Turnover rates of human muscle proteins in vivo reported in fractional, mole and absolute units

24 Deuterium oxide; heavy water; proteomics; fractional synthesis rate; biosynthetic labelling; protein 25 turnover; skeletal muscle; proteome dynamics; human 26 27 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint

28 Protein fractional turnover rates (FTR) represent measurements of flux through a protein pool, i.e. net 29 abundance (ABD) of the protein. If protein abundance is not measured or is different between 30 experimental conditions the interpretation of FTR data may be confounded. This project investigates 31 the consequences of reporting turnover rates of human muscle proteins in vivo in mole and absolute 32 units (that incorporate protein abundance data) compared to fractional (%/d) data that ignore protein 33 abundance. Three physically active males (21 ± 1 years) were recruited and underwent a 12 -d protocol 34 of daily deuterium oxide (D 2O) consumption and biopsies of vastus lateralis on days 8 and 12. Protein 35 abundances were normalised to yeast alcohol dehydrogenase, added during sample preparation, and 36 FTR was calculated from time -dependent changes in peptide mass isotopomer profiles. FTR and 37 abundance data (fmol/ μg protein) were combined to calculate mol e turnover rates (MTR; fmol/ μg 38 protein/ d) and absolute turnover rates (ATR; ng/ μg protein/ d). Abundance data were collected for 39 1,772 proteins and FTR data were calculated from 3,944 peptides representing 935 proteins (average 40 3 peptides per protein). The median (M), lower - (Q1) and upper -quartile (Q3) values for protein FTR 41 (%/d) were M = 4.3, Q1 = 2.52, Q3 = 7.84. Our analyses suggest MTR data is preferred over FTR, 42 particularly for studies on multiprotein complexes, wherein MTR takes account of potential differences 43 amongst the molecular weight of the component subunits. ATR data may be preferred over MTR and 44 FTR, particularly when comparing samples with different abundance profiles. 45 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint

46 In healthy adults, skeletal muscle is the largest tissue (by mass) in the human body, representing 47 approximately 40 % of total body mass and accounting for 50 – 70 % of total body protein. Muscle 48 proteins exist in a continuous cycle of synthesis and degradation (collectively known as protein 49 turnover), which is essential to maintain muscle quality and facilitate changes in protein abundance 50 profiles that underpin cellular adaptation. Losses in muscle mass are associated with heightened 51 disease mortality [1], and changes to the turnover of muscle protein may also be an important factor 52 underpinning muscle health. Muscle is an accessible tissue in humans and emerging techniques for 53 dynamic proteome profiling have the potential to offer new insight into the mechanisms underpinning 54 human muscle adaptation [2]. 55 The dynamic nature of proteins was established in the early 20 th Century in work led by Rudolf 56 Schoenheimer [3], which used a stable isotope -labelled amino acid ( 15N-tyrosine) to achieve 57 biosynthetic labelling of newly synthesised proteins in rats in vivo. Muscle protein turnover has since 58 been studied extensively but, for the most part, human data are constrained to reports on the average 59 synthesis rate of mixed -protein samples based on the analysis of amino acid hydrolysates [4]. Soon after 60 the turn of the century, advances in proteomic methods enabled studies on the turnover of large 61 numbers of individual proteins, and were first conducted using 2H10-leucine in yeast [5]. The 62 development of stable isotope labelling of amino acids in culture (SILAC) in mammalian cells [6] enabled 63 the turnover of individual proteins in human cell cultures using 2H3-leucine. Doherty et al [7] reports 64 the degradation rates of almost 600 proteins in human A549 adenocarcinoma cells using dynamic SILAC 65 with contemporary 13C6-labelled lysine and arginine. Similarly, Cambridge et al [8] used dynamic SILAC 66 in the mouse C2C12 muscle cell line and reported a median protein degradation rate of 1.6 %/h 67 (equating to a half-life of ~43 h) amongst 3528 proteins studied. 68 Dynamic SILAC requires extensive isotope labelling of amino acid precursors, which is readily achieved 69 in cell culture but impractical in humans. Moreover, cell studies are unable to capture the complexity 70 of human tissues in vivo and it is challenging to predict protein turnover rates in vivo from data 71 generated in cell cultures [9]. Proteome dynamic studies in humans in vivo have investigated the 72 turnover of individual proteins using the stable isotope, deuterium oxide (D 2O or ‘heavy water’), which 73 can be administered via a participant’s drinking water. Low levels of D 2O consumption are safe and 74 enable studies to be conducted under free -living conditions for periods of several days or more. 75 Combined with peptide mass spectrometry (MS) D 2O labelling can be employed to measure the 76 turnover rates of individual proteins [10] and early studies in humans reported the turnover rates of 77 specific proteins in blood [11, 12]. 78 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint To our knowledge, just 5 studies [2, 13-17] report dynamic proteome data in human muscle using D 2O, 79 and mostly these works have investigated muscle responses to exercise training. In the majority, 80 existing data on protein -specific turnover in human skeletal muscle in vivo calculate fractional synthesis 81 rates (FSR) and report the data in percent per day (%/d) units. If protein abundance is known or 82 assumed to be constant during the period of biosynthetic labelling, the term, fractional turnover rate 83 (FTR), is preferred rather than FSR. Amongst the previous literature, Scalzo et al [13] reports the 84 greatest number (n=381) of proteins analysed and found deuterium incorporation was greater in male 85 compared to female participants during a 4 -weeks of sprint interval training, but numerical data on the 86 FSR of each protein was not reported. Shankaran et al [14] and Murphy et al [15] also provide protein -87 specific data on the FSR of 273 and 190 proteins, respectively, in human muscle but did not investigate 88 the abundance of these proteins. Three earlier reports [2, 16, 17] from our laboratory include protein 89 abundance (ABD) data alongside the measurements of protein -specific FSR in human muscle but the 90 abundance and FSR data were not combined to report data in mole or absolute units. 