Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

YMGME-05935; No. of pages: 11; 4C: Molecular Genetics and Metabolism xxx (2015) xxx–xxx

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Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network Diego A. Salazar a, Alexander Rodríguez-López b,c, Angélica Herreño b, Hector Barbosa b, Juliana Herrera b, Andrea Ardila b,d, George E. Barreto a, Janneth González a,⁎, Carlos J. Alméciga-Díaz b,⁎⁎ a

Grupo Bioquímica Computacional y Bioinformática, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia 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 d Hospital Universitario San Ignacio, Bogotá D.C., Colombia b c

a r t i c l e

i n f o

Article history: Received 20 May 2015 Received in revised form 30 July 2015 Accepted 1 August 2015 Available online xxxx Keywords: Systems biology Lysosomal storage disease Disease biomarkers Flux balance analysis Diagnostic

a b s t r a c t 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. © 2015 Published by Elsevier Inc.

1. Introduction The mucopolysaccharidosis (MPS) is a group of lysosomal storage disorders (LSD) caused by total or partial deficiency on the activity of a lysosomal enzyme involved in the degradation of glycosaminoglycans (GAG) [1]. MPS are transmitted in an autosomal recessive manner excepting for MPS II, which has a X-linked inheritance pattern [2]. Abbreviations: MPS, Mucopolysaccharidosis; LSD, Lysosomal storage disease; GAG, Glycosaminoglycans; Recon2, Gene-based human metabolic network 2; FBA, Flux Balance Analysis; FVA, Flux variability analysis; DAVID, Database for Annotation, Visualization and Integrated Discovery. ⁎ Correspondence to: J.G. Santos, Grupo de terapia celular y molecular, TercerMol, Pontificia Universidad Javeriana, Cra 7 No. 43-82 Building 52, Room 107, Bogotá, Colombia. ⁎⁎ Correspondence to: C.J. Alméciga-Díaz, Proteins Expression and Purification Laboratory, Institute for the Study of Inborn Errors of Metabolism, School of Sciences, Pontificia Universidad Javeriana, Cra 7 No. 43-82 Building 53, Room 303A, Bogotá, Colombia. E-mail addresses: [email protected] (J. González), [email protected] (C.J. Alméciga-Díaz).

Currently, eleven MPS have been described, each one as a product of a deficiency in one of the enzymes involved in the stepwise degradation of heparan, dermatan, chondroitin, keratan, or hyaluronic acid [2]. Despite the difference in the affected enzyme and stored GAG, MPS are multi-systemic and progressive disorders that share clinical manifestations such as organomegaly, skeletal malformations, retarded growth, cardiac dysfunction, and in some cases neurological impairment [1,3]. Biochemical and genetic bases of MPS have allow the design of different diagnosis methods and therapeutic approaches [4]. Currently, MPS diagnosis is based on the analysis (qualitative and quantitative) of GAG in biofluids (i.e., urine or plasma) and the measure of the enzymatic activity [5]. Nevertheless, different methodologies have been used to improve the specificity, sensitivity, and accuracy of GAG detection and differentiation. Some of these methodologies include glycan reductive isotope labeling-liquid chromatography/mass spectrometry (GRIL-LC/MS), liquid chromatography mass spectrometry (LC/MS), and electrospray ionization (ESI)-tandem mass spectrometry (MS/MS) [6]. However, difficult standardization, high costs, and the need of highly trained staff limit the access

http://dx.doi.org/10.1016/j.ymgme.2015.08.001 1096-7192/© 2015 Published by Elsevier Inc.

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

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to these technologies [7]. In this sense, it is important to identify new biomarkers for disease prognosis, early diagnosis, and follow up of therapy efficacy [5,8]. New identified biomarkers include heparin cofactor IIthrombin complex, dipeptidyl peptidase IV, gangliosides GM2 and GM3, cholesterol, bis (monoacylglycero) phosphate, matrix metalloproteinases, TNF-α, chondroitin:dermatan sulfate ratio (DS/CS), interleukin (IL)-1β, are biomarkers obtained from research on humans and animal models [9,10]. Furthermore, current and new treatment options have increased the interest in the identification of new biomarkers for these disorders [11]. In addition to the GAG storage, as a consequence of the enzyme defect, there some secondary pathological processes that have been identified in several LSD. These secondary alterations include problems of vesicular network (autophagy, endosomal–lysosomal fusion, vesicle trafficking), mitochondrial function (oxidative stress, apoptosis), signaling response (neurotransmitters, TLR4, TGF-B pathway), inflammation (cytokines, chemokines), or cellular shape among others [3,8,12,13]. Although the mechanism of these secondary pathological processes is not well understood, in common they show a general cellular metabolism impairment, i.e., the synthesis and consumption of macromolecules is increased as a consequence of the lysosome disability to provide the recycled metabolites for cell metabolism, which triggers a series of cellular and hormonal processes [14]. The study of these secondary alterations might allow to understand the pathophysiology of these diseases, as well as to identify new biomarkers and therapeutic targets [15]. Nevertheless, to understand the effect of these secondary altered processes and to identify new biomarkers require to consider these diseases as a biological system, which are the product of the relation of multiple metabolic pathways, genes, proteins, and networks [16,17]. Impairment of metabolic normal states generate changes in concentrations of metabolites, consequence of disturbance of distribution of water in compartments or the high or low activity of enzymes that is altered by many mechanism [18,19]. For instance, some lysosomal enzymes reduce their activity in presence of high GAG concentration as a consequence of a physical blocking of active sites of the enzymes [20]. Furthermore, quantitation of metabolites, which concentration varies as a consequence of the disease, is important to identify new biomarkers [21]. The ‘omics’ may help to identify these changes in metabolism or signaling and the results could be integrated to get more information, since there are metabolites, genes, and proteins that are constantly interacting and that may be responsible for the disease pathophysiology [22]. Nevertheless, it is necessary to use computational tools to manipulate and integrated this data, such as Archimedes for diabetes [23], diverse signaling and metabolic networks stored at Biomodels, the network-based drug-repositioning PROMISCUOUS a database for; [24], or enhanced pharmacodynamics models [25], among others. All of these models and databases are based on a systems biology approach [26]. Systems biology is an alternative to understand, from a holistic approach, the behavior of cells, tissues, or organisms [26]. In humans, system biology has been used for the study of drug discovering using models of intracellular signaling networks [27], identification of new therapeutic targets in prion disease [28], and to understand complex process such as autophagy [29]. One of the strategies to understand a cell metabolism from a system biology approach involves the use of genome-scale metabolic constraint-based reconstructions [30]; which are assembled in a bottom-up approach based on genome annotation, biochemical, and physiological data [31]. Manual curation includes in-depth literature search to ensure high quality and coverage of the reconstruction [32]. These metabolic reconstructions are composed by a stoichiometric matrix that relates enzymes and metabolites substrates with the products of these reactions for a specific organism [32–34]. In this sense, these metabolic networks can be defined as large systems of chemical reactions [35]. The first human metabolic reconstruction (Recon1) contained 3744 reactions, which allowed the mapping of 233 inborn errors metabolism [36]. Recently, an update of this human

