Abstract

Simple Summary

   Excessive exercise can lead to muscle atrophy, which is particularly
   concerning in professional athletes. However, the effects of excessive
   exercise on the skeletal muscle system remain unclear. Here, we used a
   zebrafish model of excessive exercise to identify genes that are
   dysregulated during excessive exercise. We mapped the identified genes
   to regulatory networks to gain an understanding of their functions
   during muscle atrophy. We identified several important mechanisms by
   which excessive exercise can lead to muscle wasting and prevent
   regeneration. Our findings provide fundamental knowledge on the effect
   of overtraining on the skeletal muscle system, and will enable better
   monitoring of muscle condition in athletes.

Abstract

   Long-term imbalance between fatigue and recovery may eventually lead to
   muscle weakness or even atrophy. We previously reported that excessive
   exercise induces pathological cardiac hypertrophy. However, the effect
   of excessive exercise on the skeletal muscles remains unclear. In the
   present study, we successfully established an
   excessive-exercise-induced skeletal muscle atrophy zebrafish model,
   with decreased muscle fiber size, critical swimming speed, and maximal
   oxygen consumption. High-throughput RNA-seq analysis identified
   differentially expressed genes in the model system compared with
   control zebrafish. Gene ontology and KEGG enrichment analysis revealed
   that the upregulated genes were enriched in autophagy, homeostasis,
   circadian rhythm, response to oxidative stress, apoptosis, the p53
   signaling pathway, and the FoxO signaling pathway. Protein–protein
   interaction network analysis identified several hub genes, including
   keap1b, per3, ulk1b, socs2, esrp1, bcl2l1, hsp70, igf2r, mdm2, rab18a,
   col1a1a, fn1a, ppih, tpx2, uba5, nhlrc2, mcm4, tac1, b3gat3, and ddost,
   that correlate with the pathogenesis of skeletal muscle atrophy induced
   by excessive exercise. The underlying regulatory pathways and
   muscle-pressure-response-related genes identified in the present study
   will provide valuable insights for prescribing safe and accurate
   exercise programs for athletes and the supervision and clinical
   treatment of muscle atrophy induced by excessive exercise.

   Keywords: skeletal muscle atrophy, excessive exercise, FoxO signaling
   pathway, Wnt signaling pathway, p53 signaling pathway

1. Introduction

   Proper exercise can improve skeletal muscle adaptability through
   increased endurance and strength, by inducing mitochondrial
   regeneration, angiogenesis, and hypertrophy in skeletal muscle tissue
   [[38]1,[39]2]. In the process of adaptive formation, the recovery
   period after exercise is considered important to provide enough time
   for skeletal muscle metabolism and structural adaptation [[40]3,[41]4].
   Therefore, the balance between muscle fatigue and recovery is
   particularly critical. Unfortunately, there is a lack of information
   regarding the appropriate recovery period of skeletal muscle for
   optimizing adaptation, causing frequent occurrence of unreasonable
   exercise [[42]5]. About 60% of elite runners declared that they had
   suffered from overtraining syndrome [[43]5]. Long-term imbalance
   between fatigue and recovery may eventually lead to muscle weakness, or
   even muscle atrophy, resulting in a diminished quality of life and an
   increase in both morbidity and mortality in numerous pathologies
   [[44]6,[45]7,[46]8,[47]9].

   The most prominent feature of muscular atrophy is the loss of skeletal
   muscle, which is related to various physiological and pathological
   conditions, such as aging, trauma, immobilization, denervation, cancer,
   diabetes, inflammation, and starvation
   [[48]10,[49]11,[50]12,[51]13,[52]14,[53]15,[54]16,[55]17]. Skeletal
   muscle mass depends on the delicate equilibrium between the rates of
   protein synthesis and degradation [[56]18]. Accumulating evidence
   suggests that the accumulation of protein misfolding and endoplasmic
   reticulum (ER) stress is caused by various catabolic stimuli, including
   aging, disease, and malnutrition [[57]19,[58]20,[59]21], which may be a
   potential trigger of skeletal muscle atrophy. However, the molecular
   mechanisms of skeletal muscle atrophy induced by excessive exercise are
   unclear. Elucidating the pathogenesis of exercise-induced skeletal
   muscle atrophy is potentially helpful for controlling sports risk and
   providing new strategies for the treatment of muscular atrophy.

