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16 pages, 3223 KiB  
Article
Notoginsenoside R1 Attenuates H/R Injury in H9c2 Cells by Maintaining Mitochondrial Homeostasis
by Yuanbo Xu, Piao Wang, Ting Hu, Ke Ning and Yimin Bao
Curr. Issues Mol. Biol. 2025, 47(1), 44; https://doi.org/10.3390/cimb47010044 - 10 Jan 2025
Viewed by 413
Abstract
Mitochondrial homeostasis is crucial for maintaining cellular energy production and preventing oxidative stress, which is essential for overall cellular function and longevity. Mitochondrial damage and dysfunction often occur concomitantly in myocardial ischemia–reperfusion injury (MIRI). Notoginsenoside R1 (NGR1), a unique saponin from the traditional [...] Read more.
Mitochondrial homeostasis is crucial for maintaining cellular energy production and preventing oxidative stress, which is essential for overall cellular function and longevity. Mitochondrial damage and dysfunction often occur concomitantly in myocardial ischemia–reperfusion injury (MIRI). Notoginsenoside R1 (NGR1), a unique saponin from the traditional Chinese medicine Panax notoginseng, has been shown to alleviate MIRI in previous studies, though its precise mechanism remains unclear. This study aimed to elucidate the mechanisms of NGR1 in maintaining mitochondrial homeostasis in hypoxia/reoxygenation (H/R) H9c2 cells. The results showed that NGR1 pretreatment effectively increased cell survival rates post-H/R, reduced lactate dehydrogenase (LDH) leakage, and mitigated cell damage. Further investigation into mitochondria revealed that NGR1 alleviated mitochondrial structural damage, improved mitochondrial membrane permeability transition pore (mPTP) persistence, and prevented mitochondrial membrane potential (Δψm) depolarization. Additionally, NGR1 pretreatment enhanced ATP levels, increased the activity of mitochondrial respiratory chain complexes I–V after H/R, and reduced excessive mitochondrial reactive oxygen species (mitoROS) production, thereby protecting mitochondrial function. Further analysis indicated that NGR1 upregulated the expression of mitochondrial biogenesis-related proteins (PGC-1α, Nrf1, Nrf2) and mitochondrial fusion proteins (Opa1, Mfn1, Mfn2), while downregulating mitochondrial fission proteins (Fis1, Drp1) and reducing mitochondrial autophagy (mitophagy) levels, as well as the expression of mitophagy-related proteins (Pink1, Parkin, BNIP3) post-H/R. Therefore, this study showed that NGR1 can maintain mitochondrial homeostasis by regulating mitophagy, mitochondrial fission–fusion dynamics, and mitochondrial biogenesis, thereby alleviating H9c2 cell H/R injury and protecting cardiomyocytes. Full article
27 pages, 12788 KiB  
Article
A Multi-Omics Analysis of a Mitophagy-Related Signature in Pan-Cancer
by Nora Agir, Ilias Georgakopoulos-Soares and Apostolos Zaravinos
Int. J. Mol. Sci. 2025, 26(2), 448; https://doi.org/10.3390/ijms26020448 - 7 Jan 2025
Viewed by 339
Abstract
Mitophagy, an essential process within cellular autophagy, has a critical role in regulating key cellular functions such as reproduction, metabolism, and apoptosis. Its involvement in tumor development is complex and influenced by the cellular environment. Here, we conduct a comprehensive analysis of a [...] Read more.
Mitophagy, an essential process within cellular autophagy, has a critical role in regulating key cellular functions such as reproduction, metabolism, and apoptosis. Its involvement in tumor development is complex and influenced by the cellular environment. Here, we conduct a comprehensive analysis of a mitophagy-related gene signature, composed of PRKN, PINK1, MAP1LC3A, SRC, BNIP3L, BECN1, and OPTN, across various cancer types, revealing significant differential expression patterns associated with molecular subtypes, stages, and patient outcomes. Pathway analysis revealed a complex interplay between the expression of the signature and potential effects on the activity of various cancer-related pathways in pan-cancer. Immune infiltration analysis linked the mitophagy signature with certain immune cell types, particularly OPTN with immune infiltration in melanoma. Methylation patterns correlated with gene expression and immune infiltration. Mutation analysis also showed frequent alterations in PRKN (34%), OPTN (21%), PINK1 (28%), and SRC (15%), with implications for the tumor microenvironment. We also found various correlations between the expression of the mitophagy-related genes and sensitivity in different drugs, suggesting that targeting this signature could improve therapy efficacy. Overall, our findings underscore the importance of mitophagy in cancer biology and drug resistance, as well as its potential for informing treatment strategies. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine: 2nd Edition)
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Figure 1

Figure 1
<p><b>Mechanisms of mitophagy and associated mitochondrial pathways.</b> (<b>A</b>) PINK1/Parkin-mediated mitophagy: Under mitochondrial stress, PINK1 accumulates on the outer mitochondrial membrane and recruits the E3 ubiquitin ligase Parkin. Parkin ubiquitinates mitochondrial outer membrane proteins such as Mfn1/2, VDAC1, and Miro1. These ubiquitinated proteins are subsequently recognized by autophagy receptors (NDP52, p62, and OPTN), which facilitate the recruitment of the autophagy machinery, leading to mitochondrial degradation. This pathway is regulated by phosphorylation events mediated by TPK1. (<b>B</b>) BNIP3/NIX-mediated mitophagy and cell death pathways: BNIP3 and its homolog NIX, both regulated through phosphorylation, can promote mitophagy by directly interacting with autophagy machinery. Alternatively, BNIP3 induces mitochondrial permeabilization through Bax/Bak activation, leading to the opening of the mitochondrial permeability transition pore (mPTP) and dissociation of the COX1-UCP3 complex. This can trigger apoptosis, necrosis, or pyroptosis. In pyroptosis, BNIP3 activation is linked to Caspase-3/GSDME activity. (<b>C</b>) FUNDC1-mediated mitophagy: FUNDC1, a mitochondrial outer membrane protein, undergoes phosphorylation-dependent regulation to mediate mitophagy by interacting with autophagic components, enabling the selective degradation of damaged mitochondria.</p>
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<p><b>Differential expression of the mitophagy-related signature in pan-cancer.</b> (<b>a</b>) The bubble plot illustrates the log<sub>2</sub> fold change (FC) in the expression of the mitophagy-related genes across a spectrum of cancer types, with significance denoted by the false discovery rate (FDR) values, being represented by the color and size of the bubbles. Blue indicates downregulation, while red indicates upregulation of each gene in the tumor versus the normal tissues. The size of the circles is associated with FDR significance. Notably, <span class="html-italic">PRKN</span> and <span class="html-italic">PINK1</span> show substantial upregulation in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), while <span class="html-italic">SRC</span> and <span class="html-italic">OPTN</span> are significantly elevated in breast cancer (BRCA) and colon adenocarcinoma (COAD), respectively. <span class="html-italic">BECN1</span> was significantly downregulated in KIRC, in contrast to <span class="html-italic">BNIP3L</span>, which was upregulated in the tumor. (<b>b</b>) Boxplots for selected cancer types, showcasing significant differences in gene expression between normal and tumor tissues. (<b>c</b>) Subtype-specific expression differences, emphasizing that certain genes exhibit distinct expression patterns within cancer subtypes, such as <span class="html-italic">SRC</span> in kidney renal clear cell carcinoma (KIRC) and <span class="html-italic">OPTN</span> in BRCA. Red large circles represent deregulation in mRNA expression that is statistically significant. (<b>d</b>) The boxplots depict expression differences (log<sub>2</sub> RSEM) across molecular subtypes in different cancer types (<span class="html-italic">PINK1</span> in LUSC, <span class="html-italic">OPTN</span> in BRCA, <span class="html-italic">SRC</span> in KIRC, <span class="html-italic">MAP1LC3A</span> in GBM, <span class="html-italic">BNIP3L</span> and <span class="html-italic">BECN1</span> in BRCA, and <span class="html-italic">PRKN</span> in LUAD). (<b>e</b>) The heatmap shows a general trend of stable or decreased (in some cases like <span class="html-italic">SRC</span> in BLCA or <span class="html-italic">PINK1</span> and <span class="html-italic">OPTN</span> in ACC) expression for the mitophagy-related signature with advancing tumor stages. (<b>f</b>) The Kaplan–Meier curves show the survival rates in different types of cancer, according to gene expression. Higher expression of <span class="html-italic">PRKN</span> in LUAD and of <span class="html-italic">PINK1</span> in LUSC is associated with poorer prognosis, thereby underscoring the clinical relevance of these mitophagy-related genes in cancer progression and patient survival.</p>
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<p><b>Pathway activity associated with differential expression of mitophagy-related genes.</b> (<b>a</b>) Heatmap depicting the potential activation (A) or inhibitory (I) effects of the mRNA levels of the mitophagy-related gene signature on the activity of 10 cancer-related pathways in pan-cancer. The color scale indicates the percentage of pathway activation (red) or inhibition (blue). The percentages represent the frequency of gene association with pathway regulation in various types of cancer. (<b>b</b>) The boxplots compare the pathway activity scores (PAS) between high and low expression groups of <span class="html-italic">PRKN</span> and <span class="html-italic">OPTN</span> in BRCA. The FDR values indicate the significance of the differences in PAS found between the high and low expression groups.</p>
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<p><b>Correlation between mitophagy-related gene expression and immune cell infiltration in pan-cancer.</b> (<b>a</b>) The heatmap represents the Spearman’s correlation between the infiltration of 24 immune cell types evaluated through ImmuCellAI, and the expression of the mitophagy-related gene signature across different cancer types. The color scale indicates the strength and direction of Spearman’s correlation (blue for negative, red for positive). #, Spearman’s rho &lt;−0.4 or &gt;0.4; *, <span class="html-italic">p</span> &lt; 0.01. (<b>b</b>) Correlation between the expression of the mitophagy-related signature and immune cell infiltrates in skin melanoma (SKCM). The size of the dots represents the significance (−log<sub>10</sub>FDR values), while the color indicates the correlation coefficient (blue for negative, red for positive). (<b>c</b>) Scatter plots depicting the Spearman’s correlation between <span class="html-italic">OPTN</span> expression and specific immune cell infiltrates (Th1, neutrophils, monocytes, and central memory infiltrates) in SKCM, with trend lines and correlation coefficients. <span class="html-italic">OPTN</span> expression is positively correlated with the infiltration of Th1 cells and the central memory infiltrate score, and negatively correlated with the infiltration of neutrophils and monocytes in SKCM.</p>
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<p><b>DNA methylation analysis of mitophagy-related genes in pan-cancer and its correlation with their mRNA expression.</b> (<b>a</b>) The dot plot shows differential DNA methylation levels (tumor vs. normal) for mitophagy-related genes (<span class="html-italic">SRC</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">PRKN</span>, <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">BNIP3L</span>) across various cancer types. The color scale represents the methylation difference (tumor–normal), and the size of the dots indicates the significance (FDR values). (<b>b</b>) The dot plot illustrates the correlation between DNA methylation levels and mRNA expression for the same set of genes across multiple cancer types. The color scale represents Spearman’s correlation coefficient, with the size of the dots indicating the significance (FDR values). (<b>c</b>) Integrative genomic analysis displaying the association between <span class="html-italic">PINK1</span> and <span class="html-italic">MAP1LC3A</span> methylation, expression and CNV, and various clinical and demographic features in KIRC. The top panel summarizes patient data [age at diagnosis, hemoglobin levels, histological type, tumor recurrence, smoking history, gender, tumor stage, sample type, and overall survival (OS)]. The middle panel shows the distribution of <span class="html-italic">PINK1</span> expression levels across different copy number alterations. Sections marked with ‘−2’ indicate homozygous deletions, while ‘+1’ or ‘+2’ would indicate low-level or high-level amplifications, respectively. Higher or lower expression levels of <span class="html-italic">PINK1</span> and <span class="html-italic">MAP1LC3A</span> are indicated, showing how these levels align with methylation patterns and CNVs. The bottom panel presents a detailed view of the <span class="html-italic">PINK1</span> (left) and <span class="html-italic">MAP1LC3A</span> (right) regions, showing the relationship between DNA methylation sites, CpG islands, gene structure, and expression levels. The color coding of the CpG sites (vertical lines) likely indicates their methylation status, with different shades representing varying levels of methylation.</p>
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<p><b>Mutation landscape of <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">BNIP3L</span> across various cancer types.</b> (<b>a</b>) The waterfall plot presents the tumor mutation burden (TMB) per cancer sample and the mutation distribution of the mitophagy-related gene signature across 400 cancer samples in the TCGA. Each row represents a gene, and each column represents a cancer sample. Different colors indicate different types of mutations, including missense mutations, nonsense mutations, frame-shift deletions, frame-shift insertions, in-frame deletions, splice-site mutations, and multi-hit mutations. The bar plot on the right shows the percentage of samples with alterations in each gene. The bottom annotation panel indicates the cancer type for each sample. (<b>b</b>) Summary of variant classifications and types. Bar plots show the number of different mutation types (missense, nonsense, etc.) and variant types (SNP, insertion, deletion) for the five genes. The SNV class distribution is displayed, highlighting the most frequent base substitutions (184 C&gt;T and 138 C&gt;A). The bottom left plot shows the distribution of variants per sample, with a median of 1 variant per sample. The bottom right plot shows the variant classification summary for each gene, with the percentage of samples altered. (<b>c</b>) The heatmap shows the mutation frequency of the mitophagy-related genes across different cancer types. The numbers in each cell represent the percentage of samples with mutations in the respective gene and cancer type. The intensity of the color corresponds to the mutation frequency, with a darker red indicating a higher frequency.</p>
Full article ">Figure 7
<p><b>Copy number variations in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across different cancer types</b>. (<b>a</b>) The pie charts show the frequency and types of CNVs in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across various cancer types. Each pie chart represents a cancer type, with different colors indicating the type of CNV. (<b>b</b>) The dot plots illustrate the distribution of heterozygous amplifications (left) and heterozygous deletions (right) in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across different cancer types. The size of the dots corresponds to the percentage of samples with the respective CNV type. (<b>c</b>) The dot plots show the distribution of homozygous amplifications (left) and homozygous deletions (right) in <span class="html-italic">PRKN</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">SRC</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">MAP1LC3A</span> across various cancer types. The size of the dots represents the percentage of samples with the respective CNV type.</p>
Full article ">Figure 8
<p>Correlation of <span class="html-italic">MAP1LC3A</span>, <span class="html-italic">OPTN</span>, <span class="html-italic">SRC</span>, <span class="html-italic">PINK1</span>, <span class="html-italic">BNIP3L</span>, <span class="html-italic">BECN1</span>, and <span class="html-italic">PRKN</span> expression with drug sensitivity (IC50) in pan-cancer, using the GDSC (<b>a</b>) and CTRP (<b>b</b>) drug databases. The color, from red to blue, depicts the correlation between each gene’s mRNA expression and IC50. Also, the bubble size represents the false discovery rate (FDR), with larger circles indicating stronger statistical significance. The color gradient indicates the direction and magnitude of correlation. Blue color, negative correlation; red color, positive correlation. Significant correlations (FDR &lt; 0.05) are emphasized with bold outlines, highlighting the most critical interactions. (<b>c</b>) Regulator prioritization of the mitophagy-related gene signature. Each column is a data cohort. Genes are ranked based on their average score with multiple cohorts. The colors correspond to different score values, ranging from −3 (blue) to +3 (red).</p>
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20 pages, 11188 KiB  
Article
Changes to the Autophagy-Related Muscle Proteome Following Short-Term Treatment with Ectoine in the Duchenne Muscular Dystrophy Mouse Model mdx
by Eulàlia Gómez Armengol, Caroline Merckx, Hanne De Sutter, Jan L. De Bleecker and Boel De Paepe
Int. J. Mol. Sci. 2025, 26(2), 439; https://doi.org/10.3390/ijms26020439 - 7 Jan 2025
Viewed by 390
Abstract
The most severe form of muscular dystrophy (MD), known as Duchenne MD (DMD), remains an incurable disease, hence the ongoing efforts to develop supportive therapies. The dysregulation of autophagy, a degradative yet protective mechanism activated when tissues are under severe and prolonged stress, [...] Read more.
