Cells

16 pages, 1519 KiB

Open AccessReview

Breaking the Feedback Loop of β-Cell Failure: Insight into the Pancreatic β-Cell’s ER-Mitochondria Redox Balance

by Amira Zaher and Samuel B. Stephens

Cells 2025, 14(6), 399; https://doi.org/10.3390/cells14060399 (registering DOI) - 8 Mar 2025

Pancreatic β-cells rely on a delicate balance between the endoplasmic reticulum (ER) and mitochondria to maintain sufficient insulin stores for the regulation of whole animal glucose homeostasis. The ER supports proinsulin maturation through oxidative protein folding, while mitochondria supply the energy and redox [...] Read more.

Pancreatic β-cells rely on a delicate balance between the endoplasmic reticulum (ER) and mitochondria to maintain sufficient insulin stores for the regulation of whole animal glucose homeostasis. The ER supports proinsulin maturation through oxidative protein folding, while mitochondria supply the energy and redox buffering that maintain ER proteostasis. In the development of Type 2 diabetes (T2D), the progressive decline of β-cell function is closely linked to disruptions in ER-mitochondrial communication. Mitochondrial dysfunction is a well-established driver of β-cell failure, whereas the downstream consequences for ER redox homeostasis have only recently emerged. This interdependence of ER-mitochondrial functions suggests that an imbalance is both a cause and consequence of metabolic dysfunction. In this review, we discuss the regulatory mechanisms of ER redox control and requirements for mitochondrial function. In addition, we describe how ER redox imbalances may trigger mitochondrial dysfunction in a vicious feed forward cycle that accelerates β-cell dysfunction and T2D onset. Full article

(This article belongs to the Special Issue Endoplasmic Reticulum Stress Signaling Pathway: From Bench to Bedside)

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Figure 1
Proinsulin and insulin structure. (A) Linear proinsulin sequence highlighting the order of the six cysteine residues that form the three disulfide bonds. Sequence is colorized: brown is the B chain, green is the A chain, blue is C-peptide. (B) Structure of insulin highlighting the disulfide bonds between the A and B chains (A7-B7, A20-B19) and within the A chain (A6-A11), which are necessary for proper structure. Structure is colorized as follows: brown is the B chain; green is the A chain. Images were created using Mol* of 4EZT from the RCSB PDB [<a href="#B16-cells-14-00399" class="html-bibr">16</a>,<a href="#B17-cells-14-00399" class="html-bibr">17</a>]. Full article ">Figure 2
Overview of ER redox regulation. (A) Oxidative protein folding generates hydrogen peroxide (H2O2) through ERO1β. (B) Hydrogen peroxide is scavenged by PRDX4 in the ER lumen. (C) Hydrogen peroxide diffuses into the cytosol to be scavenged by cytosolic antioxidants such as peroxiredoxin with the aid of the redox carrier thioredoxin (TXN). (D) TXN is reduced by TXNRD1 and NADPH. In addition, electrons from TXN can be transferred into the ER via reduction-oxidation of an unknown ER membrane shuttle to aid ERdj5-like PDIs in the reduction of non-native disulfide bonds (E). Full article ">Figure 3
A model describing how ER hyperoxidation promotes mitochondrial dysfunction. With elevated demand for proinsulin synthesis, oxidative protein folding increases leading to excess ERO1β activity and hydrogen peroxide (H2O2) production. Hydrogen peroxide can oxidize PERK leading to its oligomerization, which enhances Mitochondrial-ER contacts (MERCs) tethering. Hydrogen peroxide can also oxidize IP3R and increase the transport of Ca2+ from the ER into the mitochondria through VDAC, leading to Ca2+ overload and mitochondrial dysfunction. Hydrogen peroxide can also diffuse from the ER into the mitochondria and cytosol and promote lipid peroxidation/ferroptosis and NLRP3 inflammasome activation, both of which can lead to mitochondrial dysfunction. Full article ">

25 pages, 3757 KiB

Open AccessArticle

GATAD2B O-GlcNAcylation Regulates Breast Cancer Stem-like Potential and Drug Resistance

by Giang Le Minh, Jessica Merzy, Emily M. Esquea, Nusaiba N. Ahmed, Riley G. Young, Ryan J. Sharp, Tejsi T. Dhameliya, Bernice Agana, Mi-Hye Lee, Jennifer R. Bethard, Susana Comte-Walters, Lauren E. Ball and Mauricio J. Reginato

Cells 2025, 14(6), 398; https://doi.org/10.3390/cells14060398 (registering DOI) - 8 Mar 2025

Abstract

The growth of breast tumors is driven and controlled by a subpopulation of cancer cells resembling adult stem cells, which are called cancer stem-like cells (CSCs). In breast cancer, the function and maintenance of CSCs are influenced by protein O-GlcNAcylation and the enzyme [...] Read more.