91 Fractional turnover measurements provide information on the flux through the protein pool but are 92 ignorant to the size of the pool (i.e. net protein abundance) and the potential inter -relationships 93 amongst proteins. Muscle is renowned for its plasticity and can exhibit a broad repertoire of 94 phenotypes, underpinned by different protein abundance profiles. For example, proteomic studies 95 have highlighted robust changes to the abundance profile of proteins in human muscle in the contexts 96 of exercise [18], ageing [19, 20] or disease [21]. Therefore, the abundance profile of muscle proteins 97 may need to be considered alongside protein -specific synthesis data, particularly when comparisons 98 are made across different populations with different muscle phenotypes. Label -free proteomics data 99 can be normalised to spike -in standards [22] or calculated from endogenous proteins [23]. Herein, we 100 have applied spike -in methods to investigate the abundance and turnover rates of human muscle 101 proteins in vivo using mole (e.g. fmol/ μg total protein/ day) and absolute (e.g. ng/ μg total protein/ 102 day) units, and we find the different unis of measurement alter the biological interpretation of protein -103 specific turnover data. 104 105 .CC-BY-NC-ND 4.0 International licenseperpetuity. 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106 Participant s 107 Three physically active males (21 ± 1 years; height 178 ± 1 cm; weight 75 ± 5 kg) were recruited and 108 received verbal and written information, including potential risks, prior to providing written informed 109 consent. The study was approved by the Liverpool John Moores School of Sport and Exercise Science 110 Research Committee (M18SPS006) and conformed with the Declaration of Helsinki, except registration 111 in a publicly accessible database. 112 Experimental Protocol 113 Figure 1 provides an overview of the experimental protocol which consisted of a 12 -day cross -sectional, 114 observation study including metabolic labelling of newly synthesised protein in vivo using deuterium 115 oxide (2H2O; D2O) administration. Seventy -two hours prior to the labelling period, preparatory data 116 including anthropological measurements and peak aerobic capacity, were collected. Baseline saliva, 117 blood, and muscle samples were collected on day 0, prior to daily D 2O administration. Saliva and venous 118 blood samples were collected on days 0, 4, 8 and 12 to measure body water deuterium enrichment. 119 Muscle samples were also collected on days 8 and 12 via micro -needle biopsy of the vastus lateralis. 120 Assessment of aerobic exercise capacity 121 Participants attended the laboratory in the morning after an overnight fast and their resting heart rate 122 and blood pressure were measured (DINAMAP V100, General Healthcare, UK) in a seated position. Peak 123 oxygen uptake (VrO2 peak) was measured, using an incremental exercise test to volitional exhaustion on 124 a cycle ergometer (Lode, Groningen, The Netherlands). Respiratory gases were measured using an 125 online gas collection system (CORTEX Biophysik MetaLyzer 3B stationary CPX). The test consisted of an 126 initial load of 100 W for 10 minutes, followed by 30 W increases in external load at 2 -minute intervals 127 until cadence reduced to <50 rpm, at which point the test was terminated. VrO2 peak was reported as 128 the mean oxygen uptake during the final 1 -minute of exercise. 129 Stable isotope labelling in vivo 130 Participants were instructed to maintain their habitual exercise and dietary routine throughout the 12 -131 day D2O labelling period. Participants recorded the duration and rate of perceived exertion (RPE) of 132 each training session using TrainingPeaks software (TrainingPeaks, Denver, CO, USA). Dietary intake 133 was monitored by recording meal information using a smartphone application (MyFitnessPal, Under 134 Armour, Baltimore, MD, USA) [24]. 135 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Biosynthetic labelling of newly synthesised proteins was achieved by oral consumption of deuterium 136 oxide (D2O). Consistent with our previous work [2, 16], participants consumed 50 ml of 99.8 atom % of 137 D2O four times per day for days 1 -5 commencing after the first muscle biopsy on day 0. On days 6 -12, 138 the dosage was lowered to 50 ml two times per day. All 50 ml doses were dispensed in a nutrition 139 laboratory and sealed prior to distribution to the participants. Participants were instructed to consume 140 each dose ~4 hours apart to negate any potential side effects e.g., nausea. 141 Saliva samples were collected, upon waking, by each participant using pre -labelled saliva collection kits 142 (Salivette, Sarstedt, NC, USA). Participants delivered saliva samples to the laboratory at each visit using 143 a cooled container. Collection tubes were centrifuged for 2 mins at 1000 x g and aliquots of saliva were 144 stored at -80 °C until analysis. 145 Venous blood samples were collected in EDTA-coated vacutainer tubes via a single use butterfly needle 146 (Beckton Dickson, UK) inserted into the antecubital fossa. Plasma was extracted by centrifugation 147 (1,200 x g, 4 °C for 10 min) prior to storage at -80 °C for subsequent analysis. 148 Muscle Biopsy Protocol 149 Muscle samples were obtained from the vastus lateralis of the participant’s dominant leg after an 150 overnight fast using a Bard Monopty Disposable Core Biopsy Instrument 12 -gauge x 10 cm length (Bard 151 Biopsy System, Tempe, AZ). Local anaesthesia (0.5 % Marcaine) was administered, and a 0.5 cm 152 longitudinal incision was made through the skin. The muscle fascia was then pierced, and 2 -3 muscle 153 pieces were taken to collect adequate amounts (minimum 50 mg) of sample. Samples were blotted to 154 remove excess blood, and visible fat and connective tissue were removed through dissection. Muscle 155 tissue was snap-frozen in liquid nitrogen and stored at -80 °C for subsequent analysis. 156 Calculation of D2O Enrichment 157 Body water enrichment of D 2O was measured in plasma and saliva samples against external standards 158 that were constructed by adding D 2O to PBS over the range from 0.0 to 5.0 % in 0.5 % increments. 159 Deuterium enrichment of aqueous solutions was determined by gas chromatography -mass 160 spectrometry after exchange to acetone [25]. Samples were centrifuged at 12,000 g, 4 °C for 10 min, 161 and 20 µl of sample supernatant or standard was reacted overnight at room temperature with 2 µl of 162 10 M NaOH and 4 µl of 5 % (v/v) acetone in acetonitrile. Acetone was then extracted into 500 µl 163 chloroform and water was captured in 0.5 g Na 2SO4 before transferring a 200 -µl aliquot of chloroform 164 to an auto-sampler vial. Samples and standards were analysed in triplicate by using an Agilent 5973 N 165 mass selective detector coupled to an Agilent 6890 gas chromatography system (Agilent Technologies, 166 Santa Clara, CA, USA). A CD624 -GC column (30 m, 30.25 mm 3 1.40 mm) was used in all analyses. 