metabolic reconstruction was published (Recon2), which has an updated matrix, a higher number of reactions (7440) and metabolites (5063) than Recon1, and an important set of new metabolisms (xenobiotic, vitamin E, Squalene and cholesterol, lipoate, linoleate, glycosphingolipid, arachidonic acid and androgen metabolism) [37]. Genes within these reconstructions are connected to their respective reactions based on Boolean logic by defining gene–protein-reaction (GPR) associations for each network reaction [37]. Human metabolic reconstructions have been used in several studies, including metabolic engineering, gene deletion predictions, and cellular regulatory network elucidation [35]. In the case of inborn errors of metabolism, Shlomi et al. [38] used Recon1 to study 304 metabolic disorders, which led to the construction of a list of 9600 candidate biomarkers, including 23 and 9 potential biomarkers for MPS IV and VII, respectively. In this study, we used the computational human metabolic reconstruction Recon2 to model the metabolic changes observed after silencing each one of the MPS-related reactions, as well as to predict potential new biomarkers for MPS. We found that synthesis of N-glycans, β-oxidation, and metabolites of exchange with mitochondria were most affected in MPS. Furthermore, we analyzed the MPS in the context of metabolic fluxes distribution to gain insight into common clinical features. The presented comprehensive knowledgebase of MPS will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases. 2. Materials and methods 2.1. Construction of MPS models using the human metabolic network (Recon2) The human metabolic network Recon2 [37] was used to generate the MPS models. In Recon2 each reaction has two types of restrictions: stoichiometric values (for the reactions) and mass flux limits (upper and lower bounds). Candidate reactions of MPS were identified by the E.C. number of the affected enzyme. The metabolic reactions associated with each MPS were independently silenced by setting to zero both the upper and lower limits of the reaction fluxes. When one E.C. number was associated to more than one reaction or was involved in different steps of GAG degradation pathway, all of those reactions were silenced (i.e., the upper and lower limits were set to zero). Modification and analysis of Recon2 and MPS models were done by COnstraints Based Reconstruction and Analysis (COBRA) [39]. For the analysis we focused on the cell compartments present in Recon2 (cytosol, extracellular, nucleus, mitochondria, Golgi apparatus, endoplasmic reticulum, peroxisome, lysosome), used all the 100 pathways of metabolism and no additional restrictions were applied to the model. 2.2. Flux balance analysis (FBA) FBA is an optimization method which is applied to metabolic models that allows to identify the set of reaction fluxes that maximize a given biological objective within the model [40]. The metabolic reactions are represented as a stoichiometric matrix (S), of size m ∗ n, where m represents the compounds and n the reactions and the entries in the matrix are the stoichiometric coefficients of the metabolites participating in a reaction [41]. The flux through all of the reactions in a network is represented by the vector v, which has a length of n. The concentrations of all metabolites are represented by the vector x, with length m. The systems of mass balance equations at steady state, dx/dt = 0 or S ∗ v = 0. FBA seeks to maximize or minimize an objective function which can be any linear combination fluxes, to obtain a flux for each reaction, indicating how much each reaction contributes to the objective function [40]. FBA for Recon2 and MPS models was resolved by using Gurobi Optimizer 5 (Gurobi Optimization, Inc.), setting the synthesis of adenosine triphosphate (ATP synthase, E.C. 3.6.3.14) as objective function. Models were analyzed by comparing the number of reactions that showed flux