   In zebrafish, skeletal muscle accounts for a large proportion of the
   trunk, and is highly similar to human muscle, both molecularly and
   histologically [[60]22]. The relatively simple genome and easy gene
   manipulation of zebrafish are advantageous in myopathy and muscular
   dystrophy research [[61]23,[62]24,[63]25]. Zebrafish are also an
   excellent model to facilitate excessive exercise interventions, because
   the aquatic environment is unlikely to cause injury following
   high-intensity forced exercise. In the present study, we trained
   zebrafish using a previously designed overtraining program and
   successfully constructed a model of excessive-exercise-induced skeletal
   muscle atrophy. Using this model, we applied high-throughput RNA-seq
   analysis to unravel the key genes and pathways that contribute to the
   pathogenesis of skeletal muscle atrophy induced by excessive exercise.
   Our findings contribute to the development of more biologically
   informed exercise regimens, particularly for professional sportspeople,
   which are less likely to cause injury by prescribing excessive
   exercise.

2. Materials and Methods

2.1. Animal Experiments

   AB-strain male zebrafish (approximately 6 months old, n = 60) were
   purchased from the National Zebrafish Resource Center (Wuhan, China)
   and raised in a flow-through system with a water temperature of 28 ± 1
   °C on a 12 h light/dark cycle. All animal experiments followed the
   Guidelines for Proper Conduct of Animal Experiments (Science Council of
   China) and were approved by the Ethics Committee of Hunan Normal
   University (approval number: 2018/046, approved date: 9 March 2019).

2.2. Swimming Exercises and Experimental Conditions

   The zebrafish were randomly divided into two groups, both fed normally:
   the control group (n = 30) and the excessive exercise group (n = 30).
   The excessive exercise conditions were set up as described previously
   [[64]26]. In brief, the excessive exercise group was forced to swim in
   a water current at a speed equivalent to 80% critical swimming speed
   (U[crit,] 24 cm/s) for 4 weeks (6 h for 6 days per week). A month
   later, the zebrafish were sacrificed after deep anesthetization in 300
   mg/L MS-222, and muscle tissue samples were collected and stored at −80
   °C.

2.3. Hematoxylin and Eosin Staining

   Hematoxylin and eosin (H&E) staining was performed as described
   previously [[65]27]. The muscle tissues of zebrafish were fixed in 4%
   paraformaldehyde, dehydrated through an ethanol series, dewaxed with
   xylene, and embedded in paraffin wax. The samples were transversely cut
   into 4 μm thick slices, dewaxed, dried, and stained with hematoxylin
   and eosin under standard conditions. The images were captured with a
   microscope (Leica, Heidelberg, Germany) and analyzed using ImageJ (NIH,
   Bethesda, MD, USA).

2.4. Swimming Performance and Oxygen Consumption

   Analysis of the swimming performance and oxygen consumption of
   zebrafish was performed using a miniature swimming tunnel respirator
   (Loligo Systems, Viborg, Denmark). The following formula was used to
   calculate U[crit] values for the swimming tests: U[crit] = Uf + US ×
   (Tf/TS), where Uf (cm/s) is the highest velocity, US (2.7 L) is the
   velocity increment, Tf (min) is the time elapsed at fatigue velocity,
   and TS (14 min) is the prescribed interval time. U[crit] is expressed
   in terms of body lengths per second (BL/s). Maximal oxygen consumption
   (MO[2]) was calculated using AutoRespTM 1 software (Loligo Systems,
   Viborg, Denmark).

2.5. Identification of Differentially Expressed Genes

   Total RNA was extracted from the zebrafish skeletal muscle in the
   control and exercise groups, n = 3 for each group. Paired sequencing
   was performed using an Illumina NovaSeq 6000 platform (Illumina, San
   Diego, CA, USA) at Majorbio Technology Co., Ltd. (Shanghai, China).
   Trimmomatic (Max Planck Institute of Molecular Plant Physiology,
   Berlin, Germany) was used to process the acquired raw data, and pure
   data were obtained after discarding extended adaptor sequences and
   low-quality sequences. Hisat2 (University of Texas Southwestern Medical
   Center, Dallas, TX, USA) was used for gene mapping, and FeatureCounts
   was used for quantitative analysis. Comparison of the two groups was
   performed using the DESeq2 package to identify differentially expressed
   genes (DEGs).

2.6. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment
Analysis of DEGs

   To identify the physiological changes during skeletal muscle atrophy,
   the David online database (available online:
   [66]https://david.ncifcrf.gov/ (accessed on 20 January 2021).) and
   ‘cluster profiler’ package in R were used to perform gene ontology (GO)
   and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment
   analysis of DEGs.

2.7. Clusters of Orthologous Groups Analysis of DEGs

   We used the bioMart package to obtain DEG protein sequences, and
   clusters of orthologous groups (COGs) analysis was performed using the
   COG database (NCBI, Bethesda, MD, USA).

