Quantitative Proteomics and Phosphoproteomics Analysis Revealed

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Quantitative proteomics and phosphoproteomics analysis revealed different regulatory mechanisms of Halothane and Rendement Napole genes in porcine muscle metabolism Honggang Huang, Tracy L. Scheffler, David E. Gerrard, Martin R. Larsen, and Rene Lametsch J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00294 • Publication Date (Web): 19 Jun 2018 Downloaded from http://pubs.acs.org on June 20, 2018

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Journal of Proteome Research

Quantitative proteomics and phosphoproteomics analysis revealed different regulatory mechanisms of Halothane and Rendement Napole genes in porcine muscle metabolism Honggang Huang1, 2, 3, 4*, Tracy L. Scheffler5, David E. Gerrard6, Martin R. Larsen1, René Lametsch2 1

Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-

5230, Odense M, Denmark 2

Department of Food Science, Faculty of Science, University of Copenhagen, DK-1958,

Frederiksberg, Denmark 3

The Danish Diabetes Academy, Odense, Denmark

4

Arla Foods Ingredients Group P/S, Soenderupvej 26, 6920 Videbaek, Denmark

5

Department of Animal Sciences, University of Florida, USA

6

Department of Animal and Poultry Sciences, Virginia Tech, USA

* Corresponding author E-mail: [email protected]; Tel: +45 35333483

1 ACS Paragon Plus Environment

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Abstract Pigs with the Halothane (HAL) and/or Rendement Napole (RN) gene mutations demonstrate abnormal muscle energy metabolism patterns and produce meat with poor quality, classified as pale, soft and exudative (PSE) meat, but it is not well understood how HAL and RN mutations regulate glucose and energy metabolism in porcine muscle. In order to investigate the potential signaling pathways and phosphorylation events related to these mutations, muscle samples were collected from four genotypes of pigs: wildtype, RN, HAL and RN-HAL double mutations and subjected to quantitative proteomic and phosphoproteomic analysis using the TiO2 enrichment strategy. The study led to the identification of 932 proteins from the non-modified peptide fractions and 1885 phosphoproteins with 9619 phosphorylation sites from the enriched fractions. Among them, 128 proteins at total protein level and 323 phosphosites from 91 phosphoproteins were significantly regulated in mutant genotypes. The quantitative analysis revealed that the RN mutation mainly affected the protein expression abundance in muscle. Specifically, high expression was observed for proteins related to mitochondrial respiratory chain and energy metabolism, thereby enhancing the muscle oxidative capacity. The high content of UDP-glucose pyrophosphorylase 2 (UGP2) in RN mutant animals may contribute to high glycogen storage. However, the HAL mutation mainly contribute to the up-regulation of phosphorylation in proteins related to calcium signaling, muscle contraction, glycogen, glucose and energy metabolism, and cellular stress. The increased phosphorylation of Ca2+/calmodulin dependent protein kinase II (CAMK2) in HAL mutation may play as a key regulator in these processes of muscle. Our findings indicate the different regulatory mechanisms of RN and HAL mutations in relation to porcine muscle energy metabolism and meat quality. Keywords: Porcine muscle; RN and HAL mutation; muscle metabolism; glycogen metabolism; CAMK2; phosphoproteomics 2 ACS Paragon Plus Environment

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Abbreviations: PM, postmortem; PSE meat, pale, soft, and exudative meat; HAL, Halothane; RN, Rendement Napole; PTMs, posttranslational modifications; iTRAQ, Isobaric tags for relative and absolute quantitation; LC-MS/MS, liquid chromatography tandem mass spectrometry; TiO2, titanium dioxide; HILIC, hydrophilic interaction liquid chromatography; AMPKγ3, AMP-activated protein kinase; RYR1, muscle ryanodine receptor; GP, glycogen phosphorylase; GS, glycogen synthase; UGP2, UDP-glucose pyrophosphorylase 2; CAMK2, Ca2+/calmodulin dependent protein kinase II; PKA, protein kinase A; PHKB, phosphorylase b kinase subunit beta; MYL2, myosin regulatory light chain 2;



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Introduction The two gene mutations in pigs, RN and HAL, can both independently influence the rate and extent of muscle pH decline postmortem (PM), and have a major influence on final meat quality.1, 2 RN mutant (RN-RN-) pigs possess elevated muscle glycogen deposition and demonstrate an extended glycolysis in the muscle, resulting in an abnormally low ultimate pH in meat 24 h PM.3 The mutation responsible for the RN gene is a R200Q substitution identified in the muscle speciﬁc isoform of AMP-activated protein kinase (AMPKγ3),4 which functions as the major energy sensor in muscle. The RN mutation results in constitutive AMPK activation with increased phosphorylation and enhanced skeletal muscle-specific glucose transporter (GLUT-4) expression, thereby increasing the glucose uptake and glycogen storage.5 HAL mutant (nn) pigs have a higher incidence of acute stress-induced death with lactic acidosis than wild-type pigs suggesting skeletal muscle from HAL mutant pigs possess enhanced capabilities for glycogenolysis and glycolysis.6 Meat from mutant pigs is often of poor andundesired quality, classified as pale, soft and exudative (PSE) meat resulting from rapid metabolism of stored carbohydrate in the muscle postmortem, and thus a lower pH early PM when carcass temperatures are still elevated. The causative mutation was identified in the skeletal muscle ryanodine receptor (RYR1) with a R615C mutation.7 RYR1 is responsible for regulating sarcoplasmic Ca2+ release and energy utilization, the mutation causes the increased Ca2+ release and energy utilization.8,

9

Meanwhile, the porcine R615C mutation in

RyR1can also increase muscle fiber size and cause fiber hypertrophy.10 While the exact mechanisms responsible for causing the rapid and extended glycolysis in HAL and RN muscle pig muscle respectfully are quite controversial and remain largely unknown, these mutations represent an outstanding model for studying the mechanisms controlling the conversion of muscle to meat. Four pig genotypes: homozygous normal, RN, HAL and HAL-RN double mutations have previously been used to assess the mechanisms responsible for controlling postmortem metabolism 4 ACS Paragon Plus Environment

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by Gerrard and colleagues.6, 11 RN mutant pigs had significantly higher AMPK activity through increased phosphorylation levels, this contributed to enhanced skeletal muscle specific glucose transporter 4 (GLUT-4) expression, resulting in the high glycogen content in muscles, and activation of AMPK caused muscle to assume a slower-contracting, more oxidative muscle fiber phenotype nature. Curiously, the Hal mutation blunted AMPK activation in the presence of the RN mutation.5, 12 Others have suggested that early AMPK activation can contribute to the increased content and phosphorylation level of downstream targets, and the development of PSE meat.13, 14 To better understand the influence of high AMPK activity in RN mutant pigs on downstream target proteins and the interaction between HAL and RN mutations through the regulation of AMPK phosphorylation, we performed the systematic mechanistic study using a proteomic and phosphoproteomic strategy. Proteomics is a powerful tool for studying quality variation in meat due to processing and genetic backgrounds.15-17 Development of phosphoproteomic methods enables the intensive studies of protein phosphorylation in various muscle systems,18-20 and revealed that degree of phosphorylation can affect the metabolism and contraction of muscle. In PM muscle, gel-based phosphoproteomic approaches demonstrated that pork with rapid pH decline had the highest global phosphorylation level at 1 h PM, but lowest at 24 h, whereas the pork with slow pH decline rate showed the reverse case, the phosphorylation of several glycolytic enzymes showed changes and may affect PM pH decline rate.21 However, the phosphorylation pattern of myofibrillar proteins was mainly changed with PM time, but not pH decline rate.22 Phosphorylation changes in PM pork and beef related to genetics and electrical stimulation were also characterized.23,

24

Furthermore, LC-MS/MS based

quantitative phosphoproteomics approaches have been employed to investigate phosphorylation events in PM meat.25 The work confirmed that PM muscle proteins underwent significant changes at the phosphorylation level but were relatively stable at the total protein level, suggesting that



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protein phosphorylation can regulate proteins involved in glucose metabolism and muscle contraction, thereby affecting glycolysis and rigor mortis development in PM muscle. Together, these data show the utility and promise of proteomics and phosphoproteomics in studying meat quality development. Characterization of HAL and RN mutant pigs is essential to understand the mechanisms by which these two genes regulate protein expression in living muscle and affect PM muscle metabolism, thereby affecting PM meat quality development. In this study, quantitative proteomic and TiO2 based phosphoproteomic approaches were employed to analyze the muscle samples from four genotypes of pigs: wildtype (normal), RN, HAL and RN-HAL double mutants. This study facilitated the identification of the key proteins responsible for the typical aberrant quality associated with the HAL and RN mutations. The results can also shed light on the underlying mechanisms of the HAL and RN mutations affecting PM muscle metabolism and meat quality. Experimental Procedures Animal information and characterization Pig muscle samples were obtained from Prof. Gerrard’s research group at Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), USA. The usage of pigs was reviewed and approved by the Virginia Tech Animal Care and Use Committee. The muscle samples were collected from the lumbar region of the longissimus muscle from four homozygous genotypes of pig groups: normal (NN/rn+rn+), RN mutant (NN/RN-RN-), HAL mutant (nn/rn+rn+) and double mutant (nn/RN-RN-) genotypes

26

. The genetic

characterization and collection of the biochemical data related to meat quality and muscle metabolites were performed as described.26 Porcine longissimus dorsi (LD) muscle samples collected at 45 min PM from 12 pigs (3 animals for each genotype) were analyzed in this study. The


