Identification of Potential Muscle Biomarkers in McArdle Disease: Insights from Muscle Proteome Analysis

Citation: García-Consuegra, I.; Asensio-Peña, S.; Garrido-Moraga, R.; Pinós, T.; Domínguez-González, C.; Santalla, A.; Nogales-Gadea, G.; Serrano-Lorenzo, P.; Andreu, A.L.; Arenas, J.; et al. Identification of Potential Muscle Biomarkers in McArdle Disease: Insights from Muscle Proteome Analysis. Int. J. Mol. Sci. 2022,23, 4650. https:// doi.org/10.3390/ijms23094650 Academic Editors: Jose Renato Pinto and Daniel Taillandier Received: 28 December 2021 Accepted: 18 April 2022 Published: 22 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Molecular Sciences Article Identification of Potential Muscle Biomarkers in McArdle Disease: Insights from Muscle Proteome Analysis Inés García-Consuegra 1,2,†, Sara Asensio-Peña 1,† , Rocío Garrido-Moraga 1, Tomàs Pinós2,3, Cristina Domínguez-González 1,2 , Alfredo Santalla 4, Gisela Nogales-Gadea 5, Pablo Serrano-Lorenzo 1,2 , Antoni L. Andreu 6, Joaquín Arenas 1,2, JoséL. Zugaza 7,8 , Alejandro Lucia 1,9 and Miguel A. Martín1,2,* 1Mitochondrial and Neuromuscular Disorders Group, Hospital 12 de Octubre Health Research Institute (imas12), 28041 Madrid, Spain; [email protected] (I.G.-C.); [email protected] (S.A.-P.); rociogarridor[email protected] (R.G.-M.); [email protected]g (C.D.-G.); [email protected] (P.S.-L.); joaquin.ar[email protected]g (J.A.); [email protected] (A.L.) 2Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 28029 Madrid, Spain; [email protected] 3Mitochondrial and Neuromuscular Disorders Unit, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain 4Department of Computer and Sport Sciences, Universidad Pablo de Olavide, 41013 Sevilla, Spain; [email protected] 5Grup de Recerca en Malalties Neuromusculars i Neuropediàtriques, Department of Neurosciences, Institut d’Investigacio en Ciencies de la Salut Germans Trias i Pujol i Campus Can Ruti, Universitat Autònoma de Barcelona, 08916 Barcelona, Spain; [email protected] 6EATRIS, European Infrastructure for Translational Medicine, 1019 Amsterdam, The Netherlands; [email protected] 7Achucarro Basque Center for Neuroscience, Science Park of the UPV/EHU, and Department of Genetics, Physical Anthropology, and Animal Physiology, Faculty of Science and Technology, UPV/EHU, 48940 Leioa, Spain; [email protected] 8IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain 9Faculty of Sport Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain *Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Glycogen storage disease type V (GSDV, McArdle disease) is a rare genetic myopathy caused by deficiency of the muscle isoform of glycogen phosphorylase (PYGM). This results in a block in the use of muscle glycogen as an energetic substrate, with subsequent exercise intolerance. The pathobiology of GSDV is still not fully understood, especially with regard to some features such as persistent muscle damage (i.e., even without prior exercise). We aimed at identifying potential muscle protein biomarkers of GSDV by analyzing the muscle proteome and the molecular networks associated with muscle dysfunction in these patients. Muscle biopsies from eight patients and eight healthy controls showing none of the features of McArdle disease, such as frequent contractures and persistent muscle damage, were studied by quantitative protein expression using isobaric tags for relative and absolute quantitation (iTRAQ) followed by artificial neuronal networks (ANNs) and topology analysis. Protein candidate validation was performed by Western blot. Several proteins predominantly involved in the process of muscle contraction and/or calcium homeostasis, such as myosin, sarcoplasmic/endoplasmic reticulum calcium ATPase 1, tropomyosin alpha-1 chain, troponin isoforms, and alpha-actinin-3, showed significantly lower expression levels in the muscle of GSDV patients. These proteins could be potential biomarkers of the persistent muscle damage in the absence of prior exertion reported in GSDV patients. Further studies are needed to elucidate the molecular mechanisms by which PYGM controls the expression of these proteins. Keywords: PYGM; myophosphorylase; proteomics; McArdle disease; GSDV; iTRAQ; skeletal muscle; metabolic myopathy; protein biomarkers Int. J. Mol. Sci. 2022,23, 4650. https://doi.org/10.3390/ijms23094650 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2022,23, 4650 2 of 18 1. Introduction Glycogen storage disease type V (GSDV) (OMIM#232600), also known as McArdle disease, is a rare autosomal recessive myopathy caused by biallelic pathogenic mutations in the PYGM gene [ 1 ] that result in deficiency of the skeletal muscle isoform of glycogen phosphorylase (or ‘myophosphorylase’, PYGM) [ 