Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA

International Journal of Molecular Sciences Article Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA Víctor J. Álvarez 1,2, Susana B. Bravo 3, Maria Pilar Chantada-Vazquez 3, Cristóbal Colón1, María J. De Castro 1, Montserrat Morales 4, Isidro Vitoria 5, Shunji Tomatsu 2, Francisco J. Otero-Espinar 6,7 and María L. Couce 1,*   Citation: Álvarez, V.J.; Bravo, S.B.; Chantada-Vazquez, M.P.; Colón, C.; De Castro, M.J.; Morales, M.; Vitoria, I.; Tomatsu, S.; Otero-Espinar, F.J.; Couce, M.L. Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA. Int. J. Mol. Sci. 2021,22, 226. https:// doi.org/10.3390/ijms22010226 Received: 26 November 2020 Accepted: 24 December 2020 Published: 28 December 2020 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2020 by the authors. LicenseeMDPI, Basel, Switzerland. This articleisanopenaccessarticledistributed under the terms and conditions of the CreativeCommonsAttribution(CCBY) license(https://creativecommons.org/ licenses/by/4.0/). 1Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Paediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain; josevictor.Alvar[email protected] or jvictor_alvar[email protected] (V.J.Á.); [email protected] (C.C.); mj.decastr[email protected] (M.J.D.C.) 2Skeletal Dysplasia Lab Nemours Biomedical Research Nemours/Alfred I. du Pont Hospital for Children, 1600 Rockland Road, Wilmington, DE 19803, USA; [email protected] 3Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; [email protected] (S.B.B.); [email protected] (M.P.C.-V.) 4Minority Diseases Unit Hospital Universitario12 de Octubre, 28041 Madrid, Spain; [email protected] 5Nutrition and Metabolophaties Unit, Hospital Universitario La Fe, 46026 Valencia, Spain; [email protected] 6Paraquasil Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain; [email protected] 7 Department of Pharmacology, Pharmacy and Pharmaceutical Technology, School of Pharmacy, Campus Vida, University of Santiago de Compostela, 15872 Santiago de Compostela, Spain *Correspondence: [email protected] or [email protected]; Tel.: +34-981-951-100 Abstract: Mucopolysaccharidosis type IVA (MPS IVA) is a lysosomal storage disease caused by mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Skeletal dysplasia and the related clinical features of MPS IVA are caused by disruption of the cartilage and its extracellular matrix, leading to a growth imbalance. Enzyme replacement therapy (ERT) with recombinant human GALNS has yielded positive results in activity of daily living and endurance tests. However, no data have demonstrated improvements in bone lesions and bone grow thin MPS IVA after ERT, and there is no correlation between therapeutic efficacy and urine levels of keratan sulfate, which accumulates in MPS IVA patients. Using qualitative and quantitative proteomics approaches, we analyzed leukocyte samples from healthy controls (n= 6) and from untreated (n= 5) and ERT-treated (n= 8, sampled before and after treatment) MPS IVA patients to identify potential biomarkers of disease. Out of 690 proteins identified in leukocytes, we selected a group of proteins that were dysregulated in MPS IVA patients with ERT. From these, we identified four potential protein biomarkers, all of which may influence bone and cartilage metabolism: lactotransferrin, coronin 1A, neutral alpha-glucosidase AB, and vitronectin. Further studies of cartilage and bone alterations in MPS IVA will be required to verify the validity of these proteins as potential biomarkers of MPS IVA. Keywords: biomarkers; enzyme replacement therapy; lysosomal disorders; proteomics 1. Introduction Morquio A syndrome, or mucopolysaccharidosis type IVA (MPS IVA, OMIM #253000), is an autosomal recessive disease caused by mutations in the GALNS gene. A deficiency of N-acetylgalactosamine-6-sulfatase (GALNS, E.C.3.1.6.4) [ 1 – 3 ] leads to the accumulation of keratan sulfate (KS) and chondroitin-6-sulfate (C6S) in multiple tissues, mainly bone, cartilage, heart valves, and cornea. The classical phenotype is characterized by systemic skeletal dysplasia with incomplete ossification and successive imbalance of growth [ 4 ], including short stature and neck, cervical instability, spinal cord compression, tracheal Int. J. Mol. Sci. 2021,22, 226. https://doi.org/10.3390/ijms22010226 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2021,22, 226 2 of 22 obstruction, prominent chest, kyphoscoliosis, laxity of joints, hip dysplasia, and knock knees [ 5 , 6 ]. Respiratory failure is the primary cause of death during the second and third decades of life in untreated patients [7,8]. Currently, two therapies are available for MPS IVA in clinical practice, namely enzyme replacement therapy (ERT) and hematopoietic stem cell transplantation (HSCT) [ 1 , 9 , 10 ]. ERT and HSCT are based on the principle of cross-correction, whereby lysosomal enzymes are taken up by deficient recipients’ cells and their lysosomes via the mannose-6-phosphate receptor. ERT with recombinant enzyme GALNS (elosulfase alfa) is an established treatment for MPS IVA. However, like other forms of ERT for lysosomal storage diseases (LSDs), elosulfase alfa has several limitations. It requires weekly 4–6 h infusions; is cleared rapidly due to its short half-life (35 min in human, 2 min in mouse) [ 11 – 13 ], it is expensive [ 13 , 14 ], penetration of the avascular cartilage is limited; and patients can develop an immune response against the infused enzyme [ 11 , 14 , 15 ]. In addition, clinical trials of elosulfase alfa have shown little improvement in bone growth and pathology [ 16 – 19 ]. Recent studies have demonstrated that the presence of KS, KS sulfation level, chondroitin-6-sulfate levels, and the presence of collagen type II in blood are potential biomarkers [ 20 ] associated with bone and cartilage disease in MPS IVA. Although urinary KS levels have been measured as a potential biomarker in clinical trials, there is no proof that decreases in urinary KS reduction correlate with clinical improvement. Urinary KS originates in the kidneys and does not reflect the degree of impairment of bone and other relevant tissues in MPS IVA. Therefore, urinary KS is considered a pharmacokinetic marker