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<journal-id journal-id-type="publisher-id">Front. Physiol.</journal-id>
<journal-title>Frontiers in Physiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Physiol.</abbrev-journal-title>
<issn pub-type="epub">1664-042X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1607873</article-id>
<article-id pub-id-type="doi">10.3389/fphys.2025.1607873</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Physiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Mechanisms of skeletal muscle atrophy in type 2 diabetes mellitus</article-title>
<alt-title alt-title-type="left-running-head">Yang et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphys.2025.1607873">10.3389/fphys.2025.1607873</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Jingyi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3029288/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
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<role content-type="https://credit.niso.org/contributor-roles/visualization/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Yingdong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Yuzhe</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Jia</surname>
<given-names>Xinqi</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lu</surname>
<given-names>Fangping</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>Department of Pathophysiology</institution>, <institution>Mudanjiang Medical University</institution>, <addr-line>Mudanjiang</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of General Surgery</institution>, <institution>Mudanjiang Medical University Affiliated Hongqi Hospital</institution>, <addr-line>Mudanjiang</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/350946/overview">Amilcare Barca</ext-link>, University of Salento, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/649042/overview">Daniela Sayuri Mizobuti</ext-link>, Brazilian Biosciences National Laboratory (LNBio), Brazil</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2787251/overview">Limin Shi</ext-link>, Johns Hopkins All Children&#x2019;s Hospital, United States</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Fangping Lu, <email>lufangping0910@163.com</email>; Xinqi Jia, <email>jiaxinqi0129@163.com</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>25</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1607873</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>04</day>
<month>06</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Yang, Wang, Xu, Jia and Lu.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Yang, Wang, Xu, Jia and Lu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>ERS-induced apoptosis may play a pivotal role in diabetic skeletal muscle atrophy. However, the specific mechanisms by which ERS regulates skeletal muscle atrophy in diabetes remain unclear. The research examines the impact of endoplasmic reticulum stress (ERS) on skeletal muscle atrophy in type 2 diabetes mellitus (T2DM) mice.</p>
</sec>
<sec>
<title>Methods</title>
<p>Leptin receptor-deficient <italic>Db/db</italic> mice (n &#x3d; 7, 24-week-old, male) were employed as a type 2 diabetes model, while age-matched male C57BL/6J mice (n &#x3d; 7) served as normal controls. Pathway enrichment analysis of differentially expressed genes was performed based on transcriptome sequencing data, focusing on apoptosis, ERS, and ubiquitin-proteasome pathways. Skeletal muscle morphology was assessed via anatomical observation, Laminin Staining, and immunoblotting analysis (WB). WB was used to detect ERS markers (ATF6, p-eIF2&#x3b1;, Bip, p-JNK, Chop), apoptosis-related proteins (Bcl2, Bax, Cleaved Caspase-3, CytC), p-Akt, and muscle atrophy marker Atrogin1.</p>
</sec>
<sec>
<title>Results</title>
<p>Transcriptomic enrichment analysis confirmed specific activation of apoptosis, ERS, and ubiquitin-proteasome pathways. WB revealed upregulated ERS-related proteins, increased apoptotic proteins, decreased p-Akt expression, elevated Atrogin1 levels, and enhanced proteolytic activity. <italic>Db/db</italic> mice exhibited significant skeletal muscle atrophy, with Laminin Staining demonstrating reduced cross-sectional area (CSA) of muscle fibers.</p>
</sec>
<sec>
<title>Discussion</title>
<p>These findings uncovers a dual regulatory mechanism underlying diabetic muscle atrophy. The diabetic skeletal muscle microenvironment exhibits elevated oxidative stress and significantly enhanced ER stress, which promotes direct muscle atrophy through ER stress sensor-mediated apoptosis. Concurrently, sustained ER stress suppresses Akt activity while upregulating the muscle-specific E3 ubiquitin ligase Atrogin1, thereby accelerating proteolysis and inducing indirect muscle wasting. These findings provide crucial mechanistic insights into diabetic skeletal myopathy, highlighting the ER stress signaling network as a promising therapeutic target for mitigating muscle atrophy in diabetes.</p>
</sec>
</abstract>
<kwd-group>
<kwd>type 2 diabetes mellitus</kwd>
<kwd>endoplasmic reticulum stress</kwd>
<kwd>apoptosis</kwd>
<kwd>skeletal muscle atrophy</kwd>
<kwd>proteostasis</kwd>
</kwd-group>
<contract-sponsor id="cn001">Health Commission of Heilongjiang Province<named-content content-type="fundref-id">10.13039/100017961</named-content>
