Regulation of proteins in human skeletal muscle: the role of transcription

ABSTRACT Regular low intensity aerobic exercise (aerobic training) provides effective protection against various metabolic disorders. Here, the roles played by transient transcriptome

responses to acute exercise and by changes in baseline gene expression during up-regulation of protein content in human skeletal muscle were investigated after 2 months of aerobic training.

Seven untrained males were involved in a 2 month aerobic cycling training program. Mass-spectrometry and RNA sequencing were used to evaluate proteome and transcriptome responses to training

and acute exercise. We found that proteins with different functions are regulated differently at the transcriptional level; for example, a training-induced increase in the content of

extracellular matrix-related proteins is regulated at the transcriptional level, while an increase in the content of mitochondrial proteins is not. An increase in the skeletal muscle content

of several proteins (including mitochondrial proteins) was associated with increased protein stability, which is related to a chaperone-dependent mechanism and/or reduced regulation by

proteolysis. These findings increase our understanding of the molecular mechanisms underlying regulation of protein expression in human skeletal muscle subjected to repeated stress (long

term aerobic training) and may provide an opportunity to control the expression of specific proteins (e.g., extracellular matrix-related proteins, mitochondrial proteins) through

physiological and/or pharmacological approaches. SIMILAR CONTENT BEING VIEWED BY OTHERS HIGH-INTENSITY TRAINING INDUCES NON-STOICHIOMETRIC CHANGES IN THE MITOCHONDRIAL PROTEOME OF HUMAN

SKELETAL MUSCLE WITHOUT REORGANISATION OF RESPIRATORY CHAIN CONTENT Article Open access 03 December 2021 DEEP MUSCLE-PROTEOMIC ANALYSIS OF FREEZE-DRIED HUMAN MUSCLE BIOPSIES REVEALS FIBER

TYPE-SPECIFIC ADAPTATIONS TO EXERCISE TRAINING Article Open access 12 January 2021 TRANSCRIPTOMIC ADAPTATION DURING SKELETAL MUSCLE HABITUATION TO ECCENTRIC OR CONCENTRIC EXERCISE TRAINING

Article Open access 14 December 2021 INTRODUCTION Metabolic syndrome and type 2 diabetes mellitus are among the most widespread diseases in Western countries. Regular low intensity aerobic

exercise (aerobic training) improves insulin sensitivity, fat metabolism, and endurance. This effect is associated mainly with a marked increase in skeletal muscle mitochondrial

volume/density, increased amounts and activity of mitochondrial enzymes, and increased fat oxidation in muscle both at rest and during low intensity exercise1,2,3,4. Therefore, investigation

of the molecular mechanisms underlying skeletal muscle adaptation to regular aerobic exercise (aerobic training) is of fundamental importance. Acute stress (e.g., each individual exercise

session) is associated with transient (over several hours) changes in gene expression in cells; hence, repeated exposure to stress leads to an increase in expression of specific proteins and

cellular functions5,6. However, regular aerobic exercise is associated not only with a transient transcriptome response after each exercise session, but also with a pronounced change in

expression of a large number of genes under baseline conditions7,8,9. Therefore, both repeated responses to acute exercise and changes in baseline gene expression might regulate the tissue

content of different proteins. Recent studies examined the role of baseline mRNA levels in regulating proteins in cells [see the review6] and, to a lesser extent, in various human

tissues10,11,12. However, to the best of our knowledge, only one study has examined the role of mRNA in protein regulation in human skeletal muscle after repeated stress (i.e., at baseline

before and after long term aerobic exercise training)13. The study demonstrated a lack of direct relation between transcriptional and proteomic abundances after long term aerobic exercise

training. The purpose of the present study was to evaluate the roles of transient transcriptome responses to acute aerobic exercise, and of changes in baseline gene expression after regular

exercise training in regulating the protein content of human skeletal muscle. We examined changes in basal protein expression (HPLC-MS/MS with isobaric labeling) and corresponding mRNA (RNA

sequencing) in the vastus lateralis muscle of both legs after 2 months of aerobic cycling training (1 h/day for 5 weeks) in seven untrained males. After the training program, contractile

activity-specific transcriptome responses to acute aerobic exercise (one-legged knee extensions for 1 h) were evaluated. For this, tissue samples were taken from exercised and non-exercised