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

D.A. Salazar et al. / Molecular Genetics and Metabolism xxx (2015) xxx–xxx

changes after FBA in comparison with the fluxes of Recon2. In addition, the fluxes of Recon2 and MPS models were subjected to principal component analysis (PCA) by using the statistics tools available at MATLAB 2012b (The MathWorks, Inc.). Finally, reactions that showed fluxes changes in MPS models against Recon2 were subjected to functional annotation clustering with enrichment analysis by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 [42]. 2.3. Flux variability analysis (FVA) FVA is a method that allows to explore alternative optimal solutions as well as to investigate network flexibility among other network properties [30]. FVA is used to find the minimum and maximum flux for reactions in the network while maintaining some state of the network [43]. As it was defined by Shlomi et al. [38], the minimal and maximal flux values of a reaction i are computed using FVA by solving the following two linear programming optimization problems: Min vi or Max vi s:t sv ¼ 0 vmin ≤v ≤vmax This model defines a space of feasible flux distribution that satisfy mass-balance constraints (embedded in the stoichiometric matrix S) and flux directionality constraints (embedded in the flux bound vectors vmin, vmax) [38]. Metabolic biomarkers were determined by overlapping the intervals of each reaction between healthy (Recon2) and MPS models. After FVA for all MPS models and Recon2 were carried out, the boundaries for each reaction were calculated. For an X reaction that belongs to Recon2 and has [upperR, lowerR] boundaries and for the same reaction in MPS model with [upperM, lowerM] boundaries, we define different biomarkers as follows [38]: If (upperR b upperM) and (upperR b lowerM): Then high confidence biomarker. If (upperR b upperM) or (lowerR N lowerM): Then intermediate confidence biomarker. For everything else: Low confidence biomarker. 2.4. In-vivo evaluation of predicted biomarkers To evaluate one of the predicted biomarkers for MPS IVA, total β-hexosaminidase activity was assayed in human skin fibroblasts from a healthy donor and a MPS IVA patient. Fibroblasts were cultured with complete culture media Dulbecco's Modified medium (DMEM, Gibco) supplemented with fetal bovine serum 15%, penicillin 100 U/mL and streptomycin 100 U/mL. 1 × 105 cells/well were seed in 12-well plates and lysed by using 1% sodium deoxycholate after 2 days of culture. β-hexosaminidase activity was assayed in the cell lysate by using the substrate 4-methylumbelliferyl-β-D-acetyl-glucosaminide (SigmaAldrich) as previously described [44]. One unit (U) was defined as the amount of enzyme hydrolyzing 1 nmol of substrate per hour, and specific β-hexosaminidase activity was expressed as U/mg of total protein determined by Lowry assay. All experiments were done in triplicate To evaluate the relationship between total β-hexosaminidase and N-acetylgalatosamine-6-sulfate-sulfatase (GALNS) levels, human MPS IVA fibroblasts were transduced with a lentiviral vector expressing the human GALNS cDNA (Lenti-GALNS). The GALNS cDNA was inserted into the vector pLenti6/V5-D-TOPO (Life Technologies). Lentiviral

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vector were produced on 293FT cells using the ViraPower™ Lentiviral Packaging Mix, under the manufacturer instructions (Life Technologies). Vectors were concentrated by precipitation with 8.5% polyethylene glycol (PEG) 6000 and 0.3 M NaCl [45]. Finally, vector titers were estimated by using HEK293 cells cultured in complete culture media DMEM, as described above, and supplemented with blasticidin, under the manufacturer instructions (Life Technologies). Transduction of human MPS IVA fibroblasts was carried out as previously described [46]. Briefly, 1 × 105 cells/well were seed in 12-well plates and transduced with 5 multiplicity of infection (MOI). GALNS activity was assayed in cell lysate 48 h post-transduction. GALNS activity was assayed by using the substrate 4-methylumbelliferyl-β-D-galactopyranoside-6-sulfate (Toronto Chemicals Research) [47]. One unit (U) was defined as the amount of enzyme catalyzing 1 nmol substrate per hour, and specific GALNS activity was expressed as U/mg of protein as determined by Lowry assay. All experiments were done in triplicate. To evaluate two of the predicted biomarkers for MPS VI, βglucoronidase and total β-hexosaminidase activity was assayed in leucocytes from a healthy donor and a previously diagnosed MPS VI patient. Leucocytes were lysed by 3 cycles (15 s on/45 s off) of sonication with 20% amplitude (Vibra-Cell, Sonics & Materials Inc.) at 4 °C. Total βhexosaminidase was assayed as previously described by using the substrate 4-methylumbelliferyl-β-D-acetyl-glucosaminide (Sigma-Aldrich) [44]. N-acetylgalactosamine-4-sulfatase (Arylsulfatase B) was assayed by using the substrate p-nitrocatechol sulfate (Sigma-Aldrich) as previously described [48]. β-glucoronidase activity was assayed by using the substrate 4-methylumbelliferyl-β-D-glucuronide (Sigma-Aldrich), as previously described [48]. As a control, GALNS activity was assayed as described above [47]. In all cases, one unit (U) was defined as the amount of enzyme catalyzing 1 nmol substrate per hour, and activity was expressed as U/mg of protein as determined by Lowry assay. All experiments were done in triplicate. 3. Results 3.1. Development of the MPS models MPS models were obtained from Recon2 by silencing respective enzymes of each MPS (Tables 1 and Supplemental Table 1), i.e., MPS I model was obtained after silencing the reactions that were associated to α-L-iduronidase (IDUA), which participates in the catabolism of dermatan and heparan sulfate. Metabolic models were obtained for MPS I, II, III (A to D), IV (A and B), VI and VII. The metabolic changes in the models were characterized through flux balance analysis (FBA) and flux variability analysis (FVA). The reaction adenosine triphosphate synthesis (ATP synthase, E.C. 3.6.3.14) was selected as the objective function of the models (Fig. 1A), since this may be considered as the main task of a regular cell. Recon2 without any modification, was considered as a healthy state, and processed as described for the MPS models (Supplemental Table 2). 3.2. Flux balance analysis (FBA) For all the evaluated models it was possible to obtain a set of reactions, and the corresponding fluxes, that satisfies the objective function, which suggest that silencing of MPS-related reactions does not affect the synthesis of ATP (i.e., viability of the cell). To evaluate the differences and similarities among the MPS models the FBA results were clustered through principal component analysis. Four clusters were obtained: (1) Recon2, MPS VII and MPS IIID, (2) MPS III A to C, (3) MPS IVA, IVB and VI, and (4) MPS I and II (Fig. 1B), and further interpretation of the data was performed based on these clusters. In general, the models were clustered according to the affected GAG. These results agree with the well know correlation between the type of MPS and the affected GAG. For instance, both MPS I and II have an impairment in dermatan and heparan sulfate catabolism and they share some of the clinical