</contract-sponsor>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cell Physiology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>Diabetes mellitus (DM) represents a global metabolic dysregulation syndrome, where T2DM is pathophysiologically characterized by insulin resistance. T2DM is often accompanied by various complications, including skeletal muscle atrophy. Skeletal muscle, serving as the principal regulator of systemic glucose homeostasis, functions as the primary insulin-target tissue that mediates postprandial glucose disposal through GLUT4-dependent uptake mechanisms (<xref ref-type="bibr" rid="B34">Sylow et al., 2021</xref>). Skeletal muscle atrophy is pathologically associated with diverse clinical contexts spanning: (1) age-related sarcopenia; (2) chronic diseases; and (3) malignancy-associated cachexia (<xref ref-type="bibr" rid="B11">Ishii et al., 2008</xref>; <xref ref-type="bibr" rid="B25">Mendes et al., 2015</xref>; <xref ref-type="bibr" rid="B43">Zhang H. et al., 2023</xref>; <xref ref-type="bibr" rid="B44">Zhang et al., 2024</xref>). Emerging evidence demonstrates that dysregulated mROS production and caspase-3 activation constitute pathophysiological mechanisms driving proteolytic degradation in atrophying muscle (<xref ref-type="bibr" rid="B31">Plant et al., 2009</xref>; <xref ref-type="bibr" rid="B26">Min et al., 2011</xref>; <xref ref-type="bibr" rid="B44">Zhang et al., 2024</xref>).</p>
<p>The endoplasmic reticulum (ER) serves as a central organelle for cellular proteostasis, coordinating protein synthesis, post-translational modification, secretory trafficking, and folding quality control (<xref ref-type="bibr" rid="B40">Yoshida, 2007</xref>). ERS can be triggered by various pathological and physiological factors, including Ca2&#x2b; dysregulation, redox imbalance, defective protein folding, and other stressors (<xref ref-type="bibr" rid="B13">Jiang et al., 2021</xref>). In patients with T2DM, obesity is frequently associated with enhanced adipokine secretion, which subsequently promotes excessive reactive oxygen species (ROS) generation and induces systemic oxidative stress (<xref ref-type="bibr" rid="B8">Fernandez-Sanchez et al., 2011</xref>). As the primary defense mechanism against oxidative damage, the endogenous antioxidant system mobilizes crucial enzymes including superoxide dismutase (SOD) and catalase (CAT) to establish the first-line protective barrier against oxidative assault (<xref ref-type="bibr" rid="B14">Jomova et al., 2024</xref>). This compensatory upregulation represents a critical adaptive response to maintain redox homeostasis. Furthermore, the persistent oxidative imbalance contributes to the accumulation of misfolded proteins, thereby triggering endoplasmic reticulum (ER) stress through impaired protein folding capacity (<xref ref-type="bibr" rid="B27">Ong and Logue, 2023</xref>). Physiological Unfolded Protein Response (UPR) activation under moderate ERS orchestrates proteostatic adaptation through three transmembrane sensors. Conversely, sustained ERS drives terminal UPR signaling that activates caspase-dependent apoptotic cascades, culminating in programmed cell death (<xref ref-type="bibr" rid="B29">Park et al., 2019</xref>; <xref ref-type="bibr" rid="B10">Guo et al., 2024</xref>). The UPR is mediated by three evolutionarily conserved sensors: inositol-requiring enzyme 1&#x3b1; (IRE1&#x3b1;); protein kinase R (PKR)-like ER kinase (PERK); and activating transcription factor 6 (ATF6) (<xref ref-type="bibr" rid="B3">Casas-Martinez et al., 2024</xref>). Under proteostatic equilibrium, these sensors remain sequestered by the ER chaperone BiP/GRP78. During ERS, misfolded protein overload competitively dissociates BiP/GRP78 from the sensors, enabling their oligomerization and activation. PERK activation catalyzes eIF2&#x3b1; phosphorylation, which transiently attenuates global translation while paradoxically enabling ATF4-mediated transcriptional upregulation of pro-apoptotic factor CHOP. ATF4 and CHOP synergistically stimulate the expression of genes related to apoptosis, autophagy, and antioxidant responses (<xref ref-type="bibr" rid="B21">Lu et al., 2024</xref>). IRE1&#x3b1; endoribonuclease activity executes XBP1 mRNA splicing, generating sXBP1 for UPR gene activation, while concomitantly activating apoptosis signal-regulating kinase 1 (ASK1)-JNK1 signaling (<xref ref-type="bibr" rid="B2">Brozzi et al., 2015</xref>; <xref ref-type="bibr" rid="B21">Lu et al., 2024</xref>). Activated ATF6 translocates to the Golgi for proteolytic cleavage, yielding a transcriptionally active fragment that represses anti-apoptotic Bcl-2 proteins while inducing pro-apoptotic effectors. In myocytes, ATF6 hyperactivation provokes caspase-9-mediated intrinsic apoptosis via mitochondrial permeabilization (<xref ref-type="bibr" rid="B15">Kapuy, 2024</xref>).</p>
<p>Akt (protein kinase B, PKB), a pivotal node in insulin receptor substrate (IRS) signaling, coordinates cellular proliferation, survival, and metabolic homeostasis through phosphorylation-dependent regulation of downstream effectors (<xref ref-type="bibr" rid="B36">Viglietto et al., 2002</xref>; <xref ref-type="bibr" rid="B1">Bao et al., 2004</xref>; <xref ref-type="bibr" rid="B19">Li et al., 2007</xref>). Following insulin receptor activation, phosphatidylinositol-3,4,5-trisphosphate (PIP3) recruits Akt to the plasma membrane via pleckstrin homology (PH) domain interaction, enabling PDK1-mediated phosphorylation at Thr308 within the activation loop (<xref ref-type="bibr" rid="B33">Stephens et al., 1998</xref>). Concomitant mTOR complex 2 (mTORC2)-dependent phosphorylation at Ser473 in the hydrophobic motif confers full catalytic competence (<xref ref-type="bibr" rid="B41">Zeng et al., 2007</xref>). Activated Akt modulates downstream targets, including FoxOs, cell cycle regulators, mTOR, and GSK3, to regulate diverse physiological functions (<xref ref-type="bibr" rid="B23">Manning and Cantley, 2007</xref>; <xref ref-type="bibr" rid="B35">Vantler et al., 2023</xref>).</p>