contralateral (control) muscle, and differences in gene expression between muscles at 1 and 4 h after cessation of exercise were examined (i.e., contractile-activity-specific transcriptome

responses; Fig. 1a), as described elsewhere7. RESULTS AND DISCUSSION Changes in the protein content of human skeletal muscle after regular exercise (2 months of aerobic exercise training)

were compared with changes in baseline transcriptome profiles and with contractile activity-specific transcriptome responses to acute exercise. Previously, we showed that this training

program increased ADP-stimulated mitochondrial respiration in permeabilized muscle fibers, and endurance performance in an incremental one-legged knee extension exercise and an incremental

cycling test7,14. ABSOLUTE MRNA LEVELS HAVE LITTLE EFFECT ON THE CONTENT OF HIGHLY ABUNDANT PROTEINS In total, we identified 795 muscle proteins before and after 2 months of aerobic exercise

training (Dataset 1). This covers 14% of the confirmed human skeletal muscle proteome (5749 proteins from the Human Protein Atlas with a reliability score of “Enhanced”, “Supported”, or

“Approved”). The largest groups of proteins identified in our proteomic analysis were “_Oxidoreductases_”, “_Nucleic acid binding_” (predominantly RNA binding) proteins, and “_Cytoskeletal

proteins_”, while the deepest coverage (40–55%) was found for “_Oxidoreductases_”, “_Cytoskeletal proteins_”, and “_Chaperones_” (Fig. 1b). Only a small number of regulatory proteins

(_Transcription factors_, _Signaling molecules_, _Receptors_, and _Transporters_) were identified (Fig. 1b). Thus, our analysis identified mainly highly abundant proteins (in terms of mass

concentration). We performed Spearman’s correlation analysis to examine how strongly mRNA levels affect protein levels. We found a weak correlation (ρ = 0.51, R2 = 0.26, _P_ < 0.001)

between these parameters (Fig. 1c), which is in agreement with previous studies of various human tissues10,11,12. The wide variation in the protein:mRNA expression ratio suggests that

protein expression is weakly regulated at the mRNA level and supports the notion that using mRNA levels to predict protein expression is not valid15. Another explanation for the huge

disparity is that some proteins are poorly regulated at the mRNA level whereas others are strongly dependent on mRNA. Moreover, we suggest that this characteristic depends on the function of

the protein. This hypothesis is in line with studies demonstrating that mRNAs and proteins with different functions show differing stability16,17. To identify proteins that are regulated at

the mRNA level, we compared changes in protein levels with changes in mRNA levels after 2 months of aerobic training. HIGHLY ABUNDANT MUSCLE PROTEINS WITH DIFFERENT FUNCTIONS ARE

DIFFERENTIALLY REGULATED AT THE TRANSCRIPTIONAL LEVEL Adaptation to regular aerobic exercise is associated not only with a transient (over several hours) increase in expression of many genes

after exercise, but also with a pronounced change in expression of a large number of other genes under baseline conditions7,13. Therefore, we compared baseline changes in protein expression

induced by 2 months of regular aerobic exercise with changes in mRNA levels after acute exercise and with baseline changes in mRNA levels after 2 months of training. Long term training

predominantly increased baseline expression of proteins [248 were upregulated, median and interquartile range: log2 Fold change 0.28(0.20–0.37), and only two were downregulated, log2 Fold

change −0.29 and −0.38]. The pattern of mRNA responses under all experimental conditions was quite different from that of protein responses (Fig. 2a,b). Namely, an increase in baseline

protein expression was not associated with changes in expression of the corresponding mRNA at 1 and 4 h post-acute exercise (Fig. 2c,d), whereas it was associated with either an increase [n

= 41, log2 Fold change 0.96(0.79–1.71)], no change, or even a decrease [n = 109, log2 Fold change −0.48(−0.56– −0.42)] in baseline mRNA expression (Fig. 2e) that is in line with the previous

discussion18. Remarkably, functional enrichment analysis revealed that the pattern of protein regulation depends on protein function. The group “_Protein UP–mRNA UP_” was associated with

extracellular matrix and collagen fibril organization, and the group “_Protein UP–mRNA NS (non-significant)_” was associated with mitochondrion and mitochondrial ATP synthesis. The group