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biochemical data related to meat quality and muscle metabolites specific for these 12 animals were characterized in this study, These biochemical data included the muscle pH value at 45 min and 1440 min (24h) PM, the concentration of muscle adenosine nucleotides (IMP, ADP and ATP) at PM 45min, the concentration of muscle glucose, glucose-6-phosphate, glycogen and lactate at 0 min, 45 min and 1440 min PM. Four muscle samples representing 4 genotypes were set as one experimental group for proteomic and phosphoproteomic analysis. Muscle samples from 12 pigs (3 animals for each genotype) were used for the triplicate biological experimental groups. Muscle sample preparation In the experiment, 200 mg of each frozen muscle tissue was minced and homogenized in 600ul of ice-cold homogenizing buffer containing 6M urea, 2M thiourea, 2% (w/v) SDS, 1% (w/v) DTT, complete protease inhibitor (Roche, Hvidovre, Denmark, one tablet per 50 mL) and phosphatase inhibitor PhosStop (Roche, Hvidovre, Denmark, two tablets per 50 mL) on ice with a Ultra-Turrax T25 homogenizer equipped with a S25N-18 G dispersing element (Ika Labortechnik, Staufen, Germany). Samples were homogenized twice for 30 s each at 9,500 rpm followed by twice for 30s each at 13,500 rpm. Between each homogenization step, the samples were incubated for 30s on ice. The proteins were sonicated on ice using a probe tip sonicator. Then the samples were centrifuged at 4°C for 30 min at 25,000×g; and the supernatant was carefully collected avoiding the fat layer, aliquoted and stored at −80°C until use. The protein concentration was determined using the Bradford method (Bio-Rad Protein Assay). In-solution trypsin digestion and iTRAQ labeling The extracted protein from each sample was subjected to purification using 10KDa spin filters to remove SDS, The protein pellet in filter was diluted in 500 µl 50 mM triethylammonium bicarbohydrateate buffer (TEAB) buffer, and the protein concentration was determined by Qubit



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assay (Invitrogen, Waltham, MA). Then 500 µg of proteins from each sample were reduced with 10 mM dithiothereitol (DTT) for 1 h at 25 °C and alkylated in 40 mM freshly prepared iodoacetamide for 50 min at 25 °C in the dark. The alkylated proteins were subsequently digested with 0. 04 AU Lys-C (Wako, Japan) for 3h at room temperature, and trypsin (2%, w/w) was added for further digestion at 37°C overnight. The samples were acidified to a final concentration of 2% formic acid and 0.1% trifluoroacetic acid (TFA) and centrifuged at 20000 × g for 30 mins to precipitate the lipids. The acidified peptides were desalted and purified with a Hydrophilic-Lipophilic-Balance solid phase extraction (HLB-SPE) (Waters, Bedford, MA) cartridge according to the manufacturer's instructions, and the eluted peptides were lyophilized and subsequently resuspended in 200 µL 50 mM TEAB buffer before isobaric tagging for relative and absolute quantification (iTRAQ) labeling, and the concentration was measured using Qubit assay. A total of 100 µg peptides from each sample was labeled with iTRAQTM (Applied Biosystems, Foster City, CA) as described by the manufacturer (normal/control, iTRAQ-114; RN mutant, iTRAQ-115; HAL mutant, iTRAQ-116 and RN/HAL double mutant, iTRAQ-117). After labeling, the samples were mixed 1:1:1:1 and dried by vacuum centrifugation. The ratio and labeling efficiency were evaluated by MALDI-TOF-MSMS. The experiment was performed in biological triplicate. Deglycosylation and TiO2 enrichment The iTRAQ labelled peptides were resuspended in 100 µl of 50 mM TEAB, pH 8.0 and deglycosylated with 500 U of PNGase F (New England Biolabs, Ipswich, MA) and 0.1 U Sialidase A (Prozyme, Hayward, CA) for 12 h at 37 °C to remove the glycan groups from potential N-linked glycosylated peptides, which can also bind with TiO2,27 then the peptides were dried by vacuum centrifugation. Afterward, the TiO2 enrichment was performed to enrich the phosphopeptides.28-30 The labelled peptides were dissolved in 100 µl 0.1% TFA, and then diluted 10 times with loading buffer of 80% ACN, 5% TFA and 1 M glycolic acid. A total of 2.4 mg TiO2 (0.6 mg TiO2 per 100


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µg peptides) was added to the solution, shaken for 10 min at 600 rpm and centrifuged. The supernatant was collected carefully and incubated with half the amount of TiO2. The TiO2 beads were firstly washed with 100 µL of loading buffer by mixing for 15 s, transferred to a new tube and centrifuged to pellet the beads, then washed with 100 µL of a solution containing 80% ACN and 1% TFA, and followed by washing with 100 µL of a solution containing 20% ACN and 0.1% TFA. The peptides were eluted with 100 µL eluting buffer (40 µL 28% ammonia solution in 960 µL water, pH 11.3) and centrifuged for 1 min. The eluent was collected and passed through a C8 stage tip to remove TiO2 beads and the peptides attached to the C8 tip were subsequently eluted with 10 µL 30% ACN. The eluted peptides were dried by vacuum centrifugation. The unbound peptides and subsequent washes were pooled and dried by vacuum centrifugation to get the non-modified peptide fraction. The non-modified peptide fraction was resuspended in 0.1% TFA, purified by Oligo R2 Reversed phase column and dried by vacuum centrifugation. Hydrophilic interaction liquid chromatography (HILIC) fractionation After enrichment, the non-modified peptide flow-through fractions and the enriched phosphopeptide fractions were further pre-fractionated by micro-HPLC-HILIC using an in-house packed TSKgel Amide-80 HILIC (Tosoh, 5 µm) 320 µm × 170 mm µHPLC column by using the Agilent 1200 micro-HPLC instrument. Briefly, samples were suspended in solvent B (90 % ACN, 0.1% TFA) by adding 10% TFA followed by water and finally the acetonitrile was slowly added to the aqueous solution in order to prevent peptide precipitation by the high acetonitrile concentration. Peptides were loaded onto the HILIC column and eluted at 6 µL/min by decreasing the solvent B concentration (100%−60% ACN, 0.1 % TFA in water) in 42 min. Fractions were automatically collected in a 96 well plate at 1 min intervals after UV detection at 210 nm and some fractions were pooled to a total of 10 fractions according to intensity from UV detection, dried by vacuum centrifugation, and stored at −20 °C until LC-MS/MS analysis. 9 ACS Paragon Plus Environment


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Liquid chromatography tandem mass spectrometry (LC-MS/MS) The peptides (resuspended in 0.1% FA) were automatically injected and loaded onto a ReproSil-Pur C18 AQ (Dr. Maisch, Ammerbuch-Entringen, Germany) in-house packed trap column (2 cm x 100 µm inner diameter; 5 µm), and separated on an analytical ReproSil-Pur C18 AQ (Dr. Maisch, Ammerbuch-Entringen, Germany) packed in-house column (17 cm x 75 µm; 3 µm) by reversed phase chromatography on an EASY-nanoLC system (Thermo Fisher Scientific). Mobile phase was buffer B (95% ACN/0. 1% formic acid) and buffer A (0.1% formic acid). Samples were loaded using intelligent flow control at a maximum pressure of 250 bar. Depending on the HILIC intensity of samples, gradients were run at 250 nL/min with a profile of: 0–34% buffer B over 60 min or 90 min, 30 - 50% buffer B in 10 min, 50 - 100% buffer B in 5 min and 8 min at 100% B before returning to buffer A for re-equilibration. The nano-LC was connected to a Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) operating in positive ion mode and using data-dependent acquisition. The Orbitrap acquired the full MS scan with an automatic gain control (AGC) target value of 1e6 ions and a maximum fill time of 120 ms. Each MS scan was acquired with high-resolution 70, 000 full-width half maximum (FWHM) at m/z 200 in the Orbitrap with a mass range of 400-1600 Da. The top 12 most abundant peptide ions were selected from the MS for higher energy collision-induced dissociation (HCD) MS2 fragmentation (normalized collision energy: 31V) in the Orbitrap. Fragmentation was performed at high resolution (17500 FWHM) for a target of 2e4 and a maximum injection time of 150 ms using an isolation window of 1.5 m/z and dynamic exclusion duration of 15 s with a 10 ppm tolerance around the selected precursor. Only those precursors with charge state +2, +3 and +4 were sampled for MS2, and fixed first mass of 110 was used. Raw data were viewed in Xcalibur v2.0.7 (Thermo Fisher Scientific, USA). Raw data were submitted to PRIDE (http://www. ebi. ac. uk/pride/archive/) under the project accession PXD008292 (username: reviewer24028@ebi. ac. uk, password: Nl5svtAi).


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Database searching, statistical and bioinformatics analysis The LC-MS/MS data were processed with Proteome Discoverer (Version 1.4.1.14, Thermo Fisher Scientific) and subjected to database searching using both an in-house Mascot server (Version 2. 2. 04, Matrix Science Ltd. , London, UK) and the embedded Sequest HT server with the following criteria: database, SwissProt Sus Scrofa (pig) protein database (updated to 01-11-2014); enzyme, trypsin; maximum missed cleavages, 2; variable modifications included oxidation (Met), acetyl (protein N-terminus and lysine (K) ), carbamidomethyl for Cys, deamidation for Asn and Gln, phosphorylation (Ser, Thr, and Tyr), iTRAQ were also included as a variable modification. The MS and MS/MS results were searched with a precursor mass tolerance at 10 ppm and a MS/MS mass tolerance at 0.05 Da. The results were filtered in Proteome Discoverer with the integrated Percolator algorithm31 to ensure the false discovery rate (FDR) less than 0.01. Only peptides identified with high confidence, first rank, Mascot score higher than 18 and passed the default score versus charge state for Sequest HT were accepted. Phosphosite localization probability was determined using the PhosphoRS probability algorithm.32 Peptides with different amino acid sequences or modifications were considered unique. The generated quantitative data was further filtered by removing the data with missing channels, the redundant data, and the non-unique peptides shared by multiple proteins. Then the data was subject to statistical analysis. For the datasets of non-modified peptides, the quantification was performed at the protein level. The log2-values of the measured precursor areas were normalized by the median values across an entire labelling experiment to correct for protein abundance variation. Peptides from same proteins were merged using the R Rollup function (www. rdocumentation. org) allowing for one-hit-wonders and using the mean of the normalized areas for each peptide. Then the mean over the experimental conditions for each protein in each replicate was subtracted, and data from the three replicates were merged. Significantly regulated proteins were defined as proteins with minimum 1.33 fold up-regulated or 0.75 fold down-regulated, quantified in



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at least two of the replicates and with a standard deviation lower than the 2×median of all the standard deviations of proteins as previously described.30 For the datasets of phosphopeptides, after normalization at peptide level, they were further normalized against the abundance of corresponding protein for each phosphopeptides based on the protein quantitative information from non-modified peptides, this step was essential to remove the effects caused by protein expression difference. Detection of differentially regulated phosphopeptides was carried out applying a combination of limma and rank tests.33 Resulting pvalues were corrected for multiple test.34 Statistical methods were described in more detail in.35 Perseus was also used to visualize the statistical results.36 A very stringent criteria was used to define phosphopeptides with significant regulation, which should be observed in at least two replicates, with a p-value ˂ 0.05, and showed no less than 1.5 fold change in one condition compared to the other three conditions. We applied fuzzy c-means clustering analysis37 of phosphopeptides. Annotation and classification of the identified proteins were facilitated by using Protein Center (Thermo Fisher Scientific, Odense, Denmark). The detail procedures were performed as described previously.25 Motif analysis of PTM sites was done using Motif-X online software with possibility threshold of P < 10−6 and relative occurrence rate threshold of 3% (http://motif-x. med. harvard. edu/motif-x. html). Network analysis was performed using Cytoscape (3.4.0 version) and related Apps, including clusterMarker and StringApp (http://apps. cytoscape. org/apps/stringapp) with a high confidence setting (0.7), where confident associations were shown with connecting lines. The sub-clusters were generated using Markov CLustering Algorithm (MCL) in clusterMarker app. Ingenuity pathway analysis (IPA) was used to reveal the protein interaction and signaling pathways.