167 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Samples (1 μl) were injected by using an Agilent 7683 autosampler. The temperature program began 168 at 50 °C and increased by 30 °C/min to 150 °C and was held for 1 min. The split ratio was 50:1 with a 169 helium flow of 1.5 ml/min. Acetone eluted at ~3 min. The mass spectrometer was operated in the 170 electron impact mode (70 eV) and selective ion monitoring of m/z 58 and 59 were performed by using 171 a dwell time of 10 ms/ ion. 172 Muscle processing 173 Muscle samples were pulverized in liquid nitrogen, then homogenized on ice in 10 volumes of 1 % Triton 174 X-100, 50 mM Tris, pH 7.4 (including complete protease inhibitor; Roche Diagnostics, Lewes, United 175 Kingdom) using a PolyTron homogenizer. Homogenates were incubated on ice for 15 min, then 176 centrifuged at 1000 x g, 4 °C, for 5 min to fractionate insoluble (myofibrillar) proteins from soluble 177 proteins. Soluble proteins were decanted and cleared by further centrifugation (12,000 x g, 4 °C, for 45 178 min). Insoluble proteins were resuspended in a half -volume of homogenization buffer followed by 179 centrifugation at 1000 x g, 4 °C, for 5 min. The washed pellet was then solubilized in lysis buffer (7 M 180 urea, 2 M thiourea, 4 % CHAPS, 30 mM Tris, pH 8.5) and cleared by centrifugation at 12,000 x g, 4 °C, 181 for 45 min. Protein concentrations of the insoluble and soluble protein fractions were measured by 182 Bradford assay. Aliquots containing 500 µg protein were precipitated in 5 volumes of ice -cold acetone 183 and incubated for 1 h at -20 °C. Proteins were then resuspended in lysis buffer to a final concentration 184 of 5 μg/ μl. 185 Tryptic digestion was performed using the filter -aided sample preparation (FASP) method [26]. Aliquots 186 containing 100 µg protein were precipitated in acetone and resuspended in 40 μl UA buffer (8 M urea, 187 100 mM Tris, pH 8.5). Samples were transferred to filter tubes and washed with 200 µl of UA buffer. 188 Proteins were incubated at 37 °C for 15 min in UA buffer containing 100 mM dithiothreitol followed by 189 incubation (20 min at 4 °C) protected from light in UA buffer containing 50 mM iodoacetamide. UA 190 buffer was exchanged with 50 mM ammonium bicarbonate and sequencing -grade trypsin (Promega, 191 Madison, WI, USA) was added at an enzyme to protein ratio of 1:50. Digestion was allowed to proceed 192 at 37 °C overnight then peptides were collected in 100 μl 50 mM ammonium bicarbonate containing 193 0.2 % trifluoroacetic acid. Samples containing 4 µg of peptides were de -salted using C 18 Zip-tips 194 (Millipore) and resuspended in 20 µl of 2.5 % (v/v) ACN, 0.1 % (v/v) formic acid (FA) containing 10 fmol/ 195 μl yeast alcohol dehydrogenase (ADH1; MassPrep, Waters Corp., Milford, MA). 196 Liquid chromatography -mass spectrometry of the myofibrillar protein fraction 197 Liquid chromatography -mass spectrometry of myofibrillar proteins was performed using nanoscale 198 reverse -phase ultra -performance liquid chromatography (NanoAcquity; Waters Corp., Milford, MA) 199 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint and online electrospray ionization quadrupole -time-of-flight mass spectrometry (Q -TOF Premier; 200 Waters Corp.). Samples (5 μl corresponding to 1 μg tryptic peptides) were loaded by partial -loop 201 injection on to a 180 μm ID x 20 mm long 100 Å, 5 µm BEH C 18 Symmetry trap column (Waters Corp.) 202 at a flow rate of 5 μl/ min for 3 min in 2.5 % (v/v) ACN, 0.1% (v/v) FA. Separation was conducted at 35 °C 203 via a 75 μm ID x 250 mm long 130 Å, 1.7 µm BEH C 18 analytical reverse -phase column (Waters Corp.). 204 Peptides were eluted using a non -linear gradient that rose to 37.5 % acetonitrile 0.1% (v/v) FA over 90 205 min at a flow rate of 300 nl/ min. Eluted peptides were sprayed directly into the mass spectrometer via 206 a NanoLock Spray source and Picotip emitter (New Objective, Woburn, MA). Additionally, a LockMass 207

(100 fmol/ μl Glu-1-fibrinopeptide B) was delivered to the NanoLock Spray source of the mass 208 spectrometer at a flow rate of 1.5 μl/ min and was sampled at 240 s intervals. For all measurements, 209 the mass spectrometer was operated in positive electrospray ionization mode at a resolution of 10,000 210 full width at half maximum (FWHM). Before analysis, the time -of-flight analyser was calibrated using 211 fragment ions of [Glu -1]-fibrinopeptide B from m/z 50 to 1990. 212 Mass spectra for liquid chromatography -mass spectrometry profiling were recorded between 350 and 213 1600 m/z using mass spectrometry survey scans of 0.45 -s duration with an interscan delay of 0.05 s. In 214 addition, equivalent data-dependent tandem mass spectra (MS/MS) were collected from each baseline 215 (day 0) sample. MS/MS spectra of collision -induced dissociation fragment ions were recorded from the 216 5 most abundant precursor ions of charge 2+ 3+ or 4+ detected in each survey scan. Precursor 217 fragmentation was achieved by collision-induced dissociation at an elevated (20–40 eV) collision energy 218 over a duration of 0.25 s per parent ion with an interscan delay of 0.05 s over 50 –2000 m/z. Acquisition 219 was switched from MS to MS/MS mode when the base peak intensity exceeded a threshold of 30 220 counts/s and returned to the MS mode when the total ion chromatogram (TIC) in the MS/MS channel 221 exceeded 50,000 counts/s or when 1.0 s (5 scans) were acquired. To avoid repeated selection of 222 peptides for MS/MS, the program used a 30 -s dynamic exclusion window. 223 Liquid chromatography -mass spectrometry of the myofibrillar protein fraction 224 Data-dependent label-free analysis of soluble protein fractions was performed using an Ultimate 3000 225 RSLC nanosystem (Thermo Scientific, Waltham, MA) coupled to a Fusion mass spectrometer (Thermo 226 Scientific). Samples (3 μl corresponding to 600 ng of protein) were loaded on to the trapping column 227 (Thermo Scientific, PepMap100, C 18, 75 μm X 20 mm), using partial loop injection, for 7 minutes at a 228 flow rate of 9 μl/min with 0.1 % (v/v) trifluoroacetic acid. Samples were resolved on a 500 mm analytical 229 column (Easy-Spray C 18 75 μm, 2 μm column) using a gradient of 96.2 % A (0.1 % formic acid) 3.8 % B 230 (79.9 % ACN, 20 % water, 0.1 % formic acid) to 50 % B over 90 min at a flow rate of 300 nL/min. The 231 data-dependent program used for data acquisition consisted of a 120,000 -resolution full -scan MS scan 232 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint (AGC set to 4e5 ions with a maximum fill time of 50 ms) and MS/MS using quadrupole ion selection with 233 a 1.6 m/z window, HCD fragmentation and normalised collision energy of 32 and LTQ analysis using the 234 rapid scan setting and a maximum fill time of 35 msec. The machine was set to perform as many MS/MS 235 scans as to maintain a cycle time of 0.6 sec. To avoid repeated selection of peptides for MS/MS the 236 program used a 60 s dynamic exclusion window. 237 Label-free quantitation of protein abundances 238 Progenesis Quantitative Informatics for proteomics (Non -Linear Dynamics, Newcastle, UK) was used to 239 perform label -free quantitation on samples collected at days 8 and 12 only. QToF data were LockMass 240 corrected using the doubly charged monoisotopic ion ( m/z 785.8426) of the Glu -1- fibrinopeptide B. 241 Prominent ion features were used as vectors to warp each data set to a common reference 242 chromatogram and an analysis window of 15 –105 min and 350 –1500 m/z was selected. Log -243 transformed MS data were normalized by inter -sample abundance ratio, and relative protein 244 abundances were calculated using unique peptides only. Abundance data were normalised to the 245 median abundance of 3 most abundant peptides of yeast ADH1 [22] to derive abundance 246 measurements in fmol/μg units. MS/MS spectra were exported in Mascot generic format and searched 247 against the Swiss-Prot database (2018.7) restricted to Homo -sapiens (20,272 sequences) using a locally 248 implemented Mascot server (v.2.2.03; www.matrixscience.com). Enzyme specificity was trypsin, 249 allowing 1 missed cleavage, carbamidomethyl modification of cysteine (fixed). QToF data was searched 250 using m/z errors of 0.3 Da, whereas FUSION data were searched using MS errors of 10 ppm and MS/MS 251 errors of 0.6 Da. Mascot output files (xml format), restricted to nonhomologous protein identifications, 252 were recombined with MS profile data in Progenesis. 