The growth of breast tumors is driven and controlled by a subpopulation of cancer cells resembling adult stem cells, which are called cancer stem-like cells (CSCs). In breast cancer, the function and maintenance of CSCs are influenced by protein O-GlcNAcylation and the enzyme responsible for this post-translational modification, O-GlcNAc transferase (OGT). However, the mechanism of CSCs regulation by OGT and O-GlcNAc cycling in breast cancer is still unclear. Analysis of the proteome and O-GlcNAcome, revealed GATAD2B, a component of the Nucleosome Remodeling and Deacetylase (NuRD) complex, as a substrate regulated by OGT. Reducing GATAD2B genetically impairs mammosphere formation, decreases expression of self-renewal factors and CSCs population. O-GlcNAcylation of GATAD2B at the C-terminus protects GATAD2B from ubiquitination and proteasomal degradation in breast cancer cells. We identify ITCH as a novel E3 ligase for GATAD2B and show that targeting ITCH genetically increases GATAD2B levels and increases CSCs phenotypes. Lastly, we show that overexpression of wild-type GATAD2B, but not the mutant lacking C-terminal O-GlcNAc sites, promotes mammosphere formation, expression of CSCs factors and drug resistance. Together, we identify a key role of GATAD2B and ITCH in regulating CSCs in breast cancer and GATAD2B O-GlcNAcylation as a mechanism regulating breast cancer stem-like populations and promoting chemoresistance. Full article

(This article belongs to the Special Issue Cellular Mechanisms of Anti-Cancer Therapies)

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19 pages, 3164 KiB

Open AccessArticle

Depletion of MGO or Its Derivatives Ameliorate CUMS-Induced Neuroinflammation

by Bing Liu, Ke Dong, Yun Zhao, Xue Wang, Zhaowei Sun, Fang Xie and Lingjia Qian

Cells 2025, 14(6), 397; https://doi.org/10.3390/cells14060397 (registering DOI) - 8 Mar 2025

Abstract

Advanced glycation end products (AGEs) are a series of structurally complex and harmful compounds formed through the reaction between the carbonyl group of reducing sugars (such as glucose and fructose) and the free amino groups of proteins, lipids, or nucleic acids. Excessive accumulation [...] Read more.

Advanced glycation end products (AGEs) are a series of structurally complex and harmful compounds formed through the reaction between the carbonyl group of reducing sugars (such as glucose and fructose) and the free amino groups of proteins, lipids, or nucleic acids. Excessive accumulation of AGEs in the body can trigger oxidative stress, induce inflammatory responses, and contribute to the development of diabetes, atherosclerosis, and neurological disorders. Within the category of dicarbonyl compounds, methylglyoxal (MGO)—a byproduct resulting from glucose degradation—serves as a pivotal precursor in the formation of AGEs and the induction of neurotoxicity. Specifically, AGEs generated from MGO display significant cytotoxicity toward cells in the central nervous system. Therefore, we aimed to investigate the role of MGO-AGEs in neuroinflammation mediated by CUMS. Interestingly, we found that the overexpression of glyoxalase 1 (GLO1) reduced the levels of MGO in corticosterone-treated microglia, thereby alleviating the inflammatory response. Furthermore, overexpression of GLO1 in the hippocampus of chronically stressed mice reduced MGO levels, mitigating CUMS-induced neuroinflammation and cognitive impairment. Additionally, when using the receptor for advanced glycation end products (RAGE) inhibitor FPS-ZM1 in primary microglia cells, we observed that despite corticosterone-induced elevation of MGO, no significant inflammatory response occurred. This suggests that RAGE clearance can reduce MGO-AGE-mediated neurotoxicity. Subsequently, we used FPS-ZM1 to treat chronically stressed mice and found that it significantly ameliorated neuroinflammation and cognitive dysfunction. These results suggest that targeting MGO metabolism could serve as a therapeutic approach to manage neuroinflammation in stress-related mental disorders. Full article

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18 pages, 7239 KiB

Open AccessArticle

The Effects of Electrical Stimulation on a 3D Osteoblast Cell Model

by Crystal O. Mahadeo, Alireza Shahin-Shamsabadi, Maedeh Khodamoradi, Margaret Fahnestock and Ponnambalam Ravi Selvaganapathy

Cells 2025, 14(6), 396; https://doi.org/10.3390/cells14060396 (registering DOI) - 8 Mar 2025

Viewed by 14

Abstract

Electrical stimulation has been used with tissue engineering-based models to develop three-dimensional (3D), dynamic, research models that are more physiologically relevant than static two-dimensional (2D) cultures. For bone tissue, the effect of electrical stimulation has focused on promoting healing and regeneration of tissue [...] Read more.