2 ]. GSDV has been reported to have an estimated prevalence of 1 in 100,000—350,000 people [2–5]. Because PYGM catalyzes the first rate-limiting step of glycogen metabolism (i.e., removal of terminal alpha-1,4-glycosidic bonds from the outer branches of this molecule to release glucose-1-phosphate), deficiency of this enzyme leads to a block in the use of glycogen as an energy source for muscle contraction [ 6 ]. Typical clinical features consist of muscle ‘crises’ of pain and fatigue, together with tachycardia during the first minutes of dynamic exercise (e.g., brisk walking) that are attenuated after 7–10 min have elapsed—the so-called ‘second wind’ phenomenon [ 2 , 7 ]. These episodes of early exercise intolerance are frequently accompanied by severe muscle contractures, potentially leading to rhabdomyolysis and subsequent myoglobinuria, as reflected by ‘dark urines’. Yet, another feature of the disease is a persistent status of muscle damage—(as reflected by very high circulating levels of intra-muscle proteins such as creatine kinase [CK]), even in the absence of physical exercise on the previous day(s) [8]. More than 170 pathogenic mutations (including missense, nonsense, in-frame, frameshift, and splicing variants) have been identified in the PYGM gene that cause McArdle disease [ 9 , 10 ]. Most of these mutations result in a total absence of PYGM activity [ 11 ] in the patients’ muscle tissue, except for two patients carrying deep-intronic mutations in compound heterozygosity that led to some residual (~1% of normal) enzyme activity, with subsequent amelioration in clinical phenotype [ 12 ]. There is no association between the PYGM genotype and disease phenotype, since patients with the same mutation(s) can show quite different degrees of clinical severity [ 13 ]. The pathobiology of GSDV is not fully understood, but it seems that the potential molecular consequences of the lack of glycogenolytic–derived ATP involve not only the expected energetic deficit for actin-myosin cross bridging, but also impairments in membrane pump function, excitation–contraction coupling, and sarcolemmal excitability [14]. In an attempt to identify potential muscle protein biomarkers and gain insight into the pathobiology of GSDV, we analyzed the targeted proteome in skeletal muscle biopsies obtained from both patients with histochemical and genetic diagnoses of GSDV and healthy controls. In this regard, since skeletal muscle is the only tissue that is clinically affected in all patients with GSDV, the control tissue was skeletal muscle biopsies from aged and sex-matched healthy individuals with normal PYGM activity and no signs of the typical features of McArdle disease, such as frequent exercise-induced contractures or persistent muscle damage in the absence of prior exertion. We first used isobaric tags for relative and absolute quantitation (iTRAQ) analysis [ 15 ] to compare the muscle protein expression in patients vs. controls. This was followed by a systems biology network-based approach to identify key proteins involved in distinct pathways that could be related to the GSDV phenotype, such as the breakdown of muscle fibers, muscle contractures, and impairment in calcium homeostasis or in other physiological processes of the skeletal muscle. To this end, we applied the therapeutic performance mapping system (TPMS) machine learning-based technology, particularly by applying artificial neuronal networks (ANNs) that were ‘trained’ using the human protein network and drug-pathophysiology knowledge [ 16 , 17 ]. This technology has proven useful to identify non-obvious functional relationships for drug repurposing purposes [ 18 – 20 ] and biological data analysis and prioritization of proteins according to documented relationships with pathophysiological processes [ 21 – 23 ], especially in rare diseases or when sample sizes are limited. After the prioritization process, levels of the selected candidate proteins were analyzed using Western blot analyses. Our results indicate that in addition to PYGM, myosin 1 (MYH-1), tropomyosin alpha1 chain (TPM1), sarcoplasmic/endoplasmic reticulum calcium ATPase 1 (ATP2A1, also Int. J. Mol. Sci. 2022,23, 4650 3 of 18 abbreviated as SERCA1), troponin isoforms (troponin I2, fast skeletal type [TNNI2] and troponin T3, fast skeletal type [TNNT3]), and alpha-actinin-3 (ACTN3) show a relationship with GSDV, with their levels reduced in the skeletal muscle tissue of GSDV patients with respect to healthy controls. Most of these proteins are involved in muscle contractures associated with altered calcium homeostasis. 