253 Measurement of protein turnover rates 254 Mass isotopomer abundance data from samples collected at days 8 and 12 were extracted from MS 255 spectra using Progenesis Quantitative Informatics (Non -Linear Dynamics, Newcastle, UK). Consistent 256 with our previous work, e.g. [27], the abundances of the monoisotopic peak (m0), m1, m2 and m3 mass 257 isotopomers were collected over the entire chromatographic peak for each proteotypic peptide that 258 was used for label -free quantitation of protein abundances. Mass isotopomer information was 259 processed in R version 3.5.2 (R core team., 2016). Incorporation of D 2O into newly synthesized protein 260

in a decrease in the molar fraction of the monoisotopic ( fm0) peak that follows the pattern of 261 an exponential decay. The rate constant ( k) for the decay of fm0 was calculated as a first -order 262 exponential spanning from the beginning ( t0) (day 8) to end ( t) (day 12) of the D 2O labelling period 263 (Equation 1). 264 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint 𝑘 = 1 𝑡 − 𝑡0 • −ln + 𝑓𝑚!" 𝑓𝑚!"! . 265 Equation 1 266 The rate of change in mass isotopomer distribution is also a function of the number of exchangeable 267 H-D sites, and this was accounted for by referencing each peptide sequence against standard tables 268 reporting the relative enrichment of amino acids by deuterium in humans [11] to give the fractional 269 turnover rate (FTR) for each peptide. Individual protein FTR was reported as the median of peptide 270 values assigned to each protein (decimal values were multiplied by 100 to give FTR in %/d) for each 271 participant. Mole turnover rate (MTR, fmol/μg/d) was calculated by multiplying protein FTR (expressed 272 as a decimal) by the mole abundance of the protein normalised to the yeast ADH1 spike -in. Absolute 273 turnover rate (ATR, ng/μg/d) was calculated by multiplying MTR by the predicted molecular weight 274 (kDa) of each protein. Protein half -life (t1/2) in days was estimated from decimal FTR data by Equation 275 2. 276 𝑡# $ = 𝑙𝑛2 𝐹𝑆𝑅 277 Equation 2 278 Bioinformatic Analysis 279 Functional annotation and the association of proteins with pathways of the Kyoto Encyclopaedia of 280 Genes and Genomes [KEGG; http:// www.genome.jp/kegg/, [28]] were conducted using the Perseus 281 platform [29]. Protein interactions were investigated using bibliometric mining in the Search Tool for 282 the Retrieval of Interacting Genes/proteins (STRING; http://string -db.org/) [30]. Protein physio -283 chemical characteristics, including isoelectric point ( pI) and molecular weight (MW) were calculated 284 using the Swiss Institute of Bioinformatics EXpasy ProtParam tool 285 (https://web.expasy.org/protparam/ ). Statistical analysis was performed in R (Version 3.6.2). Within -286 subject differences between samples collected on day 8 and day 12 were investigated by repeated 287 measures one -way ANOVA. Significance was identified as P ≤ 0.05 and a false -discovery rate of 5 % 288 calculated from q -values [31]. Differences amongst myosin heavy chain (MyHC) isoforms and the 289 subunits of multiprotein complexes were investigated by one -way ANOVA with Tukey’s HSD post -hoc 290 analysis. Pearson’s moment correlation analyses were used to determine relationships between 291 protein abundance and turnover rate expressed in relative (e.g., FTR or t 1/2), mole and absolute units. 292 Amino acid sequence logos were generated using Seq2Logo 2.0 293 (https://services.healthtech.dtu.dk/services/Seq2Logo -2.0/). Stoichiometry of multiprotein complexes 294 were calculated and compared to the expected stoichiometry reported within The Complex Portal [32]. 295 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Gene ontology analysis was conducted on the top quantile of proteins when ranked by ABD, FTR, MTR 296 and ATR using cluster profiler with significance set at an adjusted P value ≤ 0.05. 297 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint

298 Protein abundance measurements 299 Three physically active age -matched males were studied that had similar peak aerobic capacity and 300 body mass index (Table 1). In total, 1,885 proteins were identified and 1,772 of these proteins had at 301 least 1 unique peptide (<1 % FDR) detected in both day 8 and day 12 samples in all 3 participants. The 302 abundance of muscle proteins spanned 6 orders ( Figure 2A ) of magnitude from 0.003 fmol/μg (leucine 303 tRNA Ligase; SYLC) to 4146.35 fmol/μg (haemoglobin subunit beta; HBB). There were no statistically 304 significant differences in protein abundance between day 8 and day 12 and the R 2 for protein 305 abundance data was 0.987 (Figure 2B ). The median coefficient of variation in protein abundance was 306 6 % with an inter -quartile range from 2.97 % to 12.54 %, which demonstrates a high level of 307 repeatability between protein abundance measurements at day 8 and day 12. Gene ontology analysis 308 of the upper quartile (n = 234 proteins) of protein abundances, included proteins associated with 309 muscle contraction, muscle system process, regulation of muscle contraction, sarcomere organization 310 and striated muscle contraction as the most enriched processes in human muscle ( Figure 2C ). Whereas 311 biological processes, including tRNA aminoacylation, positive regulation of transcription, protein 312 folding, cell migration and regulation of translation were enriched amongst proteins in the lower 313 quartile of abundance measurements. 314 Protein turnover measurements 315 Body water enrichment measured in blood plasma (1.71 ± 0.08 %) on day 8 was not different (P = 316 0.1058) from values (1.89 ± 0.07 %) measured on day 12. Stringent filters were applied to select 317 peptides with clearly resolved envelopes of m 0, m 1, m 2 and m3 mass isotopomers, and in all 6,800 318 protein -specific peptides met the inclusion criteria for turnover calculations in one or more participants. 