“_Protein UP–mRNA DOWN_” showed no enrichment. Other groups showing enrichment (“_Protein NS–mRNA DOWN_” and “_Protein NS–mRNA NS_”) were associated with sarcomere-related and cytosolic

proteins, respectively (Fig. 3a, Dataset 2). Collectively, these data suggest that highly abundant skeletal muscle proteins with different functions show differential regulation at the

transcriptional level; for example, an increase in the content of extracellular matrix-related proteins is regulated at the transcriptional level, while an increase in the content of

mitochondrial proteins is not. These findings raise important questions: How are mitochondrial and some other proteins upregulated without changes in mRNA level? And what are the potential

mechanisms that determine the pattern of protein regulation? REGULAR EXERCISE UP-REGULATES CHAPERONE-RELATED PROTEINS Surprisingly, we found that a significant number (n = 209) of some

proteins (including mitochondrial proteins, n = 109) were not regulated at the transcriptional level, either at baseline after long term training or acute exercise. This is in agreement with

a study showing that increased expression of many mitochondrial proteins in HeLa cells subjected to misfolding stress occurs without increased expression of the corresponding genes19. The

amounts of individual proteins might be controlled by different post-transcriptional mechanisms, including protein synthesis on ribosomes, protein degradation (predominantly via the

ubiquitin proteasome system), and regulation of protein stability. Here, we found several dozen proteins related to the ubiquitin proteasome system, ribosomes, or chaperones. In contrast to

other groups, we found that half of detected chaperone and chaperone-related proteins were upregulated (20 of 45) at baseline after 2 months of training (Fig. 3b, Dataset 2). Importantly,

those upregulated proteins included three mitochondrial chaperones. Rodent studies that drive or suppress gene expression show that a chaperone and chaperon-related protein (HS71A, HSF1)

strongly upregulate mitochondrial density and oxidative metabolism in skeletal muscle, along with insulin sensitivity and endurance20,21. During the first hours of recovery after acute

aerobic exercise, dozens of enzymes involved in energy metabolism are downregulated22. Therefore, chaperone-related proteins seem to play a meaningful role in mitochondrial biogenesis by

improving mitochondrial proteostasis. In line with this, we revealed (Fig. 3c) that proteins in the group “_Protein UP–mRNA NS_” were significantly more stable (by ∼32–42%) than all other

proteins identified in our study, and were more stable (by ∼92–170%) than proteins in proteomics datasets from two previous studies investigating the baseline half-life of several thousand

proteins in C2C12 and mouse fibroblasts16,23. Notably, the use of data from C2C12 and fibroblasts studies gave actually the equal results (Fig. 3c). Mitochondria play a central role in ATP

production; therefore, mitochondrial biogenesis is an essential process for the vast majority of cells. A given -fold increase in expression of highly abundant proteins (including

mitochondrial proteins) involves a significantly larger number of molecules than equivalent -fold increase in expression of low abundancy proteins; hence, the total energy cost of

synthesizing highly abundant proteins is much greater than that of low abundancy proteins. This explains why, in terms of energy cost, transcriptional regulation of highly abundant proteins

is less effective than post-transcriptional regulation6. Indeed, mRNA levels and transcription rates play less important roles in regulating highly abundant proteins16,24. Collectively,

these data suggest that the increase in the content of several highly abundant proteins (including mitochondrial proteins) in human muscle might be associated, at least partially, with

chaperone-dependent mechanisms that increase protein stability. POTENTIAL MECHANISMS THAT CONTROL THE PATTERN OF PROTEIN REGULATION Two main mechanisms may be responsible for up-regulating

proteins in the group “_Protein UP–mRNA NS_” without inducing changes in mRNA expression. First, protein-specific regulation of translation in a cap-independent manner through internal

ribosome entry sites (IRESs) and N6-methyladenosine (m6A) modifications within the 5′ and 3′ UTR25. This might explain (at least in part) a mitochondrial-specific increase in protein

synthesis induced by acute aerobic exercise13,26,27. Second, protein-specific regulation of stability via physicochemical properties (protein length, molecular weight, isoelectric point, and