<p>ERS-induced apoptosis may play a pivotal role in diabetic skeletal muscle atrophy. However, the specific mechanisms by which ERS regulates skeletal muscle atrophy in diabetes remain unclear. This research aims to explore the molecular mechanisms of ERS-mediated skeletal muscle atrophy in diabetic mice. By comparing skeletal muscle alterations, ERS marker expression, and apoptosis between diabetic and control groups, we seek to provide new insights into the pathogenesis of diabetic muscle atrophy and theoretical support for developing ERS-targeted therapies.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Data collection and processing</title>
<p>The GSE22309 dataset was retrieved from the GEO database (<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</ext-link>) using the keyword &#x201c;diabetes.&#x201d; This dataset included human skeletal muscle samples from insulin-sensitive individuals and insulin-na&#xef;ve diabetic patients. Differentially expressed genes (DEGs) were identified using GEO2R (&#x7c;logFC&#x7c; &#x2265; 1, FDR &#x3c; 0.05). Gene Ontology (GO) analysis was conducted with DAVID and visualized with Cytoscape.</p>
</sec>
<sec id="s2-2">
<title>2.2 Animal experiments</title>
<p>All animal experiments were approved by the Animal Experimental Ethics Committee (IACUC-2303040). Leptin receptor-deficient <italic>Db/db</italic> mice (n &#x3d; 7, 8-week-old, male) were employed as a type 2 diabetes model, while age-matched male C57BL/6J mice (n &#x3d; 7) served as normal controls (<xref ref-type="bibr" rid="B28">Pandey and Dvorakova, 2020</xref>). Following 16 weeks of standardized housing under specific pathogen-free conditions, skeletal muscle tissues were collected for molecular characterization. All experimental animals were generously provided by Harbin Medical University.</p>
</sec>
<sec id="s2-3">
<title>2.3 Transcriptome data analysis</title>
<p>RNA from the hindlimb skeletal muscles of 5 control and 4 <italic>Db/db</italic> mice was sequenced on the Illumina platform by Metware Biotechnology Co., Ltd. (Wuhan, China).</p>
</sec>
<sec id="s2-4">
<title>2.4 Body weight and random blood glucose monitorin</title>
<p>Body weight was measured using an electronic balance (0.1 g resolution). Mice were briefly transferred to an empty cage for consistent weighing. Random blood glucose was assessed without fasting: a needle lightly punctured the tail vein, the first blood droplet was discarded, and subsequent droplets were analyzed via glucometer.</p>
</sec>
<sec id="s2-5">
<title>2.5 Intraperitoneal glucose tolerance test</title>
<p>Following 12-h fast. A sterile needle was used to puncture the tail tip, and blood droplets were collected for baseline (0-min) glucose measurement. Then glucose (2 g/kg) was administered i. p. Blood glucose was re-measured at 30, 60, and 120 min post-injection (same method) (<xref ref-type="bibr" rid="B45">Zhang Y. et al., 2023</xref>).</p>
</sec>
<sec id="s2-6">
<title>2.6 Antibodies</title>
<p>Anti-SOD (1:1,000), anti-p-eIF2&#x3b1; (1:1,000), anti-GAPDH (1:10,000), anti-ATF6 (1:1,000), anti-eIF2&#x3b1; (1:1,000), anti-p-JNK (1:1,000), anti-JNK (1:1,000), anti-CYTC (1:1,000), anti-Atrogin1 (1:1,000), anti-BiP (1:1,000), anti-Bax (1:1,000), anti-Caspase3 (1:1,000), anti-MYH4 (1:1,000), anti-CHOP (1:1,000), anti-MYOM1 (1:1,000) antibodies were purchased from Proteintech. Anti-TNNI2 (1:1,000), anti-CAT (1:1,000) antibodies were obtained from Affinity Biosciences (Qinke Biotechnology Co., Ltd.). Anti-Bcl2 (1:500) antibody was purchased from Wanlei Biotechnology Co., Ltd.</p>
</sec>
<sec id="s2-7">
<title>2.7 Immunoblotting analysis</title>
<p>Hind limb skeletal muscle tissues of mice were homogenized in pre-chilled RIPA buffer and centrifuged at low speed (3,000 rpm, 10 min, 4&#xb0;C). The supernatant was collected and vortexed intermittently (20 s every 5 min for 30 min total). After centrifugation at 12,000 rpm for 30 min (4&#xb0;C), the clarified supernatant was collected. Protein concentration was determined by BCA assay, and samples were adjusted to load 80 &#x3bc;g protein per well. The samples were mixed with 5&#xd7; loading buffer at a 1:4 ratio, boiled at 100&#xb0;C for 8 min, and separated by SDS-PAGE. Proteins were transferred to PVDF membranes (0.45 &#x3bc;m pore size) via wet transfer. Membranes were blocked for 90 min at room temperature, incubated with primary antibodies (4&#xb0;C, 16 h) and HRP-conjugated secondary antibodies (25&#xb0;C, 1 h). Protein bands were visualized by ECL and quantified using ImageJ software for normalization and statistical analysis.</p>
</sec>
<sec id="s2-8">
<title>2.8 Laminin staining</title>
<p>Immunofluorescence staining of hindlimb skeletal muscle (5 &#x3bc;m cryosections) was performed as follows: Tissues were fixed in 4% PFA, embedded in OCT compound, and sectioned using a cryostat. Antigen retrieval was performed by heating sections in citrate buffer (95&#xb0;C, 20 min). After PBS washes, sections were blocked with blocking buffer (25&#xb0;C, 30 min) and incubated with anti-laminin antibody (4&#xb0;C, 16 h). Secondary antibody labeling was conducted under light-protected conditions (25&#xb0;C, 1 h). Nuclei were counterstained with DAPI (5 min), and slides were mounted with antifade mounting medium. Images were acquired using confocal microscopy.</p>