319 The turnover rates of 935 proteins were measured in at least 1 participant ( Suppl Table S1 ), whereas 320 data were collected for 766 proteins in 2 or more participants and the synthesis rate of 444 proteins 321 was measured in all 3 participants. Unless otherwise stated, data are presented from at least n = 2 322 participants in the subsequent text and figures. The turnover of individual proteins in human vastus 323 lateralis in vivo ranged from 0.32 %/d (microtubule associated protein RP/EB family member 2; MARE2) 324 to 54.43 %/d (nuclear protein localisation protein 4 homolog; NPL4) and the median (IQR) of protein -325 specific FTR was 4.3 (2.52 – 7.84) %/d. MTR had a median of 0.04 (IQR: 0.01 – 0.10) fmol/μg/d and 326 ranged between 0.00013 (MARE2) and 56.89 fmol/μg/d haemoglobin subunit beta (HBB). ATR values 327 had a median of 1.53 (IQR: 0.48 – 4.63) ng/ μg/d and ranged between 0.005 (MARE2) and 931.98 328 ng/μg/d (albumin; ALBU). Different gene ontological classifications were highlighted amongst the 75 th 329 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint percentile of proteins when turnover data were presented in relative or absolute units ( Figure 2 ). When 330 ranked by FTR, cellular oxidant detoxification, cellular response to toxic substance, nuclear migration, 331 nucleus localization and viral process were amongst the top -ranked terms. ( Figure 2D ). In contrast, 332 proteins ranked in the 75 th percentile by MTR were associated with terms, multicellular organismal 333 movement, muscle contraction, muscle system process, sarcomere organization, and striated muscle 334 contraction ( Figure 2E ). And proteins ranked in the 75 th percentile by ATR were associated with the 335 terms muscle cell development, muscle contraction, muscle system process, sarcomere organization 336 and striated muscle cell development ( Figure 2F ). Furthermore, antioxidant enzymes, including SODM 337 (mitochondrial superoxide dismutase [Mn]), PRDX3 (peroxiredoxin 3), PRDX2 (peroxiredoxin 2) and 338 (GSTP1) glutathione S-transferase -P were also amongst the top-ranked proteins by ATR, consistent with 339 their prominent role in skeletal muscle physiology. 340 Our analysis encompassed 35 myofibrillar proteins, including each of the main components of the 341 sarcomere ( Figure 3 ), which were primarily extracted from the insoluble fraction. The myosin heavy 342 chain (MyHC) isoform profile was 65 ± 2.4 % type IIa (MYH2), 24 ± 3.5 % type I (MYH7) and 12 ± 1.2 % 343 type IIx (MYH1). Accordingly, the abundance (ABD) of MYH2 (634.50 ± 173.79 fmol/ μg) was significantly 344 (p ≤ 0.01) greater than MYH1 (114.67 ± 38.25 fmol/μg) and MYH7 (225.97 ± 24.96 fmol/μg), whereas 345 there were no significant differences amongst turnover of MyHC isoforms ( Figure 3B ) when expressed 346 in FTR (%/d) units. When rate data were expressed in mole ( Figure 3C ) or absolute terms ( Figure 3D ), 347 the turnover profile of MyHC isoforms more closely mirrored the abundance data and both the MTR 348 (8.07 ± 1.93 fmol/μg/d) and ATR (1799.2 ± 429.82 ng/μg/d) of MYH2 were significantly (p ≤ 0.01) greater 349 than MYH7 (MTR; 2.56 ± 1.13 fmol/μg/d, ATR; 571 ± 252.3 ng/μg/d) and MYH1 (MTR; 2.03 ± 1.04 350 fmol/μg/d, ATR; 453.70 ± 232.19 ng/μg/d). 351 Protein turnover data were collected for 48 proteins of the major energy metabolism pathways in 352 human muscle ( Figure 4 ), including fatty acid β-oxidation (17 of 42 annotated in gene ontology 353 databases), glycolysis (15 of 67) and the TCA cycle (16 of 30). The median (IQR) ABD of proteins involved 354 in fatty acid oxidation and the TCA cycle were 2.11 (IQR; 1.13 – 3.65) fmol/μg and 3.34 (IQR; 0.63 – 355 8.41) fmol/μg respectively, whereas the ABD for proteins involved in glycolysis was approximately 10 -356 fold greater, 37.00 (IQR; 9.12 – 105.56) fmol/μg. Despite the markedly greater abundance of glycolytic 357 enzymes, the median FTR (2.10, 1.20 – 2.40 %/d) of glycolytic proteins was not different from enzymes 358 of either TCA cycle (2.20, 1.66 – 3.04 %/d) or fatty acid oxidation pathway (2.78, 2.19 – 3.94 %/d). 359 Differences amongst the turnover of proteins of the different metabolic pathways were more 360 transparent when data were expressed in mole or absolute units. The MTR (0.69, 0.15 – 1.34 fmol/μg/d) 361 and ATR (29.23, 7.84 – 81.48 ng/μg/d) of enzymes involved in glycolytic processes was ≥10-fold greater 362 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint than the TCA cycle (MTR; 0.077, 0.022 – 0.134 fmol/μg/d, ATR; 4.06, 1.24 – 9.41 ng/μg/d) and fatty acid 363 oxidation (MTR; 0.065, 0.039 – 0.095 fmol/μg/d, ATR; 2.12, 1.29 – 3.63 ng/μg/d). 364 Predictors of protein turnover rates in human muscle in vivo 365 Protein turnover expressed in fractional units (FTR, %/d) did not correlate (r = -0.082) with mole protein 366 abundance (Figure 5A ), whereas MTR exhibited a strong (r = 0.9695) positive relationship ( Figure 5B ) 367 and ATR somewhat correlated with mole protein abundance (r = 0.6964) (Figure 5C ). Neither protein 368 molecular weight (MW; Figure 5 D -F) nor isoelectric point (p I; Figure 5 G -I) corelated with protein 369 turnover expressed in either relative or absolute units. The median (IQR) for p I and MW of proteins in 370 the current study were 41.44 (IQR; 25.97 – 63.45) KDa and pH 6.105 (IQR; 5.37 – 7.58). Pearson’s 371 corelation analysis of the top 50 proteins with the lowest and highest t 1/2 values found no correlation (r 372 = -0.0024) with predicted protein t 1/2 values calculated using the N-end rule of degradation. Similarly, 373 there was no significant enrichment of linear motifs amongst either the top ( Figure 5K ) or bottom -374 ranked proteins ( Figure 5L ). 375 Subunit stoichiometry of multiprotein complexes 376 Data were collected for 79 subunits of 6 multiprotein complexes (MPC), including the 26S proteasome 377 and complexes I, II, III, IV and V of the mitochondrial respiratory chain ( Figure 6 ). On average the mean 378 ± SD mole abundance of the 26S proteasome (0.342 ± 0.224 fmol/μg) was significantly less (P ≤ 0.05) 379 than the abundance of respiratory chain complexes III, IV and V ( Figure 6A ). Subunits of Complex I 380 (1.414 ± 0.856 fmol/ug) were significantly less abundant than subunits of Complex III (7.311 ± 4.453 381 fmol/μg) and Complex V (11.335 ± 14.349 fmol/ug). Similarly, subunits of Complex II (2.328 ± 1.944 382 fmol/ug) and complex IV (4.833 ± 3.835 fmol/ug) were also significantly less abundant than Complex V. 383 There were significant (P ≤ 0.05) differences in FTR (%/d) between the 26S proteasome and 384 mitochondrial respiratory chain complexes III and V ( Figure 6B ). In contrast to ABD data the turnover 385 of the 26S Proteasome subunit ( 8.436 ± 7.03450 %/day) was significantly higher than that of Complex 386 III (1.946045 ± 0.479 %/day) and Complex V ( 2.210 ± 1.091 %/day). Turnover data expressed in mole 387 (Figure 6C ) and absolute terms ( Figure 6D ) were in better agreement with protein abundance 388 measures, for example Complex IV (0.21 ± 0.25 fmol/μg/day) and Complex V ( 0.180 ± 0.181 389 fmol/ug/day) had significantly (P ≤ 0.05) greater mean ± SD MTR than the 26S proteasome (0.027 ± 390 0.029 fmol/μg/day). Complex IV also had a significantly higher MTR than complex I (0.09 ± 0.12 391 fmol/μg/day). Absolute values of MPC highlighted the ATR of Complex V was significantly (P ≤ 0.05) 392 greater (6.83 ± 11.62 ng/μg/day) than complex I (2.46 ± 3.66 ng/μg/day) and the 26S proteasome (1.02 393 ± 0.98 ng/μg/day). 394 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Individual subunits within a MPC display differing turnover rates. We identified 18 of the 47 subunits of 395 the 26S proteasome in at least n = 2 participants and 24 subunits in n = 1 participants ( Figure 7 ). Ten 396 subunits belonged to the 20S core particle and 13 to the 19S regulatory particle. FTR values of subunits 397 within the 26S proteasome ranged from 1.57 ± 1.06 %/d (proteasome subunit alpha type 3; PSA3) to 398 27. 