N-terminal hydrophobicity)28, and/or the presence/absence of regulatory motifs responsible for cleavage or ubiquitination binding29. We examined these parameters in proteins belonging to

the group “_Protein UP–mRNA NS_” and compared them with those for all other proteins detected in the study. Proteins belonging to the group “_Protein UP–mRNA NS_” were shorter (by 21%), of

lower mass (by 19%), and had a higher pI (by 1.01) and greater N-terminal hydrophobicity (by 0.37) than proteins belonging to other groups (Fig. 4a). To examine the relevance of differences

in motif enrichment among groups, we calculated a mean rank for both the motif frequency and the adjusted odds ratio among the groups. We found no difference in enrichment for IRES- and

m6A-associated motifs in the 5′UTR and m6A-associated motifs in the 3′UTR, which are markers of cap-independent initiation of translation (Fig. 4b). However, enrichment of 21 regulatory

motifs (out of 271 investigated motifs) did differ among the group “_Protein UP–mRNA NS_” and other examined proteins. Proteins from group “_Protein UP–mRNA NS_” showed lower levels of

enrichment for many motifs associated with protein degradation (e.g., PEST-, ubiquitin-, and SUMO-related motifs; Fig. 4b), suggesting reduced regulation via proteolysis, which partially

explains their enhanced stability (see data in Fig. 3c). An important limitation of the study is the lack of measurements of protein half-life and turnover. Protein synthesis and breakdown,

along with mRNA content, are responsible for regulation of proteins. Synthesis rate of individual labeled proteins in human skeletal muscle was examined in a study30 by mass-spectrometry.

Combination of this approach with RNA sequencing seems to be very promising for a deeper understanding of protein regulation in human tissues. In addition, transcriptomic and proteomic

responses in human tissues show higher variability than those in cells. Therefore, increasing sample size in further studies seems to be relevant strategy for improving significance of the

obtaining data. In conclusion, the pattern of regulation of highly abundant proteins during adaptation of human skeletal muscle to regular endurance exercise depends strongly on protein

function: the extracellular matrix-related protein content is regulated at the transcriptional level, whereas mitochondrial protein content is not. The increase in expression of several

proteins (including mitochondrial proteins) is associated with increased protein stability via chaperone-dependent mechanisms and/or reduced proteolysis. Taken together, the data presented

herein increase our understanding of the general molecular mechanisms underlying regulation of protein expression. In particular, they shed light on the molecular mechanisms underlying

regulation of protein expression in human skeletal muscle subjected to repeated stress (exercise). Elucidation of these mechanisms may provide an opportunity to control the expression of

specific proteins (e.g., extracellular matrix-related proteins, mitochondrial proteins) in muscle through physiological and/or pharmacological approaches. METHODS EXPERIMENTAL MODEL AND

SUBJECT DETAILS The study was approved by the Biomedicine Ethics Committee of the Institute of Biomedical Problems of the Russian Academy of Sciences and complied with the guidelines set

forth in the Declaration of Helsinki. All participants gave written informed consent to participate in the study. Experimental model was described in our previous study7. Briefly, 7

untrained males (median age, 21 years [interquartile range, 21–24 years]; weight, 74 kg [72–79 kg]; body mass index, 23 kg/m2 [23–24 kg/m2]) were involved in a 2 month aerobic training

program (5/week, 1 h/day) on a two-legged cycle ergometer (Ergoselect 200, Ergoline, Germany). The aerobic training program included alternating continuous (60 min, 70% LT4 [power at lactate

threshold at 4 mmol/l]) and intermittent ([3 min, 50% LT4 + 2 min, 85% LT4] × 12) exercise on different days. 48 h after the last exercise, each subject performed a one-legged continuous

knee extension exercise (∼65% Wmax) for 1 h on a modified electromagnetic ergometer (Ergometric 900 S, Ergoline, Germany). Biopsy samples were taken from the m. vastus lateralis of each leg

before and after a 2 month training program (to detect baseline changes in proteome and transcriptome), as well as at 1 h and 4 h after the one-legged knee extension exercise (to detect

transcriptome responses specific for contractile activity; Fig. 1a). To detect genes specifically involved in contractile activity, we examined differentially expressed genes (DEGs) between

exercised and non-exercised muscle at the same time points (at 1 h and 4 h after the one-legged knee extension exercise). This design of the study provided an opportunity to eliminate

effects associated with circadian regulation and systemic factors (e.g., changes in neural activity and blood metabolite content) and to identify transcriptome response specific for

contractile activity. No DEGs between exercised and non-exercised muscle were found prior to exercise. The muscle samples were taken, as described in our previous study7, under local

anesthesia (2 mL 2% lidocaine) using a microbiopsy technique31, quickly blotted with gauze to remove superficial blood, frozen in liquid nitrogen, and stored at −80 °C until required.