2. Results The main characteristics of the patients are shown in Tables 1and 2. Sex distribution (50% and 62.5% female in patients and controls; Chi-square test’s p= 0.625) and mean (±SD) age (patients: 38 ± 12 years; controls: 40 ± 9 years; Mann–Whitney’s U p= 0.711) did not differ between the two groups. Table 1. Demographic and disease characteristics of the GSDV patients. Patient PYGM PYGM Age Sex CK (U/L) Severity Class 2 Muscle Used for Biopsy PYGM PYGM Allele 1 1Allele 2 1(Years) Stain (Muscle) Activity (Muscle) 1c.148C>T c.148C>T 19 F1250 2Biceps brachii Absent NR p.(R50*) p.(R50*) 2c.148C>T c.1366G>A 34 F969 2Biceps brachii Absent NR p.(R50*) p.(V457M) 3c.2262delA c.2262delA 32 F500 1Vastus lateralis Absent NR p.(K754Nfs*) p.(K754Nfs*) 4c.148C>T c.148C>T 24 M 533 2Vastus lateralis Absent Absent p.(R50*) p.(R50*) 5c.148C>T c.613G>A 52 F2328 2Biceps brachii Absent NR p.(R50*) p.(G205S) 6c.148C>T c.2111C>T 48 M4889 2Biceps brachii Absent NR p.(R50*) p.(A704V) 7c.148C>T c.347T>C 55 M1330 2Biceps brachii Absent NR p.(R50*) p.(L116P) 8c.2392T>C c.2392T>C 43 M1550 2Biceps brachii Absent Absent p.(W798R) p.(W798R) 1 PYGM reference sequence: NM_005609.4; 2 as determined with the most commonly used phenotype severity scale for GSDV, the so-called ‘Martinuzzi scale’ (ranging from 0 [lowest] to 3 [highest]) for this disease [ 24 ]; where: 0 = asymptomatic or virtually asymptomatic (mild exercise intolerance, but no functional limitation in any daily life activity); 1 = exercise intolerance, contractures, myalgia, and limitation of acute strenuous exercise, and occasionally in daily life activities; no record of myoglobinuria, no muscle wasting or weakness; 2 = same as 1, plus recurrent exertional myoglobinuria, moderate restriction in exercise, and limitation in daily life activities; 3 = same as 2, plus fixed muscle weakness, with or without wasting and severe limitations on exercise and most daily life activities. Table 2. Main characteristics of the healthy control group. Control Age Sex CK Muscle Used for Biopsy PYGM Stain (Muscle) (Years) (U/L) 1 41 M N.A. Biceps brachii Normal 2 27 F <200 Vastus lateralis Normal 3 35 F <200 Biceps brachii Normal 4 52 M N.A. Biceps brachii Normal 5 35 M <200 Biceps brachii Normal 6 40 F <200 Biceps brachii Normal 7 56 F <200 Biceps brachii Normal 8 34 F N.A. Vastus lateralis Normal By quantitative proteome analysis of skeletal muscle biopsies obtained from the Biceps brachii or Vastus lateralis of eight GSDV patients and eight healthy controls using iTRAQ labeling followed by reversed-phase liquid chromatography-mass spectrometry (RP-LC-MS/MS), 178 proteins were identified. The patient and healthy control samples were separately pooled, and parallel double labeling was performed for each pool, resulting Int. J. Mol. Sci. 2022,23, 4650 4 of 18 in two label values per group (113 and 115 for patients and 114 and 116 for controls); all values were referenced to the values of the 113 patients’ pool (Supplementary Table S1). The peptide value distribution for each protein with peptide number >10 was compared between control and patient pool values, respectively, to obtain a total of 21 proteins with comparable control pool values, on the one hand, and differences between controls and patients, on the other (Table 3). These results were used to set a control/patient value ratio-based threshold, considering the mean of this value for these 21 proteins (= 1.675). Next, we detected the most differentially expressed proteins by calculating the control/patient value ratio of the global data (i.e., for all 178 proteins detected [Supplementary Table S1] regardless of the number of peptides measured) and identified 15 proteins with control/patient values ratio >1.676 (Table 4). To allow analyses with TPMS technology, all data were mapped to 14 unique reviewed SwissProtKB entries (Table 4). Nine of the fourteen proteins exhibited at least a two-fold higher change in one of the control pools compared to the 113-labeled patient pool, which was used as a reference for labeling the rest of the proteins (indicated as bold values in Table 4). The possible relationship between the most differentially expressed proteins (Table 4) and GSDV attending to their ‘molecular characterization’ was evaluated by means of ANNs (Table 5and Supplementary Table S3 show results considering GSDV as a whole or considering the different motives separately, respectively). Attending to the associated p-values, we sorted the ANN ranking score into four categories: ‘very strong’ (p< 0.01), ‘strong’ (p< 0.05) , ‘medium-strong’ (p< 0.25), and ‘weak’ (p> 0.25) (Supplementary Table S4) . Three proteins, ATP2A1, MYH1, and TPM1, showed a very strong relationship with GSDV (Table 5). These three proteins were part of the functional motif elevated cytosolic calcium levels, and specifically of the