</sec>
<sec id="s2-9">
<title>2.9 Statistical analysis</title>
<p>Data were analyzed using GraphPad Prism and expressed as mean &#xb1; SEM. Group comparisons were performed using Student&#x2019;s t-test. The normality of data for all parametric tests (Student&#x2019;s t-test) was verified using the Shapiro-Wilk test. Significance was set at &#x2a;P &#x3c; 0.05 and &#x2a;&#x2a;P &#x3c; 0.01.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Differential gene expression in diabetic skeletal muscle</title>
<p>Differentially expressed genes (DEGs) were screened using GEO2R-normalized microarray data from the GEO database (GSE22309 dataset) with thresholds of &#x7c;log2Fold Change&#x7c; &#x2265;1 and FDR &#x3c; 0.05. A total of 227 diabetes mellitus (DM)-related DEGs were identified (<xref ref-type="fig" rid="F1">Figure 1A</xref>). Protein-protein interaction network analysis conducted through the STRING database revealed gene clusters associated with ER homeostasis and apoptosis (<xref ref-type="fig" rid="F1">Figure 1B</xref>). Gene Ontology (GO) enrichment analysis demonstrated significant enrichment in biological processes (BPs) including apoptosis regulation, proteostasis maintenance, and glycogen metabolism. Cellular components (CCs) were predominantly localized to the nucleus, cytoplasm, mitochondria, and ER. Molecular functions (MFs) involved protein binding, nucleic acid binding, ubiquitin-protein ligase interactions, and unfolded protein binding (<xref ref-type="fig" rid="F1">Figure 1C</xref>). These bioinformatic analyses collectively indicate dysregulation of ER stress modulation, apoptotic damage, and proteostatic control in skeletal muscle of diabetic patients.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Screening and enrichment analysis of DEGs in the GSE22309 dataset. <bold>(A)</bold> Volcano plot of DEGs comparing skeletal muscle from insulin-sensitive individuals (Con) and untreated diabetic patients (DM). using red/blue color dots to denote upregulation and downregulation. <bold>(B)</bold> PPI network of DEGs. <bold>(C)</bold> GO enrichment analysis of DEGs. Enrichment score reflects the number of enriched genes; higher values indicate stronger enrichment.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g001.tif">
<alt-text content-type="machine-generated">Panel A shows a volcano plot from GSE22309 comparing DM and control, highlighting genes with significant expression changes. Panel B displays a complex network diagram of gene interactions. Panel C is a bar graph illustrating enrichment scores for biological processes, cellular components, and molecular functions, with categories color-coded: green for biological processes, orange for cellular components, and blue for molecular functions.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-2">
<title>3.2 Upregulated ER stress and apoptosis-associated genes in diabetic murine skeletal muscle</title>
<p>To further explore differential gene expression patterns, transcriptome sequencing was conducted on hindlimb skeletal muscle tissues from control and <italic>Db/db</italic> mice. DEGs were filtered using thresholds of &#x7c;log2Fold Change&#x7c; &#x2265; 1 and FDR &#x3c;0.05, resulting in 1,764 DEGs (1,182 upregulated and 582 downregulated in <italic>Db/db</italic> mice) (<xref ref-type="fig" rid="F2">Figure 2A</xref>). Cross-referencing analysis with 1,827 UPR-associated genes from the GeneCards database identified 106 overlapping genes (<xref ref-type="fig" rid="F2">Figure 2B</xref>). Gene Ontology (GO) enrichment analysis demonstrated significant enrichment of BPs related to ER stress, insulin response, and apoptosis (<xref ref-type="fig" rid="F2">Figure 2C</xref>). Furthermore, intersection analysis of 2,584 type 2 diabetes-associated genes curated from CTD, GeneCards, and DisGeNET databases revealed substantial overlap with ER stress and apoptosis-related genes (<xref ref-type="fig" rid="F2">Figure 2D</xref>). Upregulated ER Stress and Apoptosis-Associated Genes in Diabetic Murine Skeletal Muscle: Integrated multi-omics analyses reveal a significant association between ER stress activation and apoptotic pathway enrichment in diabetic murine models.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Screening and enrichment analysis of <italic>Db/db</italic>-associated DEGs. <bold>(A)</bold> Volcano plot of <italic>Db/db</italic>-related DEGs, using red/blue color scales to denote upregulation and downregulation. <bold>(B)</bold> Venn diagram showing integration between DEGs and UPR-related genes from GeneCards. <bold>(C)</bold> GO BP enrichment analysis of overlapping genes. Dot size reflects gene count; color intensity represents P-value (darker &#x3d; lower P). <bold>(D)</bold> Venn diagram of T2DM-related genes from DisGeNET, GeneCards, and CTD.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g002.tif">