09 ± 17.50 %/d (26S proteasome AAA -ATPase subunit RPT1; PRS7), with a combined median of 399 5.50 %/d (IQR; 3.86 – 11.58) for the 18 proteins identified in the current work. MTR and ATR values 400 ranged from 0.003 ± 0.002 fmol/μg/d (PSA3) to 0.12 ± 0.16 fmol/μg/d (proteasome subunit alpha type 401 7; PSA7) and 0.09 ± 0.06 ng/μg/d (PSA3) to 3.38 ± 4.59 ng/μg/d (PSA7) respectively. The median (IQR) 402 MTR and ATR for the 18 proteins identified in the 26S proteasome are reported as 0.015 (0.008 – 0.038) 403 fmol/μg/d and 0.51 (0.31 – 1.27) ng/μg/d respectively. 404 Complex V of the mitochondrial respiratory chain is a well -defined MPC. We herein quantified the ABD 405 and stoichiometry of Complex V (ATP synthase) and compared the measured protein abundance of 406 each subunit, against their predicted abundance from the Complex Portal database ( Figure 8A ). We 407 also compared individual protein subunit turnover in fractional ( Figure 8C ), mol e (Figure 8 D) and 408 absolute ( Figure 8E ) terms against protein abundance stoichiometry. Figure 8F -H illustrates the 409 relationship between the turnover rates and relative protein abundance of subunits of ATP synthase. 410 Values are expressed as a percentage of the total of ATP synthase. There was no relationship (r 2 = -411 0.144) between the FTR of a subunit and its relative protein ABD within the ATP synthase MPC ( Figure 412 8F). For example, ATPB made up ~ 37 % of the ABD of ATP synthase, but only 6 % of turnover (FTR). In 413 contrast, MTR (r 2 = 0.851) and ATR (r 2 = 0.933) data exhibited significant relationships with the relative 414 ABD of ATP synthase subunits (Figure 8 G -H). The measured stoichiometry of the average abundance 415 of ATP synthase subunits when normalised to ATPG (i.e. abundance ATPA:ATPB:ATPD per fmol of ATPG) 416 was 9.17: 11.62: 0.84 fmol/µg per fmol/µg of ATPG. 417 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint

418 Currently few data exist on protein -specific turnover in human skeletal muscle in vivo, and the turnover 419 rates of individual proteins have seldom been reported alongside protein abundance measurements. 420 Muscle is renowned for its plasticity and can exhibit a broad range of different phenotypes underpinned 421 by different protein abundance profiles. Therefore, it may be important to consider the abundance 422 profile of muscle proteins alongside protein -specific turnover data. Herein, we report that the unit of 423 measurement used for dynamic proteomic data influences the biological interpretation of the findings. 424 Although commonplace, the presentation of protein turnover or synthesis data in fractional terms 425 (i.e. %/d), masks the different size (molecular weight; MW) and abundance of individual proteins. Data 426 presented in mole units that account for differences in MW amongst protein subunits may be preferred 427 for studies on multiprotein complexes within samples or subcellular fractions. Alternatively, data in 428 absolute units that account for differences in protein abundance between samples may be preferred 429 for studies on longitudinal adaptation or cross -sectional analyses on differing populations. 430 Changes in protein abundance are underpinned by the balance between the synthesis and degradation 431 of individual proteins [33] but this relationship does not equate to a correlation between the turnover 432 rate of a protein and its abundance (Figure 5). Disparities between the FTR and abundance of proteins 433 make it difficult to draw conclusions about the physiological state of muscle from FTR data alone [34]. 434 In the current work, FTR data highlighted biological processes (Figure 2) that are not usually viewed as 435 being prominent in healthy muscle, and provided limited insight into the allocation of cellular resources 436 or top -ranking molecular processes. Mole turnover rates incorporate quantification of protein 437 abundance in mole terms and provide new insight into stoichiometric relationships amongst the 438 synthesis of subunits that form multiprotein complexes in muscle (Figure 6). Both mole and absolute 439 synthesis rates correlated (Figure 5) with protein abundance measurements and better reflected the 440 biological processes associated with skeletal muscle (Figure 2). 441 The median FTR of 935 proteins in human vastus lateralis muscle was 4.3 (IQR 2.52 – 7.84) %/d, which 442 equates to a median protein half -life of 16 (IQR 9 – 27) days. These values appear to differ from values 443 (~1-2 %/d) reported in previous D2O proteomic studies [15, 16] that surveyed fewer proteins. As the 444 depth of proteome coverage (i.e. number of proteins studied) increases it becomes increasingly 445 important to account for the abundance and molecular weight of each protein when estimating the 446 gross average turnover of muscle protein. Large or highly abundant proteins (e.g., myosin and actin) 447 contribute more to the gross average turnover of protein in muscle than less abundant proteins that 448 may exhibit relatively higher rates of turnover. For example, when our FTR data is weighted using 449 absolute abundance values, (i.e. ATR) the pooled FTR for human vastus lateralis muscle was 1.4 %/d 450 and in line with previous findings in active individuals. Notably, muscle proteins that exhibit high 451 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint absolute turnover rates and therefore contribute the most to the gross average turnover rate, include 452 blood proteins (e.g. albumin and haemoglobin subunits), myoglobin (MYG) and glycolytic enzymes. In 453 total, the top 10 proteins ranked by ATR account for almost 30 % of protein turnover in human skeletal 454 muscle. The prominent contribution of blood proteins and glycolytic enzymes sheds new light on the 455 interpretation of earlier mixed protein FTR data generated by analysis of amino acid hydrolysates . Such 456 analyses of muscle sarcoplasmic fractions often failed to find differences in response to ageing or diet 457 and exercise interventions. If the small number of top-ranking ATR proteins are unaffected by the 458 intervention this will overshadow potentially important responses by proteins that rank lower in ATR 459 but are nonetheless biologically important. 460 Because FTR data do not consider the different abundances or molecular weights of the proteins 461 studied, data expressed in MTR or ATR units may be more appropriate for investigating the 462 stoichiometry amongst subunits of multi -protein complexes. Consistent with studies in yeast [35], we 463 report the half -life of 26S proteasome subunits is shorter than that of most respiratory chain subunits. 464 However, the relative abundance of respiratory chain complexes is greater than the proteasome. The 465 mitochondrial respiratory chain comprises five multi -protein complexes and the relative combination 466 and organisation of each complex influences mitochondrial super complex formation [36], and is crucial 467 for subunit -complex stability [37]. The stoichiometry amongst subunits may be adaptable, for example 468 differences in physical activity levels [20] and exercise training [38] are associated with different 469 abundance profiles of respiratory chain protein subunits. ATP -synthase (respiratory chain Complex V) 470 is responsible ATP resynthesis [39], and defects in ATP-synthase structure are associated with diseases 471 and ageing [40], whereas the beta subunit of ATP -synthase is particularly responsive to exercise [41]. 