The most severe form of muscular dystrophy (MD), known as Duchenne MD (DMD), remains an incurable disease, hence the ongoing efforts to develop supportive therapies. The dysregulation of autophagy, a degradative yet protective mechanism activated when tissues are under severe and prolonged stress, is critically involved in DMD. Treatments that harness autophagic capacities therefore represent a promising therapeutic approach. Osmolytes are protective organic molecules that regulate osmotic pressure and cellular homeostasis and may support tissue-repairing autophagy. We therefore explored the effects of the osmolyte ectoine in the standard mouse model of DMD, the mdx, focusing on the autophagy-related proteome. Mice were treated with ectoine in their drinking water (150 mg/kg) or through daily intraperitoneal injection (177 mg/kg) until they were 5.5 weeks old. Hind limb muscles were dissected, and samples were prepared for Western blotting for protein quantification and for immunofluorescence for an evaluation of tissue distribution. We report changes in the protein levels of autophagy-related 5 (ATG5), Ser366-phosphorylated sequestosome 1 (SQSTM1), heat shock protein 70 (HSP70), activated microtubule-associated protein 1A/1B-light chain 3 (LC3 II) and mammalian target of rapamycin (mTOR). Most importantly, ectoine significantly improved the balance between LC3 II and SQSTM1 levels in mdx gastrocnemius muscle, and LC3 II immunostaining was most pronounced in muscle fibers of the tibialis anterior from treated mdx. These findings lend support for the further investigation of ectoine as a potential therapeutic intervention for DMD. Full article
(This article belongs to the Special Issue Molecular Insights into Muscular Dystrophy)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Protein levels in extensor digitorum longus muscle of 26-week-old mice. Total protein samples were prepared from four individual healthy control (BL 10 1–4) and four individual mdx (1–4) mice. A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>) Protein bands for autophagy-related 5 (ATG5), autophagy-related 7 (ATG7), microtubule-associated protein 1A/1B-light chain 3 (LC3), parkin (PRKN) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown. (<b>B</b>) Protein levels are reported as relative values to GAPDH. A significant difference can be observed in the ratio of phosphatidylethanolamine-conjugated LC3 (LC3 II) over unconjugated LC3 (LC3 I); <span class="html-italic">p</span> = 0.03 (*) between the BL 10 and mdx group, tested with an unpaired <span class="html-italic">t</span> test. Other protein levels are not significantly different. Graphs and statistical analyses were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Immunofluorescence staining of mdx soleus muscle of 4-week-old mice (<b>A</b>) and tibialis anterior muscle of 12-week-old mice (<b>C</b>,<b>D</b>). (<b>A</b>) Staining for autophagy-related 16-like 1 (ATG16L1; AlexaFluor 488, green) and neural cell adhesion molecule (NCAM; CY3, red) in a BL 10 control (<b>A</b>) and an mdx mouse (<b>B</b>) shows strong sarcoplasmic staining in the majority of muscle fibers. Strong NCAM staining is present in a cluster of small perifascicular muscle fibers in the mdx section, indicating that these are regenerating muscle fibers, and from these muscle fibers, ATG16L1 staining is absent. Staining for microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II; CY3, red) and macrophages (F4-80; AlexaFluor 488, green) are shown in a BL 10 control (<b>C</b>) and an mdx mouse (<b>D</b>). (<b>C</b>) In a healthy control, LC3 II staining is mostly absent, and a few scattered macrophages can be observed. (<b>D</b>) In an mdx section, sarcolemmal LC3 II staining localizes to muscle fibers in perifascicular regions. Macrophages are abundant in this microscopic field, indicated by bright green fluorescence. The diffuse and weaker staining that can be observed in the connective tissue accumulating in the perifascicular region can be considered nonspecific. Scale bars = 100 µM.</p>
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<p>Protein levels in extensor digitorum longus muscle of untreated and ectoine-treated mice. Two total protein samples were prepared per group by pooling muscles from two individual mice. A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>) Protein bands for autophagy-related 5 (ATG5), autophagy-related 7 (ATG7), microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II), sequestosome 1 (SQSTM1), parkin (PRKN) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown in control black 10 mice (BL 10 1–4), and mdx treated with ectoine in their drinking water (ECT DW 1–4), ectoine administered via intraperitoneal injection (ECT IP 1–4), and intraperitoneal saline (SAL IP 1–4), serving as a treatment control. (<b>B</b>) Protein levels are reported as values relative to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Significant changes can be observed for ATG5 levels, with an adjusted <span class="html-italic">p</span> = 0.02 between BL 10 and mdx DW ECT (*), and <span class="html-italic">p</span> = 0.04 between mdx IP ECT and mdx DW ECT (*), tested with the Brown–Forsythe test and Welch ANOVA for multiple comparisons. Graphs and statistical analyses were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Protein levels in gastrocnemius muscle of untreated and ectoine-treated mice. Total protein samples were prepared from two sets of three individual mice per group: control black 10 mice (BL 10 1–3, 4–6), mdx administered with saline intraperitoneally (SAL IP 1–3, 4–6) and mdx administered with ectoine intraperitoneally (SAL IP 1–3, 4–6). A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>,<b>C</b>) Protein bands for heat shock protein 70 (HSP70), voltage-dependent anion channels (VDACs), parkin (PRKN), microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II), sequestosome 1 (SQSTM1), SQSTM1 phosphorylated on its Ser366 residue (p-SQSTM1), peroxisome proliferator-activated receptor-gamma coactivator 1α (PGC-1α), mammalian target of rapamycin (mTOR) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown (<b>B</b>–<b>D</b>). Protein levels are reported as relative values to GAPDH. In the first sample set, significant changes can be observed between BL 10 and mdx IP ECT in HSP70 (adjusted <span class="html-italic">p</span> = 0.009 **) and p-SQSTM1 levels (adjusted <span class="html-italic">p</span> = 0.003); the latter remains significantly different when calculated as a ratio of phosphorylated over total SQSTM1 protein levels (adjusted <span class="html-italic">p</span> = 0.006 **). In the second sample set, mammalian target of rapamycin (mTOR) levels are significantly higher in IP ECT compared to BL 10 (adjusted <span class="html-italic">p</span> = 0.02 *). A significant increase in pSQSTM1 cannot be confirmed; however, the levels of the proteins p-SQSTM1 and LC3 II do display a tendency to increase. Graphs and statistical analyses with the Brown–Forsythe test and Welch ANOVA for multiple comparisons were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Autophagic efficiency responds to ectoine treatment in mdx. Theoretical autophagic efficiency is calculated as the ratio of microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II) over sequestosome 1 (SQSTM1) as relative to the ratio observed in healthy control mice (n = 6) set to 1.0, presented as boxplots showing the median and interquartile range. In comparison to control mdx treated with intraperitoneal saline (IP SAL n = 6), autophagic efficiency is significantly higher in mdx treated with ectoine (IP ECT n = 6), as demonstrated by performing an unpaired <span class="html-italic">t</span>-test (<span class="html-italic">p</span> = 0.003 **). Graphs and statistical analyses were generated with GraphPad Prism 10.2.3.</p>
Full article ">Figure 6
<p>Immunofluorescence co-staining with F4/80 antibody, which stains macrophages (AlexaFluor 488, green), and 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II, CY3, red) in tibialis anterior muscle. In a healthy control (<b>A</b>) and mdx treated with intraperitoneal saline (<b>B</b>), infrequent partial sarcolemmal LC3 II staining is observed on certain muscle fibers. (<b>C</b>) In an mdx treated with intraperitoneal ectoine, macrophage staining localizes a necrotic invaded muscle fiber, which is LC3 II-negative (asterisk). (<b>D</b>) In an mdx treated with ectoine in its drinking water, macrophages are abundant, and a small muscle fiber surrounded by an inflammatory infiltrate is strongly LC 3 II-positive (arrow). Scale bars = 50 µm.</p>
Full article ">Figure 7
<p>Mitochondrial protein levels in soleus muscle of untreated and ectoine-treated mice. Two mitochondria-enriched protein samples were prepared per group by pooling soleus muscle from four individual mice (except for the first BL 10 sample, which was prepared from three mice). (<b>A</b>) Protein bands for heat shock protein 70 (HSP70), voltage-dependent anion channels (VDACs), parkin (PRKN), microtubule-associated protein 1A/1B-light chain 3 (LC3) and the corresponding total protein stain are shown in control black 10 mice (BL 10 1–7 and in mdx) intraperitoneally administered with saline (SAL IP 1–8) or ectoine (ECT IP 1–8), or ectoine in the drinking water (ECT DW 1–8). (<b>B</b>) Protein levels are reported as relative values normalized to total protein content. Significant changes can be observed for LC3 II levels, with an adjusted <span class="html-italic">p</span> = 0.01 between mdx IP ECT and mdx DW ECT (*); significance does not remain when calculated as a ratio of LC3 II over I. Graphs and statistical analyses with the Brown–Forsythe test and Welch ANOVA for multiple comparisons were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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34 pages, 3100 KiB  
Review
Plant Secondary Metabolites as Modulators of Mitochondrial Health: An Overview of Their Anti-Oxidant, Anti-Apoptotic, and Mitophagic Mechanisms
by Julia Anchimowicz, Piotr Zielonka and Slawomir Jakiela
Int. J. Mol. Sci. 2025, 26(1), 380; https://doi.org/10.3390/ijms26010380 - 4 Jan 2025
Viewed by 538
Abstract
Plant secondary metabolites (PSMs) are a diverse group of bioactive compounds, including flavonoids, polyphenols, saponins, and terpenoids, which have been recognised for their critical role in modulating cellular functions. This review provides a comprehensive analysis of the effects of PSMs on mitochondrial health, [...] Read more.
Plant secondary metabolites (PSMs) are a diverse group of bioactive compounds, including flavonoids, polyphenols, saponins, and terpenoids, which have been recognised for their critical role in modulating cellular functions. This review provides a comprehensive analysis of the effects of PSMs on mitochondrial health, with particular emphasis on their therapeutic potential. Emerging evidence shows that these metabolites improve mitochondrial function by reducing oxidative stress, promoting mitochondrial biogenesis, and regulating key processes such as apoptosis and mitophagy. Mitochondrial dysfunction, a hallmark of many pathologies, including neurodegenerative disorders, cardiovascular diseases, and metabolic syndrome, has been shown to benefit from the protective effects of PSMs. Recent studies show that PSMs can improve mitochondrial dynamics, stabilise mitochondrial membranes, and enhance bioenergetics, offering significant promise for the prevention and treatment of mitochondrial-related diseases. The molecular mechanisms underlying these effects, including modulation of key signalling pathways and direct interactions with mitochondrial proteins, are discussed. The integration of PSMs into therapeutic strategies is highlighted as a promising avenue for improving treatment efficacy while minimising the side effects commonly associated with synthetic drugs. This review also highlights the need for future research to elucidate the specific roles of individual PSMs and their synergistic interactions within complex plant matrices, which may further optimise their therapeutic utility. Overall, this work provides valuable insights into the complex role of PSMs in mitochondrial health and their potential as natural therapeutic agents targeting mitochondrial dysfunction. Full article
(This article belongs to the Special Issue Advances in Plant Metabolite Research)
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<p>Effects of plant secondary metabolites (PSMs) on disease mechanisms. These compounds: (i) enhance mitochondrial biogenesis, (ii) reduce oxidative stress, (iii) regulate apoptosis and mitophagy, and (iv) alter mitochondrial morphology. Abbreviations: CytC, cytochrome c; Drp1, dynamin-related protein 1; Fis1, mitochondrial fission 1 protein; IMM, inner mitochondrial membrane; IMS, intermembrane space; Mff, mitochondrial fission factor; Mfn1/2, mitofusins 1/2; mtDNA, mitochondrial DNA; NO, nitric oxide; OMM, outer mitochondrial membrane; Opa1, optic atrophy 1 protein; ROS, reactive oxygen species.</p>
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<p>Key processes and associated proteins involved in mitochondrial dynamics: (i) biogenesis, mediated by PGC-1α (peroxisome proliferator-activated receptor gamma coactivator 1α), Nrf2 (nuclear factor erythroid 2-related factor 2), TFAM (mitochondrial transcription factor A), AMPK (AMP-activated protein kinase), and Sirt1 (sirtuin 1); (ii) fusion, regulated by Mfn1 and Mfn2 (mitofusins 1 and 2) and Opa1 (optic atrophy protein 1); (iii) fission, controlled by Drp1 (dynamin-related protein 1), Fis1 (mitochondrial fission protein 1), and Mff (mitochondrial fission factor); and (iv) mitophagy, involving PINK1 (PTEN-induced putative kinase 1) and Parkin. Green arrows indicate increased/decreased expression of key proteins caused by PSMs.</p>
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<p>Overview of natural compounds. The primary groups of PSMs discussed in this review include alkaloids (heterocyclic alkaloids), terpenoids, saponins, polyphenols (flavonoids, non-flavonoids, extra virgin olive oil (EVOO) polyphenols, ellagitannins, catechins), glucosinolates, and phytocannabinoids.</p>
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<p>Chemical structures of berberine (BRB), caffeine (Cof), capsaicin (CS), betanin, liensinine (LIEN), matrine (MAT), huperzine A (Hup A), rhynchophylline (RP), and piperine (PIP).</p>
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<p>Chemical structures of perillaldehyde (PAE), asiatic acid (AA), α-bisabolol (BSB), astaxanthin (AST), forskolin (FSK), carvacrol (CARV), carnosic acid (CA), linalool (LIN), and genipin.</p>
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<p>Chemical structures of Ginsenoside Rb1 (Rb1), astragaloside IV (AS-IV), and cycloastragenol (CAG).</p>
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<p>Chemical structures of oleuropein aglycone (OleA) and hydroxytyrosol (HT).</p>
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<p>Chemical structures of quercetin, naringenin (NAR), gossypitrin (Gos), silibinin (Silybin, SIL), luteolin (LUT), hesperetin (Hst), hesperidin (Hsd), diosmin (DSM), and icariin (ICA).</p>
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<p>Chemical structures of magnolol (MGN), tannic acid (TA), resveratrol (RSV), pterostilbene (PTE), and mangiferin (MGF).</p>
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<p>Chemical structures of gallocatechin gallate (GCG), urolithin A (UA), and sulforaphane (SFN).</p>
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<p>Chemical structures of cannabinol (CBN), Δ9-tetrahydrocannabinol (THC), and cannabidiol (CBD).</p>
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44 pages, 2678 KiB  
Review
Mitochondria and the Repurposing of Diabetes Drugs for Off-Label Health Benefits
by Joyce Mei Xin Yip, Grace Shu Hui Chiang, Ian Chong Jin Lee, Rachel Lehming-Teo, Kexin Dai, Lokeysh Dongol, Laureen Yi-Ting Wang, Denise Teo, Geok Teng Seah and Norbert Lehming
Int. J. Mol. Sci. 2025, 26(1), 364; https://doi.org/10.3390/ijms26010364 - 3 Jan 2025
Viewed by 1302
Abstract
This review describes our current understanding of the role of the mitochondria in the repurposing of the anti-diabetes drugs metformin, gliclazide, GLP-1 receptor agonists, and SGLT2 inhibitors for additional clinical benefits regarding unhealthy aging, long COVID, mental neurogenerative disorders, and obesity. Metformin, the [...] Read more.