but not a surrogate biomarker [20]. Proteomics allows the large-scale identification and quantification of proteins in biological fluids (serum, urine, saliva, tears, etc.), cells, tissues, or organisms in continuous change. Among the first proteomic approaches developed are one and two-dimensional polyacrylamide gel systems (1D SDS-PAGE and 2D SDS-PAGE). These methods provide a means of monitoring protein purification, protein expression, and post-transcriptional modifications [ 20 ]. Although 1D and 2D SDS-PAGE are commonly used to screen for putative biomarkers in several disorders, particularly in animal studies [21,22], they have not been applied to LSDs. Mass spectrometry (MS) is a more recent and accurate proteomics technology, and allows high-through put protein identification [ 23 , 24 ]. This approach also enables the characterization of post-transcriptional modifications, such as phosphorylation and glycosylation, similar to the 2D-PAGE approach [ 25 ]. In the last decade, MS has led to significant advances by increasing the number of potential protein biomarkers of different diseases [ 26 ]. Liquid chromatography (LC) coupled with MS is widely used to search for disease biomarkers [24] in different biological sample types [26–28]. Most quantitative methods using liquid chromatography-tandem mass spectrometry (LC-MS/MS) involve labeling of proteins/peptides with heavy and light stable-isotope pairs (SILAC, iTRAQ). More recently developed label-free quantification techniques rely on advanced software analysis. One of these techniques is SWATH-MS (sequential window acquisition of all theoretical mass spectra) analysis [ 29 , 30 ]. This method measures the concentrations of peptide analytes (10 peptides per protein in SWATH analysis) in two or more samples, using certain peptides in the samples as an internal standard. By contrast, other absolute quantification techniques use external peptides as standards to create a calibration curve. These external peptides are identified by searching using monitoring methods (monitoring of selected reactions or multiple reactions) [23]. Given that MPS IVA is a dynamic entity that involves alterations in the expression of multiple proteins, proteomics techniques can provide important biological information that can help advance our understanding of the underlying pathophysiological mechanisms. Moreover, proteomics could aid the identification of novel disease biomarkers [ 28 , 30 , 31 ], which can facilitate disease diagnosis, prognosis, and monitoring [32–42]. To address the lack of disease-specific protein biomarkers in MPS IVA, we applied quantitative and qualitative proteomics approaches to peripheral blood cell specimens from treated and untreated patients with MPS IVA to systematically identify and biochemically characterize new biomarker candidates. Int. J. Mol. Sci. 2021,22, 226 3 of 22 2. Results Leukocytesamplesfrom13 patients with MPS IVA and 6 control subjects were classified in the three following groups: healthy controls; untreated MPS IVA patients; and ERTtreated MPS IVA patients, from whom samples were acquired before (ERT-a group) and 24 h after (ERT-b group) treatment. The demographic features of participating MPS IVA patients are shown in Table 1. Table 1. Demographics of MPS IVA patients. Patient ID Sex Age at Diagnosis (y) ERT Age at Start of Treatment (y) Current Characteristics Age (y) Height (cm) 6 Minute Walk Test (m) FVC (mL) FEV1 (mL) 1 F 1 No - 31 98 250 600 500 2 M 2 No - 31 113 305 870 700 3 M 2 No - 21 95 ND * 380 260 4 M 2 No 40 99 ND * 480 360 5 F 4 No - 15 103 341 920 820 6 F 3 No - 29 99 ND * ND ** ND ** 7 F 1 No - 18 119 272 110 900 8 M unknown No - 21 103 ND * 920 700 9 M 1 Yes 12 16 100 105 690 450 10 M 2 Yes 2 6 104 450 770 720 11 M 3 Yes 13 18 113.5 472 1390 1330 12 M 3 Yes 11 19 113 234 1350 1160 13 M 5 Yes 18 22 110 344 870 730 Patients 1 and 2 are twin brothers; patients 6 and 7 are brothers * patient cannot walk; ** patient undergoing ventilation with tracheostomy. Abbreviations: ERT, enzyme replacement therapy; F, female; FEV 1 , forced expiratory volume in 1 s; FVC, forced vital capacity; ID, identification, M, male; ND, not determined. We first performed a qualitative analysis to identify the entire set of proteins expressed in leukocyte samples using LC-MS/MS technology in data-dependent acquisition (DDA) mode. Proteins were identified with only 1% error (false discovery rate (FDR) 1%). To characterize the most significant proteins, we selected those commonly found in all or all but one sample (n − 1) per group. Subsequently, quantitative analysis was performed using the SWATH method. 2.1. Qualitative Analysis of Proteins Table 2shows the number of proteins identified by LC-MS/MS in individual samples and the number of proteins commonly found in all or all but one samples. We identified 235 proteins in untreated patients, 164 proteins in health controls, 301 proteins in the ERT-a group, and 222 proteins in the ERT-b group. Int. J. Mol. Sci. 2021,22, 226 4 of 22 Table 2. Number of proteins per sample and number of common proteins per group. Only proteins with FDR < 1% were selected. Patients and Control GroupsSample ID Proteins Identified Per Sample (n) Proteins Identified in All or All but One Samples (n) Untreated Group UG 1 460 235 UG 2 330 UG 3 367 UG 4 612 UG 5 NA UG 6 NA UG 7 341 UG 8 304 ERT-a Group ERT-a 1 480 301 ERT-a 2 338 ERT-a 3 369 ERT-a 4 406 ERT-a 5 400 ERT-b Group ERT-b 1 492 222 ERT-b 2 190 ERT-b 3 437 ERT-b 4 252 ERT-b 5 557 Healthy Control Group CG 1 144 164 CG 2 470 CG 3 238 CG 4 286 CG 5 315 CG6 261 Abbreviations: ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; NA, not analyzed (due to very low quantity of leukocytes). To analyze the distribution of proteins expressed in leukocytes across the different groups, a Venn diagram was generated (Figure 1). Supplementary Table S1 shows this same list with the corresponding UniProt codes and names of the identified proteins. Int. J. Mol. Sci. 2021, 22, x 4 of 23 Table 2. Number of proteins per sample and number of common proteins per group. Only proteins with FDR < 1% were selected. Patients and Control GroupsSample ID Proteins Identified per Sample (n) Proteins Identified in All o r