persistent contraction of muscle cell sub-motif for MYH1 and TPM1 (Supplementary Tables S2 and S3). The troponin isoforms, TNNI2 and TNNT3, displayed a strong relationship with GSDV (Table 5) and were also effectors of the sub-motif persistent contraction of muscle cells. All the proteins were related to muscle structure and activity and showed a stronger relationship with GSDV definition than the enzyme PYGM, the defective protein in GSDV, which presented a medium-strong score with GSDV molecular characterization and was assigned as an effector of the glycogenolytic pathway. The PDZ and LIM domain protein 7 (PDLIM7) and alpha-actinin-3 (ACTN3) showed a medium-strong score, and the rest of the evaluated proteins showed a low probability of being related to GSDV in a molecular-dependent manner (Table 5), according to the used molecular characterization. After evaluating the relationship between the most differentially expressed proteins and each GSDV motif, as described by molecular characterization (Supplementary Table S3) , we observed that the motif that exhibited the highest probability of a relationship with the available data was elevated cytosolic calcium levels, and particularly the submotif persistent contraction of muscle cell. Most of these proteins showed a strong or medium-strong probability of a relationship with the motif and submotif. In fact, for all the candidate proteins, the highest probability score was observed for elevated cytosolic calcium levels. Int. J. Mol. Sci. 2022,23, 4650 5 of 18 Table 3. Proteins with peptide number > 10, or with comparable peptide values’ distribution between control pool values, or with different peptide value distribution between control and GSDV patient pool values, respectively. UniProt ID Protein Gene Control/Patient Values Ratio Controls (114/113 vs. 116/113) FDR q-Values Controls vs. Patients 115/113 vs. 114/113 115/113 vs. 116/113 P11217 Glycogen phosphorylase, muscle isoform PYGM 4.527 0.23 5.99 ×10−11 5.99 ×10−11 P20929-2 Nebulin NEB 1.597 0.25 7.31 ×10−71.97 ×10−7 Q14324 Myosin-binding protein C, fast-type MYPC2 2.035 0.22 3.39 ×10−55.50 ×10−6 P14618-2 Pyruvate kinase, isoform-1 PKM1 1.379 0.03 1.58 ×10−81.01 ×10−10 P08237 ATP-dependent 6-phosphofructokinase, muscle type PFKM 1.521 0.26 8.77 ×10−58.77 ×10−5 P04075 Fructose-bisphosphate aldolase A ALDOA 1.626 0.02 2.80 ×10−65.35 ×10−7 Q8WZ42-11 Titin, isoform-11 TTN 11 1.492 0.05 1.01 ×10−10 1.48 ×10−11 Q08043 Alpha-actinin-3 ACTN3 2.265 0.26 1.91 ×10−72.92 ×10−8 P00558-2 Phosphoglycerate kinase 1, isoform 2 PGK1 1.578 0.04 3.39 ×10−53.39 ×10−5 O14983-2 Isoform SERCA1A sarcoplasmic/endoplasmic reticulum calcium ATPase 1 ATP2A1 1.680 0.26 2.66 ×10−54.24 ×10−6 O60662 Kelch-like protein 41 KLHL41 1.469 0.26 2.12 ×10−52.12 ×10−5 P54296 Myomesin-2 MYOM2 1.465 0.45 2.12 ×10−52.12 ×10−5 P13929 β-enolase ENO3 1.542 0.03 3.39 ×10−55.50 ×10−6 P16615-2 Isoform 2-sarcoplasmic/endoplasmic reticulum calcium ATPase 2 ATP2A2A 1.363 0.24 7.75 ×10−31.74 ×10−3 P12882 Myosin-1 MYH-1 1.972 0.04 8.49 ×10−10 1.46 ×10−10 P04406-2 Isoform2-glyceraldehyde-3-phosphate dehydrogenase GAPDH 1.422 0.04 2.29 ×10−42.29 ×10−4 P06576 ATP synthase subunit beta, mitochondrial ATP5F1B 1.157 0.13 1.63 ×10−22.69 ×10−3 P25705 ATP synthase subunit alpha, mitochondrial ATP5F1A 1.142 0.07 1.34 ×10−21.05 ×10−2 P17661 Desmin DES 1.200 0.14 1.54 ×10−32.83 ×10−5 P06732 Creatine kinase M-type CKM 1.517 0.05 6.43 ×10−91.12 ×10−9 Q14315-2 Isoform 2-filamin-C FLNC 1.253 0.05 5.83 ×10−51.20 ×10−5 Control/patient values ratio mean 1.676 The table also shows the control/patient value ratio (considering the mean between 114/113 and 116/113 values as ‘control value’ and 115/113 value as ‘patient value’), the lowest q-value obtained for comparison within controls, and the highest q-value obtained for comparison between controls and patients. Tests applied: Student’s t-test, Wilcoxon rank-sum, or one-way ANOVA. Table 4. Most differentially expressed muscle proteins in GSDV vs. controls. UniProt ID 115/113 Value 114/113 Value 116/113 Value Control/Patient Value Ratio SwissProtKB ID 1Protein Gene P11217 0.924 4.056 4.315 4.527 P11217 Glycogen phosphorylase, muscle form PYGM H9KVA2 0.916 1.979 2.233 2.298 P45378 Troponin T, fast skeletal muscle TNNT3 C9JZN9 1.079 2.263 2.668 2.286 Q08043 0.933 2.002 2.224 2.265 Q08043 Alpha-actinin-3 ACTN3 P09493 0.987 1.955 2.265 2.137 P09493 Four and a half LIM domains protein 3 FHL3 Q13642-1 1.050 1.942 2.408 2.072 Q13642 Tropomyosin alpha-1 chain TPM1 Q14324 0.979 1.790 2.196 2.035 Q14324 Isoform 1 of four and a half LIM domains protein 1 FHL1 P12882 1.018 1.863 2.151 1.972 P12882 Myosin-1 MYH1 Q13643 1.150 1.992 2.410 1.913 Q13643 Isoform 6 of PDZ and LIM domain protein 7 PDLIM7 