This review describes our current understanding of the role of the mitochondria in the repurposing of the anti-diabetes drugs metformin, gliclazide, GLP-1 receptor agonists, and SGLT2 inhibitors for additional clinical benefits regarding unhealthy aging, long COVID, mental neurogenerative disorders, and obesity. Metformin, the most prominent of these diabetes drugs, has been called the “Drug of Miracles and Wonders,” as clinical trials have found it to be beneficial for human patients suffering from these maladies. To promote viral replication in all infected human cells, SARS-CoV-2 stimulates the infected liver cells to produce glucose and to export it into the blood stream, which can cause diabetes in long COVID patients, and metformin, which reduces the levels of glucose in the blood, was shown to cut the incidence rate of long COVID in half for all patients recovering from SARS-CoV-2. Metformin leads to the phosphorylation of the AMP-activated protein kinase AMPK, which accelerates the import of glucose into cells via the glucose transporter GLUT4 and switches the cells to the starvation mode, counteracting the virus. Diabetes drugs also stimulate the unfolded protein response and thus mitophagy, which is beneficial for healthy aging and mental health. Diabetes drugs were also found to mimic exercise and help to reduce body weight. Full article
(This article belongs to the Special Issue Role of Mitochondria in Diseases)
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<p>Diabetic medications and the mitochondria.</p>
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<p>Mode of action of existing drugs that have potential to reverse viral-induced metabolic changes in the mitochondria and hence are candidates targeting treatment of mitochondrial dysfunction in long COVID.</p>
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<p>How anti-diabetic drugs improve mental health by modulating mitochondrial dynamics and function.</p>
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<p>Diagram summarizing how metformin, gliclazide, GLP-1 receptor agonists, and SGLT-2 inhibitors could be used in combination to produce synergistic effects, resulting in more consistent weight loss outcomes.</p>
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19 pages, 3301 KiB  
Article
Administration of AICAR, an AMPK Activator, Prevents and Reverses Diabetic Polyneuropathy (DPN) by Regulating Mitophagy
by Krish Chandrasekaran, Joungil Choi, Mohammad Salimian, Ahmad F. Hedayat and James W. Russell
Int. J. Mol. Sci. 2025, 26(1), 80; https://doi.org/10.3390/ijms26010080 - 25 Dec 2024
Viewed by 369
Abstract
Diabetic peripheral neuropathy (DPN) is a common complication of diabetes in both Type 1 (T1D) and Type 2 (T2D). While there are no specific medications to prevent or treat DPN, certain strategies can help halt its progression. In T1D, maintaining tight glycemic control [...] Read more.
Diabetic peripheral neuropathy (DPN) is a common complication of diabetes in both Type 1 (T1D) and Type 2 (T2D). While there are no specific medications to prevent or treat DPN, certain strategies can help halt its progression. In T1D, maintaining tight glycemic control through insulin therapy can effectively prevent or delay the onset of DPN. However, in T2D, overall glucose control may only have a moderate impact on DPN, although exercise is clearly beneficial. Unfortunately, optimal exercise may not be feasible for many patients with DPN because of neuropathic foot pain and poor balance. Exercise has several favorable effects on health parameters, including body weight, glycemic control, lipid profile, and blood pressure. We investigated the impact of an exercise mimetic, 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), on DPN. AICAR treatment prevented or reversed experimental DPN in mouse models of both T2D and T1D. AICAR in high-fat diet (HFD-fed) mice increased the phosphorylation of AMPK in DRG neuronal extracts, and the ratio of phosphorylated AMPK to total AMPK increased by 3-fold (HFD vs. HFD+AICAR; p < 0.001). Phospho AMP increased the levels of dynamin-related protein 1 (DRP1, a mitochondrial fission marker), increased phosphorylated autophagy activating kinase 1 (ULK1) at Serine-555, and increased microtubule-associated protein light chain 3-II (LC3-II, a marker for autophagosome assembly) by 2-fold. Mitochondria isolated from DRG neurons of HFD-fed had a decrease in ADP-stimulated state 3 respiration (120 ± 20 nmol O2/min in HFD vs. 220 ± 20 nmol O2/min in control diet (CD); p < 0.001. Mitochondria isolated from HFD+AICAR-treated mice had increased state 3 respiration (240 ± 30 nmol O2/min in HFD+AICAR). However, AICAR’s protection in DPN in T2D mice was also mediated by its effects on insulin sensitivity, glucose metabolism, and lipid metabolism. Drugs that enhance AMPK phosphorylation may be beneficial in the treatment of DPN. Full article
(This article belongs to the Special Issue Mitochondrial Metabolism Alterations in Health and Disease)
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<p>Exercise consumes a large amount of ATP, elevating the AMP-to-ATP ratio. Increased AMP binds to the enzyme AMPK and induces the phosphorylation of AMPK (<a href="#ijms-26-00080-f001" class="html-fig">Figure 1</a>). On the other hand, <b>AICAR directly phosphorylates AMPK</b>. (1) Phosphorylated AMPK activates SIRT1/PGC1-α in the catabolic process of exercise (increased OXPHOS), resulting in decreased body weight, reduced triglycerides, improved HOMA-IR index and lower NEFA levels; (2) phosphorylated AMPK activates PGC-1α co-transcriptional complexes that initiate the overexpression of target genes to promote myogenesis, neurogenesis, and mitochondrial bioenergetics; and (3) phosphorylated AMPK led to phosphorylation of mitochondrial fission factor (MFF), recruitment of dynamin-like protein DRP1 to mitochondria, and activation of ULK1, an upstream kinase in autophagy and mitophagy. Mitochondrial fission allows damaged mitochondria to be selectively degraded through mitophagy pathways. The main points of this paper are boxed in red.</p>
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<p>Western blot analysis of AMPK and quantification of phospho-AMPK to total AMPK (<a href="#ijms-26-00080-f002" class="html-fig">Figure 2</a>). DRG neurons were isolated from CD (<span class="html-italic">n</span> = 6), CD+AICAR (<span class="html-italic">n</span> = 6), HFD (<span class="html-italic">n</span> = 6), and HFD+AICAR (<span class="html-italic">n</span> = 6) mice, and protein extracts were prepared. Antibodies recognize total AMPK, AMPK phosphorylated at the Thr 176 residue, and beta-actin. The levels of expression were quantified by the intensity of the bands. The ratio of pAMPK to total AMPK was calculated, and the values were analyzed by ANOVA. The significance is denoted by the following <span class="html-italic">p</span>-values: *** <span class="html-italic">p</span> &lt; 0.001; CD or CD+AICARD vs. HFD, +++ <span class="html-italic">p</span> &lt; 0.001; HFD vs. HFD+AICAR.</p>
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<p>Western blot analysis of proteins involved in mitophagy in neuronal mitochondria isolated from CD (<span class="html-italic">n</span> = 6), CD+AICAR (<span class="html-italic">n</span> = 6), HFD (<span class="html-italic">n</span> = 6), and HFD+AICAR (<span class="html-italic">n</span> = 6) mice. Western blots were carried out on the protein extracts using anti-LC3, anti-DRP1, anti-phospho (S555) ULK1, and anti-VDAC. The levels of expression were quantified based on the intensity of the bands. The relative ratio was calculated, and the values were analyzed by ANOVA. The significance is denoted by the following <span class="html-italic">p</span>-values: ** <span class="html-italic">p</span> &lt; 0.01 HFD vs. CD or CD+AICAR; +++ <span class="html-italic">p</span> &lt; 0.001 HFD+AICAR vs. HFD.</p>
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<p>Impaired ADP-stimulated mitochondrial respiration in HFD mice was prevented by AICAR treatment. Oxygen consumption rate (OCR) was measured in the presence of complex I substrates (malate/glutamate) with the subsequent and sequential addition of ADP, oligomycin, and rotenone + antimycin A to mitochondria to measure state 2 (basal), state 3 (ADP-stimulated), state 4<sub>0</sub> (oligomycin-sensitive), and FCCP-induced respiration rates were measured. ADP-stimulated respiration was significantly decreased in neuronal mitochondria from HFD-fed mice compared to CD and CD+AICAR-treated mice. Administration of AICAR to HFD-fed mice significantly increased ADP-stimulated respiration. The respiratory control ratio (RCR) was calculated. *** <span class="html-italic">p</span> &lt; 0.001 HFD vs. CD, CD+AICAR. +++ <span class="html-italic">p</span> &lt; 0.001 HFD vs. HFD+AICAR.</p>
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<p><b>AICAR prevented HFD-induced neuropathy in C57Bl6 mice (<span class="html-italic">n</span> = 6 to 8 per group).</b> WT C57BL6 mice were randomly assigned to four groups of six to eight mice per group. Group #1: CD; Group #2: CD+AICAR (500 mg/kg); Group #3: HFD; and Group #4: HFD+AICAR (500 mg/kg). Placebo or AICAR was administered for 4 months to CD and HFD mice. Mice were tested for the following parameters: SMNCV (<b>A</b>), TML (<b>B</b>), TSNCV (<b>C</b>), mechanical allodynia (MA) by Von Frey filament paw withdrawal threshold (<b>D</b>), and IENFD of the hind paw (<b>E</b>). Statistical comparisons were made among the 5 groups by ANOVA and post hoc Tukey test. *** <span class="html-italic">p</span> &lt; 0.001; HFD, Group #3 at 4 months compared to all other groups in all the parameters.</p>
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<p>Two months of AICAR treatment reversed HFD-induced neuropathy in C57BL6 mice (<span class="html-italic">n</span> = 6/group). Three-month-old C57BL6 WT mice were fed with either a control diet (CD) or a high-fat diet (HFD). Baseline NCSs were completed at the beginning of the study. At 2 months, SMNCV, TML, TSNCV, and MA were measured in the mice fed a CD and the mice fed an HFD. After confirming that consumption of the HFD for 2 months induced development of peripheral neuropathy as observed by the changes in the NCSs and MA, AICAR was administered to the CD and HFD mice at a dose of 500 mg/kg for an additional 2 months. The vehicle was administered to CD and HFD mice. Nerve conduction studies were performed 2 months after administration of the AICAR or vehicle. The protocol is described in (<b>A</b>), the results of SMNCV (<b>B</b>), TSNCV (<b>C</b>), TML (<b>D</b>), MA using the Von Frey filament paw withdrawal threshold method (<b>E</b>), and intraepidermal fiber density (<b>F</b>) are shown. Statistical comparisons were made among the three groups with the ANOVA and post hoc Tukey test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001; HFD mice at 2 months compared to 0-month-old HFD and CD mice in all parameters. <sup>++</sup> <span class="html-italic">p</span> &lt; 0.01, HFD+AICAR mice at 4 months compared to HFD at 2 months in all parameters. The administration of AICAR reversed all the deficits of HFD-induced neuropathy. The administration of AICAR to non-diabetic mice had no significant effect. There was no statistically significant difference in CMAP or SNAP amplitudes compared between groups or compared at the start, during, and at the end of the treatment. The baseline (prior to starting any treatment) CMAP sciatic amplitude (mV) was 6.17 ± 0.78; CMAP tail amplitude (mV) was 3.050 ± 0.60, and SNAP tail amplitude (μV) was 51.92 ± 13.08. The CMAP sciatic amplitudes (mV) after 4 months of treatment were as follows: CD: 5.14 ± 0.99 (SEM); CD+AICAR: 5.70 ± 1.68; HFD: 8.31 ± 1.66; HFD+AICAR: 5.09 ± 0.76. The CMAP tail amplitudes (mV) after 4 months of treatment were as follows: CD: 1.24 ± 0.24; CD+AICAR: 2.16 ± 0.41; HFD: 2.96 ± 0.96; HFD+AICAR: 1.87 ± 0.70. The SNAP tail amplitudes (μV) after 4 months of treatment were as follows: CD: 102.48 ± 24.88; CD+AICAR: 126.77 ± 22.88; HFD: 56.21 ± 19.47; HFD+AICAR: 77.375 ± 19.46. There was no statistically significant difference in the CMAP or SNAP amplitudes during the reversal study, compared between groups or compared at the start and end of the treatment. This is as expected because of considerable variability in the CMAP and SNAP amplitudes [<a href="#B26-ijms-26-00080" class="html-bibr">26</a>,<a href="#B27-ijms-26-00080" class="html-bibr">27</a>,<a href="#B28-ijms-26-00080" class="html-bibr">28</a>].</p>
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<p>AICAR reverses STZ-induced neuropathy in C57BL6 mice (<span class="html-italic">n</span> = 6/group). Three-month-old C57BL6 WT and STZ-induced diabetic mice were purchased from Jackson Labs. The mice were fed with a control diet for a month. Measurement of NCSs and MA after a month showed that the STZ mice had developed neuropathy. Some of the STZ mice were then administered AICAR at a dose of 500 mg/kg for an additional 2 months. NCSs were performed at 5 and 6 months of age, namely after 2 and 3 months of STZ-induced diabetes or after 1 or 2 months of AICAR treatment. The results are shown as follows: SMNCV (<b>A</b>), TSNCV (<b>B</b>), TML (<b>C</b>), and MA (<b>D</b>) using the Von Frey filament paw withdrawal threshold method. Statistical comparisons were made among the three groups with the ANOVA and post hoc Tukey test. *** <span class="html-italic">p</span> &lt; 0.001; STZ at experimental time 3 months compared to 1 month in all parameters. <sup>+++</sup> <span class="html-italic">p</span> &lt; 0.001, STZ+AICAR at experimental time 3 months compared to STZ at 1 month in all parameters. Thus, administration of AICAR reversed all the peripheral neuropathy deficits of STZ mice.</p>
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16 pages, 1045 KiB  
Review
The Critical Role of Autophagy and Phagocytosis in the Aging Brain
by Stephen C. Bondy and Meixia Wu
Int. J. Mol. Sci. 2025, 26(1), 57; https://doi.org/10.3390/ijms26010057 - 25 Dec 2024
Viewed by 488
Abstract
As the organism ages, there is a decline in effective energy supply, and this retards the ability to elaborate new proteins. The consequences of this are especially marked in the gradual decline in brain function. The senescence of cells and their constituent organelles [...] Read more.