All but One Samples (n) Untreated Group UG 1 460 235 UG 2 330 UG 3 367 UG 4 612 UG 5 NA UG 6 NA UG 7 341 UG 8 304 ERT-a Group ERT-a 1 480 301 ERT-a 2 338 ERT-a 3 369 ERT-a 4 406 ERT-a 5 400 ERT-b Group ERT-b 1 492 222 ERT-b 2 190 ERT-b 3 437 ERT-b 4 252 ERT-b 5 557 Healthy Control Group CG 1 144 164 CG 2 470 CG 3 238 CG 4 286 CG 5 315 CG6 261 Abbreviations: ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; NA, not analyzed (due to very low quantity of leukocytes). To analyze the distribution of proteins expressed in leukocytes across the different groups, a Venn diagram was generated (Figure 1). Supplementary Table S1 shows this same list with the corresponding UniProt codes and names of the identified proteins. Figure 1. Venn diagram showing the distribution of proteins in the 4 study groups . Abbreviations: CG: control group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; UG: untreated group. These proteins were subjected to functional analysis using the FunRich program. In this analysis, we evaluated proteins implicated in the endosome, lysosomal lumen, lysoFigure 1. Venn diagram showing the distribution of proteins in the 4 study groups. Abbreviations: CG: control group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; UG: untreated group. These proteins were subjected to functional analysis using the FunRich program. In this analysis, we evaluated proteins implicated in the endosome, lysosomal lumen, Int. J. Mol. Sci. 2021,22, 226 5 of 22 lysosomal membrane, lysosome, mitochondria, and vesicles, since these organelles are affected in patients with MPS IVA [ 42 – 44 ] (Figure 2). The percentage of proteins involved in endosome function was lower in untreated versus control samples, and was higher in ERT-a and ERT-b samples than in untreated samples, although still lower than that of control samples. A similar pattern was observed in proteins expressed in the lysosomal membrane, mitochondrion, and vesicles. By contrast, the percentage of proteins expressed in the lysosome and lysosomal lumen was lower in ERT-a and ERT-b. Int. J. Mol. Sci. 2021, 22, x 5 of 23 somal membrane, lysosome, mitochondria, and vesicles, since these organelles are affected in patients with MPS IVA [42–44] (Figure 2). The percentage of proteins involved in endosome function was lower in untreated versus control samples, and was higher in ERT-a and ERT-b samples than in untreated samples, although still lower than that of control samples. A similar pattern was observed in proteins expressed in the lysosomal membrane, mitochondrion, and vesicles. By contrast, the percentage of proteins expressed in the lysosome and lysosomal lumen was lower in ERT-a and ERT-b. Figure 2. Functional analysis using the FunRich program. This figure shows the changes in the relative percentages of leukocyte proteins expressed in the endosome, different parts of the lysosome, the mitochondria, and the vesicles in each of the 4 groups. Abbreviations: CG: control group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; UG: untreated group. Further functional analyses performed using the Reactome program allowed us to identify the metabolic pathways in which these proteins participate. In samples from ERT treated patients we observed significant differences with respect to untreated patients in the expression of proteins involved in inflammation pathways, including of leukotrienes, eoxins, and thromboxane. Expression of proteins involved in the gluconeogenesis pathway was also partially normalized in MPS IVA patients that received ERT compared to untreated patients. 2.2. Quantitative Analysis of Proteins by SWATH To identify dysregulated proteins in leukocyte samples, we performed a quantitative analysis using SWATH. To this end, we generated a protein library consisting of 690 potential biomarker proteins expressed by leukocytes in all groups analyzed. Next, we performed large-scale targeted protein analysis to quantify the levels of each protein in each group. In this analysis, we only considered dysregulated proteins with a p-value < 0.05 and a fold change (FC) > 1.4. Table 3 shows the number of dysregulated proteins found for each SWATH comparison. Also, see Supplementary Table S2. Table 3. Dysregulated proteins in leukocyte samples from MPSIVA patients. Proteins considered dysregulated are those with a p-value < 0.05 and a fold change (FC) > 1.4. Comparison Proteins Downregulated Compared with Controls Proteins Upregulated Compared with Controls Control vs. Untreated 91 73 Control vs. ERT-a 64 55 Control vs. ERT-b 49 56 Comparison Proteins down regulated compared with untreated group Proteins upregulated compared with untreated group Untreated vs. ERT-a 22 10 Untreated vs. ERT-b 10 23 Figure 2. Functional analysis using the FunRich program. This figure shows the changes in the relative percentages of leukocyte proteins expressed in the endosome, different parts of the lysosome, the mitochondria, and the vesicles in each of the 4 groups. Abbreviations: CG: control group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion; UG: untreated group. Further functional analyses performed using the Reactome program allowed us to identify the metabolic pathways in which these proteins participate. In samples from ERT treated patients we observed significant differences with respect to untreated patients in the expression of proteins involved in inflammation pathways, including of leukotrienes, eoxins, and thromboxane. Expression of proteins involved in the gluconeogenesis pathway was also partially normalized in MPS IVA patients that received ERT compared to untreated patients. 