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Results and discussion Characterization of metabolites in muscle samples from 4 genotypes The genotypes of the12 pigs used in this study were characterized in the same way as previous study26. They contained four homozygous genotypes of pig groups (3 animals for each genotype): normal (NN/rn+rn+), RN mutant (NN/RN-RN-), HAL mutant (nn/rn+rn+) and double mutant (nn/RN-RN-) genotypes. The biochemical data related to meat quality and muscle metabolites specific for these animals were analyzed in this study. The statistical results are presented in Supplemental table S1 and illustrated in Figure 1. Significant difference can be observed for these parameters between 4 genotypes. For energy consumption, animals with wild and RN mutation genotypes still have high level of ATP at 45 min PM, but for animals with HAL and RN-HAL mutation, the ATP was almost consumed completely, and converted into a high level of IMP (Figure 1A), indicating a rapid consumption of energy and depletion of energy reserves during slaughter for HAL mutant pigs. A fast consumption of phosphocreatine (PCr) was also observed in HAL mutant pigs.26 It is well known that HAL mutant pigs are particularly susceptible to stress during slaughter, which hastens energy metabolism and depletion, and results in quickly reduced PCr and ATP concentrations at exsanguination.10, 38 Consequently, the muscle from HAL mutant animals also showed a much lower pH value (less than 5. 6) compared to wild and RN mutant only pigs (higher than 6. 4) at 45 min PM (Figure 1B). For metabolites, HAL mutant animals had relatively high level of glucose, G6P and lactate, but low level of glycogen compared wild type pigs (Figure 1C, D, E, F). Pigs with RN mutation had a much higher level of glycogen storage and G6P in living muscle (0min), but for ATP level, pH value at 45 min, concentration of glucose and lactate at 0h and 45h, the differences compared to wild type animals at these time points were not as significant as RN mutant animals (Figure 1), indicating the pig with only RN mutation had a similar metabolic pattern as wild type pigs within 1h PM. However, when we compared the parameters at



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1440 min PM, the animals with RN mutation had a relatively lower pH, higher levels of glycogen and related metabolites than wild type animals (Figure 1). This was due to the fact that the RN mutant pigs possess elevated muscle glycogen deposition, and the capacity for extended glycolysis and pH decline, results in an abnormally low ultimate pH.3 RN-HAL double mutant animals were found to possess the additive results from both mutations for all the determined parameters, for example, higher glycogen storage caused by RN mutation, rapid depletion of energy reserves during slaughter and quick decreased pH value at 45 min PM caused by HAL mutation, and finally a very low pH value at 1440 min PM (Figure 1). Overall, the HAL and RN mutations negatively impact meat quality attributes. The HAL mutation typically generated pork with the characteristic PSE appearance, whereas the RN mutation tended to produce pork with greater exudation.26 These determined metabolic parameters clearly indicated the significant and differential effects of RN and HAL mutations on porcine muscle PM metabolism, and set up a solid phenotypic foundation for further proteomic characterization. Quantitative phosphoproteomic experimental strategy In order to compare and investigate the difference of protein abundance and phosphorylation in porcine muscle from these four genotypes of pigs, the LC–MS/MS based quantitative proteomic and phosphoproteomic strategies were applied in this work (Figure 2). The schematic overview of the experiment is depicted in Figure 2. For each genotype, muscle samples were collected at 45 min PM from three pigs, the genotype, weight, gender and determined metabolic parameters for each animal were presented in Supplemental table 1. After protein extraction and in-solution digestion, 100 ug peptides from sample of each genotype were subjected to iTRAQ labeling (normal/control, iTRAQ-114; RN mutant, iTRAQ-115; HAL mutant, iTRAQ-116 and RN/HAL double mutant, iTRAQ-117), and then the four differentially labeled samples were equally mixed together. The experiment had three biological replicates. Afterward, the peptide mixtures were subjected to the


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TiO2 enrichment of phosphopeptides, and both phosphopeptides and non-modified peptides were pre-fractionated with HILIC and analyzed with LC–MS/MS using Q Exactive Plus mass spectrometer. Protein identification As it can be seen from the submitted MSF files, about 77% of the enriched peptides were phosphopeptides, and more than 98. 5% of identified peptides were labeled with iTRAQ, indicating a high iTRAQ labeling efficiency. The overlap of the number of proteins identified from the flowthrough representing the total proteome and proteins identified from the enriched fractions mainly containing the phosphoproteins in three experimental replicates were indicated in Supplemental Figure S1. A total of 6378 unique peptides from 932 proteins were identified from the flow-through fractions (Supplemental Table 2), which can represent the general pig muscle proteome dataset. From the enriched fractions, in total of 14032 unique peptides were identified from 2426 proteins (Supplemental Table 3), among them, 11545 unique peptides were identified to be phosphorylated in 1885 phosphoproteins. Only 423 proteins were overlapped between non-modified proteins and phosphoproteins (Figure 3A). The identified phosphoproteins from enriched fractions were more than the identified proteins from flow-through fractions. The explanation for this observation was that much more redundant peptides and less unique peptides were identified from flow-through fractions than those from the enrich fractions, and these redundant peptides mostly originated from the high abundant proteins, such as myosin heavy chain (MYH) proteins, actin isoforms and tropomyosin isoforms. This comparison also indicated the specificity of enrichment strategy for phosphopeptides, as phosphopeptides from many low abundant phosphoproteins were specifically enriched and identified in the phosphopeptide fractions, thereby increasing the total identification number of unique peptides and phosphoproteins. Results also revealed the complexity and technical



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challenge of muscle proteomics for its high dynamicity and diversity due to the wide-ranging biochemical heterogeneity of the muscle proteins.39, 40 The 11545 unique phosphopeptides included 1210 peptides with multiple phosphorylation sites and 10335 mono-phosphopeptides. Using the pig Uniprot database, 9619 phosphorylation sites were successfully mapped to 1885 proteins (Supplemental Table 4). About 75% of phosphosites were identified in 2 or 3 experiment replicates, indicating a good reproducibility (Figure 3B). The 9619 assigned phosphosites included 6788 phosphoserine (pS), 2299 phosphothreonine (pT) and 532 phosphotyrosine (pY) residues (ratios of 70.57%, 23.9% and 5.53%), respectively, together with assigned proteins indicated in Figure 3C. Among these phosphosites, only 212 sites were annotated in the database, indicating that the pig phosphoproteome was not well characterized. Our data can greatly contribute to the database for future study. Gene ontology comparison and motif analysis Gene ontology (GO) comparison of identified non-modified proteins and phosphoproteins can reflect the difference of pig muscle proteome and phosphoproteome. The cellular components, biological processes and molecular functions of both protein datasets were characterized in this study using Protein Center (Supplemental Figure S2). Analysis indicated that phosphoproteins were over-represented from cell organelles, such as nucleus, golgi, chromosome, endosome and spliceosome, etc. and more involved in biological regulation, biogenesis, stimulus response, differentiation, component movement biological processes, and with the function of binding with protein, DNA and RNA. The majority of the phosphoproteins were found in low abundance compared to non-modified proteins. Phosphorylation motifs were analyzed using Motif-X with possibility threshold of P < 10−6 and relative occurrence rate threshold of 3%.41 The generated motifs were matched against the annotated kinase/phosphatase motifs database in the Human Protein Reference Database


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(http://www. hprd. org). In total 7 well-annotated motifs were identified using this strategy, including six phosphoserine centered motifs and one phosphothreonine centered motif. No phosphotyrosine based motifs were identified due to the low abundance of pT and pY compared to pS. These motifs were proline-directed MAPKs Kinase motif: pS/pTP, Ca2+/calmodulin dependent protein kinase II (CAMK2) motif: RXXpS/pT, protein kinase A (PKA) motif: RXpS, MDC1, BRCT, Plk1, PBD domain binding motif: SpS, AKT kinase motif: SXpS, and the Casein kinase II motif: pSXXE. The Logo-like representations of these identified motifs were demonstrated in Figure 4. Data normalization and principal component analysis Quantitative analysis was performed to compare the protein expression difference based on the nonmodified proteomic datasets and the protein phosphorylation pattern difference based on the phosphoproteomic datasets between the four genotypes of 12 animal muscle samples. For proteomic differences, comparison was conducted at protein level, but comparison of phosphorylation difference was conducted for individual phosphopeptides or phosphosites as phosphorylation was regulated at specific sites. The log2 value of intensities for peptides were normalized to the median and centered to 0, the distributions of the normalized and centered log2 value of intensities for phosphopeptides were slightly asymmetric between different genotypes in three replicates as demonstrated with boxplots in Supplemental Figure S3. In order to ensure reliability, the proteins and peptides should be quantified in at least 2 out of 3 biological replicates, a total of 511 proteins from non-modified datasets and 3001 phosphopeptides met this standard were used for further quantification analysis. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were employed to assess sample quality and get an overview of different genotypes among three experimental replicates. In the results, the PCA scoring plot showed that samples with different genotypes clearly