2.8. Total RNA Extraction and qRT-PCR

   The total RNA of zebrafish muscle tissue was extracted by
   homogenization in TRIzol solution according to the manufacturer′s
   protocol (Thermo Fisher Scientific, Waltham, MA, USA). RNA was
   converted to cDNA using a reverse transcription system kit (Takara,
   Tokyo, Japan). Real-time PCR was performed using a SYBR green master
   mix (Thermo Fisher Scientific, Waltham, MA, USA). Relative mRNA
   expression was determined using a Bio-Rad real-time PCR system (CFX96,
   Bio-Rad, Hercules, CA, USA). Sangon Biotech synthesized primers for the
   detected genes and the reference gene, gapdh. The relative mRNA
   expression was determined using the 2^−ΔΔCT method, and the primer
   sequences used are listed in [67]Table S1.

2.9. Western Blot

   The zebrafish skeletal muscle tissue was taken out and lysed in cold
   RIPA buffer (Solarbio, Beijing, China) containing a mixture of protease
   and phosphatase inhibitors (Solarbio, Beijing, China). Protein
   quantification was performed using a bicinchoninic acid (BCA) protein
   assay kit (Vazyme, E112-01/02, Nanjing, China), and the protein (30 mg)
   in each sample was separated by 10% or 15% SDS-PAGE and then
   transferred to 0.45 mm or 0.22 mm polyvinylidene difluoride (PVDF)
   membranes. The membranes of phosphorylated protein were blocked with 5%
   BSA, and others were blocked with 5% fat-free milk. Membranes were
   incubated with the primary antibodies overnight at 4 °C and washed with
   TBST. Subsequently, membranes were incubated with appropriate secondary
   HRP-linked antibodies. The proteins were detected with a gel imaging
   system (Tanon, Shanghai, China). The antibodies used in the present
   research were as follows: rabbit anti-GAPDH antibody (1:2000,
   Servicebio, Wuhan, China), rabbit anti-MURF antibody (1:2000, Abcam,
   Boston, MA, USA), rabbit anti-FBXO32 antibody (1:2000, Abcam, Boston,
   MA, USA), rabbit anti-P53 antibody (1:2000, Abcam, Boston, MA, USA),
   rabbit anti-AKT antibody (1:2000, CST, Boston, MA, USA), rabbit
   anti-p-AKT antibody (1:2000, CST, Boston, MA, USA), rabbit anti-PI3K
   antibody (1:1000, Proteintech, Wuhan, China), rabbit anti-p-PI3K
   antibody (1:2000, CST, Boston, MA, USA), rabbit anti-LC3 antibody
   (1:1000, Proteintech, Wuhan, China), rabbit anti-BCL-2 antibody
   (1:1000, 1:1000, Proteintech, Wuhan, China), and rabbit anti-AMPK
   antibody (1:1000, 1:1000, Proteintech, Wuhan, China). The expressions
   of the phosphorylated proteins p-AKT and p-PI3K were normalized to
   total AKT and PI3K, and the expressions of other proteins were
   normalized to GAPDH.

2.10. Protein–Protein Interaction Network

   The protein–protein interaction (PPI) network was predicted using the
   ‘search tool for the retrieval of interacting genes’ (STRING) online
   database. Interactions with a combined score > 0.7 were considered
   statistically significant. The bioinformatics platform Cytoscape was
   used to visualize the molecular interaction network, and the MCODE
   extension was used to cluster the PPI network of DEGs.

2.11. Statistical Analysis

   Statistical analysis was performed using SPSS (version 22.0; IBM,
   Chicago, IL, USA) or GraphPad Prism software (version 7.0; San Diego,
   CA, USA). Data were expressed as the mean ± SD of three independent
   experiments. Unpaired t-tests were used to compare the mean values of
   the two groups. All experiments were repeated three times. Statistical
   significance was set at p < 0.05.

3. Results

3.1. Excessive Exercise Caused Muscle Atrophy of Zebrafish Skeletal Muscle

   After 4 weeks of excessive exercise, we found that the body size of the
   zebrafish in the excessive exercise group was significantly smaller
   than that of the control zebrafish ([68]Figure 1A,B). H&E staining
   showed that the muscle fiber size of transverse sections in the
   excessive exercise group was 55.7% smaller than that in the control
   group ([69]Figure 1C,D). The expression levels of the muscle atrophy
   marker genes trim63a, trim63b, fbxo25, fbxo32, capn1, and casp3a
   increased in the excessive exercise group compared with the control
   group ([70]Figure 1E). Simultaneously, the expression levels of
   cyclin-encoding genes (ccnd1, ccne1, ccng2), mitochondrial cytochrome C
   protein (cyc1), NADH dehydrogenase 4 (nd4), NADH dehydrogenase 5 (nd5),
   and nuclear transcription coactivated peroxisome (ppargc1a) decreased
   compared with the control group ([71]Figure 1E). We also found that the
   protein expressions of muscle atrophy markers (Murf and Fbox32) were
   evidently upregulated in the excessive exercise group compared with the
   control group ([72]Figure 1F–I). Next, to study the changes in
   zebrafish exercise capacity under excessive exercise, we compared
   U[crit] and MO[2] in the excessive exercise and control zebrafish.
   Excessive exercise reduced the U[crit] and MO[2] of the zebrafish,
   suggesting decreased athletic ability as a result of excessive exercise
   ([73]Figure 1J,K). Overall, these data indicate that excessive exercise
   causes muscle atrophy in zebrafish skeletal muscle.