As the organism ages, there is a decline in effective energy supply, and this retards the ability to elaborate new proteins. The consequences of this are especially marked in the gradual decline in brain function. The senescence of cells and their constituent organelles is ultimately the cause of aging of the entire nervous system. What is less immediately obvious is that brain aging is also accompanied by the failure of catabolic events that lead to the removal of non-functional cells and ineffective subcellular components. The removal of non-working cellular and subcellular elements within the brain is essential in order to allow the appearance of fresh cells and organelles with a full range of capacities. Thus, the maintenance of operative mechanisms for the dispersal of failed tissue components is important, and its diminished capacity with aging is a significant contributory factor to the onset and progression of age-related neurological disorder. This report discusses the mechanisms underlying autophagy and phagocytosis and how these can be adversely modulated as aging proceeds. The means by which the effective recycling of cellular components may be reinstated in the aged brain are considered. Full article
(This article belongs to the Special Issue Molecular Research on Neuroinflammation and Brain Aging)
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<p>Autophagy and phagocytosis: breakdown of failed cells and cellular constituents resulting in beneficial metabolic changes and maintenance of cellular efficiency.</p>
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<p>Phagocytic and autophagic pathways that may experience excessive demand, leading to inflammation and potentially harmful neurodegenerative changes.</p>
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<p>Critical decision points distinguishing healthy brain aging from pathological age-related developments.</p>
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31 pages, 41731 KiB  
Article
Hepatitis C Virus NS5A Activates Mitophagy Through Cargo Receptor and Phagophore Formation
by Yuan-Chao Hsiao, Chih-Wei Chang, Chau-Ting Yeh and Po-Yuan Ke
Pathogens 2024, 13(12), 1139; https://doi.org/10.3390/pathogens13121139 - 23 Dec 2024
Viewed by 582
Abstract
Chronic HCV infection is a risk factor for end-stage liver disease, leading to a major burden on public health. Mitophagy is a specific form of selective autophagy that eliminates mitochondria to maintain mitochondrial integrity. HCV NS5A is a multifunctional protein that regulates the [...] Read more.
Chronic HCV infection is a risk factor for end-stage liver disease, leading to a major burden on public health. Mitophagy is a specific form of selective autophagy that eliminates mitochondria to maintain mitochondrial integrity. HCV NS5A is a multifunctional protein that regulates the HCV life cycle and may induce host mitophagy. However, the molecular mechanism by which HCV NS5A activates mitophagy remains largely unknown. Here, for the first time, we delineate the dynamic process of HCV NS5A-activated PINK1/Parkin-dependent mitophagy. By performing live-cell imaging and CLEM analyses of HCV NS5A-expressing cells, we demonstrate the degradation of mitochondria within autophagic vacuoles, a process that is dependent on Parkin and ubiquitin translocation onto mitochondria and PINK1 stabilization. In addition, the cargo receptors of mitophagy, NDP52 and OPTN, are recruited to the mitochondria and required for HCV NS5A-induced mitophagy. Moreover, ATG5 and DFCP1, which function in autophagosome closure and phagophore formation, are translocated near mitochondria for HCV NS5A-induced mitophagy. Furthermore, autophagy-initiating proteins, including ATG14 and ULK1, are recruited near the mitochondria for HCV NS5A-triggered mitophagy. Together, these findings demonstrate that HCV NS5A may induce PINK1/Parkin-dependent mitophagy through the recognition of mitochondria by cargo receptors and the nascent formation of phagophores close to mitochondria. Full article
(This article belongs to the Special Issue Pathogenesis of Viral Hepatitis)
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Graphical abstract
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<p>HCV NS5A induces the engulfment of mitochondria within autophagic vacuoles: (<b>A</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3 and Mito-GFP, as described in the “Materials and Methods” section, to generate Huh7/RFP-LC3/Mito-GFP cells. Then, Huh7/RFP-LC3/Mito-GFP cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The degree of colocalization between RFP-LC3-labeled autophagic vacuoles and Mito-GFP-expressing mitochondria was quantified. The data are presented as means ± SEMs (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the sequestration of Mito-GFP-labeled mitochondria by RFP-LC3 puncta. (<b>D</b>–<b>G</b>) CLEM analysis of mitochondrial sequestration by autophagic vacuoles in HCV NS5A-expressing cells. (<b>D</b>) Huh7/RFP-LC3/Mito-GFP cells were transduced with lentiviruses expressing HCV NS5A-mTagBFP2 for forty-eight hours and then processed for confocal microscopy. (<b>E</b>) The assembled Z-stacks of the confocal micrographs in (<b>D</b>) were reconstituted into a 3-D image. The white dashed boxes indicate the engulfment of Mito-GFP-expressing mitochondria within RFP-LC3 puncta. (<b>F</b>) The aligned image of the confocal micrograph (IF) and electron micrograph (EM) from the CLEM analysis of cells in (<b>D</b>) is shown. The white dashed boxes in the left panel are enlarged and shown in the magnified images in the right panel. The white arrowheads indicate the sequestration of mitochondria within autophagic vacuoles. (<b>G</b>) The enlarged images show the magnified white dashed boxes in the EM of (<b>F</b>). The white arrows indicate the phagophores wrapped around deformed mitochondria.</p>
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<p>HCV NS5A induces the translocation of Parkin to mitochondria: (<b>A</b>) Huh7 cells were transduced with lentiviruses harboring RFP-Parkin and Mito-GFP, according to the procedure described in the “Materials and Methods” section, to establish Huh7/RFP-Parkin/Mito-GFP cells. Huh7/RFP-LC3/Mito-GFP cells were transduced with (+) or without (−) pTRIP-HCV NS5A-miRFP670 lentiviruses for forty-eight hours and then analyzed via confocal microscopy. (<b>B</b>) The degree of colocalization between RFP-LC3-Parkin and Mito-GFP-labeled mitochondria was quantified. The data are presented as means ± SEMs (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the translocation of RFP-Parkin to Mito-GFP-expressing mitochondria. (<b>D</b>–<b>F</b>) CLEM analysis of the mitochondrial translocation of RFP-Parkin in HCV NS5A-expressing cells. (<b>D</b>) Huh7/RFP-Parkin/Mito-GFP cells were transduced with lentiviruses expressing HCV NS5A-miRFP670. Forty-eight hours later, the cells were analyzed via confocal microscopy. (<b>E</b>) The Z-stacks of the confocal micrograph shown in (<b>D</b>) were assembled and deconvoluted into a 3-D image. The white dashed boxes indicate the Mito-GFP-expressing mitochondria with RFP-Parkin translocation. (<b>F</b>) The aligned IF and CLEM image of the cells from (<b>D</b>) is presented. The white dashed boxes in the left panel are enlarged and shown in the magnified images in the right panel. The white arrowheads indicate the degradative mitochondria in which Parkin translocates.</p>
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<p>HCV NS5A induces mitolysosome formation: (<b>A</b>) Huh7 cells were transduced with pTRIP-Mito-QC lentiviruses, as described in the “Materials and Methods” section, generating Huh7/Mito-QC cells. Huh7/Mito-QC cells were transduced with (+) or without (−) pTRIP-HCV NS5A-miRFP670 lentiviruses. After forty-eight hours, the cells were analyzed via confocal microscopy. (<b>B</b>) The number of RFP<sup>+</sup>/GFP<sup>−</sup> mitolysosomes was quantified with Image J, as described previously [<a href="#B33-pathogens-13-01139" class="html-bibr">33</a>]. The data are presented as means ± SEMs (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the formation of RFP<sup>+</sup>/GFP<sup>−</sup> mitolysosomes. (<b>D</b>–<b>F</b>) CLEM analysis of mitolysosome formation in HCV NS5A-expressing cells. (<b>D</b>) Huh7/Mito-QC cells were transduced with lentiviruses expressing HCV NS5A-miRFP670 for forty-eight hours and then processed for confocal microscopy. (<b>E</b>) The Z-stacks of the confocal micrograph shown in (<b>D</b>) were assembled and deconvoluted into a 3-D image. The white dashed boxes indicate the loci of RFP<sup>+</sup>/GFP<sup>−</sup> mitolysosomes. (<b>F</b>) The aligned IF and CLEM image of the cells from (<b>D</b>) is shown. The white dashed boxes in the left panel are enlarged and shown in the magnified images in the right panel. The white arrowheads indicate the loci of mitolysosomes.</p>
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<p>HCV NS5A enhances mitophagic flux and induces ubiquitin recruitment to mitochondria: (<b>A</b>) Huh7 cells were transduced with pTRIP-MT-Keima lentiviruses, according to the procedure described in the “Materials and Methods” section, to establish Huh7/MT-Keima cells. Then, Huh7/MT-Keima cells were transduced with (+) or without (−) pTRIP-HCV NS5A-miRFP670 lentiviruses. Forty-eight hours later, the cells were analyzed via confocal microscopy at short (488 nm) and long (561 nm) excitation wavelengths. (<b>B</b>) The percentage of cells containing acidic MT-Keima (excitation at 561 nm) was quantified, as described previously [<a href="#B33-pathogens-13-01139" class="html-bibr">33</a>]. The data are presented as means ± SEMs (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the cells expressing acidic MT-Keima. (<b>D</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3, GFP-Ub, or Mito-miRFP670, as described in the “Materials and Methods” section, generating Huh7/RFP-LC3/GFP-Ub/Mito-miRFP670 cells. Huh7/RFP-LC3/GFP-Ub/Mito-miRFP670 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and then analyzed via confocal microscopy. (<b>E</b>) The number of RFP-LC3 puncta containing GFP-Ub on Mito-miRFP670-labeled mitochondria was quantified. The data are presented as means ± SEMs (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-Ub to Mito-miRFP670-expressing mitochondria and the subsequent sequestration of RFP-LC3-labeled autophagic vacuoles.</p>
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<p>HCV NS5A induces ubiquitin Ser65 phosphorylation and PINK1 stabilization in mitochondria: (<b>A</b>,<b>B</b>) (<b>A</b>) Huh7/RFP-LC3/Mito-GFP cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses. Forty-eight hours later, the cells were immunostained with a phospho-ubiquitin (Ub; Ser65) antibody and analyzed via confocal microscopy. (<b>B</b>) Foci of phospho-ubiquitin (Ser65) recruited onto mitophagosomes in which RFP-LC3 puncta sequestered Mito-GFP-labeled mitochondria were quantified. (<b>C</b>,<b>D</b>) (<b>C</b>) Huh7/RFP-Parkin/Mito-GFP cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours. Then, the cells were immunostained with an anti-phospho-ubiquitin (Ser65) antibody and analyzed via confocal microscopy. (<b>D</b>) Foci of phosphor-Ub recruited to RFP-Parkin-translocated Mito-GFP-expressing mitochondria were quantified. (<b>E</b>,<b>F</b>) (<b>E</b>) Huh7/RFP-LC3/Mito-GFP cells were transduced with lentiviruses expressing PINK1-miRFP670, generating Huh7/RFP-LC3/Mito-miRFP670/PINK1-miRFP670 cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>F</b>) Foci of PINK1-miRFP670 stabilized on mitophagosomes, in which RFP-LC3 puncta sequestered Mito-GFP-expressing mitochondria were quantified. (<b>G</b>,<b>H</b>) (<b>G</b>) Huh7/RFP-Parkin/Mito-GFP cells were transduced with lentiviruses expressing PINK1-miRFP670 to establish Huh7/RFP-LC3/Mito-miRFP670/PINK1-miRFP670 cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>H</b>) The number of MiRFP670-PINK1 foci recruited onto the RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. The data shown in (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). The magnified field-1 and magnified field-2 in (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) show enlarged images of white dashed boxes 1 and 2 in the top and bottom panels. The white arrowheads indicate colocalized signals.</p>
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<p>Recruitment of NDP52 and OPTN into HCV NS5A-activated mitophagy: (<b>A,B</b>) (<b>A</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3, Mito-miRFP670, or GFP-NDP52, as described in the “Materials and Methods” section, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-NDP52 cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-NDP52 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>,<b>D</b>) (<b>C</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3, Mito-miRFP670, or GFP-OPTN, as described in the “Materials and Methods” section, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-OPTN cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>D</b>) The number of GFP-OPTN molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>E</b>,<b>F</b>) (<b>E</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with lentiviruses expressing GFP-NDP52, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-NDP52 cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>F</b>) The number of GFP-NDP52 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>G</b>,<b>H</b>) (<b>G</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with lentiviruses expressing GFP-OPTN to establish Huh7/RFP-Parkin/Mito-miRFP670/GFP-OPTN cells. Then, the cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (H) The number of GFP-OPTN molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. The data shown in (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001). The magnified field-1 and magnified field-2 in (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) show enlarged images of white dashed boxes 1 and 2 in the top and bottom panels. The white arrowheads indicate colocalized signals.</p>
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<p>Recruitment of ATG5 into close proximity to mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3 and mito-miRFP670 to establish Huh7/RFP-LC3/Mito-miRFP670 cells. Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG5, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG5 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG5 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-ATG5 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-ATG5 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7 cells were transduced with lentiviruses expressing RFP-Parkin and Mito-miRFP670 to establish Huh7/RFP-Parkin/Mito-miRFP670 cells. Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG5, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG5 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG5 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-ATG5 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-ATG5 to Mito-miRFP670-labeled mitochondria after translocation by RFP-Parkin. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 7 Cont.