<alt-text content-type="machine-generated">Panel A shows a volcano plot with log2 fold change on the x-axis and negative log10 FDR on the y-axis, indicating upregulated genes in red and downregulated genes in green. Panel B features a Venn diagram and bar charts comparing db-db vs. control and unfolded protein response, highlighting shared and unique elements. Panel C presents a bubble chart of enriched biological pathways, with bubble size representing count and color indicating p-value significance. Panel D contains a Venn diagram and bar charts illustrating overlaps among CTD, GC, and DisGeNET datasets.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-3">
<title>3.3 Metabolic characterization of diabetic murine models</title>
<p>To validate the establishment of T2DM in murine models, bodyweight, random blood glucose levels, and glucose tolerance were systematically assessed in 8-week-old mice. <italic>Db/db</italic> mice demonstrated significantly greater bodyweight relative to controls (<xref ref-type="fig" rid="F3">Figure 3A</xref>). The random blood glucose concentrations in diabetic mice were substantially elevated compared to controls (<xref ref-type="fig" rid="F3">Figure 3B</xref>). Intraperitoneal glucose tolerance testing (IPGTT) further revealed impaired glycemic regulation in <italic>Db/db</italic> mice pared to control mice (<xref ref-type="fig" rid="F3">Figure 3C</xref>). Quantification of the glucose tolerance curve area under the curve (AUC) showed significantly elevated values in diabetic versus controls (<xref ref-type="fig" rid="F3">Figure 3D</xref>). These findings collectively demonstrate disrupted glucose homeostasis in <italic>Db/db</italic> mice, confirming successful T2DM model establishment.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Metabolic Profile Characterization in Control and <italic>Db/db</italic> Murine models. <bold>(A)</bold> Body weight comparison at 8 weeks post-modeling. <bold>(B)</bold> Random blood glucose measurements between Control and <italic>Db/db</italic> cohorts. <bold>(C)</bold> Intraperitoneal glucose tolerance test (IPGTT) trajectories. <bold>(D)</bold> Quantitative area under the curve (AUC) analysis of glycemic responses. Data: mean &#xb1; SEM. &#x2a;<italic>P</italic> &#x3c; 0.05 was considered statistically significant. n &#x3d; 3.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g003.tif">
<alt-text content-type="machine-generated">Bar and line graphs labeled A to D compare control and Db/db groups. A: Bar graph shows higher body weight in Db/db. B: Bar graph shows increased glucose levels in Db/db. C: Line graph shows glucose over time, higher in Db/db across all times. D: Bar graph of area under the curve (AUC) shows Db/db with higher AUC. Significant differences are noted with asterisks.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4">
<title>3.4 ERS activation in <italic>Db/db</italic> murine models</title>
<p>ERS has been mechanistically linked to oxidative stress, ubiquitin-proteasome pathway activation, and inflammatory signaling (<xref ref-type="bibr" rid="B43">Zhang H. et al., 2023</xref>). The quantity levels of critical antioxidant enzymes superoxide dismutase (SOD) and catalase (CAT), which counteract oxidative damage (<xref ref-type="bibr" rid="B14">Jomova et al., 2024</xref>), were significantly downregulated in <italic>Db/db</italic> mice compared to Control cohorts (<xref ref-type="fig" rid="F4">Figures 4A&#x2013;C</xref>), indicative of exacerbated oxidative stress. Furthermore, proteomic analysis revealed elevated expression of ERS markers including the ER transmembrane sensor ATF6, phosphorylated eukaryotic initiation factor 2&#x3b1; (p-eIF2&#x3b1;) as a downstream target of PERK signaling, and the ER chaperone BiP/GRP78 in <italic>Db/db</italic> mice compared to control mice (<xref ref-type="fig" rid="F4">Figures 4D&#x2013;G</xref>). These findings collectively demonstrate concomitant augmentation of oxidative stress and ERS pathways in diabetic murine models.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>ERS Protein Levels in Control vs. <italic>Db/db</italic> Mice. <bold>(A)</bold> superoxide dismutase (SOD) and catalase (CAT) protein levels. Quantitative analysis of <bold>(B)</bold> SOD, n &#x3d; 5, <bold>(C)</bold> CAT, n &#x3d; 4. <bold>(D)</bold> Activating transcription factor 6 (ATF6), immunoglobulin heavy chain binding protein (Bip), and Phosphorylation of eukaryotic initiation factor-2&#x3b1; (p-eIF2&#x3b1;) protein levels. Quantitative analysis of <bold>(E)</bold> ATF6, n &#x3d; 4, <bold>(F)</bold> Bip, n &#x3d; 4, <bold>(G)</bold> p-eIF2&#x3b1;, n &#x3d; 3. Data: mean &#xb1; SEM. &#x2a;<italic>P</italic> &#x3c; 0.05 was considered statistically significant.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g004.tif">
<alt-text content-type="machine-generated">Western blot and bar graphs comparing protein expression in control and Db/db groups. Panel A shows SOD, CAT, and GAPDH bands. Panel B and C display bar graphs for SOD/GAPDH and CAT/GAPDH ratios, respectively, with significant decreases in Db/db (&#x2a;, &#x2a;&#x2a;). Panel D shows ATF6, p-eIF2&#x3b1;, eIF2&#x3b1;, Bip, and GAPDH bands. Panels E, F, and G display bar graphs for ATF6/GAPDH, Bip/GAPDH, and p-eIF2&#x3b1;/eIF2&#x3b1; ratios, respectively, with significant increases in Db/db (&#x2a;).</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-5">
<title>3.5 Enhanced apoptotic activation in <italic>Db/db</italic> murine models</title>