Figure 1.

   [74]Figure 1
   [75]Open in a new tab

   Excessive exercise causes skeletal muscle atrophy in zebrafish. (A)
   Representative image of excessive exercise and control zebrafish. (B)
   Changes in the body weight of zebrafish with or without excessive
   exercise over a 4-week period. (C) Representative photomicrographs of
   muscle sections stained with H&E. Scale bars = 20 μm. (D) Average fiber
   size (based on H&E staining). (E) The expression of
   muscle-atrophy-related genes (trim63a, trim63b, fbxo25, fbxo32, capn1,
   and casp3a), cyclin (ccnd1, ccne1, and ccng2), mitochondrial function
   markers (cyc1, nd4, and nd5), and ppargc1a in muscle tissue detected
   using real-time PCR. (F–I) Western blot analysis of Murf and Fbox32
   proteins (representative blot). All uncut western blot images are
   provided in [76]Supplementary Materials. Data represent mean ± SD (n =
   6). (J,K) The critical swimming speed (U[crit]) and oxygen consumption
   (MO[2]) were measured using the Loligo System. * p < 0.05, ** p < 0.01,
   and *** p < 0.001.

3.2. Functional Annotation of DEGs

   We next investigated the underlying mechanisms of
   excessive-exercise-induced skeletal muscle atrophy using
   high-throughput transcriptome analysis. Compared with the control
   group, 972 genes (3.7%) were specifically expressed and 2661 genes
   (10.2%) were not detected in the exercise-atrophied zebrafish skeletal
   muscle ([77]Figure 2A). In the paired control and skeletal muscle
   atrophy samples, 2499 mRNA transcripts were differentially expressed
   between the groups (Padj < 0.05, fold changes > 2). Among these, 1337
   were upregulated and 1162 were downregulated as a result of excessive
   exercise ([78]Figure 2B). GO enrichment analysis showed that the
   enriched terms for the upregulated genes included autophagy, myeloid
   cell homeostasis, erythrocyte differentiation, response to abiotic
   stimulus, circadian rhythm, positive regulation of the apoptotic
   process, regulation of caspase activation, negative regulation of cell
   cycle, regulation of TORC1 signaling, inhibition of RNA synthesis, and
   response to oxidative stress ([79]Figure 2C, [80]Table S2). Conversely,
   the downregulated genes were significantly enriched in skeletal system
   development, extracellular matrix (ECM) organization, microtubule
   cytoskeleton organization, prereplicative complex assembly,
   noncanonical Wnt signaling pathway synapse assembly, axon guidance,
   cell junction assembly, and protein folding ([81]Figure 2D, [82]Table
   S3).

Figure 2.

   [83]Figure 2
   [84]Open in a new tab

   Gene ontology (GO) enrichment analysis of differentially expressed
   genes in zebrafish skeletal muscles. (A) Venn diagram of the total
   number of identified genes. (B) Volcano plot constructed using fold
   change values and Padj value. Red: upregulated, green: downregulated,
   gray: nonsignificant. (C) GO terms enriched in upregulated genes. (D)
   GO terms enriched in downregulated genes.

3.3. KEGG Pathway and COG Analysis of DEGs

   The DEGs were mapped to known pathways by KEGG pathway analysis, and
   most of the upregulated genes were enriched in the p53 signaling
   pathway, lysosomes, FoxO signaling pathway, apoptosis, regulation of
   autophagy, RNA degradation, adipocytokine signaling pathway, protein
   processing in the ER, RIG-I-like receptor signaling pathway, and ABC
   transporters ([85]Figure 3A). The downregulated genes were enriched in
   ECM–receptor interaction, protein processing in the ER, focal adhesion,
   N-glycan biosynthesis, gap junction, phagosomes, oocyte meiosis, and
   GnRH signaling pathway ([86]Figure 3B). We also classified the DEGs
   according to the homology group database in the COG database. Signal
   transduction mechanism, transcription, posttranslational modification,
   and cytoskeleton were the most representative functional clusters among
   the DEGs ([87]Figure 3C).