<p>Recruitment of ATG5 into close proximity to mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7 cells were transduced with lentiviruses expressing RFP-LC3 and mito-miRFP670 to establish Huh7/RFP-LC3/Mito-miRFP670 cells. Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG5, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG5 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG5 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-ATG5 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-ATG5 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7 cells were transduced with lentiviruses expressing RFP-Parkin and Mito-miRFP670 to establish Huh7/RFP-Parkin/Mito-miRFP670 cells. Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG5, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG5 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG5 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-ATG5 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-ATG5 to Mito-miRFP670-labeled mitochondria after translocation by RFP-Parkin. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Recruitment of DFCP1 into the proximity of mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-DFCP1, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-DFCP1 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-DFCP1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-DFCP1 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The frames of selected live images show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-DFCP1 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-DFCP1, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-DFCP1 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-DFCP1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-DFCP1 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The frames of selected live images show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-DFCP1 to Mito-miRFP670-labeled mitochondria after translocation by RFP-Parkin. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 8 Cont.
<p>Recruitment of DFCP1 into the proximity of mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-DFCP1, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-DFCP1 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-DFCP1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-DFCP1 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The frames of selected live images show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-DFCP1 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-DFCP1, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-DFCP1 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-DFCP1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-DFCP1 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The frames of selected live images show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-DFCP1 to Mito-miRFP670-labeled mitochondria after translocation by RFP-Parkin. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Translocation of ATG14 into close proximity to mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG14, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG14 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG14 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-ATG14 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequester Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-ATG14 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG14, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG14 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG14 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-ATG14 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-ATG14 to Mito-miRFP670-labeled mitochondria after RFP-Parkin translocation. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 9 Cont.
<p>Translocation of ATG14 into close proximity to mitochondria for HCV NS5A-induced mitophagy: (<b>A</b>) Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG14, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG14 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-ATG14 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-ATG14 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequester Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-ATG14 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ATG14, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG14 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-ATG14 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-ATG14 molecules recruited to RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-ATG14 to Mito-miRFP670-labeled mitochondria after RFP-Parkin translocation. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Translocation of ULK1 into the proximity of mitochondria for HCV NS5A-activated mitophagy: (<b>A</b>) Huh7/RFP-LC3/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ULK1, generating Huh7/RFP-LC3/Mito-miRFP670/GFP-ULK1 cells. Then, Huh7/RFP-LC3/Mito-miRFP670/GFP-ULK1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>B</b>) The number of GFP-ULK1 molecules recruited onto mitophagosomes, in which RFP-LC3 puncta sequestered Mito-miRFP670-expressing mitochondria, was quantified. (<b>C</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>A</b>). The white arrowheads indicate the recruitment of GFP-ULK1 to Mito-miRFP670-labeled mitochondria before sequestration by RFP-LC3 puncta. (<b>D</b>) Huh7/RFP-Parkin/Mito-miRFP670 cells were transduced with retroviruses expressing GFP-ULK1, generating Huh7/RFP-Parkin/Mito-miRFP670/GFP-ULK1 cells. Then, Huh7/RFP-Parkin/Mito-miRFP670/GFP-ULK1 cells were transduced with (+) or without (−) pTRIP-HCV NS5A-mTagBFP2 lentiviruses for forty-eight hours and analyzed via confocal microscopy. (<b>E</b>) The number of GFP-ULK1 molecules recruited to the RFP-Parkin-translocated Mito-miRFP670-labeled mitochondria was quantified. (<b>F</b>) The selected live imaging frames show the magnified area in the white dashed box of the top panel in (<b>D</b>). The white arrowheads indicate the recruitment of GFP-ULK1 to Mito-miRFP670-labeled mitochondria after RFP-Parkin translocation. The data shown in (<b>B</b>,<b>E</b>) represent the mean ± SEM (<span class="html-italic">n</span> = 10, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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15 pages, 1125 KiB  
Review
Alpha-Synuclein Effects on Mitochondrial Quality Control in Parkinson’s Disease
by Lydia Shen and Ulf Dettmer
Biomolecules 2024, 14(12), 1649; https://doi.org/10.3390/biom14121649 - 22 Dec 2024
Viewed by 688
Abstract
The maintenance of healthy mitochondria is essential for neuronal survival and relies upon mitochondrial quality control pathways involved in mitochondrial biogenesis, mitochondrial dynamics, and mitochondrial autophagy (mitophagy). Mitochondrial dysfunction is critically implicated in Parkinson’s disease (PD), a brain disorder characterized by the progressive [...] Read more.
The maintenance of healthy mitochondria is essential for neuronal survival and relies upon mitochondrial quality control pathways involved in mitochondrial biogenesis, mitochondrial dynamics, and mitochondrial autophagy (mitophagy). Mitochondrial dysfunction is critically implicated in Parkinson’s disease (PD), a brain disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra. Consequently, impaired mitochondrial quality control may play a key role in PD pathology. This is affirmed by work indicating that genes such as PRKN and PINK1, which participate in multiple mitochondrial processes, harbor PD-associated mutations. Furthermore, mitochondrial complex-I-inhibiting toxins like MPTP and rotenone are known to cause Parkinson-like symptoms. At the heart of PD is alpha-synuclein (αS), a small synaptic protein that misfolds and aggregates to form the disease’s hallmark Lewy bodies. The specific mechanisms through which aggregated αS exerts its neurotoxicity are still unknown; however, given the vital role of both αS and mitochondria to PD, an understanding of how αS influences mitochondrial maintenance may be essential to elucidating PD pathogenesis and discovering future therapeutic targets. Here, the current knowledge of the relationship between αS and mitochondrial quality control pathways in PD is reviewed, highlighting recent findings regarding αS effects on mitochondrial biogenesis, dynamics, and autophagy. Full article
(This article belongs to the Section Biomacromolecules: Proteins)
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Figure 1
<p>Potential pathological interactions of αS with mitochondrial quality control pathways in PD. (<b>a</b>) αS and mitochondrial biogenesis. (<b>i</b>) αS may act as a transcriptional modulator of PGC-1α under oxidative stress, binding to its promoter sequence to repress PGC-1α function. (<b>ii</b>) An excess of exogenous αS oligomers and fibrils may interfere with Parkin’s degradation of PARIS, thus increasing the PARIS-mediated transcriptional repression of PGC-1α. (<b>b</b>) αS and mitochondrial dynamics. (<b>i</b>) Pathogenic αS may increase the cleavage of OPA1 in mitochondrial fusion. (<b>ii</b>) αS-induced alterations to mitochondrial fission may be independent of or dependent upon DRP1: αS may interact directly with mitochondrial membranes or may increase the translocation of DRP1 to mitochondria. (<b>c</b>) αS and mitochondrial autophagy. (<b>i</b>) The overexpression of αS may stabilize Miro proteins, which are required for the formation of mitochondrial-derived vesicles (MDVs). (<b>ii</b>) αS may downregulate Parkin expression and activity as described above, having negative impacts on MDV trafficking. (<b>iii</b>) During autophagosome formation in mitophagy, αS may aberrantly stabilize Miro at the OMM as previously described, causing delays in mitophagy initiation. αS may also impact autophagosome formation by causing a reduction in Parkin levels, affecting the ubiquitination of mitochondrial proteins. (<b>iv</b>) By binding to spectrin, αS may excessively stabilize the actin cytoskeleton, resulting in the mislocalization of key proteins involved in autophagosome trafficking. This mislocalization may also have global effects, disrupting other forms of cellular trafficking. (<b>v</b>) Overexpressed αS may decrease SNAP29 activity, affecting the SNARE complex that mediates autophagosome–lysosome fusion during the last step of mitophagy. Figure created with BioRender. Partially adapted from Thorne and Tumbarello [<a href="#B32-biomolecules-14-01649" class="html-bibr">32</a>].</p>
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20 pages, 3844 KiB  
Article
Inhibition of TFAM-Mediated Mitophagy by Oroxylin A Restored Sorafenib Sensitivity Under Hypoxia Conditions in HepG2 Cells
by Shufan Ji, Xuefen Xu, Yujia Li, Sumin Sun, Qiuyu Fu, Yangling Qiu, Shuqi Wang, Siwei Xia, Feixia Wang, Feng Zhang, Ji Xuan and Shizhong Zheng
Pharmaceuticals 2024, 17(12), 1727; https://doi.org/10.3390/ph17121727 - 20 Dec 2024
Viewed by 466
Abstract
Background: Liver cancer treatment encounters considerable therapeutic challenges, especially because hypoxic microenvironments markedly reduce sensitivity to chemotherapeutic agents. TFAM (mitochondrial transcription factor A) plays a crucial role in maintaining mitochondrial function. Oroxylin A (OA), a flavonoid with potential therapeutic properties, demonstrated prospects in [...] Read more.
Background: Liver cancer treatment encounters considerable therapeutic challenges, especially because hypoxic microenvironments markedly reduce sensitivity to chemotherapeutic agents. TFAM (mitochondrial transcription factor A) plays a crucial role in maintaining mitochondrial function. Oroxylin A (OA), a flavonoid with potential therapeutic properties, demonstrated prospects in cancer treatment. However, the mechanism of the sensitizing effect of OA on cancer cells has not been elucidated. Methods: MTT assays were utilized to evaluate a hypoxia-induced resistance model. Plate colony formation assays, TEM, and JC-1 staining were used to examine the effects of siTFAM on proliferation and mitochondrial damage of HepG2 cells. Cox8-EGFP-mCherry plasmid transfection, LysoTracker and MitoTracker colocalization analysis, and WB were conducted to evaluate the influence of OA on mitophagy. The effect of OA on p53 ubiquitination levels was investigated by Co-IP and the CHX chase assay. A mouse xenograft tumor model was utilized to assess the therapeutic effect of OA on HepG2 cells in vivo. Results: OA significantly improved the inhibitory effect of sorafenib by inhibiting mitophagy on HepG2 cells in in vitro and in vivo models. Notably, the molecular docking and thermal shift assays indicated a clear binding of OA and TFAM. Further research revealed that OA suppressed p53 acetylation and promoted its degradation by downregulating TFAM expression, which ultimately inhibited mitophagy in hypoxia. Conclusions: OA has demonstrated the potential to enhance the efficacy of sorafenib treatment for liver cancer, and TFAM may be one of its targets. Full article
(This article belongs to the Section Natural Products)
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Graphical abstract
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<p>TFAM contributed to hypoxia-induced resistance in HepG2 cells. (<b>A</b>) HepG2 cells were cultured in complete medium containing 50 μM CoCl<sub>2</sub> to establish a hypoxia model. HIF1α levels were assessed and analyzed by Western blot in the presence or absence of CoCl<sub>2</sub> solution. (<b>B</b>) After treatment with sorafenib for 24 h, the impact of sorafenib on HepG2 cell viability was evaluated using an MTT assay under hypoxic and normoxic conditions, respectively. (<b>C</b>) DCFH-DA flow cytometry was employed to measure ROS levels in the presence of sorafenib in hypoxic and normoxic environments. ROS levels under normoxic conditions as a control. (<b>D</b>) With the addition of 2 mmol/L NAC for 1 h under normal conditions, the influence of sorafenib on HepG2 cell viability was determined via the MTT assay. (<b>E</b>) The TCGA database was analyzed to identify differences in TFAM expression between liver cancer tissues and adjacent non-tumor tissues. (<b>F</b>) Patients with high TFAM expression in liver cancer exhibited worse prognosis. (<b>G</b>) RT-PCR showed that TFAM mRNA significantly increased in HepG2 cells under hypoxic conditions. GAPDH mRNA was used to normalize the mRNA level of each gene. (<b>H</b>) TFAM was knocked down by TFAMsiRNA, then HepG2 cells were treated with 10 μM sorafenib for 24 h under hypoxic conditions. DCFH-DA flow cytometry revealed that knockdown of TFAM enhanced the ROS levels in sorafenib-treated HepG2 cells in the resistance model. (<b>I</b>) The plate cloning assay showed that TFAM siRNA significantly decreased colonies of HepG2 cells treated with sorafenib for 24 h under hypoxic conditions. Data are expressed as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 versus control.</p>
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<p>TFAM contributed to hypoxia-induced resistance in HepG2 cells. (<b>A</b>) HepG2 cells were cultured in complete medium containing 50 μM CoCl<sub>2</sub> to establish a hypoxia model. HIF1α levels were assessed and analyzed by Western blot in the presence or absence of CoCl<sub>2</sub> solution. (<b>B</b>) After treatment with sorafenib for 24 h, the impact of sorafenib on HepG2 cell viability was evaluated using an MTT assay under hypoxic and normoxic conditions, respectively. (<b>C</b>) DCFH-DA flow cytometry was employed to measure ROS levels in the presence of sorafenib in hypoxic and normoxic environments. ROS levels under normoxic conditions as a control. (<b>D</b>) With the addition of 2 mmol/L NAC for 1 h under normal conditions, the influence of sorafenib on HepG2 cell viability was determined via the MTT assay. (<b>E</b>) The TCGA database was analyzed to identify differences in TFAM expression between liver cancer tissues and adjacent non-tumor tissues. (<b>F</b>) Patients with high TFAM expression in liver cancer exhibited worse prognosis. (<b>G</b>) RT-PCR showed that TFAM mRNA significantly increased in HepG2 cells under hypoxic conditions. GAPDH mRNA was used to normalize the mRNA level of each gene. (<b>H</b>) TFAM was knocked down by TFAMsiRNA, then HepG2 cells were treated with 10 μM sorafenib for 24 h under hypoxic conditions. DCFH-DA flow cytometry revealed that knockdown of TFAM enhanced the ROS levels in sorafenib-treated HepG2 cells in the resistance model. (<b>I</b>) The plate cloning assay showed that TFAM siRNA significantly decreased colonies of HepG2 cells treated with sorafenib for 24 h under hypoxic conditions. Data are expressed as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 versus control.</p>