<p>To further investigate whether ERS induces elevated apoptosis in <italic>Db/db</italic> mice, we performed protein-level analyses. CHOP and JNK, downstream targets of the PERK and IRE1&#x3b1; pathways, are well-known apoptosis-related proteins (<xref ref-type="bibr" rid="B17">Lakshmanan et al., 2011</xref>). CHOP-deficient mice exhibit reduced caspase-3 activation, increased Bcl-2/Bax ratios, and attenuated apoptosis in cardiac tissues (<xref ref-type="bibr" rid="B9">Fu et al., 2010</xref>). Additionally, colistin-induced JNK activation has been shown to significantly elevate the Bax/Bcl-2 ratio (<xref ref-type="bibr" rid="B22">Lu et al., 2017</xref>). Our results revealed increased expression of CHOP and phosphorylated JNK (p-JNK) in <italic>Db/db</italic> mice compared to control mice (<xref ref-type="fig" rid="F5">Figures 5A&#x2013;C</xref>). Furthermore, we assessed the levels of apoptosis-related protein markers and observed elevated expression of pro-apoptotic proteins, including cleaved caspase-3 (C-Caspase3), cytochrome C (CytC), and Bax, alongside reduced levels of the anti-apoptotic protein Bcl-2 in <italic>Db/db</italic> mice versus control mice (<xref ref-type="fig" rid="F5">Figures 5D&#x2013;H</xref>). These findings collectively indicate that ERS contributes to enhanced apoptosis in <italic>Db/db</italic> mice.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Apoptotic Pathway Activation Profile in Control and <italic>Db/db</italic> Murine models. <bold>(A)</bold> Immunoblot analysis of phosphorylated c-Jun N-terminal kinase (p-JNK) and C/EBP Homologous Protein (CHOP). Quantitative analysis of <bold>(B)</bold> p-JNK, n &#x3d; 3, <bold>(C)</bold> CHOP, n &#x3d; 3. <bold>(D)</bold> Protein levels of Cleaved caspase-3 (c-Casp3), Cytochrome c (Cyt c), B-cell lymphoma 2 (Bcl-2), and Bcl-2-associated X protein (Bax). Quantitative analysis of <bold>(E)</bold> Bcl-2, n &#x3d; 3, <bold>(F)</bold> Bax, n &#x3d; 3, <bold>(G)</bold> c-Casp3, n &#x3d; 3, <bold>(H)</bold> Cyt c, n &#x3d; 4. Data: mean &#xb1; SEM. &#x2a;<italic>P</italic> &#x3c; 0.05 was considered statistically significant.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g005.tif">
<alt-text content-type="machine-generated">The image displays panels A to H, which include Western blot analysis and bar graphs comparing control and Db/db samples. Panel A shows bands for p-JNK, JNK, Chop, and GAPDH. Panel B shows increased Chop/GAPDH expression in Db/db. Panel C shows increased p-JNK/JNK in Db/db. Panel D shows bands for Bcl2, Bax, Caspase3, CytC, and GAPDH. Panel E shows decreased Bcl2/GAPDH in Db/db. Panel F shows increased Bax/GAPDH in Db/db. Panel G shows increased Caspase3/GAPDH in Db/db. Panel H shows increased CytC/GAPDH in Db/db. Significance is indicated by asterisks.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-6">
<title>3.6 Reduced Akt activity and activation of E3 ubiquitin ligase in <italic>Db/db</italic> murine models</title>
<p>A significant decrease in phosphorylated Akt (p-Akt) levels was observed in <italic>Db/db</italic> mice compared to control mice (<xref ref-type="fig" rid="F6">Figures 6A,B</xref>). Additionally, Atrogin-1 protein levels were markedly elevated compared to the Control (<xref ref-type="fig" rid="F6">Figures 6C,D</xref>). These results indicate that ER stress in <italic>Db/db</italic> mice likely reduces Akt activity, leading to the activation of the E3 ligase Atrogin-1 and subsequent skeletal muscle atrophy.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Protein Levels in Control vs. <italic>Db/db</italic> Mice. <bold>(A)</bold> Phosphorylated Protein Kinase B (p-Akt) protein levels. <bold>(B)</bold> Quantification of p-Akt. <bold>(C)</bold> Muscle Atrophy F-box protein 1 (Atrogin-1) protein levels. <bold>(D)</bold> Quantification of Atrogin-1. Statistical significance as above. Data: mean &#xb1; SEM. &#x2a;<italic>P</italic> &#x3c; 0.05 was considered statistically significant. n &#x3d; 3.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g006.tif">
<alt-text content-type="machine-generated">Panel A shows Western blot bands for p-Akt, Akt, and GAPDH in Control and Db/db samples. Panel B is a bar graph indicating a significant decrease in p-Akt/Akt ratio in Db/db compared to Control. Panel C shows Western blot bands for Atrogin1 and GAPDH in Control and Db/db samples. Panel D presents a bar graph indicating a significant increase in the Atrogin1/GAPDH ratio in Db/db compared to Control. An asterisk denotes statistical significance.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-7">
<title>3.7 Skeletal muscle atrophy in <italic>Db/db</italic> murine mice</title>
<p>To assess skeletal muscle atrophy in <italic>Db/db</italic> mice, we first examined gastrocnemius muscle morphology, which revealed a significant reduction in muscle mass compared to control mice (<xref ref-type="fig" rid="F7">Figures 7A,B</xref>). Immunofluorescence staining of laminin further confirmed atrophy, demonstrating a marked decrease in the cross-sectional area (CSA) of muscle fibers in <italic>Db/db</italic> mice compared to control mice (<xref ref-type="fig" rid="F7">Figures 7C,D</xref>). Consistent with these morphological changes, Western blot analysis showed significantly reduced expression levels of key skeletal muscle structural proteins&#x2014;TNNI2, MYH4, and MYOM1&#x2014;in <italic>Db/db</italic> mice compared to controls (<xref ref-type="fig" rid="F7">Figures 7E&#x2013;H</xref>). Together, these findings provide conclusive evidence that <italic>Db/db</italic> mouse exhibit skeletal muscle atrophy.