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formed distinguishable clusters Figure 5. For the protein expression based on the non-modified proteomic datasets, animals with wild and HAL mutant genotypes were clustered in a closer area, while animals with RN and RN-HAL double mutant genotypes were closely clustered in another area, indicating the RN mutation had significant effects on protein expression in muscle compared to HAL mutation and wild types. However, the PCA of phosphopeptides data showed that HAL and RN-HAL double mutant genotypes were clustered together and clearly separated from wildtype and RN mutant genotypes, which were relatively close to each other. These data indicated HAL mutation has greater effects on protein phosphorylation pattern compared to RN mutation. Hierarchical clustering analysis showed similar cluster patterns as PCA for both the differentially expressed proteins and regulated phosphopeptides among four genotypes as presented in Supplemental Figure S4. In general, PCA and HCA analysis demonstrated the RN mutation had a major influence on protein expression while the HAL mutation had more effects on protein phosphorylation in the comparison among four genotypes, therefore suggesting different regulatory mechanisms for RN and HAL mutations in PM muscle development. Protein interaction analysis of proteins regulated at total protein level At total protein level, 511 proteins were quantified in 2 or more replicates, and 128 proteins were found to show significant difference between 4 genotypes (Supplemental table 5). Compared to wildtype, animals harboring the RN mutation had 40 proteins up-regulated and 30 proteins downregulated. Muscle of HAL mutant pigs had 10 regulated proteins, with 7 proteins down-regulated. Muscle of RN-HAL double mutant pigs had 37 up-regulated proteins and 14 down-regulated proteins. The differentially expressed proteins were overlapped between three genotypes, as indicated in Figure 6A, the up and down-regulation pattern of these proteins were also indicated in red and blue colors. In general, there were much more regulated proteins in RN and RN-HAL mutant genotypes compared to HAL genotype, and most of the proteins were upregulated,


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especially in RN-HAL mutant genotype. Results herein suggested that the RN mutation had a more significant effect on protein expression, thereby affecting the total protein abundance in muscle compared to the HAL mutation. String-based network analysis was performed to reveal the potential protein-protein interaction network of all proteins with significant difference between genotypes (Figure 6B), and the protein abundant difference between 4 genotypes were also indicated in the figure. A total of 9 closely clustered sub-networks were identified, including the proteins involved in mitochondrial respiratory chain, muscle contraction, carbohydrate and energy metabolism, ribosome proteins, collagen proteins and proteins related to glyocogen synthesis, redox, stress response and hemoglobin binding. The first three groups were much larger than the rest in the network. Interestingly, most proteins in mitochondrial respiratory chain protein cluster showed higher abundance in RN and RN-HAL genotypes, which may be due to that there are more type-1 red fiber in RN mutant pigs. However, ribosome proteins and collagen proteins had lower abundance in RN genotype compared to other genotypes. For the other protein clusters, the proteins shown varied in abundance between 4 genotypes. RN mutation affected proteins related to mitochondrial respiratory chain and energy metabolism The porcine RN mutation with R200Q in the AMPKγ3-subunit and the equivalent mutation in the mouse (R225Q) result in constitutive AMPK activation. Because AMPKγ3 is highly expressed in white skeletal muscle,42 this mutation contributes to increased mitochondrial protein content and enhanced oxidative capacity by increasing mitochondrial biogenesis in glycolytic skeletal muscle.43, 44

We have previously found that AMPKγ3 R200Q enhances muscle oxidative capacity,

mitochondrial density and content, such as mRNA and protein contents.45 Our findings revealed that at total protein level, SDHA, SDHB, cytochrome c somatic (CYCS), cytochrome c-1 (CYC1),



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COX4I1, SLC25A4, NADH dehydrogenase 1 (ND1) components (NDUFB11 and NDUFAB1), UQCRC1, UQCRC2, UQCRH, and 4 mitochondrial specific ATP synthases were essential components of mitochondrial respiratory electron transport chain, and they showed increased abundance in RN and RN-HAL mutant pigs compared to wild and HAL mutant pigs (Figure 6B). The increased expression of ND1, SDHA and COX4 were determined previously in RN and RNHAL mutant pigs.45 Therefore, the results here confirmed the AMPKγ3 R200Q increased mitochondrial density and protein content, and revealed that the mostly up-regulated proteins were related to mitochondrial respiratory electron transport chain. In our results, the expression of UDP-glucose pyrophosphorylase 2 (UGP2) was much higher in RN mutant animals compared to wild type and HAL mutant animals. Other studies also revealed that the UGP2 content was increased in RN mutant pigs, and it may contribute the glycogen storage by promoting the flux of glucose toward glycogen synthase.46, 47 The HAL mutant can increase muscle fiber size and cause fiber hypertrophy.10 Only 10 proteins were identified to be regulated at total protein level in HAL mutant pigs, and most of them were also regulated in the RN genotypes. Musculoskeletal embryonic nuclear protein 1 (MUSTN1) was the only protein found to be upregulated only in HAL mutant pigs in this study, this protein was found to be expressed during skeletal muscle regeneration, activated satellite cells and differentiating myoblasts, and it be involved in the development and regeneration of the musculoskeletal system.48,

49

The high abundance of MUSTN1 in HAL mutant muscle may

contribute to the fiber hypertrophy by promotion of muscle development and regeneration. Protein interaction analysis of proteins regulated at phosphorylation level For phosphopeptides, after internal normalization, they were further normalized against the protein abundance to remove the effects caused by the differential protein expression. These data were then subjected to the limma test.35 A stringent criteria was used to define phosphopeptides with


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significant regulation, which should be observed in at least two replicates, with p-values ˂ 0.05 in Limma test, and showed no less than 1.5 fold change in one genotype compared to the other genotypes. A total of 3001 phosphopeptides were quantified in 2 or more replicates, and 339 phosphopeptides containing 323 phosphosites from 91 proteins were found to be significantly regulated at phosphorylation level between 4 genotypes (Supplemental table 6). Motif analysis of regulated phosphorylation sites identified 4 motifs, they were Proline-directed MAPKs kinase Serine/Threonine motifs (pS/pTP), CAMK2 kinase motifs (RXXpS/pT) and PKA kinase motifs (RXpS), these four motifs were already identified in the previous motif analysis for all identified phosphosites, so they were highlighted with underline in Figure 4. The fuzzy c-means clustering analysis was performed to reveal the phosphorylation pattern of phosphopeptides between the four genotypes. Seven clusters were revealed and presented in Figure 7, the significantly regulated phosphoproteins and related phosphorylation sites within each cluster were also indicated in Supplemental table 6. Cluster 3 and Cluster 5 had more significantly regulated phosphorylation sites than the other clusters, and obviously, the phosphorylation patterns in these two clusters were mostly related to the HAL mutation, as they showed similar patterns in wild and RN genotypes, and then similar change patterns in both HAL and RN-HAL double mutant genotypes with down-regulation in cluster 3 and up-regulation in cluster 5. Similarly, the phosphorylation sites in Cluster 6 and Cluster 7 appear to be regulated by RN mutation, however, much less regulated phosphorylation sites were in these two clusters compared to cluster 3 and 5. For the clusters 1, 2 and 4, they showed varied change patterns not related to specific genotypes. In general, the cluster analysis revealed that the HAL mutation affected more proteins at phosphorylation level compared to RN mutation, indicating that, compared to RN mutation, HAL mutation affects muscle metabolism and meat quality mainly through the regulation of phosphorylation events, thereby modulation of protein function and activity.



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String network analysis was used to analyze the proteins with significantly regulated phosphorylation sites based on their cluster information. Proteins with phosphorylation sites in clusters 3 and 5 were considered to be affected by HAL mutation and analyzed together (Figure 8A). Four protein sub-networks were clustered from 74 proteins in cluster 3 and 5. These proteins were related to calcium signaling, muscle contraction, glycogen and glucose metabolism, and stress response. Most of the proteins in this network were from cluster 5 (marked in red), as indicated, the phosphorylation of their regulated phosphosites was increased in HAL and RN-HAL mutant muscle samples compared to wildtype and RN mutant muscle, only few proteins were identified with significantly down-regulated phosphosites in cluster 3 (in green) or with both up and downregulated phosphosites in both cluster 3 and 5 (in yellow). For the 4 subnetworks, the proteins related to muscle contraction and glycometabolism had both up and down-regulated phosphosites. The proteins related to calcium signaling and stress response were determined with only upregulated phosphorylation sites. The phosphorylation patterns of proteins in Cluster 6 and Cluster 7 were considered to be affected by RN mutation as they showed similar pattern in both RN and RN-HAL mutations, but different from wild type and HAL mutation. There were only 18 proteins in cluster 6 and 7, String network analysis of these proteins (Figure 8B) revealed two small sub-networks with proteins related to glycogen metabolism and muscle contraction, and these proteins also demonstrated varied phosphorylation patterns and with color-marked in a similar way as for cluster 3 and 5. The increased phosphorylation of CAMK2 and its potential roles in muscle of HAL mutant animals Muscle specific CAMK2 was identified to be highly phosphorylated and regulated in HAL mutant muscle, six phosphosites (T36, T254, S280, T287, T307, S315) showed an increased phosphorylation level in HAL mutant muscle, and one phosphosite S333 showed decreased


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phosphorylation level in HAL mutant muscle compared to wild type muscle. A previous study also observed increased phosphorylation of CAMK2 in muscle of HAL mutant pigs.5 CAMK2 is regulated by the Ca2+/calmodulin complex and has been shown to be a crucial kinase in muscle signaling and plasticity.50 Increased intracellular Ca2+ concentration can activate and elevate CAMK2 activity in skeletal muscle.51 The T287 is an autophosphorylation site of CAMK2, this site can be phosphorylated in response to an increased level of Ca2+ during exercise, and there was a rapid and transient positive-linear correlation increase in autonomous CAMK2 activity and CAMK2 phosphorylation at T287 in skeletal muscle during exercise.52 Additionally, the autophosphorylation of T287 may allow CAMK2 to remain partially active between Ca2+ transients by disrupting autoinhibition.53 The observed high phosphorylation level of T287 in HAL mutant muscle may indicate the high activity of CAMK2 in muscle of HAL mutant animals, suggesting that increase in calcium level by HAL mutation can cause CAMK2 activation through increased phosphorylation. The CAMK2 motif was also the most highly over-represented motif identified from the regulated phosphosites, a total of 46 regulated phosphosites were identified with the CAMK2 motif (RXXpS/pT),54 they were listed in Supplemental table S7, and majority of these sites (27 out of 46) were clustered in cluster 5, suggesting the phosphorylation of these sites were up-regulated in muscles of HAL and RN-HAL double mutant animals, . This may be caused by the increased activity of CAMK2 due to HAL mutation. These results indicated the essential regulatory role of CAMK2 through protein phosphorylation in skeletal muscle. It was observed that CAMK2 activation and phosphorylation of proteins in downstream cascade signalings were essentially related to the muscle hypertrophy and exercise, and that augmentation in intracellular Ca2+ can activate and elevated CAMK2 activity in hypertrophied skeletal muscle.51 We hypothesize that the increased sensitivity and enhanced release of Ca2+ in HAL mutant muscle lead to the increased phosphorylation and activation of CAMK2, and that subsequently activation of CAMK2 may