Figure 3.

   [88]Figure 3
   [89]Open in a new tab

   Kyoto encyclopedia of genes and genomes and clusters of orthologous
   groups (COGs) analysis of DEGs. The David online database and
   ‘clusterProfiler’ package in R were used to analyze the gene ontology
   (GO) terms associated with upregulated DEGs (A) and the GO terms
   associated with downregulated DEGs (B). (C) The BioMart package and COG
   database were used to obtain the COG assignments of the DEGs. The
   vertical axis represents the number of DEGs in each category, and the
   horizontal axis represents the COG functional category.

   Next, to identify the high-level GO terms that explain the pathology of
   muscle atrophy after excessive exercise, we validated the expression of
   genes enriched in these GO terms by RT-qPCR. We found that genes
   involved in protein ubiquitination, including the FoxO signaling
   pathway (foxo1a, foxo3, foxo4, and mdm2), p53 signaling pathway (tp53,
   cdkn1a, and gadd45bb), apoptosis (casp9, baxa, bcl2l1, ankrd1a, apaf1,
   bbc3, pmaip1, and hif3α), and autophagy (prkaa1, tsc1b, tsc2, becn1,
   sqstm1, atg9a, atg9b, and lc3a) were upregulated after excessive
   exercise ([90]Figure 4A). Genes involved in ECM remodeling, including
   ECM–receptor interaction (col1a2, col1a1a, col1a1b, col2a1b, and fn1a),
   focal adhesion (itga11a, tnc, vwf, and rac3b), gap junctions (tuba1c
   and gnaq), protein synthesis (igf1, igf1ra, pik3r4, pik3r3b, pik3r2,
   akt3a, and ztor), and cell cycle (elavl1a, mcm6, and mcm3) were
   downregulated ([91]Figure 4B). Further, we validated the expression of
   the PI3K–AKT signal, the key signal of protein synthesis, and found
   that the phosphorylation levels of Pi3k and Akt were downregulated, and
   the expression of Ampk was upregulated ([92]Figure 5A). We also
   verified the expression of proteins in the apoptosis signal (p53 and
   Bcl2) and autophagy signal (Lc3) and found that the expressions of
   these proteins in the excessive exercise group were upregulated
   compared with the control group ([93]Figure 5B,C).

Figure 4.

   [94]Figure 4
   [95]Open in a new tab

   qRT-PCR was used to quantify key genes related to muscle atrophy caused
   by excessive exercise. (A) Upregulated genes. (B) Downregulated genes.
   The data represent mean ± SD (n = 6). * p < 0.05, ** p < 0.01, and ***
   p < 0.001.

Figure 5.

   [96]Figure 5
   [97]Open in a new tab

   Western blot was used to validate key proteins in signals involved in
   excessive-exercise-induced muscle atrophy. (A) The expression of
   proteins in the PI3K–AKT signal (representative blot). (B) The
   expression of proteins in the apoptosis signal (representative blot).
   (C) The expression of proteins in the autophagy signal (representative
   blot). Right panels are quantitative analysis data of the protein
   expression level by Image J and GraphPad Prism. All uncut Western blot
   images are provided in [98]Supplementary Materials. Data represent mean
   ± SD (n = 6). * p < 0.05, ** p < 0.01.

3.4. Construction of PPI Network and Cluster Identification

   The PPI networks of the upregulated and downregulated genes were
   generated using the STRING database and Cytoscape software ([99]Figure
   S1A,B), and the top 10 clusters in the PPI networks were generated
   using the MCODE extension ([100]Figure 6A,B). The genes keap1b, per3,
   ulk1b, socs2, esrp1, bcl2l1, hsp70, igf2r, mdm2, and rab18a were the
   hub genes in the clusters of the PPI network of upregulated genes with
   the highest fold change. Similarly, col1a1a, fn1a, ppih, tpx2, uba5,
   nhlrc2, mcm4, tac1, b3gat3, and ddost were the hub genes in the
   clusters of the PPI network of downregulated genes ([101]Table 1).
   Finally, we performed an additional round of GO enrichment analysis on
   the proteins in the PPI network. The 10 representative biological
   process GO terms are listed in [102]Table 2. We found that most of the
   upregulated genes were involved in protein ubiquitination, circadian
   regulation of gene expression, response to abiotic stimulus, response
   to hydrogen peroxide, erythrocyte homeostasis, ADP metabolic process,
   negative regulation of the biosynthetic process, embryonic hemopoiesis,
   proteasome-mediated ubiquitin-dependent protein catabolic process, and
   autophagy. The most downregulated genes were involved in the ECM
   organization, skeletal system development, axon guidance, regulation of
   the retinoic acid receptor signaling pathway, noncanonical Wnt
   signaling pathway, glycoprotein biosynthetic process, carbohydrate
   derivative biosynthetic process, retinoic acid metabolic process, glial
   cell differentiation, and cellular hormone metabolic process.