PROTEIN EXTRACTION, SAMPLE PREPARATION, AND MASS-SPECTROMETRY A piece of frozen tissue (15 mg) was homogenized in 10 volumes of a lysing buffer (4% sodium dodecyl sulfate in 0.1M Tris-HCl pH

7.6, 0.1M dithiothreitol) in a 1.5 mL tube using a plastic pestle, vortexed, and incubated for 5 min at 95 °C. The samples were sonicated (2 × 10 s at 100 W) and centrifuged (5 min, 16 000 

g). The supernatant (8 μL) was loaded onto the YM-30 filter (Millipore, Ireland) for alkylation and trypsinolysis (12 h, 2 μg of trypsin [Tripsin Gold, Promega, United States] in 40 μL of

0.1 M triethylammonium bicarbonate), according to the FASP method32. Peptides were washed off the filter by centrifugation (10 min, 14000 g); 40 μL of 0.1 M triethylammonium bicarbonate was

added to the filter and the sample was centrifuged. Peptide concentrations were measured by the bicinchoninic assay; then peptides were labeled using iTRAQ 8-plex kit (Sciex, USA) according

to manufacture instruction. The mixture of labeled peptides was concentrated and subjected to reverse-phase LC fractionation at a high pH using the XBridge C18 column (250 × 4.6 mm, particle

size 5 μm; Waters, Ireland) and an Agilent 1200 Series HPLC (Agilent, United States), as described elsewhere33. The collected fractions were dried using a vacuum concentrator and dissolved

in a volume of 20 μL; the fractions were combined to obtain 10 mixed fractions and subjected to HPLC-MS/MS analysis using the HPLC Ultimate 3000 RSLCnano system (Thermo Scientific, United

States). Prior to analytical separation, the peptides were concentrated on an Accalaim μ-Precolumn (0.5 mm × 3 mm, particle size 5 μm; Thermo Scientific) in the isocratic mode at a 10 μL/min

flow for 5 min in the mobile phase B (2% acetonitrile, 0.1% formic acid). The peptides were then separated on the Acclaim Pepmap C18 column (75 μm × 150 mm, particle size 2 μm; Thermo

Scientific) in a gradient mode of elution. The gradient (90 min) was formed by the mobile phase A (0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) at a 0.3 μL/min flow. The

column was washed with the 2% mobile phase B for 5 min; the mobile phase B concentration was increased linearly to 35% for 65 min and to 99% over 5 min. After washing the column (5 min, 99%

B), the mobile phase B concentration was decreased to the initial conditions of 2% of the mobile phase B over 5 min. Mass spectrometric analysis was performed, using a Q Exactive HF Hybrid

Quadrupole-Orbitrap mass spectrometer (Thermo Scientific) using the nanoelectrospray ion source in the negative mode of ionization (Thermo Scientific). The ionizing voltage was 2.1 kV.

Panoramic MS spectra were acquired at the 400–1200 m/z range; fragment ions were mass scanned from m/z 100 to the upper m/z value as assigned by the charge state of the precursor ion, but

not greater than 2100 m/z. All tandem MS scans were performed on ions with a charge state from z = 2+ to z = 6+. Synchronous precursor selection facilitated the simultaneous isolation of up

to 20 MS2 fragment ions. The maximum ion accumulation times were set to 50 ms for precursor ions and 110 ms for fragment ions. RNA EXTRACTION, LIBRARY CONSTRUCTION, AND SEQUENCING RNA was

extracted from the frozen samples (~20 mg) using an RNeasy mini kit (Qiagen, Germany) with DNase I treatment (Fermentas, Lithuania), as described in our previous study7. The RNA

concentration was measured in a fluorimeter (Qubit 3.0; Thermo Scientific) and RNA integrity was checked by capillary electrophoresis using a Bioanalyzer 2100 (Agilent, USA); all samples

were at least 7 in RIN. Libraries were constructed, as described in our previous study7, from 300 ng of RNA using a NEB Next Ultra II RNA kit (New England Biolabs, USA). For the first stage,

poly-A mRNA was isolated using magnetic particles associated with 24-dT oligonucleotides by means of NEB Next Poly(A) mRNA Magnetic Isolation Module. RNA was fragmented and reverse

transcribed using 6-nucleotide random primers, followed by second strand cDNA synthesis. Adapters were ligated to the obtained cDNA, and libraries were amplified using a universal primer