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contribute to fiber hypertrophy through phosphorylation and activation of proteins in downstream cascade signalings. HAL mutation increased the phosphorylation of proteins involved Ca2+ signaling and stress response RYR1 on the sarcoplasmic reticulum (SR) is the major calcium (Ca2+) release channel required for skeletal muscle excitation–contraction (EC) coupling. RYR1 function is essentially modulated by phosphorylation kinases, such as PKA and CAMK2.55 The HAL mutation with RyR1R615C can also increase sensitivity to agents that stimulate channel opening and enhance luminal Ca2+ activation of RYR1.56 In this study, notably, RYR1 was identified with 30 phosphosites, and 6 sites (S1338, T1361, T1405, S2754, T2835, T3121) were determined to be up-regulated in HAL and RN-HAL mutant animals. Network analysis indicated RYR1 directly interacted with several proteins involved in calcium signaling network, such as juntophilins (JPH1 and JPH2), Sarcoplasmic/endoplasmic reticulum calcium ATPase 1 (ATP2A1). JPH1 and JPH2 contribute to the formation of junctional membrane complexes and maintain Ca2+ homoeostasis through interaction with RYR1 in muscle.57 ATP2A1 is the key regulator of striated muscle performance by acting as the major Ca2+ ATPase responsible for the reuptake of cytosolic Ca2+ into the sarcoplasmic reticulum, and the phosphorylation can greatly increase the catalytic activity of enzyme.58 In our study, multiple novel phosphorylation sites in JPH1, JPH2 and ATP2A1 were observed to be highly phosphorylated in muscle of HAL and RN-HAL double mutant animals. The high phosphorylation level of proteins involved in Ca2+ signaling should be an adaptive response to high basic cytosol Ca2+ concentration in muscles due to HAL mutation. Several stress response related proteins were also identified with upregulated phosphorylation sites in HAL mutant animals, such as heat shock proteins (HSPB1, HSPA1B and HSPB6). Heat shock proteins (HSPs) are involved in stress resistance and apoptotic signaling pathway.59 Stress induces


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an increase of expression and rapid phosphorylation of HSPB1. Phosphorylated HSPB1 can protect cells exposed to different stress factors, and inhibit stress induced cell apoptosis.60-62 Our previous study also observed high phosphorylation of HSPB1 and HSPB6 in porcine muscle a short time after slaughter, which may be caused by slaughter stress. In the current study, we observed high phosphorylation level of the HSPs proteins in HAL mutant animals which could be an adaptive response, and related to the high stress sensitivity of HAL mutation. HAL mutation affected phosphorylation of proteins related to glycogen metabolism Phosphorylase b kinase (PHK) phosphorylates and activates glycogen phosphorylase b to promote glycogen degradation. It can be activated by both Ca2+ release and phosphorylation by the 3', 5'cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA).63 Increased phosphorylation level on phosphosites from PHK subunits PHKB, PHKA1, and PKA subunits PRKAR1A and PRKAR2A were determined in HAL mutant animals. Protein phosphatase 1 (PP1) plays a crucial role in the regulation of glycogen metabolism by mediation of the activity of glycogen phosphorylase a and GYS1, as PP1 can activate GYS1 via dephosphorylation.64 We observed multiple phosphosites from two PP1 subunits PPP1R1A and PPP1R3A, except S50 of PPP1R3A, all other 18 sites showed increased phosphorylation in HAL mutant animals. All these proteins with increased phosphorylation should contribute to the accelerated glycogen degradation and metabolism in HAL mutant animals. GYS1 is the key enzyme in glycogen synthesis, and it is activated by the allosteric stimulator glucose-6-phosphate (G6P), but inhibited by phosphorylation through multiple kinases.65 In this study, S652, S653 of GYS1 showed decreased phosphorylation in muscles of HAL mutant animal, and S653 was a confirmed regulatory site regulated by glycogen synthase kinase 3 (GSK3).66 In the network, several proteins involved in glycolysis were also identified with up-regulated phosphorylation sites in HAL mutant muscle, including GPI, PGAM2, PGK and ENO3. Additionally, two key enzymes for energy metabolism adenylate kinase (AK1)



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and creatine kinase muscle type (CKM) were also identified with regulated phosphorylation sites in HAL mutant muscle. Taken together, the phosphorylation of above proteins would contribute to the enhanced glycogen breakdown and promote the increased rate of glycolysis and production of lactic acid, and finally lead to fast pH decline in muscle of HAL mutant pigs within 45min PM. Muscle contraction proteins affected by HAL mutation may be related to the hyperacute rigor mortis Pigs with RYR1 R615C and mice with identical RYR1 R613C mutation demonstrated malignant hyperthermia with increased rectal temperature, increased respiratory rate, hyperacute rigor mortis with full body muscle contracture within short time of the last breath.67 In our previous study, muscle contraction related proteins were also found to be significantly regulated at phosphorylation level within 24h PM, and degree of phosphorylation was postulated to affect the rigor mortis development.25 Network analysis of regulated phosphoproteins in cluster 3 and 5 (potentially affected by HAL mutation) revealed that the largest sub-network was composed of proteins related to muscle contraction activity. It included 31 proteins identified with regulated phosphorylation sites, and most of these sites were up-regulated in muscle from pigs carrying HAL mutation. Interestingly, among these up-regulated phosphosites, 17 sites from 13 muscle contraction related proteins were within the CAMK2 motif (RXXpS/pT), as indicated in Supplemental table 7 and Figure 8. The increased phosphorylation of these sites in muscle contraction related proteins may be attributed to the activation and increased activity of CAMK2 by the increased intracellular Ca2+ concentration in muscles of HAL mutant animals as discussed before. Titin (TTN) was known to highly phosphorylated in PM porcine muscle, 7 titin phosphosites were reported to be upregulated in porcine muscle PM, and were maintained at a high level throughout the 24 h PM, and it was postulated as a negative response to the rigor mortis development.25 In this study, 11 novel phosphosites from TTN were found to be up-regulated in HAL mutant animals, and


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may be related to the hyperacute rigor mortis. MYL2 phosphorylation plays essential roles in the regulation of muscle contraction through activation of contraction in smooth muscle and modulation of force production.68 Studies in PM muscle revealed that MYL2 demonstrated an increased phosphorylation level during meat aging, and the regulated sites were S15, S16 and S20.22, 25, 69 Surprisingly, in this study, S15, S16 and a novel phosphotyrosine site Y117 were all identified to be down-regulated in muscle of HAL mutant animals. Phosphorylation of S15 in mouse cardiac MYL2 (identical to S16 of skeletal muscle MYL2) can affect secondary structure and modulates the Ca2+ sensitivity of contraction in cardiac tissue.70,

71

In skeletal muscle, phosphorylation of S16 is

mediated by skeletal myosin light chain kinase (MYLK2), and ablation of MYLK2 was shown to reduce the maximal possible power output of mouse fast skeletal muscle.72 In our work, we observed 6 regulated phosphosites on MYLK2 related to HAL and RN-HAL mutations, 2 were downregulated, and 4 were up-regulated. MYLK2 can be activated by Ca2+/calmodulin and regulated by phosphorylation through kinases including CAMK2, AMPK, for instance, AMPK can attenuate contraction by phosphorylating and inactivating MYLK2.73, 74 RN mutation increased the phosphorylation of GYS1, thereby decreasing its activity The RN gene, AMPKγ3 with gene name PRKAG3 was identified with 5 phosphosites, but, it was not quantified at total protein level. The interaction analysis indicated PRKAG3 interacted with GYS1 and 2 glycolytic enzymes PFKM, GAPDH (Figure 8B). Three adjacent phosphorylation sites S641, S645 and S649 on GYS1 were found to be up-regulated in RN and RN-HAL mutant animals. All these three sites can be phosphorylated by GSK3.66 Phosphorylation at S641 can significantly inhibit the activity of GYS1, and the phosphorylation of S645 and S649 may facilitate the phosphorylation at S641, and result in potent inhibition.75 The result suggested the activity of GYS1 should be inhibited in muscle 45min PM from RN mutant animals by increased phosphorylation. In agreement, the decreased activity of GYS1 was confirmed in muscles of RN and RN-HAL mutant



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pigs.47 Up-regulated phosphorylation of S146, T182 on GAPDH, T669 and T677 on PFKM was also determined in RN and RN-HAL mutant animals. These two proteins can be phosphorylated by different kinases, and since PFK is the rate limiting enzyme in glycolysis, phosphorylation seems to augment PFK activity in glycolysis.76 In conclusion, the quantitative proteomic and phosphoproteomic characterization of muscle samples from pigs with wildtype, RN, HAL and RN-HAL double mutant genotypes in this study revealed different regulatory mechanisms of RN and HAL mutations in porcine muscle metabolism. The RN mutation mainly affected the protein expression at the total protein level. Specifically, RN mutant animals had increased abundance of proteins related to mitochondrial respiratory chain and energy metabolism, thereby enhancing the muscle oxidative capacity. The high content of UGP2 in RN mutant animals compared to other genotypes may contribute to increased glycogen storage. However, the HAL mutation resulted in major effects on the regulation of protein phosphorylation. Specifically, most affected phosphoproteins were determined with up-regulated phosphorylation sites, potentially influencing calcium signaling, muscle contraction, glycogen, glucose and energy metabolism, and cellular stress. The increase of calcium level by HAL mutation can increase phosphorylation of CAMK2, and lead to activation of CAMK2 as well as its downstream signaling cascades. The phosphorylation changes may contribute to accelerated glyocogen metabolism, muscle fiber hypertrophy, acute stress response and hyperacute rigor mortis. Our findings revealed a set of protein targets in RN and HAL mutations known to alter muscle energy metabolism and shed light on the regulatory mechanisms underlying postmortem energy metabolism in pig muscle. Author contributions: H.H., M.R.L. and R.L. conceived and designed the experiments, H.H. and R.L. wrote the paper. H.H. performed the experiments and data analysis. T.L.S. and D.E.G. collected animal samples and determined the related metabolic data.