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

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Table 1 Summary table of enzymes blocked in Recon2. hs_deg: heparan sulfate, degradation product, iduor: L-iduronate, cs_b_deg: chondroitin sulfate B/dermatan sulfate (IdoA2S-GalNAc4S), degradation product, acgam: N-acetyl-D-glucosamine, accoa: acetylCoA, ksi_deg: keratan sulfate I, degradation product, cs_d: chondroitin sulfate D (GlcNAc6S-GlcA2S), free chain, glcur: D-glucuronate,

[l]: Lysosomal, [c]: cytosol.

Mucopolysaccharidosis type

Name of deficient enzyme related in Recon2

Formula reaction in Recon2

I (Hurler syndrome)

Alpha-L-iduronidase

II (Hunter syndrome)

Iduronate-2-sulfatase

IIIA (Sanfilippo syndrome A) IIIB (Sanfilippo syndrome B) IIIC (Sanfilippo syndrome C) IIID (Sanfilippo syndrome D)

Heparan-N-sulfatase Alpha-N-acetylglucosaminidase Heparan-glucosaminide N-acetyltransferase N-acetylglucosamine-6-sulfatase

IVA (Morquio syndrome A)

Galactose-6-sulfate sulfatase

IVB (Morquio syndrome B) VI (Maroteaux-Lamy syndrome) VII (Sly syndrome)

Beta-galactosidase N-acetylgalactosamine-4-sulfatase Beta-glucuronidase

h2o[l] + hs_deg[l] ⟹ hs_deg[l] + idour[l] h2o[l] + cs_b_deg[l] ⟹ cs_a_deg[l] + idour[l] h2o[l] + hs_deg[l] ⟹ h[l] + so4[l] + hs_deg[l] h2o[l] + cs_b_deg[l] ⟹ h[l] + so4[l] + cs_b_deg[l] h2o[l] + hs_deg[l] ⟹ h[l] + so4[l] + hs_deg[l] h2o[l] + hs_deg[l] ⟹ acgam[l] + hs_deg[l] accoa[c] + hs_deg[l] ⟹ coa[c] + h[l] + hs_deg[l] h2o[l] + hs[l] ⟺ h[l] + so4[l] + hs_deg[l] h2o[l] + ksi_deg[l] ⟺ h[l] + so4[l] + ksi_deg[l] h2o[l] + ksi_deg[l] ⟺ h[l] + so4[l] + ksi_deg[l] h2o[l] + cs_d[l] ⟺ h[l] + so4[l] + cs_d_deg1[l] h2o[l] + ksi_deg[l] ⟹ gal[l] + ksi_deg[l] h2o[l] + cs_a[l] ⟺ h[l] + so4[l] + cs_a_deg[l] h2o[l] + cs_a_deg[l] ⟹ glcur[l] + cs_a_deg[l] h2o[l] + cs_d_deg[l] ⟹ glcur[l] + cs_d_deg[l] 2 h2o[l] + ha_pre[l] ⟹ acgam[l] + glcur[l]

manifestations of the disease [2]. Nevertheless, MPSIIID and MPS VII were clustered with Recon2 model, which might suggest a reduced change in the flux balance in comparison with Recon2 FBA, or the presence of bypass reactions that reduce the impact of the reaction silencing. Models for the LSD Fabry, Gaucher and Pompe were created by silencing the respective reactions and they were processed through FBA (Supplemental Table 3). FBA results were compared against those obtained for the MPS models to identify the reactions with similar flux values. It was observed that less than 6% of the reactions had similar fluxes between MPS and the Fabry, Gaucher and Pompe models, which suggest that MPS models have a different metabolic profile than that observed for other LSD. Optimization of Recon2 required 1942 reactions to accomplish the objective function, while MPS models used on average 488 more reactions than Recon2, excepting MPS IIID and VII (Fig. 1B, blue bars). On average, 1983 reactions showed a flux change in the MPS models in comparison to those observed in the Recon2 model (Fig. 1C, red bars), excepting MPS IIID and VII for which 1290 reactions showed flux changes. Reactions with flux changes include both activated (flux XRecon2 = 0 and flux XMPS ≠ 0) and inactivated (flux XRecon2 ≠ 0 and flux XMPS = 0) reactions, as well as reactions that remained active but that showed a different flux (flux XRecon2 ≠ flux XMPS). In this sense, although MPS IIID and VII were grouped with Recon2 after PCA, the silencing of the MPS-related reactions produced several changes in the cellular homeostasis, although in a lower proportion than those observed for the other MPS. To facilitate the analysis of these results, the reactions within the models were classified according to the KEGG Database (Supplemental Table 4). The comparison of the affected groups in MPS vs. Recon2 showed a wide metabolic alteration as a result of impairment in GAG catabolism (Table 2). The main metabolic changes include an increase in: 1) synthesis of N-glycan and keratan sulfate, 2) fluxes through glycolysis and pentose phosphate pathways, 3) β-oxidation, and 4) nucleotide interconversion. Furthermore, the flux of several reactions involved in the transport of metabolites between cell compartments was also increased. Taken together, these metabolic changes showed an activated cell metabolism in the MPS models in comparison with that observed in the healthy state (Recon2) (Supplemental Table 5). Experimental evidence have shown that the cell metabolism in some MPS adapts to the deficiency of recycled substrates through the activation of cell metabolism [49,50]. Comparison between Recon2 and MPS models, allowed to identify specifically the reactions that were activate (flux XRecon2 = 0 and flux XMPS ≠ 0)) or inactivated (flux XRecon2 ≠ 0 and flux XMPS = 0) (Fig. 2A). On average, 608 ± 243 reactions were activated, while 136 ± 32 reactions were inactivated (Fig. 2A). Since Recon2 correlates