2.2. Quantitative Analysis of Proteins by SWATH To identify dysregulated proteins in leukocyte samples, we performed a quantitative analysis using SWATH. To this end, we generated a protein library consisting of 690 potential biomarker proteins expressed by leukocytes in all groups analyzed. Next, we performed large-scale targeted protein analysis to quantify the levels of each protein in each group. In this analysis, we only considered dysregulated proteins with a p-value < 0.05 and a fold change (FC) > 1.4. Table 3shows the number of dysregulated proteins found for each SWATH comparison. Also, see Supplementary Table S2. Table 3. Dysregulated proteins in leukocyte samples from MPSIVA patients. Proteins considered dysregulated are those with a p-value < 0.05 and a fold change (FC) > 1.4. Comparison Proteins Downregulated Compared with Controls Proteins Upregulated Compared with Controls Control vs. Untreated 91 73 Control vs. ERT-a 64 55 Control vs. ERT-b 49 56 Int. J. Mol. Sci. 2021,22, 226 6 of 22 Table 3. Cont. Comparison Proteins down regulated compared with untreated group Proteins upregulated compared with untreated group Untreated vs. ERT-a 22 10 Untreated vs. ERT-b 10 23 Comparison Proteins down regulated compared with ERT-a group Proteins upregulated compared with ERT-b group ERT-a vs. ERT-b 4 12 Abbreviations: ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion. 2.2.1. Proteins Down Regulated in MPS IVA Patients Relative to Healthy Controls We next assessed the distribution of proteins that were downregulated in the untreated, ERT-a, and ERT-b groups relative to controls (Figure 3). Int. J. Mol. Sci. 2021, 22, x 6 of 23 Comparison Proteins down regulated compared with ERT-a group Proteins upregulated compared with ERT-b group ERT-a vs. ERT-b 4 12 Abbreviations: ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion. 2.2.1. Proteins Down regulated in MPS IVA Patients Relative to Healthy Controls We next assessed the distribution of proteins that were downregulated in the untreated, ERT-a, and ERT-b groups relative to controls (Figure 3). Figure 3. Venn diagram showing the distribution of leukocyte proteins that were downregulated the untreated, ERT-a, and ERT-b groups relative to healthy controls. Abbreviations: CG, control group; UG, untreated group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion. Table 4 shows the 91proteins that were down regulated in untreated patients relative to healthy controls, and indicates the changes observed in these proteins in the ERTa and ERT-b groups. These proteins corresponded to the 36 proteins exclusively downregulated in the untreated group, the 15 downregulated proteins common to the untreated and ERT-a groups, the 6 proteins common to the untreated and ERT-b groups, and the 34 proteins common to the untreated, ERT-a, and ERT-b groups (Figure 3). We observed partial normalization (i.e., proteins remained dysregulated, p > 0.05) of the expression of proteins involved in the pyruvate metabolic pathway (KPYM, G3P, LDHA, PGK1, LDHB, ODPB and ALDOA; Table 4, gray rows) and complete normalization (protein expression was fully restored with treatment; p < 0.05 ) in some proteins involved in glucose metabolism (G6PI, PGM1 and G6PD; Table 4, green rows). Expression of most proteins involved in cytoskeletal organization (MOES, DEST, ARPC3, CORO1A, ANXA1, TPM1, K1C9, K22E, ARC1B, K2C1, K2C6A, TPM4, and K1C14) was normalized after ERT (Table 4, clear gray rows), although some remained downregulated (ARP2, ARPC5, ACTB). The following proteins are also involved in cytoskeletal organization: ADDB, a membrane-cytoskeleton-associated protein that promotes the assembly of spectrin-actin in a network of actin filaments (F-actin) and binds to actin monomers (Gactin); CNN2, which is involved in the regulation and modulation of smooth muscle contraction (Table 4, clear gray rows); SPTN1, which interacts with calmodulin in a calcium-dependent manner and regulates the movement of the cytoskeleton in the calciumdependent membrane; VASP, which is involved in the remodeling of the cytoskeleton and all of them were normalized after ERT; and SEPT7, which is required for the normal organization of the actin cytoskeleton, and WDR1, which is involved in chemotactic cell migration by restricting the protrusions of the lamellipodial membrane, were further downregulated after ERT (Table 4, clear gray rows). Our findings reveal dysregulation of proteins involved in the lysosome/endosome system in untreated MPS IVA patients. Proteins involved in lysosomal membrane repair (LEG3 and VPS35) were normalized after ERT (Table 4, blue rows). Expression of STXB2 (Table 4, dark blue rows), which proFigure 3. Venn diagram showing the distribution of leukocyte proteins that were downregulated the untreated, ERT-a, and ERT-b groups relative to healthy controls. Abbreviations: CG, control group; UG, untreated group; ERT-a, patients sampled before enzyme infusion; ERT-b, patients sampled 24 h after enzyme infusion. Table 4shows the 91proteins that were down regulated in untreated patients relative to healthy controls, and indicates the changes observed in these proteins in the ERT-a and ERT-b groups. These proteins corresponded to the 36 proteins exclusively downregulated in the untreated group, the 15 downregulated proteins common to the untreated and ERT-a groups, the 6 proteins common to the untreated and ERT-b groups, and the 34 proteins common to the untreated, ERT-a, and ERT-b groups (Figure 3). We observed partial normalization (i.e., proteins remained dysregulated, p> 0.05) of the expression of proteins involved in the pyruvate metabolic pathway (KPYM, G3P, LDHA, PGK1, LDHB, ODPB and ALDOA; Table 4, gray rows) and complete normalization (protein expression was fully restored with treatment; p< 0.05) in some proteins involved in glucose metabolism (G6PI, PGM1 and G6PD; Table 4, green rows). Expression of most proteins involved in cytoskeletal organization (MOES, DEST, ARPC3, CORO1A, ANXA1, TPM1, K1C9, K22E, ARC1B, K2C1, K2C6A, TPM4, and K1C14) was normalized after ERT (Table 4, clear gray rows), although some remained downregulated (ARP2, ARPC5, ACTB). The following proteins are also involved in cytoskeletal organization: ADDB, a membrane-cytoskeletonassociated protein that promotes the assembly of spectrin-actin in a network of actin filaments (F-actin) and binds to actin monomers (G-actin); CNN2, which is involved in the regulation and modulation of smooth muscle contraction (Table 4, clear gray rows); SPTN1, which interacts with calmodulin in a calcium-dependent manner and regulates the movement of the cytoskeleton in the calcium-dependent membrane; VASP, which is involved in the remodeling of the cytoskeleton and all of them were normalized after ERT; and SEPT7, which is required for the normal organization of the actin cytoskeleton, and Int. J. Mol. Sci. 2021,22, 226 7 of 22 WDR1, which is involved in chemotactic cell migration by restricting the protrusions of the lamellipodial membrane, were further downregulated after ERT (Table 4, clear gray rows). Our findings reveal dysregulation of proteins involved in the lysosome/endosome system in untreated MPS IVA patients. Proteins involved in lysosomal membrane repair (LEG3 and VPS35) were normalized after ERT (Table 4, blue rows). Expression of STXB2 (Table 4, dark blue rows), which promotes vesicle trafficking and vesicle fusion with membranes in the SNARE position, was partially normalized in treated leukocytes (FC values decreased from 2.4 in untreated patients to 1.8 and 1. 