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Acknowledgements: We thank Dr. Colin Ray (Arla Foods Ingredients Group P/S) for editing and proofreading of the manuscript. This work was supported by the postdoctoral fellowship (H. H) from the Danish Diabetes Academy financed by the Novo Nordisk Foundation, the Lundbeck Foundation (M. R. L Junior Group Leader Fellowship) and by a generous grant from the VILLUM Foundation to the VILLUM Center for Bioanalytical Sciences at the University of Southern Denmark. Conflict of interest The authors declare no competing financial interests. Supporting information: Supplemental Figure S1: The Venn diagrams of the numbers of identified proteins from the flowthrough fractions and the enriched fractions in three experimental replicate. Supplemental Figure S2: The gene ontology comparison of identified non-modified proteins, phosphoproteins. Supplemental Figure S3: The boxplots indicated the distributions of the normalized log2 value of intensities for phosphopeptides between different genotypes in triplicates. Supplemental Figure S4: The heatmaps (hierarchical clustering analysis) of differentially expressed proteins and regulated phosphopeptides among four genotypes. Supplemental Table S1: The background information of 12 animals used in this study, including related metabolites determination and statistical analysis. Supplemental Table S2: List of identified unique peptides from flow-through of TiO2 enrichment.



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Supplemental Table S3: List of identified unique peptides from the enriched phosphopeptide fractions. Supplemental Table S4: List of mapped phosphorylation sites with information of localization on protein. Supplemental Table S5: List of significantly regulated proteins at total protein level. Supplemental Table S6: List of significantly regulated phosphopeptides and statistical information. Supplemental Table S7: List of regulated phosphorylation sites with the CAMK2 kinase motif (RXXpS/pT) and their cluster information. Reference 1. Leach, L. M.; Ellis, M.; Sutton, D. S.; McKeith, F. K.; Wilson, E. R., The growth performance, carcass characteristics, and meat quality of halothane carrier and negative pigs. Journal of Animal Science 1996, 74 (5), 934-43. 2. Lundstrom, K.; Andersson, A.; Hansson, I., Effect of the RN gene on technological and sensory meat quality in crossbred pigs with Hampshire as terminal sire. Meat Science 1996, 42 (2), 145-53. 3. Le Roy, P.; Elsen, J. M.; Caritez, J. C.; Talmant, A.; Juin, H.; Sellier, P.; Monin, G., Comparison between the three porcine RN genotypes for growth, carcass composition and meat quality traits. Genetics Selection Evolution 2000, 32 (2), 165-186. 4. Milan, D.; Jeon, J. T.; Looft, C.; Amarger, V.; Robic, A.; Thelander, M.; Rogel-Gaillard, C.; Paul, S.; Iannuccelli, N.; Rask, L.; Ronne, H.; Lundstrom, K.; Reinsch, N.; Gellin, J.; Kalm, E.; Le Roy, P.; Chardon, P.; Andersson, L., A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 2000, 288 (5469), 1248-1251. 5. Park, S.; Scheffler, T. L.; Gunawan, A. M.; Shi, H.; Zeng, C.; Hannon, K. M.; Grant, A. L.; Gerrard, D. E., Chronic elevated calcium blocks AMPK-induced GLUT-4 expression in skeletal muscle. Am J Physiol-Cell Ph 2009, 296 (1), C106-C115. 6. Scheffler, T. L.; Gerrard, D. E., Mechanisms controlling pork quality development: The biochemistry controlling postmortem energy metabolism. Meat Science 2007, 77 (1), 7-16. 7. Fujii, J.; Otsu, K.; Zorzato, F.; Deleon, S.; Khanna, V. K.; Weiler, J. E.; Obrien, P. J.; Maclennan, D. H., Identification of a Mutation in Porcine Ryanodine Receptor Associated with Malignant Hyperthermia. Science 1991, 253 (5018), 448-451. 8. Oliver, M. A.; Gispert, M.; Diestre, A., The Effects of Breed and Halothane Sensitivity on Pig Meat Quality. Meat Science 1993, 35 (1), 105-118. 9. Mickelson, J. R.; Gallant, E. M.; Rempel, W. E.; Johnson, K. M.; Litterer, L. A.; Jacobson, B. A.; Louis, C. F., Effects of the Halothane-Sensitivity Gene on Sarcoplasmic-Reticulum Function. American Journal of Physiology 1989, 257 (4), C787-C794.


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10. Essen-Gustavsson, B.; Karlstrom, K.; Lundstrom, K., Muscle fibre characteristics and metabolic response at slaughter in pigs of different halothane genotypes and their relation to meat quality. Meat Sci 1992, 31 (1), 1-11. 11. Scheffler, T. L.; Park, S.; Gerrard, D. E., Lessons to learn about postmortem metabolism using the AMPK gamma 3(R200Q) mutation in the pig. Meat Science 2011, 89 (3), 244-250. 12. Park, S. K.; Sheffler, T. L.; Spurlock, M. E.; Grant, A. L.; Gerrard, D. E., Chronic activation of 5 '-AMP-activated protein kinase changes myosin heavy chain expression in growing pigs. Journal of Animal Science 2009, 87 (10), 3124-3133. 13. Shen, Q. W. W.; Means, W. J.; Underwood, K. R.; Thompson, S. A.; Zhu, M. J.; McCormick, R. J.; Ford, S. P.; Ellis, M.; Du, M., Early post-mortem AMP-activated protein kinase (AMPK) activation leads to phosphofructokinase-2 and-1 (PFK-2 and PFK-1) phosphorylation and the development of pale, soft, and exudative (PSE) conditions in porcine longissimus muscle. Journal of Agricultural and Food Chemistry 2006, 54 (15), 5583-5589. 14. Zhao, J. X.; Yan, X.; Tong, J. F.; Means, W. J.; McCormick, R. J.; Zhu, M. J.; Du, M., Mouse AMP-activated protein kinase gamma 3 subunit R225Q mutation affecting mouse growth performance when fed a high-energy diet. Journal of Animal Science 2010, 88 (4), 1332-1340. 15. Bendixen, E., The use of proteomics in meat science. Meat Science 2005, 71 (1), 138-149. 16. Bendixen, E.; Danielsen, M.; Hollung, K.; Gianazza, E.; Miller, I., Farm animal proteomics - A review. Journal of Proteomics 2011, 74 (3), 282-293. 17. Hollung, K.; Veiseth, E.; Jia, X. H.; Faergestad, E. M.; Hildrum, K. I., Application of proteomics to understand the molecular mechanisms behind meat quality. Meat Science 2007, 77 (1), 97104. 18. Gannon, J.; Staunton, L.; O'Connell, K.; Doran, P.; Ohlendieck, K., Phosphoproteomic analysis of aged skeletal muscle. International Journal of Molecular Medicine 2008, 22 (1), 33-42. 19. Hojlund, K.; Bowen, B. P.; Hwang, H.; Flynn, C. R.; Madireddy, L.; Geetha, T.; Langlais, P.; Meyer, C.; Mandarino, L. J.; Yi, Z. P., In vivo Phosphoproteome of Human Skeletal Muscle Revealed by Phosphopeptide Enrichment and HPLC-ESI-MS/MS. Journal of Proteome Research 2009, 8 (11), 4954-4965. 20. Hou, J. J.; Cui, Z. Y.; Xie, Z. S.; Xue, P.; Wu, P.; Chen, X. L.; Li, J.; Cai, T. X.; Yang, F. Q., Phosphoproteome Analysis of Rat L6 Myotubes Using Reversed-Phase C18 Prefractionation and Titanium Dioxide Enrichment. Journal of Proteome Research 2010, 9 (2), 777-788. 21. Huang, H. G.; Larsen, M. R.; Karlsson, A. H.; Pomponio, L.; Costa, L. N.; Lametsch, R., Gelbased phosphoproteomics analysis of sarcoplasmic proteins in postmortem porcine muscle with pH decline rate and time differences. Proteomics 2011, 11 (20), 4063-4076. 22. Huang, H. G.; Larsen, M. R.; Lametsch, R., Changes in phosphorylation of myofibrillar proteins during postmortem development of porcine muscle. Food Chemistry 2012, 134 (4), 1999-2006. 23. Lametsch, R.; Larsen, M. R.; Essen-Gustavsson, B.; Jensen-Waern, M.; Lundstrom, K.; Lindahl, G., Postmortem Changes in Pork Muscle Protein Phosphorylation in Relation to the RN Genotype. J Agric Food Chem 2011, 59 (21), 11608-15. 24. Li, C. B.; Li, J.; Zhou, G. H.; Lametsch, R.; Ertbjerg, P.; Bruggemann, D. A.; Huang, H. G.; Karlsson, A. H.; Hviid, M.; Lundstrom, K., Electrical stimulation affects metabolic enzyme phosphorylation, protease activation, and meat tenderization in beef. Journal of Animal Science 2012, 90 (5), 1638-1649. 25. Huang, H.; Larsen, M. R.; Palmisano, G.; Dai, J.; Lametsch, R., Quantitative phosphoproteomic analysis of porcine muscle within 24h postmortem. J Proteomics 2014, 106C, 125-139. 26. Copenhafer, T. L.; Richert, B. T.; Schinckel, A. P.; Grant, A. L.; Gerrard, D. E., Augmented postmortem glycolysis does not occur early postmortem in AMPK73-mutated porcine muscle of halothane positive pigs. Meat Science 2006, 73 (4), 590-599. 27. Palmisano, G.; Parker, B. L.; Engholm-Keller, K.; Lendal, S. E.; Kulej, K.; Schulz, M.; Schwammle, V.; Graham, M. E.; Saxtorph, H.; Cordwell, S. J.; Larsen, M. R., A novel method for the simultaneous enrichment, identification, and quantification of phosphopeptides and sialylated