472 We quantified the ABD of individual proteins constituting the ATP -synthase complex to investigate the 473 stoichiometry amongst its subunits and their synthesis rates in human muscle in vivo . Data were 474 normalised to ATPG, which constitutes the singular central stalk (gamma subunit) of each ATP synthase 475 multiprotein complex. A stoichiometry of 1:9:12 was found between ATPG: ATPA and ATPB abundance, 476 which differs from human mitochondrial DNA models in yeast [42] that report ATP -synthase contains 3 477 subunits each of ATPA and ATPB for every 1 subunit of ATPG. The greater ratio of ATPA and ATPB 478 subunits in human muscle in vivo may indicate a pool of subunits that are not assembled into mature 479 complexes. This could either present a burden to proteostasis mechanisms, including transport systems 480 of the inner and outer mitochondrial membranes, or represent a beneficial, ready source of 481 replacement subunits that helps to maintain the quality of the multiprotein complex. The FTR of ATP 482 synthase subunits was similar when normalised relative to the FTR of ATPG, but the relative relationship 483 between subunits was different (Figure 9) when data were expressed in MTR units. While our current 484 data can be used for within -sample comparisons of FTR, MTR and ATR data, further work is required to 485 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint investigate the stoichiometry of ATP -synthase subunits using purified subcomplexes or mitochondria -486 enriched fractions. Notably, our current data from the soluble protein fractions, which encompasses 487 both cytosolic and mitochondrial compartments cannot distinguish complexes from individual subunits 488 or the potential different cellular locations of proteins. 489 Theoretically, the turnover rate of a protein may be dictated by both its intrinsic properties, including 490 sequence elements and physiochemical properties, and by extrinsic factors, including the cell 491 environment and regulatory processes acting on protein translation and degradation. In HeLa cells, 492 highly abundant proteins have slower than average synthesis rates than low abundant proteins [43] 493 and in our data (Figure 5A) FTR tended to be inversely related (r -0.082) to protein abundance . In yeast, 494 the intrinsic properties of proteins, including physiochemical properties, linear sequence motifs, 495 biological function and mRNA half -life, provide some predictive relationship with protein turnover 496 under stable culture conditions [35]. N-terminal linear sequence motifs have been associated with high - 497 and low -turnover rate proteins but we report no relationship between N -terminal sequence and 498 protein half -life (Figure 5J), which is consistent with earlier studies using sequence information from 499 protein databases [7, 43], or empirically determined sequences of N -terminal peptides using tandem 500 mass spectrometry [44]. We also found the intrinsic properties of a protein such as their predicted 501 molecular weight (MW) and isoelectric point (p I) have little to no relationship with protein turnover 502 rates in vivo reported in FTR units (Figure 5), which is consistent with studies in human cells in vitro [7, 503 43]. When studied using 2 -dimensional gel electrophoresis, the majority of human muscle proteins 504 resolve as multiple proteoforms [45], which cannot be distinguished in LC -MS/MS analyses of tryptic 505 peptide digests used here and in previous studies [7, 43]. In particular, proteoforms with different post -506 translational modifications may exhibit marked differences in p I from their predicted values and this 507 may contribute to the lack of association between FTR and predicted p I in the current data. However, 508 in rat soleus [46], proteoforms of creatine kinase share similar turnover rates, whereas the turnover 509 rate differed between the 2 proteoforms of albumin studied. 510 The half-life of proteins is shorter in vitro compared to studies on intact tissue in vivo [9]. In addition, 511 the influence of extrinsic factors on the turnover of proteins may be more prominent in vivo than in 512 vitro, even when studied, as here, under steadystate conditions. In rats the turnover rate of a particular 513 protein in vivo is not always consistent across different muscles from an individual animal [46]. For 514 example, the FTR of the primary proteoform of the blood protein, albumin, is similar (range 4.8 %/d – 515 6.3 %/d) regardless of whether the data is extracted from samples of heart, diaphragm, soleus or 516 extensor digitorum longus. In contrast, the FTR of the cardiac/ slow muscle myosin essential light chain 517 (MYL3) is 7.4 %/d in EDL, 10.7 %/d in diaphragm and 6.4 %/d in heart [46]. Moreover, the rank order of 518 protein turnover rates is not consistent amongst different muscles [46] and the turnover rate of a 519 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint protein can change in response to environmental stimuli, including muscle contraction and exercise 520 training [2, 33]. Therefore, attempts to predict protein turnover in vivo from the intrinsic properties of 521 the protein seem unlikely to be rewarding. 522

523 In summary, the units chosen for reporting protein turnover data affect the biological interpretation of 524 dynamic proteome profiling studies. MTR data is preferred over FTR, particularly for studies on 525 multiprotein complexes, wherein MTR takes account of potential differences amongst the molecular 526 weight of the component subunits. ATR data may be preferred over MTR and FTR, particularly when 527 the aim is to compare between samples that may exhibit different abundance profiles. Protein 528 abundance and other physiochemical characteristics do not predict FTR. Therefore, co -analysis of the 529 abundance and synthesis rates of proteins in human muscle is required for correct insight and 530 interpretation. 531 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Author Contributions: Conceptualization, P.J.L. and J.G.B.; methodology, J.S.B., S.B., J.P., J.A.S., G.L.C. 532 and J.G.B; formal analysis, B.N.S., J.S.B., S.B., C.A.S., J.P. and J.B.L.; investigation, S.O.S., J.A.S., J.B.L., 533 G.L.C. and P.J.L; resources, G.L.C and J.G.B.; data curation, C.A.S and J.G.B. ; writing —original draft 534 preparation, B.N.S., C.A.S. and J.G.B; writing —review and editing, J.S.B. S.B. J.P., S.O.S., J.A.S., J.B.L., 535 G.L.C., and P.J.L.; visualization, B.N.S and C.A.S.; supervision, J.P., S.O.S., J.A.S., J.B.L. and J.G.B.; project 536 administration, J.G.B.; funding acquisition, G.L.C., P.L.J. and J.G.B. All authors have read and agreed to 537 the published version of the manuscript. 538 Funding: This research received no external funding 539 Institutional Review Board Statement: The study was approved by the Liverpool John Moores School 540 of Sport and Exercise Science Research Committee (M18SPS006) and conformed with the Declaration 541 of Helsinki, except registration in a publicly accessible database. 542 Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 543 Data Availability Statement: The data presented in this study are available in Suppl Table S1 and the 544 raw mass spectrometry files are deposited in ProteomeXchange, PXD046509. 545 Conflicts of Interest: The authors declare no conflict of interest. 546 547 548 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint

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P., Samaras, P., et al., Peptide level turnover measurements enable the 651 study of proteoform dynamics. Molecular and Cellular Proteomics 2018, 17, 974 -992. 652 [45] Holloway, K. V., Gorman, M. O., Woods, P., Morton, J. P., et al., Proteomic investigation of changes 653 in human vastus lateralis muscle in response to interval -exercise training. Proteomics 2009, 9, 5155 -654 5174. 655 [46] Hesketh, S., Srisawat, K., Sutherland, H., Jarvis, J. C., Burniston, J. G., On the Rate of Synthesis of 656 Individual Proteins within and between Different Striated Muscles of the Rat. Proteomes 2016, 4, 12 -657 12. 658 659 660 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Table 1 – Participant characteristics 661 X Y Z Age (y) 21 22 22 Height (m) 1.79 1.77 1.78 Weight (kg) 72.20 69.10 79.70 BMI (kg/m2) 23.50 21.10 25.30 V̇ O2 peak (L/min) 3.59 3.79 4.27 V̇ O2 peak (ml/kg/min) 49.9 54.8 53.6 Peak Power Output (W) 280 280 340 662 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure Legends 663 Figure 1 – Experiment design 664 Visual schematic of the experimental design employed in the current study. Performance measures and 665 biometric data (outlined in Table 1) were collected 3 days prior to the study commencement. Blood 666 and saliva were collected on days 0, 4, 8 and 12, to calculate D2O precursor enrichment. Fifty millilitres 667 of 99.8 % D2O was consumed by all participants 4x per day for the first 6 days. This dose was lowered 668 to 2x per day for days 6 – 12. Vastus lateralis biopsies were taken on day 8 and day 12 of the 669 experimental period. Diet and physical activity monitoring were conducted throughout the 670 experimental period (day 0 -12). 671 Figure 2 – Proteome profiling of human vastus lateralis muscle in vivo. 672 Proteins were extracted from the vastus lateralis of humans and turnover rates for 935 proteins were 673 quantified in at least one participant. (A) Log10 transformed distribution plot, of proteins ranked by 674 ATR (ng/μg/d). Proteins of interest are labelled using their UniProt ID. (B) Linear regression of within -675 subject protein abundance data at day 8 and day 12. Panels C -F report the 5 most significant enriched 676 GO Biological Processes amongst the top -ranking proteins contained within the upper quartile when 677 the protein dataset was ranked by either (C) abundance, (D) fractional, (E) molar or (F) absolute 678 turnover rate. 679 Figure 3 – Turnover rates of muscle sarcomeric proteins 680 Box plots representing the MyHC profile of the vastus lateralis are displayed for abundance (A), FTR (B), 681 MTR (C) and ATR (D) measurements (N = 3). *Significant (P ≤ 0.05) differences between proteins 682 determined by ANOVA and TUKEY’s HSD. Panel E is a visual representation of the skeletal muscle 683 sarcomere with major proteins labelled. Protein abundance (F), FTR (G), MTR (H) and ATR (I) 684 measurements for sarcomeric proteins detected in the insoluble fraction (N = 2 -3) are presented as 685 individual points. Bars represent mean values ± SE. All proteins are labelled using their UniProt ID. 686 687 Figure 4 – Proteomic profiling of metabolic enzyme pathways 688 Individual protein abundance (ABD) turnover rates in fractional (FTR), mole (MTR) and absolute (ATR) 689 units for proteins involved in fatty acid oxidation, glycolysis and the TCA cycle are reported Data are 690 mean ± SD from n = 2-3 participants except for CPT2, which was measure in 1 participant only. Common 691 names of proteins are labelled in coloured boxes, with adjacent boxes labelled using UniProt protein 692 IDs. 693 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 5 – Predictors of Protein Turnover Rates 694 (A-I) Each turnover measurement was correlated (Pearson’s) with protein abundance, MW, and P I to 695 attempt to predict the turnover of individual proteins. (J) Bland Altman plot of the top 50 proteins with 696 the shortest (blue) and longest (red) protein t 1/2 values against the predicted protein t 1/2 values 697 calculated using the N end rule of degradation. (K -L) P weighted Kullback -Leibler logos of the top 698 quartile (K) and bottom (L) quartile of proteins ranked by t 1/2. 699 Figure 6 – Proteomic profiling of multiprotein complexes in humans in vivo 700 Representative box plots of the ABD (A), FTR (B), MTR (C) and ATR (D) for proteins identified within 701 each of the mitochondrial respiratory chain complexes (I -V) and the 26S proteasome. Individual dots 702 represent mean values (n = 2 -3 participants) for each protein within each multiprotein complex (MPC). 703 Outliers are highlighted in red and median values for the corresponding MPC are indicated by solid 704 black lines within the box plot. * Indicate complexes that are significantly different from one another 705 as determined by ANOVA and Tukey’s HSD (P ≤ 0.05). 706 Figure 7 – Proteomic profiling of individual subunits of the 26S Proteasome 707 Proteomic data were extracted for individual proteins contained within the 26S proteasome ( n = 2-3 708 participants). ABD (A), FTR (B), MTR (C) and ATR (D) measurements are reported. Bars and error -bars 709 represent mean ± SD, respectively. Points represent values for individual participants . 710 Figure 8 – Subunit stoichiometry of mitochondrial respiratory chain Complex V - ATP 711 Synthase, and relationship between protein turnover and protein abundance. 712 (A) Predicted vs measured abundance values of subunits with known stochiometric ratios in humans 713 normalized to the γ-subunit (ATPG), which forms the central stalk of ATP synthase. The stoichiometry 714 for all quantified subunits in the data set are reported for ABD (B), FTR (C), MTR (D) and ATR (E). Protein 715 measurements are normalised to the regulatory subunit ATPG. Measurements of ATPG are represented 716 by red lines and dashed lines represent 0.5x 2x and 3x the reported value of ATPG. Linear regression 717 between the abundance of subunits of ATP synthase and turnover rates in (F) fractional, (G) molar and 718 (H) absolute units. 719 720 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 1 721 722 723 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 2 724 725 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 3 726 727 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 4 728 729 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 5 730 731 732 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 6 733 734 735 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 7 736 737 738 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint Figure 8 739 740 741 742 743 .CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2024. ; https://doi.org/10.1101/2024.01.21.576451doi: bioRxiv preprint