6 in ERT-a or ERT-b groups, respectively). Expression of ubiquitin protein, UB2L3 (Table 4, clear blue rows), which is involved in vesicle trafficking from the endoplasmic reticulum to the lysosome, it was partially normalized. Expression of other proteins involved in membrane trafficking (ERP29 and PICAL; Table 4, dark blue rows) was also normalized after ERT. Table 4. Downregulated proteins in untreated and ERT-treated MPS IVA patients (ERT-a, ERT-b) with respect to healthy controls. Proteins considered dysregulated are those with a p-value < 0.05 and a fold change (FC) > 1.4. UniProt Code UniProt Name Protein Name Fold Change Relative to Healthy Controls Untreated ERT-a ERT-b P14618 KPYM Pyruvate kinase PKM 0.0537 0.0770 0.1210 P04406 G3P Glyceraldehyde-3-phosphate dehydrogenase 0.0539 0.0729 0.0632 P00338 LDHA L-lactate dehydrogenase A chain 0.0629 0.1039 0.1176 P00558 PGK1 Phosphoglycerate kinase 1 0.0902 0.1124 0.1742 P07195 LDHB L-lactate dehydrogenase B chain 0.2261 0.2049 0.2358 P11177 ODPB Pyruvate dehydrogenase E1 component subunit beta. mitochondrial 0.3005 0.2520 0.3246 P04075 ALDOA Fructose-bisphosphatealdolase A 0.4012 0.5957 0.6128 P06744 G6PI Glucose-6-phosphate isomerase 0.1645 0.3401 0.3358 P36871 PGM1 Phosphoglucomutase-1 0.3284 0.2524 0.5816 P11413 G6PD Glucose-6-phosphate 1-dehydrogenase 0.3951 0.4585 0.5714 P26038 MOES Moesin 0.1282 0.1284 0.2848 P60981 DEST Destrin 0.3604 0.2756 0.4310 O15145 ARPC3 Actin-related protein 2/3 complex subunit 3 0.5094 0.4480 0.5276 P31146 CORO1A Coronin-1A 0.1283 0.1418 0.2020 P09493 TPM1 Tropomyosin alpha-1 chain 0.2997 0.6517 0.7882 P35527 K1C9 Keratin. type I cytoskeletal 9 0.3019 0.5949 0.6857 P35908 K22E Keratin. type II cytoskeletal 2 epidermal 0.2940 0.6149 0.8652 O15143 ARC1B Actin-related protein 2/3 complex subunit 1B 0.3132 0.4472 0.4072 P04264 K2C1 Keratin. type II cytoskeletal 1 0.3301 0.6055 0.7358 P02538 K2C6A Keratin. type II cytoskeletal 6A 0.4352 1.0918 2.2909 P67936 TPM4 Tropomyosin alpha-4 chain 0.4602 0.8777 0.5522 P02533 K1C14 Keratin. type I cytoskeletal 14 0.5186 0.8316 0.7752 P61160 ARP2 Actin-related protein 2 0.3608 0.3315 0.3566 O15511 ARPC5 Actin-related protein 2/3 complex subunit 5 0.4050 0.5206 0.4899 P60709 ACTB Actin. cytoplasmic 1 0.3010 0.2164 0.2500 Q99439 CNN2 Calponin-2 0.5653 1.2121 0.9130 P35612 ADDB Beta-adducin 0.4505 0.5356 0.7874 Q13813 SPTN1 Spectrin alpha chain. non-erythrocytic 1 0.6223 0.9501 0.9308 P50552 VASP Vasodilator-stimulated phosphoprotein 0.4809 0.7258 0.6589 O75083 WDR1 WD repeat-containing protein 1 0.6081 0.5059 0.4914 Q16181 SEPT7 Septin-7 0.3998 0.2507 0.3119 P17931 LEG3 Galectin-3 0.3903 0.4969 0.6100 Q96QK1 VPS35 Vacuolar protein sorting-associated protein 35 0.4053 0.3453 0.7819 Q15833 STXB2 Syntaxin-binding protein 2 0.4043 0.5382 0.6145 P68036 UB2L3 Ubiquitin-conjugating enzyme E2 L3 0.3696 0.6336 0.5254 P30040 ERP29 Endoplasmic reticulum resident protein 29 0.5751 0.8206 0.6180 Q13492 PICAL Phosphatidylinositol-binding clathrin assembly protein 0.6298 0.6427 0.6980 P30049 ATPD ATP synthasesubunit delta. mitochondrial 0.5906 0.8645 0.9240 P25705 ATPA ATP synthasesubunit alpha. mitochondrial 0.3060 0.2776 0.5251 O75390 CISY Citrate synthase. mitochondrial 0.6794 0.5843 0.6636 Int. J. Mol. Sci. 2021,22, 226 8 of 22 Table 4. Cont. UniProt Code UniProt Name Protein Name Fold Change Relative to Healthy Controls Untreated ERT-a ERT-b P30044 PRDX5 Peroxiredoxin-5. mitochondrial 0.4486 0.6043 0.5455 P06576 ATPB ATP synthase subunit beta. mitochondrial 0.4890 0.7480 0.7076 Q99798 ACON Aconitatehydratase. mitochondrial 0.5100 0.4129 0.6735 P13804 ETFA Electron transfer flavoprotein subunit alpha. mitochondrial 0.2472 0.1565 0.1542 P10809 CH60 60 kDa heat shock protein. mitochondrial 0.1905 0.4708 0.3593 P61604 CH10 10 kDa heat shock protein. mitochondrial 0.3779 0.3537 0.3176 P00505 AATM Aspartate aminotransferase. mitochondrial 0.5693 0.4255 0.3930 P40926 MDHM Malate dehydrogenase. mitochondrial 0.6669 0.5397 0.6540 P69905 HBA Hemoglobin subunit alpha 0.2772 0.6311 0.4201 P68871 HBB Hemoglobin subunit beta 0.2063 0.6678 0.3896 P55072 TERA Transitional endoplasmic reticulum ATPase 0.2347 0.1977 0.3911 Q14697 GANAB Neutral alpha-glucosidase AB 0.5454 0.4364 0.4613 P08133 ANXA6 Annexin A6 0.4899 0.7086 0.9723 P09525 ANXA4 Annexin A4 0.4646 0.6825 0.7587 P04083 ANXA1 Annexin A1 0.2010 0.5650 0.6752 P52209 6PGD 6-phosphogluconate dehydrogenase decarboxylating 0.1136 0.1570 0.2218 P05089 ARGI1 Arginase-1 0.2834 0.4069 0.4421 P29401 TKT Transketolase 0.3432 0.3358 0.2993 Q16762 THTR Thiosulfate sulfurtransferase 0.4924 0.7856 0.8081 P30566 PUR8 Adenylosuccinatelyase 0.2364 0.1361 0.7371 Q00013 EM55 55 kDa erythrocyte membrane protein 0.4414 0.4275 0.6262 P02766 TTHY Transthyretin 0.4808 0.4167 0.5573 Q9H2U2 IPYR2 Inorganic pyrophosphatase2.mitochondrial 0.5533 0.6667 0.7373 Q7L5Y6 DET1 DET1 homolog 0.3518 0.5613 0.6315 P19971 TYPH Thymidine phosphorylase 0.2521 0.2706 0.4983 P00488 F13A Coagulation factor XIII A chain 0.2992 0.3087 0.3043 P06737 PYGL Glycogen phosphorylase. liver form 0.1167 0.1441 0.2094 P62136 PP1A Serine/threonine-protein phosphatase PP1-alpha catalytic subunit 0.5690 0.4578 0.5951 P30101 PDIA3 Protein disulfide-isomerase A3 0.5598 1.0088 1.1788 P55786 PSA Puromycin-sensitive aminopeptidase 0.4389 0.6256 0.6560 P07741 APT Adenine phosphoribosyltransferase 0.2659 0.2516 0.3201 Q06323 PSME1 Proteasome activator complexsubunit 1 0.3035 0.4744 0.4342 P08571 CD14 Monocyte differentiation antigen CD14 0.4434 0.3594 1.0551 