</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>Morphological and Structural Protein Expression Differences in Skeletal Muscle Between Control and <italic>Db/db</italic> Murine Mice. <bold>(A)</bold> Gross anatomy of gastrocnemius muscles. <bold>(B)</bold> Quantification of gastrocnemius muscle mass. <bold>(C)</bold> Laminin immunofluorescence staining of skeletal muscle cross-sections, Scale bar &#x3d; 100 &#x3bc;m. <bold>(D)</bold> Quantification of muscle fiber cross-sectional area (CSA). <bold>(E)</bold> Western blot analysis of skeletal muscle structural proteins: Troponin I, Fast Skeletal Muscle9 (TNNI2), Myosin Heavy Chain 4 (MYH4), and Myomesin 1 (MYOM1). Quantitative analysis of <bold>(F)</bold> MYH4, <bold>(G)</bold> MYOM1, <bold>(H)</bold> TNNI2. Data: mean &#xb1; SEM. &#x2a;<italic>P</italic> &#x3c; 0.05 was considered statistically significant. n &#x3d; 3.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g007.tif">
<alt-text content-type="machine-generated">A collage of scientific images and graphs:A. Comparison of two muscle samples with a ruler for scale; the control is larger than Db/db.B. Bar graph showing the relative size of gastrocnemius muscles in control and Db/db groups; control is larger.C. Fluorescence microscopy images showing LAMININ staining and DAPI in control and Db/db muscle fibers; control shows more defined structure.D. Bar graph comparing cross-sectional area of muscle fibers; control is significantly larger.E. Western blot analysis of TNNI2, MYH4, MYOM1, and GAPDH in control and Db/db.F-H. Bar graphs showing protein expression relative to GAPDH; control is higher in each protein.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>Maintaining redox homeostasis is crucial for normal physiological functions. Under pathological conditions, oxidative stress disrupts this balance and participates in the pathogenesis of multiple diseases including DM, inflammatory responses, and tumors (<xref ref-type="bibr" rid="B24">Martemucci et al., 2023</xref>). As a vital component of cellular stress responses, ERS interacts closely with oxidative stress, the UPS, and inflammatory signaling pathways through molecular mechanisms (<xref ref-type="bibr" rid="B20">Liu et al., 2023</xref>). SOD and CAT, serving as the first line of defense against oxidative stress, constitute the most robust protective barrier against oxidative damage (<xref ref-type="bibr" rid="B14">Jomova et al., 2024</xref>). Glutathione (GSH), a central antioxidant, maintains redox homeostasis in biological systems. Studies in the epididymis of T2DM rodents demonstrated significantly reduced glutathione peroxidase (GPx) activity and elevated glutathione reductase (GR) levels (<xref ref-type="bibr" rid="B6">Diniz et al., 2022</xref>). Another study further revealed that diminished GSH pools in diabetic systems exacerbate oxidative stress and inflammatory responses through redox imbalance (<xref ref-type="bibr" rid="B5">Dawi et al., 2024</xref>). These findings collectively imply that disrupted glutathione metabolism may underlie diabetes-associated tissue dysfunction. Research have revealed that oxidative stress disrupts nascent ER protein folding, triggering the UPR to handle misfolded proteins and activating downstream stress sensors that induce ER stress (<xref ref-type="bibr" rid="B30">Peserico et al., 2020</xref>). Our results demonstrated that diabetic mice exhibited elevated oxidative stress levels accompanied by reduced protein expression of antioxidant enzymes SOD and CAT. Concurrently, increased protein levels of ER chaperone Bip, UPR stress sensors ATF6 and IRE1&#x3b1;, along with their downstream regulators p-eIF2&#x3b1;, CHOP, and p-JNK were observed. Persistent ER stress ultimately leads to apoptosis-mediated cell death (<xref ref-type="bibr" rid="B39">Yang et al., 2008</xref>), while experimental evidence shows that knocking down CHOP or JNK protein levels attenuates apoptosis induction (<xref ref-type="bibr" rid="B38">Yang et al., 2021</xref>). The release of CytC into cytoplasm represents a critical event in the mitochondrial-dependent intrinsic apoptotic pathway, where cytosolic CytC combines with apoptotic protease-activating factors to initiate caspase cascade reactions, ultimately causing cellular damage (<xref ref-type="bibr" rid="B37">Wang et al., 2016</xref>). Our results showed upregulation of cleaved Caspase-3, Bax and CytC (pro-apoptotic) with concomitant downregulation of Bcl-2 (anti-apoptotic), indicating the occurrence of apoptosis that directly contributes to skeletal muscle atrophy.</p>
<p>Skeletal muscle atrophy primarily arises from three key mechanisms: increased proteolysis, reduced protein synthesis, and impaired myofiber regeneration. These processes are regulated by multiple molecular pathways, including the ubiquitin-proteasome system (UPS), autophagy-lysosomal pathway (ALP), calpain system, caspase pathway, IGF-1/Akt protein synthesis pathway, myostatin signaling, and muscle satellite cells (<xref ref-type="bibr" rid="B12">Ji et al., 2022</xref>). Among these, the PI3K/Akt/mTOR signaling pathway plays a central role in modulating protein synthesis in skeletal muscle. Activation of Akt inhibits FoxO-mediated transcription of muscle-specific E3 ubiquitin ligases, such as MuRF1 and Atrogin-1, while simultaneously promoting protein synthesis (<xref ref-type="bibr" rid="B7">Egerman and Glass, 2014</xref>). Chronic ER stress has been shown to impair the RTK/PI3K/AKT signaling pathway (<xref ref-type="bibr" rid="B32">Qin et al., 2010</xref>; <xref ref-type="bibr" rid="B22">Lu et al., 2017</xref>). For example, ER stress in aged rat livers suppresses insulin signaling, inhibits Akt, and upregulates FOXO expression (<xref ref-type="bibr" rid="B16">Kim et al., 2019</xref>). FoxOs are transcription factors that regulate E3 ubiquitin ligases. When inhibiting the Akt pathway, nuclear FoxOs promote the expression of MuRF1/atrogin-1, activating the UPS and driving protein degradation and skeletal muscle atrophy (<xref ref-type="bibr" rid="B12">Ji et al., 2022</xref>). Similarly, dietary selenium deficiency or excess in rainbow trout modulates the Akt/FoxO3a pathway, accelerating ubiquitin-mediated muscle protein degradation (<xref ref-type="bibr" rid="B42">Zhang et al., 2022</xref>).</p>