reactions with genes and metabolites, the genes associated with the activated and inactivated reactions were extracted and studied by and enrichment gene analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7. From these results, we selected the 20 groups with greatest enrichment score values (Fig. 2B and C, and Supplementary Tables 6 and 7). The main clusters of activated genes corresponded to processes related with mitochondria (oxidative phosphorylation) and metabolism of carbohydrates, lipids and cofactors, as well as biosynthesis of glycans. Among the genes, it was highlighted the activation of NADH dehydrogenase, cytochrome c oxidase, superoxide dismutase, catalase, phospholipase C, lipoprotein lipase, oxide nitric and Aβ-binding alcohol dehydrogenase (ABAD). On the other hand, the main clusters of inactivated genes corresponded to processes related with metabolism of xenobiotics and amino acids, components of the mitochondria (cytochromes, dehydrogenases, transferases among others), and the transport of some amino acids. 3.3. Prediction of biomarkers for MPS A flux variability analysis (FVA) was used to predict potential MPS biomarkers. We selected the methodology previously proposed for the prediction of biomarkers for a list of inborn errors of metabolism [38, 51], or cancer drug targets [51]. This method defines a space of feasible flux distribution that satisfies mass-balance constraints and flux directionality constraints [40]. In this sense, limits values for each reaction in Recon2 (healthy state) were compared to the limits values for each reaction in the MPS models. Shlomi et al. [38] used this methodology to predict high- and low-confidence biomarkers, according to the overlap of the flux limits for each reaction in the disease model against the limits in the healthy model. We defined three types of biomarkers: high, intermediate and low confidence biomarkers (Fig. 3A). In high confidence biomarkers there is not an overlap of the flux limits between healthy and disease models, while in intermediate it may be that the upper limit is greater than the limit Recon2 or the lower limit is less than the limit of Recon2. The low confidence means that both limits are overlapping with the Recon2 limits. High, intermediate, and low confidence biomarkers were identified for MPS IVA, IVB, VI and VII (Fig. 3B and Supplemental Table 8). The pathways for these biomarkers were associated with the pentose phosphate pathway, starch and sucrose metabolism, synthesis of sphingolipids pathway, and the steroid metabolism (retinol synthesis) (Fig. 3B). Other processes in which potential biomarkers of different confidence were identified include the transport to endoplasmic reticulum, lysosome, or Golgi apparatus, as well as several exchange/demand reactions (Fig. 3B). Although 172 biomarkers of high, intermediate, and low confidence were predicted, only biomarkers of high and intermediate confidence

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

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Fig. 1. Behavior of metabolic fluxes in Recon2 and MPS models. A. Objective function for optimization: synthesis of ATP mapped on BIGG map. ATPS4m: synthesis of ATP from ADP and Pi mediated by ATP synthase complex in the inner mitochondria membrane. B. Principal component analysis for the fluxes of the MPS models and recon2. The colors are indicating in the scale bar. C. Number of reactions used in the model to satisfy the objective function (gray bars) and number of reactions that changed in MPS models (white bars). These last reactions include those with different flux values compared to Recon2 and reactions that were not used in Recon2 but were activated in MPS models. D. MPS models showed an impaired metabolism compared to Recon2, taking into account that both accomplished the same objective function. The values correspond to the number of reactions that change from the total of the cluster. Bars are comparison between columns, i.e., the decrease columns in MPS I–II, the minimum value is 0 and the maximum is 306.