P02652 APOA2 Apolipoprotein A-II 0.3696 0.2974 0.8084 P30086 PEBP1 Phosphatidylethanolamine-binding protein 10.5324 0.6353 0.8049 P17612 KAPCA cAMP-dependent protein kinase catalytic subunit alpha 0.2921 0.2831 0.4253 P01860 IGHG3 Immunoglobulin heavy constant gamma 3 0.5132 0.4999 0.4892 Q5VTE0 EF1A3 Putative elongation factor 1-alpha-like 3 0.3466 0.3686 0.4249 Q9NTK5 OLA1 Obg-like ATPase 1 0.1099 0.3431 0.2186 P62826 RAN GTP-binding nuclear protein Ran 0.1277 0.1178 0.3363 O00299 CLIC1 Chloride intracellular channel protein 1 0.2541 0.4800 0.6103 P38606 VATA V-type proton ATPase catalytic subunit A 0.3386 0.4063 0.3411 P31948 STIP1 Stress-induced-phosphoprotein 1 0.3071 0.5992 0.5741 Q15366 PCBP2 Poly(rC)-binding protein 2 0.4584 0.3282 0.4541 P61978 HNRPK Heterogeneous nuclear ribonucleoprotein K 0.5087 0.5807 0.6304 P09651 ROA1 Heterogeneous nuclear ribonucleoprotein A1 0.5798 0.6174 0.6532 P26583 HMGB2 High mobility group protein B2 0.1873 0.4159 0.6264 P16402 H13 Histone H1.3 0.0761 0.2883 0.2277 P16401 H15 Histone H1.5 0.0896 0.7384 1.0462 P40199 CEAM6 Carcinoembryonic antigen-related cell adhesion molecule 6 0.2537 0.4984 0.7378 Q92882 OSTF1 Osteoclast-stimulating factor 1 0.2963 0.6296 0.9094 Fold-change values in red indicate proteins that were not significantly dysregulated compared with healthy controls (p> 0.05). For more details, Supplementary Table S3. Protein related to metabolic pathway of pyruvate is shown in grey, glucose metabolism in green, cytoskeleton organization in clear gray, lysosome membranes reparation in blue, vesicle traffic and vesicle fusion in dark blue, oxygen transport in dark pink, mitochondrial organelle in pink, N-glycan metabolism pathway in yellow, DNA binding in clear orange, metabolite interconversion activity in dark green, metabolic and cellular process in dark orange, catalytic activity or proteins related to transport in purple and other proteins in white. Int. J. Mol. Sci. 2021,22, 226 9 of 22 We observed post-treatment normalization of the expression of several proteins involved in mitochondrial energy activity, assembly, or function (Table 4, pink rows) (ATPD, ATPA, CISY, PRDX5, ATPB), although others remained downregulated after ERT (ETFA, CH60, CH10, AATM, MDHM). TERA, which is involved in the formation of the transitional endoplasmic reticulum (tER), it was partially normalized only in the ERT-b group. GANAB, a catalytic subunit of glucosidase II of Glc2Man9GlcNAc2 oligosaccharide, acts as an immature precursor of glycoproteins involved in the N-glycan metabolism pathway, and was down regulated relative to healthy controls in all MPS IVA patient groups (Table 4, yellow rows). We also observed normalization after ERT of the expression of cytoplasmic and extracellular proteins (HBA, HBB) involved in oxygen transport and of proteins implicated in DNA binding (H13, H15). Proteins involved in the interconversion of metabolites, including hydrolases, dehydrogenases, transketolases, and isomerases (6PGD, ARGI1, TKT, THTR, PUR8, EM55, TTHY, IPYR2, DET1) were normalized after ERT (Table 4, dark gray rows). Among proteins involved in metabolic and cellular processes we observed normalization of PDIA3 and PSA; partial normalization of APT, PSME1, CD14, APOA2, PEBP1, and KAPCA; and no change in the expression of IGHG3 and EF1A3 (Table 4, dark pink rows). Several proteins with catalytic activity or transport-related functions showed partial normalization of expression after ERT (OLA1, RAN, CLIC1), although one such protein (VATA) showed no changes in expression (Table 4, orange rows). Proteins with DNA-binding functions (STIP1, PCBP2, HNRPK, ROA1, HMGB2) were partially or fully normalization after ERT (Table 4, clear red rows). Similarly, partial normalization of expression was observed for CEAM6, which belongs to the carcinoembryonic antigen-related cell adhesion molecule family, and OSTF1, which participates in bone resorption, interacts with secretion factors during bone formation, and participates in osteoclast development (Table 4, white rows). Dysregulated proteins for which partial or complete normalization was observed after ERT are shown in the interaction diagrams in Figure 4. Figure 4A depicts the string network analysis of all 91 proteins that were downregulated in untreated MPS IVA patients relative to healthy controls. The results of the same analysis performed after treatment reveal a less complex network, given that many proteins were normalized by ERT. Int. J. Mol. Sci. 2021, 22, x 10 of 23 expression (Table 4, orange rows). Proteins with DNA-binding functions (STIP1, PCBP2, HNRPK, ROA1, HMGB2) were partially or fully normalization after ERT (Table 4, clear red rows). Similarly, partial normalization of expression was observed for CEAM6, which belongs to the carcinoembryonic antigen-related cell adhesion molecule family, and OSTF1, which participates in bone resorption, interacts with secretion factors during bone formation, and participates in osteoclast development (Table 4, white rows). Dysregulated proteins for which partial or complete normalization was observed after ERT are shown in the interaction diagrams in Figure 4. Figure 4A depicts the string network analysis of all 91 proteins that were downregulated in untreated MPS IVA patients relative to healthy controls. The results of the same analysis performed after treatment reveal a less complex network, given that many proteins were normalized by ERT. (A) (B) Figure 4. String network analysis. (A) Proteins downregulated in untreated MPS IVA patients. (B) Proteins (n = 32) that remain dysregulated or partially dysregulated in ERT-treated MPS IVA patients. Abbreviations: MPS IVA, Mucopolysaccharidosis type IVA; ERT, enzyme replacement therapy. Coronin 1A (CORO1A) is a crucial component of the cytoskeleton of highly mobile cells. This protein is involved in the invagination of large pieces of the plasma membrane and in the formation of protrusions of the plasma membrane, thereby contributing to cell locomotion. We carried out an interactome study using STING. As shown in Figure 5, we observed that CORO1A interacts with other specific proteins such as OSTF1, which participates in bone resorption and enhances osteoclast formation. In bone, CORO1A also regulates cathepsin K, which promotes degradation of collagen I and II. Figure 4. String network analysis. ( A ) Proteins downregulated in untreated MPS IVA patients. ( B ) Proteins (n= 32) that remain dysregulated or partially dysregulated in ERT-treated MPS IVA patients. Abbreviations: MPS IVA, Mucopolysaccharidosis type IVA; ERT, enzyme replacement therapy. Int. J. Mol. Sci. 2021,22, 226 16 of 22 of proteins expressed in all conditions combining these two approaches allowed us to confirm the effect of ERT on the expression of dysregulated proteins in MPS IVA [76,77]. 