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glycopeptides applied to a temporal profile of mouse brain development. Mol Cell Proteomics 2012, 11 (11), 1191-202. 28. Larsen, M. R.; Thingholm, T. E.; Jensen, O. N.; Roepstorff, P.; Jorgensen, T. J. D., Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Molecular & Cellular Proteomics 2005, 4 (7), 873-886. 29. Thingholm, T. E.; Jorgensen, T. J.; Jensen, O. N.; Larsen, M. R., Highly selective enrichment of phosphorylated peptides using titanium dioxide. Nat Protoc 2006, 1 (4), 1929-35. 30. Huang, H.; Haar Petersen, M.; Ibanez-Vea, M.; Lassen, P. S.; Larsen, M. R.; Palmisano, G., Simultaneous Enrichment of Cysteine-containing Peptides and Phosphopeptides Using a Cysteine-specific Phosphonate Adaptable Tag (CysPAT) in Combination with titanium dioxide (TiO2) Chromatography. Mol Cell Proteomics 2016, 15 (10), 3282-3296. 31. Kall, L.; Canterbury, J. D.; Weston, J.; Noble, W. S.; MacCoss, M. J., Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature methods 2007, 4 (11), 923-5. 32. Taus, T.; Kocher, T.; Pichler, P.; Paschke, C.; Schmidt, A.; Henrich, C.; Mechtler, K., Universal and confident phosphorylation site localization using phosphoRS. Journal of proteome research 2011, 10 (12), 5354-62. 33. Breitling, R.; Armengaud, P.; Amtmann, A.; Herzyk, P., Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 2004, 573 (1-3), 83-92. 34. Storey, J. D., A direct approach to false discovery rates. Journal of the Royal Statistical Society Series B-Statistical Methodology 2002, 64, 479-498. 35. Schwammle, V.; Leon, I. R.; Jensen, O. N., Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates. J Proteome Res 2013, 12 (9), 3874-83. 36. Tyanova, S.; Temu, T.; Sinitcyn, P.; Carlson, A.; Hein, M. Y.; Geiger, T.; Mann, M.; Cox, J., The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016, 13 (9), 731-40. 37. Schwammle, V.; Jensen, O. N., A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics 2010, 26 (22), 2841-8. 38. Lundstrom, K.; Essen-Gustavsson, B.; Rundgren, M.; Edfors-Lilja, I.; Malmfors, G., Effect of halothane genotype on muscle metabolism at slaughter and its relationship with meat quality: A withinlitter comparison. Meat Sci 1989, 25 (4), 251-63. 39. Deshmukh, A. S.; Murgia, M.; Nagaraj, N.; Treebak, J. T.; Cox, J.; Mann, M., Deep proteomics of mouse skeletal muscle enables quantitation of protein isoforms, metabolic pathways, and transcription factors. Mol Cell Proteomics 2015, 14 (4), 841-53. 40. Ohlendieck, K., Skeletal muscle proteomics: current approaches, technical challenges and emerging techniques. Skelet Muscle 2011, 1 (1), 6. 41. Schwartz, D.; Gygi, S. P., An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol 2005, 23 (11), 1391-8. 42. Mahlapuu, M.; Johansson, C.; Lindgren, K.; Hjalm, G.; Barnes, B. R.; Krook, A.; Zierath, J. R.; Andersson, L.; Marklund, S., Expression profiling of the gamma-subunit isoforms of AMP-activated protein kinase suggests a major role for gamma3 in white skeletal muscle. Am J Physiol Endocrinol Metab 2004, 286 (2), E194-200. 43. Barnes, B. R.; Marklund, S.; Steiler, T. L.; Walter, M.; Hjalm, G.; Amarger, V.; Mahlapuu, M.; Leng, Y.; Johansson, C.; Galuska, D.; Lindgren, K.; Abrink, M.; Stapleton, D.; Zierath, J. R.; Andersson, L., The 5 '-AMP-activated protein kinase gamma 3 isoform has a key role in carbohydrate and lipid metabolism in glycolytic skeletal muscle. J Biol Chem 2004, 279 (37), 38441-38447. 44. Garcia-Roves, P. M.; Osler, M. E.; Holmstrom, M. H.; Zierath, J. R., Gain-of-function R225Q Mutation in AMP-activated Protein Kinase gamma 3 Subunit Increases Mitochondrial Biogenesis in Glycolytic Skeletal Muscle. J Biol Chem 2008, 283 (51), 35724-35734. 32 ACS Paragon Plus Environment

Page 33 of 45 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


45. Scheffler, T. L.; Scheffler, J. M.; Park, S.; Kasten, S. C.; Wu, Y.; McMillan, R. P.; Hulver, M. W.; Frisard, M. I.; Gerrard, D. E., Fiber hypertrophy and increased oxidative capacity can occur simultaneously in pig glycolytic skeletal muscle. Am J Physiol-Cell Ph 2014, 306 (4), C354-C363. 46. Hedegaard, J.; Horn, P.; Lametsch, R.; Moller, H. S.; Roepstorff, P.; Bendixen, C.; Bendixen, E., UDP-glucose pyrophosphorylase is upregulated in carriers of the porcine RN- mutation in the AMPactivated protein kinase. Proteomics 2004, 4 (8), 2448-2454. 47. Scheffler, T. L.; Park, S.; Roach, P. J.; Gerrard, D. E., Gain of function AMP-activated protein kinase gamma3 mutation (AMPKgamma3R200Q) in pig muscle increases glycogen storage regardless of AMPK activation. Physiol Rep 2016, 4 (11). 48. Krause, M. P.; Moradi, J.; Coleman, S. K.; D'Souza, D. M.; Liu, C.; Kronenberg, M. S.; Rowe, D. W.; Hawke, T. J.; Hadjiargyrou, M., A novel GFP reporter mouse reveals Mustn1 expression in adult regenerating skeletal muscle, activated satellite cells and differentiating myoblasts. Acta Physiol (Oxf) 2013, 208 (2), 180-90. 49. Lombardo, F.; Komatsu, D.; Hadjiargyrou, M., Molecular cloning and characterization of Mustang, a novel nuclear protein expressed during skeletal development and regeneration. FASEB J 2004, 18 (1), 52-61. 50. Chin, E. R., Role of Ca2+/calmodulin-dependent kinases in skeletal muscle plasticity. Journal of Applied Physiology 2005, 99 (2), 414-423. 51. Fluck, M.; Waxham, M. N.; Hamilton, M. T.; Booth, F. W., Skeletal muscle Ca(2+)independent kinase activity increases during either hypertrophy or running. J Appl Physiol (1985) 2000, 88 (1), 352-8. 52. Rose, A. J.; Kiens, B.; Richter, E. A., Ca2+-calmodulin-dependent protein kinase expression and signalling in skeletal muscle during exercise. J Physiol 2006, 574 (Pt 3), 889-903. 53. Hudmon, A.; Schulman, H., Structure-function of the multifunctional Ca2+/calmodulindependent protein kinase II. Biochem J 2002, 364 (Pt 3), 593-611. 54. White, R. R.; Kwon, Y. G.; Taing, M.; Lawrence, D. S.; Edelman, A. M., Definition of optimal substrate recognition motifs of Ca2+-calmodulin-dependent protein kinases IV and II reveals shared and distinctive features. J Biol Chem 1998, 273 (6), 3166-72. 55. Lanner, J. T.; Georgiou, D. K.; Joshi, A. D.; Hamilton, S. L., Ryanodine receptors: structure, expression, molecular details, and function in calcium release. Cold Spring Harb Perspect Biol 2010, 2 (11), a003996. 56. Jiang, D.; Chen, W.; Xiao, J.; Wang, R.; Kong, H.; Jones, P. P.; Zhang, L.; Fruen, B.; Chen, S. R., Reduced threshold for luminal Ca2+ activation of RyR1 underlies a causal mechanism of porcine malignant hyperthermia. J Biol Chem 2008, 283 (30), 20813-20. 57. Lee, E. H.; Cherednichenko, G.; Pessah, I. N.; Allen, P. D., Functional coupling between TRPC3 and RyR1 regulates the expressions of key triadic proteins. J Biol Chem 2006, 281 (15), 10042-10048. 58. Narayanan, N.; Xu, A., Phosphorylation and regulation of the Ca2+-pumping ATPase in cardiac sarcoplasmic reticulum by calcium/calmodulin-dependent protein kinase. Basic Res Cardiol 1997, 92, 25-35. 59. Takayama, S.; Reed, J. C.; Homma, S., Heat-shock proteins as regulators of apoptosis. Oncogene 2003, 22 (56), 9041-9047. 60. Chen, H. F.; Chen, C. Y.; Lin, T. H.; Huang, Z. W.; Chi, T. H.; Ma, Y. S.; Wu, S. B.; Wei, Y. H.; Hsieh, M., The protective roles of phosphorylated heat shock protein 27 in human cells harboring myoclonus epilepsy with ragged-red fibers A8344G mtDNA mutation. FEBS J 2012, 279 (16), 2987-3001. 61. Charette, S. J.; Lavoie, J. N.; Lambert, H.; Landry, J., Inhibition of Daxx-mediated apoptosis by heat shock protein 27. Mol Cell Biol 2000, 20 (20), 7602-7612. 62. Huot, J.; Houle, F.; Spitz, D. R.; Landry, J., HSP27 phosphorylation-mediated resistance against actin fragmentation and cell death induced by oxidative stress. Cancer Research 1996, 56 (2), 273-9. 63. Brushia, R. J.; Walsh, D. A., Phosphorylase kinase: the complexity of its regulation is reflected in the complexity of its structure. Front Biosci 1999, 4, D618-41. 33 ACS Paragon Plus Environment