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<p>Silencing TFAM enhanced the sensitivity of HepG2 cells to sorafenib by inhibiting mitophagy. The changes in mitochondria were observed after sorafenib intervention for 24 h in HepG2 cells, with or without TFAM knockdown. (<b>A</b>) Upon TFAM knockdown, the mitochondrial morphology was detected by TEM in sorafenib-treated HepG2 cells under hypoxia conditions. The black arrows indicated normal mitochondria, and the red arrows indicated the damaged mitochondria. The left scale bar: 2.0 μm; the right scale bar: 500 nm. (<b>B</b>) Mitophagy-related proteins (parkin and pink1) were analyzed by Western blot. (<b>C</b>) Flow cytometry measured the effect of TFAM knockdown on MMP levels in hypoxia. HepG2 cells were treated with 5 μM CCCP for 24 h. The experiment was repeated three times. (<b>D</b>) Plate cloning assays showed that combination of 5 μM CCCP and TFAM knockdown under hypoxia conditions significantly promoted the inhibitory effect on cell proliferation. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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<p>OA enhanced the sensitivity of HepG2 cells to sorafenib by inhibiting mitophagy. HepG2 cells were treated with different concentrations of OA or 10 μM sorafenib for 24 h under hypoxia conditions. (<b>A</b>) The MTT assay determined the effect of OA on the viability of HepG2 cells. (<b>B</b>) The inhibitory effects of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on HepG2 cells were evaluated under hypoxia conditions. (<b>C</b>) The DCFH-DA assay determined the effect of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on ROS levels. (<b>D</b>) Flow cytometry detected the effect of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on MMP levels. (<b>E</b>) The mitochondrial count was evaluated using Mito-Tracker Red CMXRos after individual or combined treatments. Scale bar: 10 μm. (<b>F</b>) The levels of mitophagy-related proteins were determined by Western blot after individual or combined treatments. (<b>G</b>) HepG2 cells were transiently transfected with the Cox8-EGFP-mCherry plasmid and subsequently treated with OA, either alone or in combination with sorafenib for 24 h. The Cox8-EGFP-mCherry dual fluorescence reporter system was analyzed using confocal microscopy. Both OA treatment alone and the combined treatment with sorafenib markedly enhanced the green fluorescence intensity (EGFP). Scale bar: 2.5 μm. (<b>H</b>) Laser confocal microscopy assessed the colocalization of mitochondria and lysosomes. Scale bar: 10 μm. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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<p>OA enhanced the sensitivity of HepG2 cells to sorafenib by inhibiting mitophagy. HepG2 cells were treated with different concentrations of OA or 10 μM sorafenib for 24 h under hypoxia conditions. (<b>A</b>) The MTT assay determined the effect of OA on the viability of HepG2 cells. (<b>B</b>) The inhibitory effects of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on HepG2 cells were evaluated under hypoxia conditions. (<b>C</b>) The DCFH-DA assay determined the effect of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on ROS levels. (<b>D</b>) Flow cytometry detected the effect of OA (10 μM) and sorafenib (10 μM), either individually or in combination, on MMP levels. (<b>E</b>) The mitochondrial count was evaluated using Mito-Tracker Red CMXRos after individual or combined treatments. Scale bar: 10 μm. (<b>F</b>) The levels of mitophagy-related proteins were determined by Western blot after individual or combined treatments. (<b>G</b>) HepG2 cells were transiently transfected with the Cox8-EGFP-mCherry plasmid and subsequently treated with OA, either alone or in combination with sorafenib for 24 h. The Cox8-EGFP-mCherry dual fluorescence reporter system was analyzed using confocal microscopy. Both OA treatment alone and the combined treatment with sorafenib markedly enhanced the green fluorescence intensity (EGFP). Scale bar: 2.5 μm. (<b>H</b>) Laser confocal microscopy assessed the colocalization of mitochondria and lysosomes. Scale bar: 10 μm. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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<p>OA targeted TFAM to inhibit mitophagy in HepG2 cells. HepG2 cells were treated with OA and 10 μM sorafenib, either individually or in combination, for 24 h under hypoxic conditions. (<b>A</b>) Molecular docking results showed a binding pocket between OA and TFAM in the 3D structure. OA formed hydrogen bonds with TYR211 and LYS145 of TFAM with hydrogen bond lengths of 2.9 and 4.0, respectively. The compound formed hydrophobic interactions with ARG157 and LYS156 of TFAM and π–cation interactions with LYS154 and LYS146 of the protein. (<b>B</b>) Thermal shift assays showed that treatment with OA decreased the degradation rate of TFAM. The thermal melting curve displayed a significant rightward shift following the administration of OA. (<b>C</b>) The effects of OA on TFAM expression were determined and analyzed by Western blot in hypoxia-induced resistance. (<b>D</b>) OA reduced the expression of mitophagy-related proteins in a concentration-dependent manner, which was detected by Western blot. (<b>E</b>) Western blot revealed that either knocking down TFAM or using OA reduced mitophagy-related proteins in HepG2 cells under hypoxia conditions. (<b>F</b>) Western blot analysis indicated that overexpression of TFAM could reverse the OA-mediated inhibition of mitophagy in hypoxia-induced resistant HepG2 cells. (<b>G</b>,<b>I</b>) Laser confocal microscopy analysis revealed that TFAM overexpression could counteract the OA-mediated suppression of mitophagy under hypoxia conditions. Scale bar: 10 μm. (<b>H</b>,<b>J</b>) Flow cytometry analysis showed that overexpression of TFAM could reverse OA-induced downregulation of MMP level in HepG2 cells. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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<p>OA suppressed mitophagy by downregulating TFAM to reduce p53 acetylation under hypoxia conditions. (<b>A</b>) Western blot showed that knocking down TFAM could reduce p53 expression under hypoxia conditions. (<b>B</b>) RT-PCR analysis was used to determine the levels of p53 mRNA in HepG2 cells after TFAM knockdown. (<b>C</b>) Western blot assessed the effects of combining CHX or MG132 with OA on p53 protein. (<b>D</b>) After treatment with CHX in the presence or absence of OA, Western blot analysis evaluated the expression of p53 protein at the indicated time. (<b>E</b>) Co-IP detected the ubiquitination levels of p53 following treatment with either TFAM knockdown or 10 μM OA for 24 h. (<b>F</b>) Western blot investigated the effects of TFAM knockdown on the expression of acetylated p53 protein and its downstream target proteins. (<b>G</b>) After treatment with OA for 24 h, Western blot assessed the impact of OA on acetylated p53 and its downstream target protein. (<b>H</b>) Western blot showed that overexpressing TFAM could reverse the effects of OA on p53 acetylation and its downstream target genes. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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<p>OA enhanced the therapeutic effect of sorafenib on xenograft tumor in vivo. (<b>A</b>) Changes in body weight of mice in each group. (<b>B</b>) Quantification of tumor weight in each group. (<b>C</b>) Volume changes of tumors in each group. (<b>D</b>) Immunohistochemical analysis of TFAM expression in tumor tissues of mice in each group. (<b>E</b>) Western blot analysis of the expression of mitophagy-related proteins in tumor tissues. Data are expressed as mean ± SEM, where * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 denote statistical significance.</p>
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22 pages, 2994 KiB  
Review
Apolipoprotein-L Functions in Membrane Remodeling
by Etienne Pays
Cells 2024, 13(24), 2115; https://doi.org/10.3390/cells13242115 - 20 Dec 2024
Viewed by 615
Abstract
The mammalian Apolipoprotein-L families (APOLs) contain several isoforms of membrane-interacting proteins, some of which are involved in the control of membrane dynamics (traffic, fission and fusion). Specifically, human APOL1 and APOL3 appear to control membrane remodeling linked to pathogen infection. Through its association [...] Read more.
The mammalian Apolipoprotein-L families (APOLs) contain several isoforms of membrane-interacting proteins, some of which are involved in the control of membrane dynamics (traffic, fission and fusion). Specifically, human APOL1 and APOL3 appear to control membrane remodeling linked to pathogen infection. Through its association with Non-Muscular Myosin-2A (NM2A), APOL1 controls Golgi-derived trafficking of vesicles carrying the lipid scramblase Autophagy-9A (ATG9A). These vesicles deliver APOL3 together with phosphatidylinositol-4-kinase-B (PI4KB) and activated Stimulator of Interferon Genes (STING) to mitochondrion–endoplasmic reticulum (ER) contact sites (MERCSs) for the induction and completion of mitophagy and apoptosis. Through direct interactions with PI4KB and PI4KB activity controllers (Neuronal Calcium Sensor-1, or NCS1, Calneuron-1, or CALN1, and ADP-Ribosylation Factor-1, or ARF1), APOL3 controls PI(4)P synthesis. PI(4)P is required for different processes linked to infection-induced inflammation: (i) STING activation at the Golgi and subsequent lysosomal degradation for inflammation termination; (ii) mitochondrion fission at MERCSs for induction of mitophagy and apoptosis; and (iii) phagolysosome formation for antigen processing. In addition, APOL3 governs mitophagosome fusion with endolysosomes for mitophagy completion, and the APOL3-like murine APOL7C is involved in phagosome permeabilization linked to antigen cross-presentation in dendritic cells. Similarly, APOL3 can induce the fusion of intracellular bacterial membranes, and a role in membrane fusion can also be proposed for endothelial APOLd1 and adipocyte mAPOL6, which promote angiogenesis and adipogenesis, respectively, under inflammatory conditions. Thus, different APOL isoforms play distinct roles in membrane remodeling associated with inflammation. Full article
(This article belongs to the Special Issue Evolution, Structure, and Functions of Apolipoproteins L)
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Figure 1
<p>Structural features of APOL1 and APOL3. The colored cylinders represent different α-helices, some of which are numbered, according to Ultsch et al. [<a href="#B5-cells-13-02115" class="html-bibr">5</a>]. HC1, HC2 = hydrophobic clusters 1, 2; LZ1, LZ2 = leucine zippers 1, 2; CRAC-1, CRAC-2 = cholesterol recognition amino acid consensuses 1, 2 (represented by red stars); TM = potential transmembrane hairpin helix; MAD = membrane-addressing domain. At acidic pH, the APOL1 TM hairpin can form weak anion pores, but pH neutralization confers high cation conductance. HC2 amino acids involved in pore pH-gating are highlighted in yellow. The boxes illustrate the folding of the N- and C-terminal APOL1 domains. In the isolated N-terminal domain, helix 5 can adopt two positions, preventing (bound) or not preventing (open) helix 4 accessibility [<a href="#B5-cells-13-02115" class="html-bibr">5</a>]. APOL1 SID represents the Smallest Interacting Domain between N- and C-terminal regions. This interaction, driven by LZ1-LZ2 pairing, is affected either by acidic conditions, as in trypanosome endosomes, or by LZ2 mutations, as in the natural G1 or G2 variants. In APOL3, LZ2 interacts with helix 5.</p>
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<p>APOL2 sequence comparison with APOL1 and APOL3. Hydrophobic residues characterizing HC2 and LZ2 are highlighted in violet and pink, respectively. APOL1 CRAC-2 residues are boxed. Key APOL2 HC2 and LZ2 differences from APOL1 are in orange and red, respectively. The boxed sequence alignments show antisense pairing between helix 5 and LZ2, based on hydrophobic heptad repeats (highlighted in green).</p>
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<p>WT or C-terminal variant APOL1 interactions and activities. The same symbols and colors as in <a href="#cells-13-02115-f001" class="html-fig">Figure 1</a>. In the last scheme, hypothetical cation driving to the membrane pore at neutral pH [<a href="#B14-cells-13-02115" class="html-bibr">14</a>] is symbolized by a dotted red arrow.</p>
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<p>APOL3 interactions and activities. The same symbols, colors and numbers as in <a href="#cells-13-02115-f001" class="html-fig">Figure 1</a>. NCS1 and CALN1 are alternative APOL3 binders activating or inhibiting PI4KB, depending on calcium concentration. ARF1 binds to APOL3, and inflammation-mediated ARF1 activation promotes its binding to PI4KB, possibly dissociating APOL3-PI4KB interaction. VAMP8 interacts with both helices 4–5 and MAD, promoting membrane fusion.</p>
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<p>Intracellular traffic of proteins involved in infection-induced changes in membrane dynamics. Detection of pathogen DNA triggers the synthesis of cyclic GMP-AMP (cGAMP), which binds to STING and disrupts STING-cholesterol interactions, allowing STING binding to PI(4)P for translocation to the Golgi. In the Golgi, STING undergoes oligomerization, which induces IFN-I inflammatory signaling. IFN-I activates ARF1, leading to STING, PI4KB and APOL3 dissociation from the Golgi in ATG9A vesicles trafficking to MERCSs, promoting membrane fission and fusion events linked to auto/mitophagy and apoptosis. This pathway allows inflammation termination due to STING autophagic degradation. Through association with NM2A and PHB2, APOL1 could direct ATG9A vesicles to MERCSs, where mitophagy is initiated. Red stars represent cholesterol interactions. The double-arrowed black dotted line represents the involvement of endolysosomes in both mitochondrion fission and autophagosome formation by ATG9A vesicles.</p>
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<p>Sequence alignment between human APOL3 (above) and mouse APOL7c (below), using Clustal Omega (<a href="https://www.ebi.ac.uk/Tools/msa/clustalo/" target="_blank">https://www.ebi.ac.uk/Tools/msa/clustalo/</a> (accessed on 4 November 2024)). Insertion of clustered acidic residues, highlighted in red, characterizes the murine APOL7 family.</p>
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<p>The two APOL1-like domains of APOLd1. Positively charged residues of helix 5 are highlighted in red, and the two helices of the putative transmembrane domain are highlighted in blue, with acidic residues in green. Hydrophobic residues characterizing the HC2 and LZ2 helices are highlighted in violet and pink, respectively. The amino acids involved in pH gating of the APOL1 pore are highlighted in yellow. The APOL1 residues defining CRAC-2 are boxed, and the loop sequences between the two helices of the double-stranded HC2-LZ2 helix hairpin are in bold. The boxed sequence alignment shows antisense pairing between APOLd1 helix 5 and LZ2, based on hydrophobic heptad repeats (highlighted in green).</p>
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24 pages, 3346 KiB  
Review
Mitophagy in Doxorubicin-Induced Cardiotoxicity: Insights into Molecular Biology and Novel Therapeutic Strategies
by Heng Zhang, Saiyang Xie and Wei Deng
Biomolecules 2024, 14(12), 1614; https://doi.org/10.3390/biom14121614 - 17 Dec 2024
Viewed by 608
Abstract
Doxorubicin is a chemotherapeutic drug utilized for solid tumors and hematologic malignancies, but its clinical application is hampered by life-threatening cardiotoxicity, including cardiac dilation and heart failure. Mitophagy, a cargo-specific form of autophagy, is specifically used to eliminate damaged mitochondria in autophagosomes through [...] Read more.