3.1. Limitations of the Study In our cohort we identified and analyzed 690 proteins with 99% reliability. The SWATH method is a specific, reproducible, and sensitive approach, allowing relative or absolute protein quantification. However, some limitations of this approach should be noted. First, the number of proteins identified/quantified is largely limited by the composition of the spectral library. In this study, the library was generated with a pool of proteins for each condition, and consisted of a total of 690 proteins, all with a FDR < 1%. In contrast to the selected reaction monitoring (SRM) technique, in which only three transitions are quantified, in the SWATH technique, seven transitions are quantified for each peptide. Therefore, 10 peptides per protein must be identified in order to extract the peak areas necessary for quantification. Despite the precision of this technique, this also constitutes a limitation, as proteins in the library for which less than 10 peptides are identified will not be quantified. Due to this limitation, the SWATH method may be unable to detect collagens, pro-inflammatory factors and lysosomal enzymes that degrade substrates in the lysosome. Interestingly, our analysis detected TRFL, which modulates interleukins, and PRTN3, which degrades collagen, but failed to detect any collagens, interleukins, or lysosomal enzymes. Another limitation of our study is that normalization of protein expression in leukocytes does not necessarily correlate with normalization in chondrocytes in the avascular region. The enzyme is easily taken up by the leukocytes, restoring the function of dysregulated metabolic pathways in these cells while the infused enzyme circulates in the blood. It is critical to understand whether these proteins are dysregulated in bone and cartilage in MPS IVA patients in order to identify potential diagnostic biomarkers of disease severity. When a bone-penetrating drug becomes available, surrogate biomarkers will be essential. 3.2. Conclusions Lysosomal disorders are characterized by significant alterations in proteins caused by the accumulation of undegraded substrate. We have identified a large set of proteins that are dysregulated in leukocytes from untreated MPS IVA patients, and many of which are fully or partially normalized following ERT. Several of these proteins are implicated in bone metabolism and are therefore potential biomarkers of the severity of bone disease in MPS IVA. These candidate proteins should be investigated in bone and cartilage specimens in MPS IVA patients to determine the extent to which they truly reflect bone pathology. 4. Materials and Methods 4.1. Study Work Flow The graphic below outlines the workflow used to identify dysregulated proteins in leukocytes. Analyses consisted of both qualitative (LC-MS/MS) and quantitative (SWATHMS) proteomic methods (Figure 9). Blood samples were collected from 3 groups of participants: untreated MPS IVA patients; ERT-treated MPS IVA patients (sampled before ERT and 1day after), and healthy controls. Blood samples were separated into plasma and leukocytes. The leukocytes were lysed, and proteomic analyses performed. The workflow in Figure 9depicts the qualitative analysis (left), which identified proteins expressed in leukocytes, and the quantitative analysis (right), which determined the number of specific proteins expressed in leukocytes. After these two distinct proteomics approaches, bioinformatics analyses were performed to obtain more information about the identified/quantified proteins. Int. J. Mol. Sci. 2021,22, 226 17 of 22 Int. J. Mol. Sci. 2021, 22, x 18 of 23 the blood. It is critical to understand whether these proteins are dysregulated in bone and cartilage in MPS IVA patients in order to identify potential diagnostic biomarkers of disease severity. When a bone-penetrating drug becomes available, surrogate biomarkers will be essential. 3.2. Conclusions Lysosomal disorders are characterized by significant alterations in proteins caused by the accumulation of undegraded substrate. We have identified a large set of proteins that are dysregulated in leukocytes from untreated MPS IVA patients, and many of which are fully or partially normalized following ERT. Several of these proteins are implicated in bone metabolism and are therefore potential biomarkers of the severity of bone disease in MPS IVA. These candidate proteins should be investigated in bone and cartilage specimens in MPS IVA patients to determine the extent to which they truly reflect bone pathology. 4. Materials and Methods 4.1. Study Work Flow The graphic below outlines the workflow used to identify dysregulated proteins in leukocytes. Analyses consisted of both qualitative (LC-MS/MS) and quantitative (SWATH-MS) proteomic methods (Figure 9). Figure 9. Schematic illustrating the study workflow showing both qualitative (LC-MS/MS, left) and quantitative (SWATH-MS, right) proteomic methods. Blood samples were collected from 3 groups of participants: untreated MPS IVA patients; ERT-treated MPS IVA patients (sampled before ERT and 1day after), and healthy controls. Blood samples were separated into plasma and leukocytes. The leukocytes were lysed, and proteomic analyses performed. The workflow in Figure 9 depicts the qualitative analysis (left), which identified proteins expressed in leukocytes, and the quantitative analysis (right), which determined the number of specific proteins expressed in leukocytes. After these two distinct proteomics approaches, bioinformatics analyses were performed to obtain more information about the identified/quantified proteins. 