Figure 6.

   [103]Figure 6
   [104]Open in a new tab

   PPI networks of the cluster proteins relating to muscle atrophy caused
   by excessive exercise. The STRING online database and Cytoscape
   bioinformatics platform were used to analyze the upregulated (A) and
   downregulated DEGs (B) (p < 0.05, fold change > 2).

Table 1.

   Hub genes of clusters of protein–protein interaction networks.
   Hub Gene Description
   UP keap1b kelch-like ECH-associated protein 1b
   per3 period circadian clock 3
   ulk1b unc-51-like kinase 1b
   socs2 suppressor of cytokine signaling 2
   esrp1 epithelial splicing regulatory protein 1
   bcl2l1 bcl2-like 1
   hsp70 heat shock cognate 70-kd protein
   igf2r insulin-like growth factor 2 receptor
   mdm2 MDM2 oncogene, E3 ubiquitin protein ligase
   rab18a RAB18A, member RAS oncogene family
   DOWN col1a1a collagen, typeI, alpha1a
   fn1a fibronectin 1a
   ppih peptidylprolyl isomerase h
   tpx2 tpx2, microtubule-associated, homolog
   uba5 ubiquitin-like modifier activating enzyme 5
   nhlrc2 nhl repeat containing 2
   mcm4 minichromosome maintenance complex component 4
   tac1 tachykinin 1
   b3gat3 beta-1,3-glucuronyltransferase 3
   ddost dolichyl-diphosphooligosaccharide-protein glycosyltransferase
   subunit
   [105]Open in a new tab

Table 2.

   Gene ontology enrichment analysis of the intersect proteins of gene
   clusters.
   ID Term p-Value
   Up GO:0016567 protein ubiquitination 8.60 × 10^−14
   GO:0032922 circadian regulation of gene expression 9.17 × 10^−12
   GO:0009628 response to abiotic stimulus 9.19 × 10^−8
   GO:0042542 response to hydrogen peroxide 2.22 × 10^−6
   GO:0034101 erythrocyte homeostasis 1.089 × 10^−5
   GO:0046031 ADP metabolic process 1.34 × 10^−5
   GO:0009890 negative regulation of biosynthetic process 1.76 × 10^−5
   GO:0035162 embryonic hemopoiesis 3.25 × 10^−5
   GO:0043161 proteasomal pathway 3.61 × 10^−5
   GO:0006914 autophagy 4.51 × 10^−5
   Down GO:0030198 extracellular matrix organization 2.00 × 10^−7
   GO:0001501 skeletal system development 4.01 × 10^−5
   GO:0007411 axon guidance 0.00045
   GO:0048385 regulation of retinoic acid receptor signaling pathway
   0.00051
   GO:0035567 noncanonical Wnt signaling pathway 0.00054
   GO:0009101 glycoprotein biosynthetic process 0.0013
   GO:1901137 carbohydrate derivative biosynthetic process 0.0014
   GO:0042573 retinoic acid metabolic process 0.0015
   GO:0010001 glial cell differentiation 0.0016
   GO:0034754 cellular hormone metabolic process 0.0020
   [106]Open in a new tab

4. Discussion

   Overtraining is a potential cause of muscle atrophy [[107]7]. After
   applying an overtraining regimen in zebrafish to model excessive
   exercise, the morphology of zebrafish skeletal muscles displayed
   decreased mass and cross-sectional muscle fiber size. The U[crit] and
   MO[2] of zebrafish in the excessive exercise model also decreased
   significantly compared with the control group. Based on these factors,
   we successfully established a zebrafish model of exercise-induced
   muscle atrophy that presented a measurable decline in exercise
   performance. Transcriptome analysis of the skeletal muscle of zebrafish
   under excessive exercise revealed that overtraining could lead to
   changes in protein synthesis and degradation, redox homeostasis, ER
   stress and apoptosis, muscle regeneration, and ECM remodeling, which
   may contribute to the pathological mechanism of
   excessive-exercise-induced muscle atrophy. A schematic regulatory
   network for excessive-exercise-induced muscle atrophy in zebrafish is
   presented in [108]Figure 7.

Figure 7.