Doxorubicin is a chemotherapeutic drug utilized for solid tumors and hematologic malignancies, but its clinical application is hampered by life-threatening cardiotoxicity, including cardiac dilation and heart failure. Mitophagy, a cargo-specific form of autophagy, is specifically used to eliminate damaged mitochondria in autophagosomes through hydrolytic degradation following fusion with lysosomes. Recent advances have unveiled a major role for defective mitophagy in the etiology of DOX-induced cardiotoxicity. Moreover, specific interventions targeting this mechanism to preserve mitochondrial function have emerged as potential therapeutic strategies to attenuate DOX-induced cardiotoxicity. However, clinical translation is challenging because of the unclear mechanisms of action and the potential for pharmacological adverse effects. This review aims to offer fresh perspectives on the role of mitophagy in the development of DOX-induced cardiotoxicity and investigate potential therapeutic strategies that focus on this mechanism to improve clinical management. Full article
(This article belongs to the Section Molecular Medicine)
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<p>Overview of DOX-induced cardiotoxicity in cardiomyocytes. DOX treatment initiates a complex interplay of multi-focal signaling pathways that play critical roles in cellular responses. The HMGB1/TLRs/NF-κB signaling pathway is activated, leading to enhanced inflammatory responses, while the NOX/Nrf2/HO-1/ROS pathway contributes to oxidative stress by generating reactive oxygen species. Additionally, the AMPK/P53 pathway can induce cell cycle arrest and apoptosis, and the PI3K/AKT/STAT3 pathway is involved in survival signaling. Collectively, these interconnected events result in oxidative stress, mitochondrial DNA dysfunction, increased inflammation, and several forms of cell death, including apoptosis, necrosis, and autosis, ultimately impacting tissue homeostasis and function.</p>
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<p>Landscape of the mitophagy mechanisms. Mitochondrial autophagy involves multiple interconnected mechanisms that can be broadly categorized into ubiquitin (Ub)-dependent and ubiquitin-independent pathways. The PINK1/Parkin pathway is the most well-studied ubiquitin-dependent mechanism. In addition, a group of mitochondrial autophagy receptors, such as Nix, Bnip3, and FUND1, can directly bind to LC3 without requiring extensive ubiquitination, which characterizes the ubiquitin-independent pathway. These distinct yet complementary pathways play crucial roles in regulating mitochondrial quality control and cellular homeostasis.</p>
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18 pages, 4694 KiB  
Article
BNIP3 Downregulation Ameliorates Muscle Atrophy in Cancer Cachexia
by Claudia Fornelli, Marc Beltrà, Antonio Zorzano, Paola Costelli, David Sebastian and Fabio Penna
Cancers 2024, 16(24), 4133; https://doi.org/10.3390/cancers16244133 - 11 Dec 2024
Viewed by 702
Abstract
Background and Aims: Cancer cachexia is a complex syndrome affecting most cancer patients and is directly responsible for about 20% of cancer-related deaths. Previous studies showed muscle proteolysis hyper-activation and mitophagy induction in tumor-bearing animals. While basal mitophagy is required for maintaining muscle [...] Read more.
Background and Aims: Cancer cachexia is a complex syndrome affecting most cancer patients and is directly responsible for about 20% of cancer-related deaths. Previous studies showed muscle proteolysis hyper-activation and mitophagy induction in tumor-bearing animals. While basal mitophagy is required for maintaining muscle mass and quality, excessive mitophagy promotes uncontrolled protein degradation, muscle loss and impaired function. BNIP3, a key mitophagy-related protein, is significantly increased in the muscles of both mice and human cancer hosts. This study aimed to define the potential of mitigating mitophagy via BNIP3 downregulation in preserving mitochondrial integrity, counteracting skeletal muscle loss in experimental cancer cachexia. Methods: Two in vivo gene delivery methods were performed to knock down muscle BNIP3: electroporation of a BNIP3-specific shRNA expression vector or adenovirus injection. Results: The electroporation effectively reduced muscle BNIP3 in healthy mice but was ineffective in C26 tumor-bearing mice. In contrast, adenovirus-mediated BNIP3 knockdown successfully decreased BNIP3 levels also in tumor hosts. Although BNIP3 knockdown did not impact overall on body or muscle mass, it improved muscle fiber size in C26-bearing miceh2, suggesting partial prevention of muscle atrophy. Mitochondrial respiratory chain complexes (OxPhos) and TOM20 protein levels were consistently rescued, indicating improvements in mitochondrial mass, while H2O2 levels were unchanged among the groups, suggesting that BNIP3 downregulation does not impair the endogenous control of oxidative balance. Conclusions: These findings suggest that a fine balance between mitochondrial disposal and biogenesis is fundamental for preserving muscle homeostasis and highlight a potential role for BNIP3 modulation against cancer-induced muscle wasting. Full article
(This article belongs to the Section Molecular Cancer Biology)
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<p>Mitophagy induction in C26 tumor-bearing mice. Representative immunoblotting showing the expression of BNIP3 and LC3-I/II in the skeletal muscle (<b>A</b>,<b>C</b>). Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold change related to C SCR (<b>B</b>,<b>D</b>). Body weight (<b>E</b>), GSN (<b>F</b>) and TA (<b>G</b>) weight correlation to BNIP3 protein expression. Data are means ± SD of 6 mice in the control group and 10 mice in the C26 group. Statistical significance: ** <span class="html-italic">p</span> &lt; 0.01 vs. C SCR.</p>
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<p>Silencing BNIP3 through electroporation does not prevent its overexpression in the skeletal muscle of tumor-bearing mice. Scheme of experimental protocol and timeline (<b>A</b>). Representative immunoblotting showing the expression of BNIP3 in TA muscle (<b>B</b>,<b>D</b>). Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold of C SCR (<b>C</b>) and C26 SCR (<b>E</b>), respectively. Differences in tibialis anterior (TA) and gastrocnemius (GSN) muscles (<b>F</b>) expressed in milligrams and normalized for 10 g of body weight. TA weight expressed in mg (<b>G</b>). Data are means ± SD of 6 mice in the control group and 11 mice in the C26 group. Statistical significance: ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001 vs. C SCR.3.3. Adenovirus-Mediated BNIP3 Silencing Improves Muscle Atrophy, Preserving Mitochondrial Homeostasis and Oxidative Capacity.</p>
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<p>Adenovirus-mediated BNIP3 silencing prevents BNIP3 overexpression in the skeletal muscle of C26 hosts. Scheme of experimental protocol and timeline (<b>A</b>). Representative immunoblotting showing the expression of GFP (<b>B</b>) and BNIP3 (<b>C</b>) in GSN skeletal muscle protein extracts. Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold of C SCR (<b>D</b>). Tibialis anterior (TA) and gastrocnemius (GSN) muscles (<b>E</b>,<b>F</b>), weight expressed in mg and normalized for 10 g of body weight. Data are means ± SD of 6–8 mice in the control group and 9–10 mice in the C26 group. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 vs. C SCR and ### <span class="html-italic">p</span> &lt; 0.001 vs. C26 SCR.</p>
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<p>BNIP3 silencing via adenovirus improves muscle atrophy while preserving mitochondrial homeostasis and oxidative capacity. Representative images at 10× magnification of H&amp;E staining performed on tibialis anterior cryostatic sections (<b>A</b>). Myofiber cross-sectional area (CSA) of TA muscles expressed as % relative to the C SCR group (<b>B</b>). Representative immunoblotting showing the expression of complexes of mitochondrial respiratory chain (<b>C</b>) and TOM20 (<b>E</b>) in TA skeletal muscle protein extracts. Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold of C SCR (<b>D</b>,<b>F</b>). Evaluation of H2O2 levels in GSN protein extracts (<b>G</b>) expressed as pmol per milligram of protein. Data are means ± SD of 6–8 mice in the control group and 9–10 mice in the C26 group. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 vs. C SCR and #### <span class="html-italic">p</span> &lt; 0.0001 vs. C26 SCR.</p>
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<p>Total representative immunoblotting showing the expression of BNIP3 (<b>A</b>,<b>E</b>,<b>G</b>), LC3 I/II (<b>C</b>) in skeletal muscle protein extracts. Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold of C SCR (<b>B</b>,<b>D</b>,<b>F</b>) and C26 SCR (<b>H</b>). Representative images at 10× magnification of GFP, H&amp;E and SDH staining performed on tibialis anterior cryostatic section (<b>I</b>). Red boxes highlight the representative immunoblotting presented in the main text. Data are means ± SD of 6 mice in the control group and 11 mice in the C26 group. Statistical significance: ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001 vs. C SCR.</p>
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<p>Representative immunoblotting showing the expression of GFP (<b>A</b>) and BNIP3 (<b>B</b>) in GSN, TOM20 (<b>D</b>) and complexes of mitochondrial respiratory chain (<b>F</b>) in TA skeletal muscle. Densitometric analysis is normalized for the corresponding vinculin content and expressed as fold change related to C SCR (<b>C</b>,<b>E</b>,<b>G</b>). Red boxes highlight the representative immunoblotting presented in the main text. Data are means ± SD of 6–8 mice in the control group and 9–10 mice in the C26 group. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 vs. C SCR and ### <span class="html-italic">p</span>&lt; 0.001 vs. C26 SCR.</p>
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<p>Representative images of subsarcolemmal mitochondria of gastrocnemius muscles obtained with transmission electron microscopy (<b>A</b>); quantification of area (<b>B</b>), circularity (<b>C</b>) and aspect-ratio (<b>D</b>) parameters of mitochondria. White circles correspond to mean values/animal (<span class="html-italic">n</span> = 3), whereas small grey circles correspond to every individual measurement. Statistical analysis: two-tailed Student’s <span class="html-italic">t</span>-test.</p>
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17 pages, 4693 KiB  
Article
Cadmium-Induced Oxidative Damage and the Expression and Function of Mitochondrial Thioredoxin in Phascolosoma esculenta
by Shenwei Gu, Xuebin Zheng, Xinming Gao, Yang Liu, Yiner Chen and Junquan Zhu
Int. J. Mol. Sci. 2024, 25(24), 13283; https://doi.org/10.3390/ijms252413283 - 11 Dec 2024
Viewed by 461
Abstract
Phascolosoma esculenta is a unique aquatic invertebrate native to China, whose habitat is highly susceptible to environmental pollution, making it an ideal model for studying aquatic toxicology. Mitochondrial thioredoxin (Trx2), a key component of the Trx system, plays an essential role in scavenging [...] Read more.