were analyzed. High confidence biomarkers correspond for MPS IVB and VII (Table 2). Eight biomarkers were predicted for MPS IVB (Table 3), while 13 potential biomarkers were predicted for VII. Intermediate confidence biomarkers for MPS IVA, IVB and VII are shown in Fig. 3D. These biomarkers correspond to metabolites or enzymes for which the concentration or activity, respectively, was decreased or increased. 3.4. In-vivo evaluation of β-hexosaminidase activity To evaluate the potential of the predicted biomarkers, βhexosaminidase was assayed in human MPS VIA skin fibroblasts, while β-hexosaminidase and β-glucoronidase activity were assayed in leucocytes from a MPS VI patient. Human MPS VIA skin fibroblasts showed an intracellular GALNS activity of 0.004 ± 0.003 U/mg,

which correspond to 4% of the levels observed in human healthy skin fibroblasts (0.10 ± 0.01 U/mg, P b 0.01). Similarly, total βhexosaminidase in human MPS VIA skin fibroblasts showed activity levels of 40 ± 17 U/mg, which were significantly lower than those observed in human healthy skin fibroblasts (857 ± 0.01 U/mg, P b 0.01). (Fig. 3C). To evaluate the relationship between total βhexosaminidase and GALNS activity levels, human MPS IVA fibroblasts were transduced with a lentiviral vector expressing the human GALNS cDNA (Lenti-GALNS). Previously, we reported that the highest GALNS activity after transduction of HEK293 cells with Lenti-GALNS vector was observed with 5 MOI [52]. Transduced human MPS VIA skin fibroblasts with 5 MOI Lenti-GALNS showed enzyme activity levels of 0.03 ± 0.007 U/mg, which were 7.5-fold higher than those of non-transduced MPS IVA fibroblasts (0.004 ± 0.003 U/mg, P b

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

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Table 2 High confidence biomarkers obtained from flux variability analysis for MPS types IVB and VII. Results are shown as increased (I) or decreased (D) in comparison with Recon2. Reaction name exchange Exchange of L-iduronate Globoside exchange Sialidase, lysosomal Beta-1,3-galactosyltransferase 3 Lactosylceramide 4-alpha-galactosyltransferase N-acetylgalactosaminidase, betaGloboside transport

MPS IVB

D-Xylose

transport, extracellular Ceramide transport protein N-acetyl-galactosamine intracellular transport Globoside intracellular transport Glutamate transport (lysosome) Glycine reversible transport via proton symport (lysosome) Iduronate transport (lysosome)

I I I I I I I D

L-iduronate

L-alanine

reversible transport via proton symport (lysosome)

L-proline

reversible transport via proton symport (lysosome) N-acetylneuraminate transport (lysosome) Transport of L-Histidine by hPT3 or hPT4 peptide transporters UDP intracellular transport UDP-N-acetyl-galactosamine intracellular transport

MPS VII D

I D D I I D I I D I I D

0.01), although lower than those of human healthy fibroblasts (0.10 ± 0.01 U/mg, P b 0.01). After transduction, total β-hexosaminidase levels were increased to 109 ± 28 U/mg, which were 2.7-fold higher than those observed in non-transduced MPS IVA fibroblasts (P b 0.05) (Fig. 3C). Arylsulfatase B was not detected on MPS VI leucocytes, while an activity of 231 ± 5 U/mg was observed in human healthy leucocytes. Total β-hexosaminidase in MPS VI leucocytes showed activity values of 2321 ± 584 U/mg, which was similar to that of human healthy leucocytes (2226 ± 61 U/mg). However, β-glucoronidase activity in MPS VI leucocytes was 1515 ± 12 U/mg, which was 60% of the levels observed in human healthy leucocytes (2591 ± 171 U/mg). Finally, similar GALNS enzyme activity levels were observed both in MPS VI (0.112 ± 0.01 U/mg) and human healthy leucocytes (0.139 ± 0.01 U/mg), which were considered within the normal range according to the reference values of our laboratory, and discard a multiple sulfatase deficiency. 4. Discussion Human metabolic reconstruction has been used to study different inborn errors of metabolism, showing the potential of this approach for identifying biomarkers and drug targets, construct specific tissue models, and modeling different pathologies [38]. Metabolic biomarkers were predicted by Shlomi et al. [38] for a list of inborn errors of the metabolism by using Recon1, while Resendis-Antonio et al. [53] proposed that this metabolic networks are the platform to explore metabolic alterations of different diseases. However, the human metabolic reconstruction has not been used to specifically model the metabolic changes produced as a consequence of the impairment in the lysosomal metabolism of the GAG. In this sense, we used the genome-scale human metabolic reconstruction Recon2 to model the effect of MPS-related reaction silencing on cell homeostasis, and to identify potential new biomarkers for this group of diseases. Recon2 is a human metabolic reconstruction that includes 7440 reactions, and it has been used to generate tissue-specific models [37]. In this sense, Recon2 can be considered as a model that represents a whole organism. However, since each mucopolysaccharidoses, or subgroups of them, has specific impaired tissues, future analysis should include the use of tissue-specific or multi-tissue models. Alterations of several secondary cellular processes have been described as a consequence of the lysosomal accumulation of partially degraded substrates, which trigger different pathologic cascades [54,55].