Q9NR12-6 1.027 1.844 1.919 1.832 Q9NR12 Myosin-binding protein C, fast-type MYBPC2 Int. J. Mol. Sci. 2022,23, 4650 6 of 18 Table 4. Cont. UniProt ID 115/113 Value 114/113 Value 116/113 Value Control/Patient Value Ratio SwissProtKB ID 1Protein Gene Q96A32 1.078 1.712 2.079 1.759 Q96A32 Isoform 1A of sarcoplasmic/endoplasmic reticulum calcium ATPase 1 ATP2A1 P15121 0.910 1.473 1.676 1.731 P15121 Myosin regulatory light chain 2, skeletal muscle isoform MYLPF A0A087WXS0 0.961 1.686 1.614 1.718 P48788 Troponin I, fast skeletal muscle TNNI2 Q96DG6 0.942 1.606 1.592 1.697 Q96DG6 Carboxymethylenebutenolidase homolog CMBL O14983-2 1.030 1.758 1.705 1.680 O14983 Aldose reductase AKR1B1 1 Proteins showing at least a two-fold change in one control pool compared with the patient 113 pool are in boldface. UniProt ID as mapped by the SEQUEST algorithm. Values in boldface indicate patient values with respect to 113-labeled control values > 2. SwissProtKB ID indicates the corresponding reviewed SwissProt KB identifier associated with each proteome result. Table 5. Relationship between candidate proteins and GSDV described by molecular characterization. Gene 1Uniprot ID ANN Score 2Score Category 3GSDV Effector and Motif 4Interactor GSDV Effector Genes MYH (*) P12882 93 Very strong Elevated cytosolic calcium levels - Persistent contraction of muscle cell ATP2A1 [25] O14983 93 Very strong Elevated cytosolic calcium levels - TPM1 (*) P09493 92 Very strong Elevated cytosolic calcium levels MYH11;MYH6;MYH8;MYL1;MYL6;TNNC1;TNNI1;TNNI2;TNNT1; TNNT3;TPM2;TPM3;TPM4 Persistent contraction of muscle cell TNNI2 (*) P48788 85 Strong Elevated cytosolic calcium levels MYH6;MYH8;MYL1;TNNC1;TNNI1;TNNT1;TNNT3;TPM1;TPM2; TPM3;TPM4 Persistent contraction of muscle cell TNNT3 (*) P45378 83 Strong Elevated cytosolic calcium levels MYH6;MYH8;MYL1;TNNC1;TNNI1;TNNI2;TNNT1;TPM1;TPM2; TPM3;TPM4 Persistent contraction of muscle cell PYGM (**) P11217 75 Medium-Strong Glycogenolysis blockade GBE1 *; GYS1 *; PHKA1 *; TPM2 PDLIM7 Q9NR12 66 Medium-Strong - TPM2 ACTN3 Q08043 43 Medium-Strong - MYH6;MYH8;MYL1;TNNC1;TNNI1;TNNI2;TNNT1;TNNT3;TPM1; TPM2;TPM3;TPM4 MYBPC2 Q14324 38 Weak - MYH6;MYH8;MYL1;TNNC1;TNNI1;TNNI2;TNNT1;TNNT3;TPM1; TPM2;TPM3;TPM4 FHL3 Q13643 36 Weak - - FHL1 Q13642 27 Weak - - MYLPF Q96A32 18 Weak - MYH11;MYL6;MYLK2;SLC2A4 *; TPM1;TPM2;TPM3;TPM4 AKR1B1 P15121 17 Weak - - CMBL Q96DG6 10 Weak - - 1 Superscript near the gene indicates the source from which the information for column ‘GSDV effector and motif’ was obtained: (*) Molecular Cell Biology 4th Edition, Textbook, ISBN-13, 978-0-7167-3706-3; (**) OMIM#232600, Online Mendelian Inherited in Man, https://omim.org (accessed on 15 February 2021); 2 ranking scores for the probability of relationship with the whole characterization of GSDV by means of ANNs; 3 category of the ANN ranking score (see Supplementary Table S2); 4 whether protein was previously described as implicated in GSDV or processes associated with muscle degradation. Int. J. Mol. Sci. 2022,23, 4650 7 of 18 To further understand the intermolecular relationships identified by ANNs, we generated a protein interactome with the human protein network used for model construction and based on publicly available sources. This allowed us to identify the interaction between the most differentially expressed (‘candidate’) proteins and the effector proteins identified as important in GSDV molecular characterization (Table 5, Figure 1). Most of the candidates showed an interaction with effectors of the biological motives elevated cytosolic calcium levelspersistent contraction of muscle cell (Supplementary Tables S3 and S5). However, two of the most differentially expressed proteins, the skeletal muscle isoform of the myosin regulatory light chain 2 (MYLPF) and PYGM, interacted with proteins belonging to the modulation of alternative metabolic pathways for energy obtainment-increased glucose uptake motif. In addition, according to the databases used (see topological analysis in the methods section), six of the most differentially expressed proteins (i.e., MYH1, ATP2A1, isoform 1 of four and a half LIM domains protein 1 (FHL1), four and a half LIM domains protein 3 (FHL3), aldose reductase (AKR1B1) and carboxymethylenebutenolidase homolog (CMBL)) did not directly interact with any of the GSDV effectors nor with any other most differentially expressed proteins (Table 5). Int.J.Mol.Sci.2022,23,xFORPEERREVIEW8of18    Figure1.Interactomenetworkmapbetweenthemostdifferentiallyexpressedproteinsandthe genesofGSDVeffectors.Thefigureshowsthemostdifferentiallyexpressedproteinsandtheirin‐ ternalrelationships,aswellastherelationshipswiththeGSDVeffectors.Symbolsindicatewhether theproteinisincludedasaneffectorofeachGSDVpathophysiologicalmotif.Themostdifferentially expressedproteinsarehighlightedaccordingtoacoloredgradientshowingthecontrol/patientval‐ uesratio.Proteinswithexpressionlevelsatleasttwo‐foldhigherinonecontrolthaninpatient113 (i.e.,114/113or116/113values)aremarkedbyathickborderinthecorrespondingsymbol.GSDV effectorsnotdetectedwithinthemostdifferentiallyexpressedproteinsaredepictedinpurplecolor. NetworkbuiltusingTPMShumanproteinnetwork[16,17]andvisualizedusingCytoscapeversion 3.0.0.