Page 34 of 45

64. Saltiel, A. R., New perspectives into the molecular pathogenesis and treatment of Type 2 diabetes. Cell 2001, 104 (4), 517-529. 65. Bouskila, M.; Hunter, R. W.; Ibrahim, A. F.; Delattre, L.; Peggie, M.; van Diepen, J. A.; Voshol, P. J.; Jensen, J.; Sakamoto, K., Allosteric regulation of glycogen synthase controls glycogen synthesis in muscle. Cell Metab 2010, 12 (5), 456-66. 66. Rylatt, D. B.; Aitken, A.; Bilham, T.; Condon, G. D.; Embi, N.; Cohen, P., Glycogen-Synthase from Rabbit Skeletal-Muscle - Amino-Acid-Sequence at the Sites Phosphorylated by Glycogen-Synthase Kinase-3, and Extension of the N-Terminal Sequence Containing the Site Phosphorylated by PhosphorylaseKinase. Eur J Biochem 1980, 107 (2), 529-537. 67. Yang, T.; Riehl, J.; Esteve, E.; Matthaei, K. I.; Goth, S.; Allen, P. D.; Pessah, I. N.; Lopez, J. R., Pharmacologic and functional characterization of malignant hyperthermia in the R163C RyR1 knock-in mouse. Anesthesiology 2006, 105 (6), 1164-75. 68. Sweeney, H. L.; Yang, Z.; Zhi, G.; Stull, J. T.; Trybus, K. M., Charge replacement near the phosphorylatable serine of the myosin regulatory light chain mimics aspects of phosphorylation. Proc Natl Acad Sci U S A 1994, 91 (4), 1490-4. 69. Muroya, S.; Ohnishi-Kameyama, M.; Oe, M.; Nakajima, I.; Shibata, M.; Chikuni, K., Double phosphorylation of the myosin regulatory light chain during rigor mortis of bovine longissimus muscle. Journal of Agricultural and Food Chemistry 2007, 55 (10), 3998-4004. 70. Josephson, M. P.; Sikkink, L. A.; Penheiter, A. R.; Burghardt, T. P.; Ajtai, K., Smooth muscle myosin light chain kinase efficiently phosphorylates serine 15 of cardiac myosin regulatory light chain. Biochem Biophys Res Commun 2011, 416 (3-4), 367-71. 71. Szczesna, D., Regulatory light chains of striated muscle myosin. Structure, function and malfunction. Curr Drug Targets Cardiovasc Haematol Disord 2003, 3 (2), 187-97. 72. Bowslaugh, J.; Gittings, W.; Vandenboom, R., Myosin light chain phosphorylation is required for peak power output of mouse fast skeletal muscle in vitro. Pflug Arch Eur J Phy 2016, 468 (11-12), 20072016. 73. Horman, S.; Morel, N.; Vertommen, D.; Hussain, N.; Neumann, D.; Beauloye, C.; El Najjar, N.; Forcet, C.; Viollet, B.; Walsh, M. P.; Hue, L.; Rider, M. H., AMP-activated protein kinase phosphorylates and desensitizes smooth muscle myosin light chain kinase. J Biol Chem 2008, 283 (27), 18505-18512. 74. Stull, J. T.; Tansey, M. G.; Tang, D. C.; Word, R. A.; Kamm, K. E., Phosphorylation of Myosin Light-Chain Kinase - a Cellular Mechanism for Ca2+ Desensitization. Molecular and Cellular Biochemistry 1993, 128, 229-237. 75. Kuma, Y.; Campbell, D. G.; Cuenda, A., Identification of glycogen synthase as a new substrate for stress-activated protein kinase 2b/p38beta. Biochem J 2004, 379 (Pt 1), 133-9. 76. Sola-Penna, M.; Da Silva, D.; Coelho, W. S.; Marinho-Carvalho, M. M.; Zancan, P., Regulation of Mammalian Muscle Type 6-Phosphofructo-1-kinase and Its Implication for the Control of the Metabolism. Iubmb Life 2010, 62 (11), 791-796.


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Figure legends: Figure 1: Metabolites determination in longissimus muscles from tested pigs with wildtype, RN, HAL and RN-HAL double mutant genotypes. A, Adenosine nucleotides; B, pH value; C, glycogen; D, glucose; E glucose 6-phosphate (G6P); F, lactate. (all are expressed in lmol/g wet weight). Data are mean ±SE. (3 pigs per genotype). Figure 2: The experimental workflow for the quantitative proteomics and phosphoproteomics analysis of 45min postmortem porcine muscles with wildtype, RN, HAL, and RN-HAL double mutant genotypes. Figure 3: The identification summary. A, the venn diagram of identified non-modified proteins and phosphoproteins. B, the venn diagram of identified unique phosphorylation sites between three replicates with high overlap, indicating an excellent reproducibility. C, the distribution of identified phosphorylation sites and related proteins. Figure 4: The Logo-like representations of putative motifs identified from all phosphorylation sites. The height of the residues represents the frequency with which they occur at the respective positions. The color of the residues represents their physicochemical properties. The four motifs highlighted with underline were also identified in the regulated phosphorylation sites. Figure 5: the principle component analysis (PCA) of quantified proteins at total protein level (A) and quantified phosphopeptides (B) from four genotypes in triplicate. Figure 6: The analysis of regulated proteins at total protein level. A, the venn diagram of regulated proteins in RN, HAL and RN-HAL double mutations compared to wild type. The downregulated proteins were in blue color, and the up-regulated proteins were in dark red color. B, in the String protein-protein interaction network, the interested biological subnetworks were indicated



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with red dash line, and the relative abundant information was indicated on top of each protein for each genotypes. Figure 7: The fuzzy c-means clustering analysis of phosphorylation sites. The numbers of proteins and phosphorylated sites in each cluster were indicated. Figure 8: the cluster based String network analysis of regulated phosphoproteins. A, the phosphoproteins from the fuzzy c-means cluster 3 and 5 were considered to be regulated by HAL mutation and analyzed together in String network analysis, the phosphoproteins identified with upregulated phosphorylation sites in RN and RN-HAL mutant genotypes were marked in red color, green color for proteins with downregulated phosphorylation sites, and yellow color for proteins with both up and down-regulated phosphorylation sites. B, the phosphoproteins from the fuzzy cmeans cluster 6 and 7 were considered to be regulated by RN mutation and analyzed together in String network analysis, the colors were indicated in a similar way.


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For TOC only



8

µmol/g

6

Adenosine nucleotides concentration at 45 min IMP ADP ATP

4 2

W

C)

RN HAL Genotypes

RN‐HAL

6.44

6.5

pH 1440 min 5.43

100 80

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Glucose concentration

20

60

5.26

5.56 5.33

5.4

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5

D) 0 min 45 min 1440 min

pH 45min

6

Glycogen concentration

120

pH value

6.68

5.5

0

µmol/g

B)

RN‐HAL 0 min 45 min 1440 min

15 µmol/g

Figure 1 A)

40

10 5

20 0

0 W

RN

HAL

W

RN‐HAL

RN

E)

G6P concentration

14 12 10 8 6 4 2 0

0 min 45 min 1440 min

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Genotypes

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Lactate concentration

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0 min 45 min 1440 min

100 µmol/g

µmol/g

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

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80 60 40 20 0

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RN

HAL

RN‐HAL

W

Genotypes

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Genotypes ACS Paragon Plus Environment

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Figure 2 Samples

Wild type

RN mutant type

HAL mutant type

RN‐HAL double mutant type

Protein extraction and digestion

iTRAQ labeling and equally mixed

iTRAQ114

iTRAQ115

iTRAQ116

iTRAQ117

TiO2 Enrichment

p p

p p

Phosphopeptides

Flow‐through

HILIC

LC‐MS/MS

Total proteome

Phosphoproteome

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Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

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Figure 3 A)

Identified proteins NM proteins

B)

C)

Identified phosphosites between replicates Exp2 6809

Phosphoproteins

Distribution of phosphosites and proteins

8000 7000 6000

1083 509

423

1462

528

730 4468

Exp1 6707

1131

580

6788

5000

Phosphosites

4000

Proteins

3000 1099

2000 Exp3 6877

1756

2299 835

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0

Lists contain 9619 unique elements


pS

pT

pY

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Figure 4



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Figure 5 A)

PCA of Proteins

B)


PCA of Phosphopeptides

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Figure 6 A)

Regulated proteins

Hemoglobin binding

B)

Collagens

HAL 10 MUSTN1

BIN1 TRDN CSRP3 Glycogen PDLIM1, ANXA6, HNRNPK ASPH COX4I1, HSD17B12, MYL6 synthesis TSC22D3 SYPL2 MIF, NUTF2, ANXA5, GAPDH, CYCS, ACADM, ZP2 NDUFA3, EIF4EBP1, UGP2, ACTN4, ATP2A3_tv1, CCT8, NDUFB11, NDUFAB1, HSPB7, UQCRC1, MYH9, HP, SOD2, PRDX3, FH, PFDN2, B4GALNT2, DLAT, LTA4H, UQCRH, UCHL1, HBB, CTSD, SUCLG1, GOT2, HBA, SLC25A4, ATP5H, IDH2, PTER, CLIC5, SH3BGR, ACTC1 UQCRC2, ACTB, SDHB, PSMA1, UCHL3, MAP4, SNX3, PPIE, HSPB1, ITGB1BP, SDHA, PDHX, QDPR, COL14A1, ODC1, SGCB, MYH1, CYC1 VDAC2, BZW2, NSFL1C, CCHCR1, PSME1, TAS1R1, PGRMC2, MYH2 MYH4, HAGH, GCC2, LDHB, DHX29, ATL2, CA3, PGAM2, TRIM62, HSD17B10, HIST1H2BK, COL1A1, ARL6IP5 PRDX1 RPL8, DNAH10, RPL7A, Redox RHOC, ATP2B1

RN 70

Energy metabolism related enzymes

Mitochondrial respiratory chain

RN‐HAL 51

Stress response

Relative abundance W RN HAL RN‐HAL

Muscle contraction activity ACS Paragon Plus Environment

Ribosome proteins Gene name


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Figure 7 Proteins: 4 pS: 4 pT: 1

Proteins: 6 pS: 7 pT: 2

Proteins: 21 pS: 22 pT: 4 pY: 2






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Figure 8 A)

cluster 3, 5 B)

Muscle contraction activity

cluster 6, 7 Muscle contraction activity

Calcium signaling Stress response

Glycogen and energy metabolism


Glycogen Metabolism

Quantitative Proteomics and Phosphoproteomics Analysis Revealed

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