4.2. Samples Cell samples for proteomic analyses were obtained from MPS IVA patients in 3 hospitals in Spain after receiving informed consent: 8 untreated patients and 5 patients who underwent weekly ERT. In the ERT group, blood samples were obtained before Figure 9. Schematic illustrating the study workflow showing both qualitative (LC-MS/MS, left) and quantitative (SWATH-MS, right) proteomic methods. 4.2. Samples Cell samples for proteomic analyses were obtained from MPS IVA patients in 3 hospitals in Spain after receiving informed consent: 8 untreated patients and 5 patients who underwent weekly ERT. In the ERT group, blood samples were obtained before (ERT-a) and 24 h after (ERT-b) treatment. All patients presented the classical MPS IVA phenotype. For the control group, blood samples were obtained from 6 healthy donors. 4.3. Protein Extraction Leukocytes were sonicated to rupture the membrane and release proteins, and then centrifuged for 10 min at 10,000 × rpm and 4 ◦ C. Protein extracts were recovered from the supernatant and subsequently frozen at −20 ◦C. 4.4. Enzyme Activity Test An enzyme activity test was used to analyze GALNS enzymatic activity in samples [ 78 ]. Results are shown in Table 7(physiologicalrange, 1.8–20.0 nM/h/mg). Table 7. Enzymatic activity of GALNS. UG EA nM/h/mg Before ERT EA nM/h/mg After ERT EA nM/h/mg Healthy Controls EA nM/h/mg 1 0.2 ERT-a 1 0.7 ERT-b 1 1.8 CG 1 4.8 2 0.2 ERT-a 2 0.6 ERT-b 2 2.1 CG 2 14.2 3 0.2 ERT-a 3 0.7 ERT-b 3 2.2 CG 3 2.7 4 0.0 ERT-a 4 1.0 ERT-b 4 2.8 CG 4 3.6 5 0.1 ERT-a 5 1.6 ERT-b 5 6.7 CG 5 3.1 6 0.2 - - - - CG 6 4.8 7 0.1 - - - - - - 8 0.2 - - - - - - EA, enzymatic activity; ERT, enzyme replacement therapy; ERT-a, MPS IVA patients sampled before ERT; ERT-b, MPS IVA patients sampled 24 h after ERT; UG, untreated group. 4.5. Proteomic Analysis Protein identification and quantification were performed as described in bibliography [ 61 , 79 – 82 ]. For protein identification, digested peptides from each sample were separated using reverse phase chromatography. The gradient was developed using a micro liquid chromatography system (Eksigent Technologies nanoLC 400, Sciex, Redwood City, CA, USA) coupled to a high-speed Triple TOF 6600 mass spectrometer (Sciex, Redwood Int. J. Mol. Sci. 2021,22, 226 18 of 22 City, CA, USA) with a microflow source. The analytical column used was a Chrom XP C18 silica-based reversed-phase column (150 × 0.30 mm) with a 3mm particle size and 120Å pore size (Eksigent, Sciex Redwood City, CA, USA). The trap column was a YMCTRIART C18 (YMC Technologies Teknokroma Analítica, Barcelona, Spain), with a 3mm particle size and 120Å pore size, that was switched on-line with the analytical column. Data were acquired with a TripleTOF 6600 System (Sciex, Redwood City, CA, USA) using a data-dependent workflow (DDA). For the SWATH analysis a spectral library was created using pooled samples from each group (healthy controls, untreated patients, and the ERT-a and ERT-b groups) using a DDA. Next, peak extraction was performed using the MS/MSALL add-in for PeakView Software (v. 2.2., Sciex, Redwood City, CA, USA) with the SWATH Acquisition MicroApp (v. 2.0., Sciex, Redwood City, CA, USA). Only peptides with a confidence score > 99% (as obtained from a Protein Pilot database search) were included in the spectral library. SWATH–MS acquisition was performed on a TripleTOF ® 6600 LC-MS/MS system (Sciex, Redwood City, CA, USA). Samples from each group were analyzed using the data-independent acquisition (DIA) method. Targeted data extraction of the fragment ion chromatogram traces from the SWATH runs was performed in PeakView (v. 2.2) using the SWATH Acquisition MicroApp (v. 2.0). The integrated peak areas (processed mrkvw files from PeakView) were directly exported to MarkerView software (Sciex, Redwood City, CA, USA) for relative quantitative analysis. Unsupervised multivariate statistical analysis using principal component analysis (PCA) was performed to compare data across the samples using a range scale. The average MS peak area for each protein was derived from the biological replicates of the SWATH-MS of each sample, followed by analysis using a Student’s t-test (MarkerView software, sciex, Redwood City, CA, USA) to compare between samples based on the averaged total area of all transitions for each protein. The t-test result (p-value) indicates how well each variable distinguishes the two groups. Candidate proteins were selected for each library based on t-test results (p-value < 0.05 and FC (increase or decrease) > 1.4). Functional analysis was performed by FunRich (Functional Enrichment analysis tool) open-access software for functional enrichment and interaction network analysis (http: //funrich.org/index.html).Reactome (https://reactome.org). STRING (https://string-db. org).PANTHER (http://pantherdb.org). Supplementary Materials: The following are available online at https://www.mdpi.com/1422-006 7/22/1/226/s1. Author Contributions: Contributions were provided by the authors in the statements: conceptualization, V.J.Á., S.B.B. and M.L.C.; methodology, S.B.B., V.J.Á., M.P.C.-V. and C.C.; validation, V.J.Á. and S.B.B.; sample collection. I.V., M.J.D.C. and M.M.; formal analysis, V.J.Á., S.B.B. and C.C.; writing— original draft preparation, V.J.Á., S.B.B.; writing—review and editing, M.L.C., S.T.; visualization, S.B.B.; supervision, M.L.C., F.J.O.-E. and S.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: This study was approved by the local Ethics Committee of Santiago-Lugo (data approval 22 May 2017 Reference number 2017/298). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available in this article and this Supplementary Material. Acknowledgments: We acknowledge to the patients and controls for allowing us to collect and analyze the samples. Conflicts of Interest: The authors have no conflicts of interest to declare. Int. J. Mol. Sci. 2021,22, 226 19 of 22 References 1. Hendriksz, C.J.; Berger, K.I.; Giugliani, R.; Harmatz, P.; Kampmann, C.; Mackenzie, W.G.; Raiman, J.; Villarreal, M.S.; Savarirayan, R. International Guidelines for the Management and Treatment of Morquioa Syndrome. Am. J. Med. Genet. A 2015 ,167A, 11–25. [CrossRef] [PubMed] 2. 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