[26]. TovalidatethepredictiveresultsobtainedusingtheANNanalysisstrategy,skeletal musclelevelsofaselectedgroupofcandidateproteinswerealsoanalyzedbyWestern blotinGSDVpatientsandhealthycontrols.Weselectedascandidatesthemostdifferen‐ tiallyexpressedproteinsthatwereclassifiedinthe‘verystrong’and‘strong’categories accordingtotheirrelationshipwithGSDVasawhole(Table5):MYH1,ATP2A1,TPM1, TNNI2,andTNNT3.Besidestheseproteins,ACTN3wasalsoconsideredacandidateand analyzeddespitebeingrankedinthemedium‐strongcategory,duetotworelevantrea‐ sons:(i)ithasbeendocumentedtointeractwithPYGMandimplicatedinalteredmuscle calciumhandlingintheActn3deficient(knockout)mousemodel[27],and(ii)atleastin femalepatients,ACTN3genotypesmightcontributetoexplainingindividualvariability inthephenotypicmanifestationofthisdisorder[28,29].Weshowedthattheexpression levelsofalltestedcandidates(MYH1,ATP2A1,TPM1,TNNI2,TNNT3,andACTN3)were significantlylowerinpatientsthanincontrols(Figure2andSupplementaryFigureS1). Figure 1. Interactome network map between the most differentially expressed proteins and the genes of GSDV effectors. The figure shows the most differentially expressed proteins and their internal relationships, as well as the relationships with the GSDV effectors. Symbols indicate whether the protein is included as an effector of each GSDV pathophysiological motif. The most differentially expressed proteins are highlighted according to a colored gradient showing the control/patient values ratio. Proteins with expression levels at least two-fold higher in one control than in patient 113 (i.e., 114/113 or 116/113 values) are marked by a thick border in the corresponding symbol. GSDV effectors not detected within the most differentially expressed proteins are depicted in purple color. Network built using TPMS human protein network [ 16 , 17 ] and visualized using Cytoscape version 3.0.0. [26]. From the list of the most differentially expressed proteins, myosin light chain, phosphorylatable, fast skeletal muscle (MYLPF, Q96A32), and myosin binding protein (MYBPC2, Q14324) were not predicted to be related to GSDV (weak relationship in Table 5); however, Int. J. Mol. Sci. 2022,23, 4650 8 of 18 they appeared highly connected to proteins within the elevated cytosolic Ca 2+ levels motif (Figure 1), which could explain the medium-strong signal detected between these proteins and this motif (Supplementary Table S3) despite not being its effectors. To validate the predictive results obtained using the ANN analysis strategy, skeletal muscle levels of a selected group of candidate proteins were also analyzed by Western blot in GSDV patients and healthy controls. We selected as candidates the most differentially expressed proteins that were classified in the ‘very strong’ and ‘strong’ categories according to their relationship with GSDV as a whole (Table 5): MYH1, ATP2A1, TPM1, TNNI2, and TNNT3. Besides these proteins, ACTN3 was also considered a candidate and analyzed despite being ranked in the medium-strong category, due to two relevant reasons: (i) it has been documented to interact with PYGM and implicated in altered muscle calcium handling in the Actn3 deficient (knockout) mouse model [ 27 ], and (ii) at least in female patients, ACTN3 genotypes might contribute to explaining individual variability in the phenotypic manifestation of this disorder [ 28 , 29 ]. We showed that the expression levels of all tested candidates (MYH1, ATP2A1, TPM1, TNNI2, TNNT3, and ACTN3) were significantly lower in patients than in controls (Figure 2and Supplementary Figure S1). Int.J.Mol.Sci.2022,23,xFORPEERREVIEW9of18    Figure2.Westernblotanalysisinskeletalmuscletissueofthecandidateproteinsthatwereclassified inthe‘verystrong’and‘strong’categoriesinthe‘molecularcharacterization’ofGSDVbymeansof artificialneuralnetworkanalysis.Upperpanel:RepresentativeblotsfromGSDVpatientsand healthycontrols(allpatients’andcontrols’Westernblotsforeachcandidateproteindisplayedin SupplementaryFigureS1);alpha‐tubulinwasusedasloadingcontrol.Bottompanel:Proteinlevels inGSDVpatients(P,n=7)comparedtohealthycontrols(C,n=6).*p<0.05.