Phascolosoma esculenta is a unique aquatic invertebrate native to China, whose habitat is highly susceptible to environmental pollution, making it an ideal model for studying aquatic toxicology. Mitochondrial thioredoxin (Trx2), a key component of the Trx system, plays an essential role in scavenging reactive oxygen species (ROS), regulating mitochondrial membrane potential, and preventing ROS-induced oxidative stress and apoptosis. This study investigated the toxicity of cadmium (Cd) on P. esculenta and the role of P. esculenta Trx2 (PeTrx2) in Cd detoxification. The results showed that Cd stress altered the activities of T-SOD and CAT, as well as the contents of GSH and MDA in the intestine. After 96 h of exposure, histological damages such as vacuolization, cell necrosis, and mitophagy were observed. Suggesting that Cd stress caused oxidative damage in P. esculenta. Furthermore, with the prolongation of stress time, the expression level of intestinal PeTrx2 mRNA initially increased and then decreased. The recombinant PeTrx2 (rPeTrx2) protein displayed dose-dependent redox activity and antioxidant capacity and enhanced Cd tolerance of Escherichia coli. After RNA interference (RNAi) with PeTrx2, significant changes in the expression of apoptosis-related genes (Caspase-3, Bax, Bcl-2, and Bcl-XL) were observed. Proving that PeTrx2 rapidly responded to Cd stress and played a vital role in mitigating Cd-induced oxidative stress and apoptosis. Our study demonstrated that PeTrx2 is a key factor for P. esculenta to endure the toxicity of Cd, providing foundational data for further exploration of the molecular mechanisms underlying heavy metal resistance in P. esculenta. Full article
(This article belongs to the Special Issue Mechanisms of Heavy Metal Toxicity: 3rd Edition)
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Figure 1

Figure 1
<p>Changes of oxidative stress indicators in the intestine of <span class="html-italic">P. esculenta</span> under Cd stress. (<b>A</b>) T-SOD activity. (<b>B</b>) CAT activity. (<b>C</b>) GSH content. (<b>D</b>) MDA content. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). Different letters indicate significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Microstructural changes in the intestine of <span class="html-italic">P. esculenta</span> after 96 h of Cd stress. (<b>A1</b>,<b>A2</b>) Cross-sectional structure of the intestine in the control group. The morphology of epithelial cells is normal, and the structures of cilia and microvilli are clearly visible. (<b>B1</b>,<b>B2</b>) Cross-sectional structure of the intestine in the 6 mg/L group. The epithelial cells are vacuolated (white pentagram), but the structures of cilia and microvilli remain clear. (<b>C1</b>,<b>C2</b>) Cross-sectional structure of the intestine in the 24 mg/L group. The epithelial cells are vacuolated (white pentagram), with blurred microvilli and disappeared cilia (blue box). (<b>D1</b>,<b>D2</b>) Cross-sectional structure of the intestine in the 96 mg/L group. The epithelium cells are severely vacuolated (black pentagram), with necrotic cells (red arrows), blurred microvilli, and disappeared cilia (blue box). Red box shows A2, B2, C2, and D2, respectively. MD: mucosal fold; SM: submucosa; ML: muscle layer; AD: adventitia; IL: intestinal lumen; EP: epithelium cell; CI: cilia; MV: microvilli.</p>
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<p>Ultrastructural changes in the intestine of <span class="html-italic">P. esculenta</span> after 96 h of Cd stress. (<b>A1</b>) Control group, 15,000×, the microvilli of the intestinal epithelial cells are densely arranged. (<b>A2</b>) Control group, 20,000×, the nucleus is regular, the nuclear membrane structure is clear, and the cell membrane is intact (black arrow). (<b>A3</b>) Control group, 25,000×, the mitochondrial cristae is clear, the morphological structure is normal, and the cell membrane is intact (black arrow). (<b>B1</b>) 96 mg/L group, 12,000×, the microvilli on the free surface of intestinal epithelial cells was still intact, while numerous vacuoles appeared in the cells. (<b>B2</b>) 96 mg/L group, 20,000×, the nuclear membrane structure was damaged (black arrow), with condensed chromatin accumulating near the inner side of the nuclear membrane. (<b>B3</b>) 96 mg/L group, 20,000×, the cristae are blurred, a large number of autophagosomes (red arrow) and autophagy-like vesicles (yellow arrow) appeared in cells, and the nuclear membrane structure is damaged (black arrow). MV: microvilli, N: nucleus, M: mitochondria, VE: vacuole.</p>
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<p>Characterization of <span class="html-italic">Pe</span>Trx2. (<b>A</b>) Full-length cDNA sequence and deduced amino acid sequence of <span class="html-italic">Pe</span>Trx2. The start codon and stop codon are marked with red fonts, the polyadenylate tail signal is marked with a black box, the redox-active site “CGPC” is marked with a red box, the black underlines represent conservative cysteines, and the Trx domain is marked with a green box. (<b>B</b>) The 3D structure of <span class="html-italic">Pe</span>Trx2. (<b>a</b>) The N- and C-terminal of <span class="html-italic">Pe</span>Trx2. (<b>b</b>) The yellow part shows the Trx domain, which has an active site. (<b>C</b>) Multiple sequence alignment of <span class="html-italic">Pe</span>Trx2 and its homologs. The red box indicates the conserved redox active site, and the green box indicates the Trx domain. (<b>D</b>) Phylogenetic tree analysis of <span class="html-italic">Pe</span>Trx2. <span class="html-italic">P. esculenta</span> is shown in bold font, and <span class="html-italic">Pe</span>Trx2 belongs to the invertebrate branch.</p>
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<p>Tissue-specific expression of <span class="html-italic">Pe</span>Trx2 mRNA and its expression changes under Cd stress. (<b>A</b>) Expression of <span class="html-italic">Pe</span>Trx2 mRNA in different tissues. CF: coelom fluid, I: intestine, BW: body wall, CM: retractor muscle, N: nephridium. (<b>B</b>) The relative expression level of <span class="html-italic">Pe</span>Trx2 mRNA in the intestine of <span class="html-italic">P. esculenta</span> following Cd stress. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). Different letters indicate significant differences among the tissues or groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Expression and purification of r<span class="html-italic">Pe</span>Trx2. (<b>A</b>) Induction effect of r<span class="html-italic">Pe</span>Trx2 over time. Line 1: the cell lysate of r<span class="html-italic">Pe</span>Trx2 without induction; Line 2: the cell lysate of r<span class="html-italic">Pe</span>Trx2 induced for 1 h; Line 3: the cell lysate of r<span class="html-italic">Pe</span>Trx2 induced for 3 h; Line 4: the cell lysate of r<span class="html-italic">Pe</span>Trx2 induced for 5 h; Line 5: the cell lysate of r<span class="html-italic">Pe</span>Trx2 induced for 7 h; Line 6: the supernatant of the cell lysate; Line 7: the precipitation of the cell lysate; Line 8: marker. (<b>B</b>) Purification of r<span class="html-italic">Pe</span>Trx2. Line 1–5: purified proteins with different imidazole elution ladders; Line 6: marker. The red boxes show the target bands.</p>
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<p>The antioxidant activity of r<span class="html-italic">Pe</span>Trx2. (<b>A</b>) Insulin disulfide bond reducing activity of r<span class="html-italic">Pe</span>Trx2. The absorbance (650 nm) of the reaction mixture was monitored following the addition of DTT. The reaction system without r<span class="html-italic">Pe</span>Trx2 was set as the control, and the reaction system without DTT and r<span class="html-italic">Pe</span>Trx2 was set as the negative control. (<b>B</b>) ABTS radical scavenging activity of r<span class="html-italic">Pe</span>Trx2. Different concentrations of GSH were set as the positive control. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of Cd stress on the growth of pET28a and pET28a-<span class="html-italic">Pe</span>Trx2. (<b>A</b>) 0.3 mM CdCl<sub>2</sub> group; (<b>B</b>) 0.6 mM CdCl<sub>2</sub> group. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Changes in the relative expression of <span class="html-italic">Pe</span>Trx2 mRNA. <span class="html-italic">GAPDH</span> was used as an inference. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Changes in the relative expression levels of apoptosis-related genes. (<b>A</b>) <span class="html-italic">Caspase</span>-3. (<b>B</b>) <span class="html-italic">Bax</span>. (<b>C</b>) <span class="html-italic">Bcl</span>-2. (<b>D</b>) <span class="html-italic">Bcl-XL</span>. <span class="html-italic">GAPDH</span> was used as an inference. Data were shown as mean ± SD (<span class="html-italic">n</span> = 3). ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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25 pages, 8089 KiB  
Article
Protective Effects of Exogenous Melatonin Administration on White Fat Metabolism Disruption Induced by Aging and a High-Fat Diet in Mice
by Dongying Lv, Yujie Ren, Jiayan Chen, Ziyao Pang, Yaxuan Tang, Lizong Zhang, Laiqing Yan, Xiufeng Ai, Xiaoping Xv, Dejun Wang and Zhaowei Cai
Antioxidants 2024, 13(12), 1500; https://doi.org/10.3390/antiox13121500 - 9 Dec 2024
Viewed by 703
Abstract
Obesity has emerged as a major risk factor for human health, exacerbated by aging and changes in dietary habits. It represents a significant health challenge, particularly for older people. While numerous studies have examined the effects of obesity and aging on fat metabolism [...] Read more.
Obesity has emerged as a major risk factor for human health, exacerbated by aging and changes in dietary habits. It represents a significant health challenge, particularly for older people. While numerous studies have examined the effects of obesity and aging on fat metabolism independently, research on their combined effects is limited. In the present study, the protective action against white fat accumulation after a high-fat diet (HFD) exerted by exogenous melatonin, a circadian hormone endowed with antioxidant properties also involved in fat metabolism, was investigated in a mouse model. For this purpose, a battery of tests was applied before and after the dietary and melatonin treatments of the animals, including epididymal white adipose tissue (eWAT) histological evaluations, transcriptomic and lipidomic analyses, real-time PCR tests, immunofluorescence staining, Western blot, the appraisal of serum melatonin levels, and transmission electron microscopy. This study found that aged mice on a high-fat diet (HFD) showed increased lipid deposition, inflammation, and reduced antioxidant glutathione (GSH) levels compared to younger mice. Lipidomic and transcriptomic analyses revealed elevated triglycerides, diglycerides, ceramides, and cholesterol, along with decreased sphingomyelin and fatty acids in eWAT. The genes linked to inflammation, NF-κB signaling, autophagy, and lipid metabolism, particularly the melatonin and glutathione pathways, were significantly altered. The aged HFD mice also exhibited reduced melatonin levels in serum and eWAT. Melatonin supplementation reduced lipid deposition, increased melatonin and GSH levels, and upregulated AANAT and MTNR1A expression in eWAT, suggesting that melatonin alleviates eWAT damage via the MTNR1A pathway. It also suppressed inflammatory markers (e.g., TNF-α, NLRP3, NF-κB, IL-1β, and CEBPB) and preserved mitochondrial function through enhanced mitophagy. This study highlights how aging and HFD affect lipid metabolism and gene expression, offering potential intervention strategies. These findings provide important insights into the mechanisms of fat deposition associated with aging and a high-fat diet, suggesting potential intervention strategies. Full article
(This article belongs to the Special Issue Antioxidant Therapy for Obesity-Related Diseases)
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Figure 1
<p>Effect of high-fat diet (HFD) and aging on eWAT of mice. (<b>A</b>) Schematic diagram of the experimental procedure; (<b>B</b>) body weight curves; (<b>C</b>) average weights of the mice; (<b>D</b>) average eWAT weight/weight of the mice; (<b>E</b>) mean food consumption; (<b>F</b>) representative images of the eWAT of the mice; (<b>G</b>) histopathological assessment of the eWAT using H&amp;E staining; (<b>H</b>) adipocyte surface area; (<b>I,J</b>) IL-1β and TNF-α level in serum of mice detected by ELISA; (<b>K</b>) GSH level in serum; (<b>L</b>) GSH in eWAT; (<b>L</b>) data are expressed as the mean ± SEM (n = 7). All the data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Changes in the overall lipid composition and distribution in the eWAT of the OC and OH group. (<b>A</b>) Left: Venn diagram of shared differentially expressed Lipidomics analysis. Right: the numbers of lipid classes and scores (PCA) in the OC and OH groups. (<b>B</b>,<b>C</b>) Heatmap for Lipid in OC and OH groups classification bubble chart; (<b>D</b>) Lipid ontology (GO) annotations analysis in OC and OH groups; (<b>E</b>) KEGG enrichment analysis in the OC and OH groups. Data are expressed as the mean ± SEM (n = 6). All the data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Characterization of mice eWAT transcriptomic characteristics. (<b>A</b>) Left: Venn diagram of shared differentially expressed genes. Right: volcano plot of differentially expressed genes in the OC and OH groups. The yellow dots represent upregulated genes, black dots represent downregulated genes, and gray dots represent genes with no significant differences. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001. (<b>B</b>) Heatmap and hierarchical clustering of genes involved in different stages. The colors represent the relative gene expression values after normalization adjustments. The red and green colors refer to up- and downregulation, respectively. (<b>C</b>) Strategy for the enrichment analysis of metabolites in the highlighted cluster with MetaboAnalyst 5.0 based on the KEGG and SMPDB (the Small Molecule Pathway Database) database. (<b>D</b>,<b>E</b>) PGC-1α, Leptin, Sptlc3, Sptlc1, NLRP3, TNF-α, CEBPB, IL-6, IL-10, PINK1, Parkin, and LC3II mRNA transcription assays, mouse eWAT from the OC group (normal diet aged mice) and OH group (HFD aged mice) followed by qPCR analysis (n = 6, mean ± SEM). (<b>F</b>) Serum melatonin levels in the groups Young, Old, and Old + HFD. (<b>G</b>) eWAT melatonin levels in the groups Young, Old, and Old + HFD. All the data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Correlation analysis of transcriptome and lipidomics revealed the changes in the regulation of eWAT by a high-fat diet in the aged mice. (<b>A</b>,<b>B</b>) Correlation KEGG enrichment analysis for differential genes and lipid molecules. (<b>C</b>) From left to right: heatmap of the genes with the most significant differences between the OH and OC group; the molecular changes in different kinds of lipids between the OH and OC group; and correlation analysis of genes and lipids. (<b>D</b>) Summary of the regulatory pathway changes in eWAT through lipidome and transcriptome.The red arrow in the figure indicates upregulation of expression and the green arrow indicates downregulation.</p>
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<p>Effect of melatonin on the eWAT of the aged mice with high-fat diet. (<b>A</b>) Schematic diagram of the experimental procedure; (<b>B</b>) body weight curves; (<b>C</b>) average weights of the mice; (<b>D</b>) average eWAT weight/weight of the mice; (<b>E</b>) representative images of the eWAT of the mice; (<b>F</b>) histopathological assessment of the eWAT using H&amp;E staining; (<b>G</b>) adipocyte surface area; (<b>H</b>,<b>I</b>) melatonin level in serum and eWAT detected by liquid chromatography; (<b>J</b>) GSH level in serum; (<b>K</b>) GSH in eWAT. All the data are represented as mean ± SEM. <sup>ns</sup> <span class="html-italic">p</span> &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Melatonin increases AANAT and MTNR1A expression in eWAT. (<b>A</b>) AANAT and MTNR1A in eWAT detected by immunofluorescence double staining (scale bar is 50 μm); (<b>B</b>) immunofluorescence analysis of CEBPB in eWAT (scale: 50 μm); (<b>C</b>–<b>E</b>) statistical graph of MTNR1A,AANAT and CEBPB positive expression rate; (<b>F</b>) Western blot analysis of MTNR1A protein expression in eWAT; (<b>G</b>) statistical graph of protein expression; (<b>H</b>) relative expression of MTNR1A, AANAT and CEBPB mRNA expression by qPCR analysis. All data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Melatonin improves mitochondrial autophagy and inflammation in eWAT. (<b>A</b>–<b>C</b>) immunofluorescence analysis of NLRP3, NF-κB, and IL-1β in eWAT (scale: 50 μm); (<b>D</b>,<b>F</b>) statistical graph of NLRP3, NF-κB, and IL-1β positive expression rate; (<b>E</b>) Western blot analysis of NLRP3, NF-κB, MMP9, and IL-1β protein expression in eWAT; (<b>G</b>,<b>H</b>) IL-1β, TNF-α level in serum of mice detected by ELISA; (<b>I</b>) transmission electron microscope picture of eWAT (scale: 50 nm); (<b>J</b>) Western blot analysis of PINK1 and PARKIN protein expression in eWAT; (<b>K</b>) statistical graph of protein expression. The red arrow points to the mitochondria. All data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Summary of signaling pathways involved in adipocyte metabolic disorders caused by melatonin decline with aging and HFD in eWAT. Upward arrows indicate upregulated expression levels and downward arrows indicate downregulated expression levels.</p>
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<p>High-fat-induced increases in body weight and eWAT weight were exacerbated in aged mice. (<b>A</b>) Average weight gain (left) and eWAT weight/weight gain (right) of mice; (<b>B</b>) melatonin levels in liver and eWAT detected by liquid chromatography. All data are represented as mean ± SEM. *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Changes in antioxidant-related genes in eWAT of the OC and OH groups. (<b>A</b>) Transcriptome GO enrichment barplot of annotations analysis in OC and OH groups. (<b>B</b>) Violin plot shows the relative expression levels of three antioxidant-related genes (GPST2, GPX3, and GPX4) in the OC and OH groups in the transcriptome. (<b>C</b>) Top left: GSEA of genes regulating glutathione transferase activity, enrichment score NES = −1.97, <span class="html-italic">p</span> &lt; 0.01; bottom right: ranked gene list of GSEA. All the data are represented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The melatonin content was detected by liquid chromatography, and the characteristic ion mass chromatograph of the melatonin standard solution was used (0.05 μg/L).</p>
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