   [109]Figure 7
   [110]Open in a new tab

   The hypothetical pathogenesis mechanism of muscle atrophy induced by
   excessive exercise in zebrafish. The upregulated genes are shown in red
   ovals, and the downregulated genes are shown in green ovals. In
   response to excessive exercise, the muscle cells may undergo the
   following events: (1) the inhibition of IGF–PI3K signaling and
   upregulation of AMPK inhibit the function of the TORC1 complex to
   suppress protein synthesis and promote autophagy; (2) excessive
   exercise activates FoxO signaling, which promotes the expression of
   protein hydrolysis, autophagy, and muscle-atrophy-related gene
   expression; (3) excessive exercise induces oxidative stress, and the
   accumulation of misfolded proteins in the endoplasmic reticulum and the
   activation of p53 signaling lead to proteolysis, autophagy, and
   apoptosis; (4) overtraining inhibits Wnt signaling and the expression
   of the MCM complex, which suppresses the proliferation of muscle
   satellites; and (5) excessive exercise destroys ECM–receptor
   interaction, focal adhesion, and the gap junction of muscle cells,
   leading to cytoskeleton damage and inducing apoptosis. (Created with
   BioRender.com.)

   Skeletal muscle mass depends on the balance between the rate of protein
   synthesis and degradation. Only when the rate of protein synthesis
   exceeds the rate of degradation will the muscle mass increase;
   otherwise, the muscle will decrease in mass, or even atrophy may occur
   in severe cases [[111]28]. Protein synthesis depends on intracellular
   mTOR signaling, which is activated by insulin/IGF-1–PI3K–Akt cascade
   phosphorylation signals [[112]29,[113]30]. The genetic inactivation of
   IGF1R or mTOR in the muscle can result in decreased muscle fiber size
   or muscular dysplasia [[114]31,[115]32]. However, in the excessive
   exercise zebrafish model, the PI3K–AKT signal igf1, igf1ra, pik3r4,
   pik3r3b, pik3r2, and akt3a were significantly downregulated.
   Importantly, the phosphorylation levels of Pi3k and Akt proteins were
   downregulated. The downregulation of PI3K–AKT phosphorylation signals
   may contribute to inhibited protein synthesis after excessive exercise
   training. In contrast, members of the FoxO family, such as foxO1,
   foxO3, and foxO4, as negative regulators of insulin/IGF-1 signaling,
   were significantly upregulated. FoxO signaling, which is an important
   intracellular emergency response to starvation or hypoxia, can cause
   protein degradation through the ubiquitin–proteasome and
   autophagy–lysosome pathways [[116]33]. Consistently, the GO analysis of
   upregulated genes showed that autophagy was the most significantly
   enriched term. The transcriptional targets of FoxO proteins, such as
   atrogin-1 (fbox32) and MuRF-1 (trim63a, trim63b), which mediate the
   ubiquitination and subsequent degradation of muscle structural proteins
   [[117]33,[118]34], were also significantly upregulated. The inhibition
   of mTOR signaling and the activation of FoxO signaling suggest that the
   total amount of protein in zebrafish skeletal muscles is reduced after
   excessive training. Under normal conditions, autophagy can maintain
   cell homeostasis by clearing metabolic waste. Autophagy is a mechanism
   of maintaining survival under external pressure, hypoxia, starvation,
   and ER stress. According to the transcriptional analysis, the
   protein-folding-related genes in the ER were downregulated, while
   oxidative stress response genes were upregulated, indicating that ER
   stress and oxidative stress also cause autophagy. Reactive oxygen
   species (ROS) are metabolites of oxygen consumption and important
   signaling molecules in muscles that maintain increased contractile
   activity [[119]35]. However, excessive ROS levels that exceed the
   abilities of antioxidant defenses and disturb redox homeostasis
   negatively affect muscle contractile proteins, mitochondrial
   phospholipids, and DNA, and are associated with the pathophysiology of
   muscle aging (sarcopenia) and various muscular disorders
   [[120]36,[121]37,[122]38,[123]39]. The mitochondria are the main sites
   of ROS production. Oxidative stress can trigger mitochondrial autophagy
   to recover nutrients and reduce ROS. In the excessive exercise
   zebrafish model, the upregulated genes were significantly enriched in
   mitochondrial autophagy and oxidative stress. In addition to the
   mitochondria, the ER of skeletal muscle cells also produces ROS to
   trigger muscle contraction. Recent evidence suggests that ER stress is
   associated with skeletal muscle atrophy caused by aging, disease, and
   malnutrition [[124]40,[125]41]. Many stimuli, such as exercise,
   hypoxia, redox balance changes, and nutrition/energy deprivation, can
   interfere with the homeostasis of the ER, which may lead to the
   accumulation of unfolded and misfolded proteins in the ER lumen,
   eventually resulting in ER stress and autophagy to maintain protein
   homeostasis by scavenging nonfunctional proteins. In addition, ER
   stress can activate p53 signaling and induce apoptosis
   [[126]42,[127]43,[128]44,[129]45]. In our excessive exercise zebrafish
   model, p53 signaling and apoptosis were significantly enriched, and the
   RT-qPCR showed that p53-signaling-pathway-related genes, such as tp53,
   cdkn1α, and gadd45bb, and apoptosis-related genes, such as lc3a, baxa,
   bcl2l1, ankrd1a, and casp9, were upregulated. Although p53 signaling
   can respond to cell pressure and regulate autophagy, apoptosis, and the
   cell cycle to maintain cell homeostasis, dysfunctional p53 signaling is
   associated with a variety of diseases. Increased expression of p53 and
   its target genes has been previously observed in muscle atrophy models
   of aging [[130]46], muscle denervation [[131]47], and Huntington′s
   disease [[132]48]. Therefore, we speculate that the starvation and
   oxidative stress induced by excessive exercise lead to ER pressure and
   activate p53 signaling. Autophagy and apoptosis induced by excessive
   exercise are two of the causes of exercise-induced muscular atrophy.