(the same for all libraries) and index primers. Then, libraries were cleaned using AMPure XP magnetic particles (Beckman Coulter, USA). After PCR and purification, the cDNA concentration was

measured in a Qubit 3.0 fluorimeter and the length distribution of library fragments was checked by capillary electrophoresis using a Bioanalyzer 2100. The effective amplicon concentration

was evaluated by real-time PCR. The libraries were diluted to 2 nM and sequenced on a HiSeq 2500 and NextSeq 500 instruments (Illumina, USA), according to the manufacturer’s instructions.

Mean amount of reads was 47 [35–50] million reads per sample. Raw data has been deposited to NCBI GEO: GSE120862. PROCESSING OF MASS-SPECTROMETRY DATA The search, identification of peaks,

and estimation of the intensity of the peaks of the reporter ions were carried out using the MaxQuant computational platform (1.5.7.4; Max Planck Institute of Biochemistry) at default

settings for the FDR of 1%. Processing of proteomic data and its comparison with transcriptomic data were done using the Perseus computational platform (1.6.1.2; Max Planck Institute of

Biochemistry). After filtration (potential contaminants, reverse peptides, and peptides identified only by site) the ratios of intensities of the reporter ions (after training to before

training) were calculated for each protein. To reduce variations related with the difference in the amount of the total proteins in samples, normalization to reference proteins was

applied34. The ratios of intensities were normalized to the median ratio of three reference proteins showing the highest P-level (the Wilcoxon signed rank test). Then, to examine

significantly changed proteins (the ratios different from 1), the Wilcoxon signed rank test with a cut off criteria for Padj (Benjamini-Hochberg correction, Q-value) <0.05 was used. RNA

SEQUENCING DATA PROCESSING Data processing was described in our previous study7. Briefly, the quality of RNA sequencing data for each library was assessed using FASTQC (v0.11.4), and adaptor

sequences and low quality reads were trimmed using the Timmomatic tool (v0.36). Reads were aligned to the reference genome GRCh38 (with Ensembl annotation GRCh38.90) and splice junctions

were detected using HISAT2 v2.1.0. HTSeq v0.6.1 was used to count the read numbers mapped to known exons of each gene (using Ensembl annotation GRCh38.90). Differential expression analysis

was performed using the DESeq2 R package (analysis of paired samples) with a cut off criteria for Padj (Benjamini-Hochberg correction) <0.05 and for |log2(Fold Change)| > log2(1.25).

To estimate individual fold changes, the normalized read counts (the median normalization) were calculated. As a result of processing of mass-spectrometry data, several proteins might fall

in a Protein group. Therefore, for comparison of mRNA changes with protein changes, the sum of the normalized read counts for proteins in a Protein group was used. FUNCTIONAL GENE ONTOLOGY

ENRICHMENT Functional enrichment of protein groups in relation to all detected proteins (795 background proteins) was performed by the DAVID 6.8 using GOTERM_BP_Direct and GOTERM_CC_Direct

databases. GO terms with Padj < 0.05 (Fisher exact test, Benjamini correction) were regarded as significantly enriched. If several proteins fell in a Protein group, then, for the

functional enrichment analysis, a gene with the greatest mRNA expression level among genes corresponding to proteins in a Protein group was chosen (_Main gene_ in Dataset 1). PROTEIN AND

MRNA MOTIFS ENRICHMENT To evaluate the relevance of difference in motif enrichment between different protein (or mRNA) groups we used a mean rank for both the motif abundance (frequency) and

the adjusted odds ratio between the groups, because both parameters explain the difference in the regulation of proteins (or mRNA translation initiation). For each motif the odds ratio

between the groups with Padj < 0.05 (Fisher exact test, Benjamini-Hochberg correction) was identified. Statistically corrected (adjusted) odds ratio was calculated as the left and the

right value of the 95% confidence interval for the odds ratio >1 and <1, respectively. Finally, the false discovery rate-controlling procedure (FDR < 0.05) was applied. The

calculations were performed in the R environment. _PEST motifs_ in protein were predicted using the epestfind algorithm (EMBOSS v6.6.0) at threshold score +5. Known _linear protein motifs_