<p>In our research, we observed reduced Akt activity and elevated Atrogin-1 expression in the skeletal muscle of diabetic mice compared to control mice, accompanied by significant muscle atrophy. This muscular degeneration inevitably leads to impaired motor function, severely impacting the quality of life of affected individuals and imposing substantial personal, familial, and societal burdens.</p>
<p>While this study revealed significant dysregulation of ERS-related molecular markers in diabetic muscle atrophy, the causal relationships remain to be directly validated through functional interventions. Notably, previous investigations have demonstrated that 4-phenylbutyric acid (4-PBA), a chemical chaperone and ERS inhibitor, can ameliorate triptolide (TP)/lipopolysaccharide (LPS)-induced ERS-associated apoptosis and hepatic oxidative stress (<xref ref-type="bibr" rid="B4">Cheng et al., 2024</xref>). Furthermore, emerging evidence indicates that pharmacological inhibition of ERS with Salubrinal effectively attenuates ferroptosis and cellular damage in diabetic myocardial ischemia/reperfusion injury (<xref ref-type="bibr" rid="B18">Li et al., 2020</xref>). Based on these mechanistic insights, we recommend the following strategies to establish causal relationships: (1) Pharmacological validation using FDA-approved ERS inhibitors (e.g., 4-PBA or tauroursodeoxycholic acid [TUDCA]) in both <italic>Db/db</italic> mice and diabetic myotube models to assess potential reversal of muscle atrophy phenotypes; (2) Genetic validation through targeted knockdown of core ERS regulators (such as PERK, ATF6, or IRE1&#x3b1;) to systematically evaluate their regulatory effects on apoptotic pathways and ubiquitin-proteasome-mediated protein degradation. These investigations will advance the development of ERS-targeted therapies for diabetic sarcopenia.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>As demonstrated in <xref ref-type="fig" rid="F8">Figure 8</xref>, our research uncovers a dual regulatory mechanism underlying diabetic muscle atrophy. The diabetic skeletal muscle microenvironment exhibits elevated oxidative stress and significantly enhanced ER stress, which promotes direct muscle atrophy through ER stress sensor-mediated apoptosis. Concurrently, sustained ER stress suppresses Akt activity while upregulating the muscle-specific E3 ubiquitin ligase Atrogin-1, thereby accelerating proteolysis and inducing indirect muscle wasting. These findings provide crucial mechanistic insights into diabetic skeletal myopathy, highlighting the ER stress signaling network as a promising therapeutic target for mitigating muscle atrophy in diabetes. Future studies should elucidate the long-term consequences of ER stress modulation, identifying additional molecular players in this pathological cascade, and developing comprehensive intervention strategies to prevent and treat this debilitating complication.</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>Mechanism diagram of skeletal muscle atrophy in T2DM.</p>
</caption>
<graphic xlink:href="fphys-16-1607873-g008.tif">
<alt-text content-type="machine-generated">Diagram comparing control and T2DM impacts on skeletal muscle. The left side shows control with redox homeostasis and activated Akt leading to maintained muscle. The right side depicts oxidative stress in T2DM with ER stress, leading to pathways involving eIF2&#x3b1;, ATF4, JNK, and CHOP, causing apoptosis and muscle atrophy.</alt-text>
</graphic>
</fig>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>The animal study was approved by the Animal Experimental Ethics Committee (IACUC-2303040). The study was conducted in accordance with the local legislation and institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>JY: Writing &#x2013; original draft, Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization. YW: Conceptualization, Data curation, Formal Analysis, Investigation, Writing &#x2013; original draft. YX: Conceptualization, Data curation, Investigation, Methodology, Writing &#x2013; original draft. XJ: Software, Supervision, Writing &#x2013; review and editing. FL: Conceptualization, Funding acquisition, Project administration, Supervision, Writing &#x2013; review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by Health Commission of Heilongjiang Provincial (Grant numbers: 20230202080190).</p>
</sec>
<ack>
<p>We are grateful to Wuhan Metware Biotechnology Co., Ltd. for assisting in sequencing and/or bioinformatics analysis.</p>
</ack>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s11">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec sec-type="disclaimer" id="s12">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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