**p<0.01,***p<0.001 usingMann–Whitneytest.Eachdotrepresentsthemeanoftwoquantifications(i.e.,twotechnical replicates).Dispersionlinesindicatemean±SD. 3.Discussion GSDVisametabolicmyopathytypicallycharacterizedbyexerciseintolerance(i.e., musclepainandearlyexertionalfatigue).Iftheexercisestressisnotreducedorhalted, severemusclecontractures(beyondtheusualbaselinestateof‘persistent’musclecontrac‐ tionanddamage)andeventualrhabdomyolysismightoccur,whichinsomecases,could resultinacuterenalfailure[4,30].Althoughtheknowledgeofthemolecularandpatho‐ physiologicmechanismsofGSDVhasimprovedduringthelasttwodecades,particularly withinsightsprovidedbyclinical,molecular,orphysiologicalstudiesinpatients [4,14,24,31–35],aswellasbystudiesinpreclinicalmodels[21,34,36–40],thereisstillno explanation(atleastatthemolecularlevel)forsomerecognizedclinicalfeaturesofthe disease,notablythepersistentmuscledamageintheabsenceofpreviousphysicalexercise [8]. Wethereforeaimedatinvestigatingindepththemuscleproteomeandthemolecular networksassociatedwithmuscledysfunctioninGSDVpatientsinanattempttoidentify keymuscleproteinsasbiomarkersthatcouldhelptounderstandtheunderlyingmolecu‐ larmechanismsofmuscledysfunctionordamage.Tothebestofourknowledge,this questionhasnotbeenexploredpreviously.Inacase‐controldesignwithmusclebiopsies fromhistochemicalandgeneticallyprovenGSDVpatientsandfromhealthycontrols,we assessedquantitativeproteinexpressionusingtheiTRAQtechniqueandthenperformed asystemsbiology‐basedstrategy,particularlyapplyingANNsandtopologyinteractome networkstoidentifythebestcandidates.Ouranalysissuggestedthatsomeoftheidenti‐ fiedcandidateproteinsarerelatedtoGSDVdiseasepredominantlythroughthemotifper‐ sistentcontractionofmusclecellsduetoelevatedcytosoliccalciumlevels,withtheproteins ACTN3,ATP2A1,MYH1,TNNT3,TPM1,andTNNI2showingthehighestpredictiveval‐ uesamongalltheproteinsevaluated.Furthermore,thetopologicalanalysisindicatedthat thecandidateproteinsidentifiedinthisstudyinteractwithproteinsinvolvedintheper‐ sistentcontractionofmusclecellsduetoelevatedcytosoliccalciumlevelsandthemodu‐ lationofalternativemetabolicpathwaysforenergyobtainment. ThelevelsofACTN3,ATP2A1,MYH1,TNNT3,TPM1,andTNNI2proteinswere significantlylowerintheskeletalmuscleofpatientscomparedwithhealthycontrols. MYH1isaskeletalmuscleproteinthat,incoordinationwithactin,playsanessentialrole inthegenerationofenergyformusclecontractionthroughATPhydrolysis[41].ATP2A1, thesarcoplasmic/endoplasmicreticulumcalciumATPase1(previouslyknownas SERCA1),isamembraneproteinthatisresponsibleforthetransportofcalciumfromthe Figure 2. Western blot analysis in skeletal muscle tissue of the candidate proteins that were classified in the ‘very strong’ and ‘strong’ categories in the ‘molecular characterization’ of GSDV by means of artificial neural network analysis. Upper panel: Representative blots from GSDV patients and healthy controls (all patients’ and controls’ Western blots for each candidate protein displayed in Supplementary Figure S1); alpha-tubulin was used as loading control. Bottom panel: Protein levels in GSDV patients (P, n= 7) compared to healthy controls (C, n= 6). * p< 0.05. ** p< 0.01,*** p< 0.001 using Mann–Whitney test. Each dot represents the mean of two quantifications (i.e., two technical replicates). Dispersion lines indicate mean ±SD. 3. Discussion GSDV is a metabolic myopathy typically characterized by exercise intolerance (i.e., muscle pain and early exertional fatigue). If the exercise stress is not reduced or halted, severe muscle contractures (beyond the usual baseline state of ‘persistent’ muscle contraction and damage) and eventual rhabdomyolysis might occur, which in some cases, could result in acute renal failure [ 4 , 30 ]. Although the knowledge of the molecular and pathophysiologic mechanisms of GSDV has improved during the last two decades, particularly with insights provided by clinical, molecular, or physiological studies in patients [ 4 , 14 , 24 , 31 – 35 ], as well as by studies in preclinical models [ 21 , 34 , 36 – 40 ], there is still no explanation (at least at the molecular level) for some recognized clinical features of the disease, notably the persistent muscle damage in the absence of previous physical exercise [8]. We therefore aimed at investigating in depth the muscle proteome and the molecular networks associated with muscle dysfunction in GSDV patients in an attempt to identify key muscle proteins as biomarkers that could help to understand the underlying molecular mechanisms of muscle dysfunction or damage. To the best of our knowledge, this question has not been explored previously. In a case-control design with muscle biopsies from histochemical and genetically proven GSDV patients and from healthy controls, we assessed Int. J. Mol. Sci. 2022,23, 4650 9 of 18 quantitative protein expression using the iTRAQ technique and then performed a systems biology-based