(270) were taken from the ELM database. The presence of a linear motif in a protein was assessed using the Sequence Manipulation Suite (Version 2). _m6A site_ were predicted in 5′UTR and

near 3′UTR (+/−100 bp from the stop-codon) mRNA using the SRAMP at moderate confidence level (10% false positive rate). The UTRs and stop-codons were evaluated by the coordinates of most

abundant transcripts in human (young males) skeletal muscle in the GTEx database (GTEx_Analysis_2016–01–15_v7). _IRES_ site were predicted in 5′UTR mRNA using the IRESPred. THE

PHYSICOCHEMICAL PROPERTIES OF PROTEINS The protein half-life was evaluated using data from two previous studies investigating the baseline half-life of several thousand proteins in C2C12 and

mouse fibroblasts16,23. Protein molecular mass, pI, and N-terminal (2–16 aa) hydrophobicity (the grand average of hydropathy [GRAVY]) were evaluated using the Sequence Manipulation Suite

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unexpected mitochondrial specialization. _EMBO Rep._ 16, 387–395 (2015). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS This work was supported by the Russian Foundation

for Basic Research (RFBR) grant No. 17-00-00308 K (17-00-00242). The authors thank Dr. Dmitriy Perfilov (Institute of Biomedical Problems) for tissue acquisition and Maria Logacheva, PhD

(M.V. Lomonosov Moscow State University) for assistance with RNA sequencing. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Institute of Biomedical Problems of the Russian Academy of

Sciences, Moscow, 123007, Russia Pavel A. Makhnovskii, Roman O. Bokov, Evgeny A. Lysenko, Olga L. Vinogradova & Daniil V. Popov * V.N. Orekhovich Research Institute of Biomedical

Chemistry, Moscow, 119121, Russia Victor G. Zgoda * Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, Kazan, 420012, Russia Elena I. Shagimardanova, 

Guzel R. Gazizova & Oleg A. Gusev * KFU-RIKEN Translational Genomics Unit, Cluster for Science, Technology and Innovation Hub, RIKEN, Saitama, 351-0198, Japan Oleg A. Gusev * RIKEN

Preventive Medicine & Diagnosis Innovation Program, RIKEN, Yokohama, 230-0045, Japan Oleg A. Gusev * RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, 230-0045, Japan Oleg

A. Gusev * Faculty of Fundamental Medicine, M.V. Lomonosov Moscow State University, Moscow, 119991, Russia Evgeny A. Lysenko, Olga L. Vinogradova & Daniil V. Popov * Institute of

Computational Technologies of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia Fedor A. Kolpakov Authors * Pavel A. Makhnovskii View author publications

You can also search for this author inPubMed Google Scholar * Victor G. Zgoda View author publications You can also search for this author inPubMed Google Scholar * Roman O. Bokov View

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* Guzel R. Gazizova View author publications You can also search for this author inPubMed Google Scholar * Oleg A. Gusev View author publications You can also search for this author

inPubMed Google Scholar * Evgeny A. Lysenko View author publications You can also search for this author inPubMed Google Scholar * Fedor A. Kolpakov View author publications You can also

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publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS D.V.P. and O.L.V. – conceptualization and design of the study. D.V.P., V.G.Z., E.I.S., G.R.G. and

E.A.L. – performance of experiments and acquisition of data. P.A.M., D.V.P., R.O.B. and F.A.K. – analysis and interpretation of data. D.V.P., O.L.V., P.A.M., F.A.K. and O.A.G. – drafting and

revising the manuscript. All authors approved of the version of the manuscript to be published. CORRESPONDING AUTHOR Correspondence to Daniil V. Popov. ETHICS DECLARATIONS COMPETING

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CITE THIS ARTICLE Makhnovskii, P.A., Zgoda, V.G., Bokov, R.O. _et al._ Regulation of Proteins in Human Skeletal Muscle: The Role of Transcription. _Sci Rep_ 10, 3514 (2020).

https://doi.org/10.1038/s41598-020-60578-2 Download citation * Received: 30 October 2019 * Accepted: 07 February 2020 * Published: 26 February 2020 * DOI:

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