   In addition to the homeostasis of muscle cells, the strong regenerative
   ability of muscle satellite cells also plays a key role in the
   maintenance of adult muscle tissue homeostasis. Stimulated by growth
   signals or physical trauma, activated satellite cells proliferate and
   differentiate into mature myocytes, replacing lost or senescent
   myocytes. In a mouse model with severe muscle injury, the proliferation
   of stem cells could completely regenerate muscles, and normal exercise
   could be resumed after severe muscle injury [[133]49,[134]50].
   Unfortunately, in the present study, the cell cycle arrest genes
   cdkn1a, cdkn3, and cdkn1bb were upregulated. According to the GO
   enrichment analysis of the DEGs, the Wnt signaling pathway, which has
   been implicated in muscle repair and regulates satellite cell
   differentiation and self-renewal, was inhibited, potentially affecting
   myocyte regeneration. The suppression of the cell cycle and Wnt
   signaling suggests that weakened muscle regenerative capabilities also
   contribute to skeletal muscle atrophy induced by excessive exercise.

   Transcriptional analysis showed that ECM–receptor interactions and the
   focal adhesion of overexercised zebrafish skeletal muscle were also
   repressed. The ECM plays a key role in maintaining the quality and
   strength of skeletal muscles [[135]51]. Degradation of the ECM leads to
   muscle damage caused by structural and functional changes to myocytes
   [[136]52]. The loss of collagen and fibroblast function, and the
   increase in matrix metalloproteinase activity, can promote the
   degradation of the ECM, leading to cell shrinkage and senescence
   [[137]53]. qRT-PCR verified that ECM (col1a2, col1a1a, col1a1b,
   col2a1b, and fn1a), focal adhesion (itga11a, vwf, and rac3b), and gap
   junction (tuba1c and gnaq) genes were downregulated by excessive
   exercise, indicating that the ECM of the skeletal muscle was degraded.
   However, whether this indicates a phenotype or the cause of muscle
   atrophy needs further study.

   Although nerve loss is the main characteristic of spinal muscular
   atrophy, we found that some key factors of axon guidance were
   significantly downregulated by overtraining in zebrafish. The key to
   repairing defects of the peripheral nervous system is to promote the
   regeneration of axons and prevent the atrophy of muscles under the
   control of the nervous system [[138]54]. During such repair, axon
   guidance molecules play an important role in the orderly growth of
   axons [[139]55]. In the present study, the downregulation of key
   factors relating to axon guidance indicates that axons may be unable to
   form in an orderly manner during or after excessive exercise, further
   contributing to muscle atrophy. Further research and analysis of these
   axon guidance factors could enable the prevention and treatment of
   muscle atrophy caused by peripheral nervous system defects.

5. Conclusions

   Through the construction of an excessive exercise zebrafish model and
   high-throughput sequencing analysis, we uncovered the potential
   mechanisms of excessive-exercise-induced skeletal muscle atrophy.
   However, the order of these events has not been clearly unraveled.
   Further, detailed exercise intervention regimes and sampling analysis
   will provide more information about the order of these events during
   muscle atrophy. As zebrafish and human muscles are extremely similar,
   this model could be used for testing newly developed drugs targeting
   muscle atrophy. Furthermore, the underlying regulatory pathways and hub
   genes that correlate with the pathogenesis of skeletal muscle atrophy
   induced by excessive exercise can be used to facilitate informed
   clinical decision making and to prescribe safe training regimens for
   athletes. Taken together, these pathways will provide valuable insights
   for the supervision and clinical treatment of muscle atrophy induced by
   excessive exercise.

Acknowledgments