strategy, particularly applying ANNs and topology interactome networks to identify the best candidates. Our analysis suggested that some of the identified candidate proteins are related to GSDV disease predominantly through the motif persistent contraction of muscle cells due to elevated cytosolic calcium levels, with the proteins ACTN3, ATP2A1, MYH1, TNNT3, TPM1, and TNNI2 showing the highest predictive values among all the proteins evaluated. Furthermore, the topological analysis indicated that the candidate proteins identified in this study interact with proteins involved in the persistent contraction of muscle cells due to elevated cytosolic calcium levels and the modulation of alternative metabolic pathways for energy obtainment. The levels of ACTN3, ATP2A1, MYH1, TNNT3, TPM1, and TNNI2 proteins were significantly lower in the skeletal muscle of patients compared with healthy controls. MYH1 is a skeletal muscle protein that, in coordination with actin, plays an essential role in the generation of energy for muscle contraction through ATP hydrolysis [ 41 ]. ATP2A1, the sarcoplasmic/endoplasmic reticulum calcium ATPase 1 (previously known as SERCA1), is a membrane protein that is responsible for the transport of calcium from the sarcoplasm back into the sarcoplasmic reticulum after each sarcomeric contraction, and whose function is dependent on the energy delivered by ATP hydrolysis. Likewise, ATPA21 contributes to the excitation/contraction balance involved in muscle activity [ 42 ]. A decrease in ATP2A1 levels would result in an impairment in the reuptake of calcium back into the sarcoplasmic reticulum after each contraction, with subsequent accumulation of this ion in the sarcoplasm and impairment of muscle fiber relaxation—that is, permanent muscle contraction and muscle contractures. Interestingly, besides the association of primary pathogenic genetic variants in the ATP2A1 gene with Brody myopathy (OMIM#601003, a rare autosomal recessive disorder characterized by painless muscle cramping and exercise-induced impaired muscle relaxation) [ 43 ], other conditions linked with aging, neurodegeneration, and muscular dystrophy also depress ATP2A1 function with the potential to impair intracellular calcium homeostasis and contribute to muscle atrophy and weakness [ 42 ]. There is some controversy on how to assess calcium homeostasis in different human diseases since most research has been performed in murine models [ 44 – 47 ]. On the other hand, the stability of actin filaments in the muscle fibers is ensured by the function of tropomyosin (TPM1), which, in association with the troponin complex (TNNI2 and TNNT3), plays a key role in the regulation of calcium-dependent interactions during muscle contraction [ 48 ]. In addition, ACTN3 plays an important role in the stability of the contractile apparatus at the Z-line, where this protein cross-links and anchors actin filaments [ 49 ]. Therefore, our findings suggest that decreased expression of the aforementioned proteins in GSDV could be associated, at least in part, with the altered muscle contractile function and a probable alteration of muscle calcium kinetics in this disorder. On the other hand, PYGM could also be involved not only in energy generation from glycogen breakdown, but also in the O-linked β -N-acetylglucosamine (O-GlcNa)c post-translational modifications of some proteins [ 6 , 50 ]. In this effect, O-GlcNAcylation plays an important role in several skeletal muscle functions, including optimal modulation of calcium homeostasis in fibers [51,52]. Our study is limited by the small sample size, although we believe this is justifiable in the context of a rare condition such as McArdle disease. We also failed to collect all the samples from the same muscle, although the vast majority of samples corresponded to the Biceps brachii, and the proportion of muscle type (i.e., 6/2 for Biceps brachii/Vastus lateralis) was identical in patients and healthy controls. Importantly, our approach also lacked a comparison group of patients with similar features to those of McArdle disease, such as muscle contractures—although we are not aware of any neuromuscular condition where muscle contractures are as frequent or persistent as in McArdle disease—and therefore we cannot address if the detected differentially expressed proteins are primarily or secondarily regulated. In addition, it must be kept in mind that with regard to potential biomarkers of McArdle disease, our findings must be viewed as mechanistic—hopefully providing useful insights and framework for future research—rather than practical ones since muscle Int. J. Mol. Sci. 2022,23, 4650 16 of 18 15. 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