Nevertheless, there is limited information for some other LSD about these mechanisms. For instance, some mechanisms of cell death have been proposed in MPS: permeabilization of lysosomal membrane, dysregulation of calcium concentration, impaired mitochondria, and accumulation of proteins, among others [9,56]. In this sense, to further understand the molecular bases of the MPS, as well as other LSD, it is necessary to take a systems biology approach instead of the classical reductionist methodology [57]. For instance, a systems biology study of autophagy showed how different intermediaries are involved in the regulation of lysosomal and autophagy processes: TFEB, mTOR, and SERBP1 are involved in lipid metabolism, GAG degradation, glycosphingolipid biosynthesis, sphingolipid metabolism, and glycan degradation pathways [29]. Woloszynek et al. [50] in MPS I mice, showed that autophagy, which normally is trigger by starvation, is altered as a consequence of the impairment in the auto/endolysosomes fusion. Alteration in autophagy led to a reduction in recycling of substrates and monomeric products (e.g., UDP or activated aminosugars) [50]. The objective function chosen was synthesis of ATP, since this may be considered as the main task of a regular cell. In addition, the lysosome plays an important role in metabolism homeostasis that allows an efficiently use of cell energy [58–60]. MPS and Recon2 models were optimized using FBA, and fluxes were further analyzed through PCA. Overall, MPS that share an affected GAG showed similar flux changes and were clustered within the same PCA groups. This result is associated with the silencing of the same metabolic pathway in those diseases. However, each MPS should have unique characteristics that lead to specific phenotypes, which suggest the need of additional constraints (e.g., kinetic constants or specific biologic constraints) to achieve these differences at the metabolic level [30]. Recon2 and MPS IIID and VII were clustered within the same group, which could be associated to the fact that the silenced reactions were located at the end of the GAG degradation pathway included in the reconstruction (Supplemental Fig. 1A). Reactions within the model may allow the recycling of some metabolites, as aminosugars or sulfate, while in other MPS the position of the reaction will limit the production of these metabolites. For the case of MPS IIID, there are 17 reactions associated with the enzyme N-acetylglucosamine-6-sulfatase, but in 14 of these reactions it was observed an alternative reaction catalyzed by beta-N-acetylhexosaminidase A (Supplemental Fig. 1B), reducing the impact of the metabolic blocking. In summary, the fact that MPS IIID and VII where grouped with Recon2 model after PCA, suggest that further improvements and constrains are necessary in this human metabolic reconstruction to properly model the metabolic changes in these diseases. Flux balance analysis showed that the whole cellular metabolism is altered after silencing the reactions involve in GAG degradation. Nevertheless, the MPS models were able to accomplished the objective function (i.e., synthesis of ATP), which suggest that the system (i.e., cell) adapts to the deficiency of the recycled metabolites, as result of the GAG degradation impairment. We observed that silencing the GAG degradation reactions reduced the synthesis of glycans and the interconversion of nucleotides. βoxidation and glycolysis were also increased in all the MPS models. These results agree with those reports showing an energy imbalance in several LSD that is compensated by affecting other pathways such as lipids metabolism (decreased adiposity), cholesterol biosynthesis, carbohydrates metabolism, and protein catabolism [13,49,61]. Also, the biosynthesis of new macromolecules as GAG (as keratan sulfate in MPS I and II) is induced due to the deficiency of recycled of substrates, which causes and increase in energy requirement for the cell [50]. As a result, it is possible that these energy requirements generate an active mitochondria that finally produce a ROS excess. In the MPS models several reactions associated with ROS production increased their flux values, such as cytochrome C oxidase, NADH dehydrogenase, and the activation of protective mechanism as glutathione. Although several studies have shown the processes affected in some MPS animal models

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

D.A. Salazar et al. / Molecular Genetics and Metabolism xxx (2015) xxx–xxx

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Fig. 2. Reactions activated or inactivated in MPS models. A. Number of reactions that were activated or inactivated in each MPS model, gray bars corresponds to inactivated reactions and white bars corresponds to activated reactions. B. Gene enrichment analysis for gene activated obtained from the reactions in MPS models. C. Gene enrichment analysis for gene inactivated obtained from the reactions in MPS models.

and cells [62–65], the present study shows for the first time, for all MPS an analysis of the adaptation to the metabolism alterations caused by the blocking of GAG catabolism. Understanding this adaptation process may contribute to understand the pathophysiology of these diseases and to identify therapeutic targets and biomarkers. For instance, it was proposed that dietary interventions may contribute to outcomes of

traditional therapies (i.e., enzyme replacement therapy, substrate reduction therapy, gene therapy, molecular chaperones) [50]. In this sense, our results suggest that dietary interventions should be focus on reduction of precursors of GAG as sugar or carbohydrates, the administration of fatty acids as source of energy, or the reduction of caloric intake that favors a more efficiently energy production.

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

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D.A. Salazar et al. / Molecular Genetics and Metabolism xxx (2015) xxx–xxx

Fig. 3. Prediction of biomarkers. A. According to the possible limits obtained by FVA three types of biomarkers were defined: high, medium, and low confidence. B. Principal pathways where the biomarkers were identified. C. Total beta-hexosaminidase (beta-Hexo) and N-acetylgalatosamine-6-sulfate-sulfatase (GALNS) activities in human MPS IVA healthy (normal) fibroblasts. Human MPS IVA fibroblasts were transduced with a lentiviral vector carried the human GALNS cDNA (LentiGALNS) at 5 MOI. D. Arysulfatase B, total β-hexosaminidase (beta-Hexo), and β-glucoronidase (GUSB) activities in leucocytes from a MPS VI patient.

Woloszynek et al. [50], observed metabolic alterations in energy expenditure in MPS I mice, similar to the results observed in the current study, including decreased of fatty acids such as palmitoleate, palmitate and arachidate, monosaccharides as fructose, maltose, mannitol, and increase in most amino acids concentration. They attributed these changes to the metabolic activation of autophagy as a consequence of lysosome dysfunction. Recently, it was described the finding of oxidative stress in

MPS IVA patients due to the accumulation of toxic substrates [66]. The MPS models predicted a hypothetical mechanism that contributing to the oxidative stress. It was observed the increase in the production of amino acids (glycine, serine, alanine, glutamate, methionine, cysteine and proline), the consumption of different fatty acids and the production of UDP-monosaccharides, which are precursors of N-glycans. In this sense, the continuous production of energy from the mitochondria

Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001

D.A. Salazar et al. / Molecular Genetics and Metabolism xxx (2015) xxx–xxx Table 3 Intermediate confidence biomarkers obtained from flux variability analysis for MPS types IVA, IVB and VI. Results are shown as increased (I) or decreased (D) in comparison with Recon2. Reaction name

MPS IVA

MPS IVB

MPS VI

MPS VII

Beta-N-acetylhexosaminidase, lysosomal Beta-glucuronidase, lysosomal

D

I I

D

D D

cause an oxidative stress that is reflected in the increased activity of mitochondrial enzymes previously mentioned. All models showed a similar behavior excepting MPS VII. In addition, other studies have unlinked the inflammation as a cause of oxidative stress in the MPS IIIB neurodegeneration [67] and it is in discussion the mechanisms that trigger the excessive production of free radicals and the saturation of the defense systems against these molecules [66]. The present results suggest that there may be a metabolic contribution to this oxidative stress. The identification and validation of biomarkers in MPS could be a tool to improve the diagnosis and treatment. Although GAG are the universal biomarker for MPS, they have several limitations including the lack of a correlation with disease severity or treatment effect [7]. Here we used FVA to compare MPS models and Recon2 to predict potential biomarkers. The high confidence biomarkers predicted in this study were mainly related with the sphingolipids metabolism. Among these, it was found ceramide and globosides, which are important glycolipids for the structure of cell membranes [68]. Ceramide has been related in cognitive impaired in neurodegenerative diseases as Alzheimer, which suggest that the presence of this molecule in MPS could induce similar cellular alterations [69,70]. These biomarkers could be used in the disease progression from pre-symptomatic stages of MPS, since a biomarker has not been described in MPS to follow up the progressive neurological impartment [10]. The impairment (increase or decrease) of secondary enzymes has been reported for several LSD [9]. According to the prediction results, in MPS IVA and VII it was observed a reduction in the flux of the reaction catalyzed by βhexosaminidase, while it was increased in MPS IVB and did not change in MPS VI. On the other hand, β-glucoronidase was increased in MPS IVB and decreased on MPS VI and VII. These in-silico results agree with the in-vivo evaluation of total β-hexosaminidase, which was decreased in MPS IVA skin fibroblast but did not change in MPS VI leucocytes. Furthermore, β-hexosaminidase levels were increased in MPS IVA skin fibroblast after the treatment with a lentiviral vector carrying the human GALNS cDNA, suggesting that β-hexosaminidase activity was impaired as a result of GALNS deficiency. However, β-hexosaminidase activity did not increase in the same proportion than GALNS activity (i.e., 12% vs. 30% of wild-type levels, respectively), suggesting that βhexosaminidase activity could have a clinical value during the follow up of MPS IVA patients receiving a therapy. These results contrast with those reported for the knock-out MPS IVA mouse, which exhibited slight but not statistically significant elevation of β-hexosaminidase in several tissues [72], but agree with normalization of β-hexosaminidase activity in MPS IIIB mice after the treatment with a recombinant α-Nacetylglucosaminidase [71]. In addition, these results agree with those observed in MPS VI cats, in which Arylsulfatase B activity was normalized against total β-hexosaminidase activity, suggesting the βhexosaminidase was not affected in these cats [73]. Finally, in MPS VI leucocytes β-glucoronidase activity was reduced about 40% of the normal levels of MPS VI leucocytes, which correlates with its character as intermediate confidence biomarker. 5. Conclusions The impairment of cell metabolism as a result of the alteration in one of the steps of lysosomal metabolism has been reported for several LSD, including some MPS [54,61]. The use of comprehensive mathematical metabolic models for the analysis of MPS let us to report for the first

9

time a system biology approach to the study of the cell process affected in MPS. This metabolic reconstruction allowed to model metabolic alterations such as oxidative stress and activation of other energy sources including β-oxidation, synthesis of ROS by NADH dehydrogenase, and cytochrome C oxidase. These MPS models allowed to predict potential biomarkers for MPS IVB and MPS VII, which were related with sphingolipids metabolism. Although the models of MPS behaved similar to the experimental evidence, it is necessary to improve the models by gap filling or by introducing kinetic data to obtain even more accurate results. In vivo evaluation of predicted biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction, which shows that a system biology approach through the use of a computational metabolic reconstruction, may allow to understand the pathophysiology of these diseases, and to identify new therapeutic targets and biomarkers. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ymgme.2015.08.001. Conflict of interest Diego Salazar, Alexander Rodríguez-López, Angélica Herreño, Hector Barbosa, Juliana Herrera, Andrea Ardila, George E. Barreto, Janneth Gonzalez, and Carlos Alméciga-Díaz declare that they have no conflict of interest. Details of the contributions of individual authors • Diego Salazar, Alexander Rodríguez-López, George E. Barreto, Janneth Gonzalez, and Carlos Alméciga-Díaz conceived and designed the study and the analysis. • Diego Salazar and Alexander Rodríguez-López performed the bioinformatics analysis. • Angélica Herreño, Hector Barbosa, Juliana Herrera and Andrea Ardila performed and analyzed the experiments with MPS IVA fibroblasts and lentiviral vectors, and with MPS VI leucocytes. • Diego Salazar, Alexander Rodríguez-López, George E. Barreto, Janneth Gonzalez, and Carlos Alméciga-Díaz wrote the manuscript.

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Please cite this article as: D.A. Salazar, et al., Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network, Mol. Genet. Metab. (2015), http://dx.doi.org/10.1016/j.ymgme.2015.08.001