Electrical stimulation has been used with tissue engineering-based models to develop three-dimensional (3D), dynamic, research models that are more physiologically relevant than static two-dimensional (2D) cultures. For bone tissue, the effect of electrical stimulation has focused on promoting healing and regeneration of tissue to prevent bone loss. However, electrical stimulation can also potentially affect mature bone parenchymal cells such as osteoblasts to guide bone formation and the secretion of paracrine or endocrine factors. Due to a lack of physiologically relevant models, these phenomena have not been studied in detail. In vitro electrical stimulation models can be useful for gaining an understanding of bone physiology and its effects on paracrine tissues under different physiological and pathological conditions. Here, we use a 3D, dynamic, in vitro model of bone to study the effects of electrical stimulation conditions on protein and gene expression of SaOS-2 human osteosarcoma osteoblast-like cells. We show that different stimulation regimens, including different frequencies, exposure times, and stimulation patterns, can have different effects on the expression and secretion of the osteoblastic markers alkaline phosphatase and osteocalcin. These results reveal that electrical stimulation can potentially be used to guide osteoblast gene and protein expression. Full article

(This article belongs to the Collection Feature Papers in 'Tissues and Organs')

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(a) Schematic of the biofabrication process and (b) culture conditions of the SaOS-2 3D constructs formed using bioink with a cell density of 3 × 106 cells/mL and collagen-to-medium ratio of 1:4 maintained in static condition 1 (C1) or 6 different dynamic culture conditions (C2–C7) explaining primary and secondary stimuli. The frequency, determined by the wave and pulse width, for all primary or secondary stimulations with an x3 = 50 ms was 10 Hz, and those with an x3 = 25 ms was 20 Hz. GM: growth medium. x1 total duration of stimulation, x2 total rest time, x3 pulse width. Full article ">Figure 2
Electrical stimulation regimen affects protein expression. (a) Three-dimensional cellular constructs and cell distribution (low- and high-magnification): (i,ii) bright field microscopy of constructs) and (iii,iv) fluorescence images of phalloidin stained constructs. Effect of different stimulation conditions on (b) osteocalcin and (c) alkaline phosphatase secretion. ** p < 0.01, and **** p < 0.0001, ns not significant. Error bars represent SEM. One-way ANOVA, post hoc Tukey multiple comparisons. This experiment was performed 3 times for a total n of 12/group, with each sample assayed in duplicate. Full article ">Figure 3
Electrical stimulation affects osteocalcin and alkaline phosphatase gene expression. (a) Significantly higher osteocalcin mRNA levels in constructs from stimulation condition C7 as compared to non-stimulated controls. (Kruskal–Wallis * p = 0.036, post hoc Dunn test between control C1 vs. C7 groups * p = 0.045). Samples were measured in triplicate. (b) Alkaline phosphatase mRNA levels were significantly lower in bone constructs from stimulation conditions C7 and C4 compared to non-stimulated controls (C1). One-way ANOVA ** p = 0.009; post hoc Dunn test C1 vs. C4 * p = 0.015 and C1 vs. C7 * p = 0.012. Osteocalcin and alkaline phosphatase mRNA levels were normalized to GAPDH, which did not vary between conditions. Error bars represent SEM. This experiment was repeated 4 times for a total n of 12–15/group. Full article ">Figure 4
Hydroxyapatite added to collagen ECM creates an in vivo like microenvironment. (a) Non-uniform distribution of hydroxyapatite particles in the constructs (low- and high-magnification construct images using the Infinity Optical System microscope from top and bottom panels in a side view), (b) uniform distribution of cells despite non-uniformity of hydroxyapatite (HA) particles shown by staining cells with phalloidin for actin, (c) collagen-only constructs resulted in significantly higher levels of osteocalcin protein secreted into the medium compared to collagen and HA combined constructs. Student’s t-test ** p = 0.005. n = 3, Col: collagen-only construct, Col/HAp: construct with both collagen and HA as their ECM. Error bars represent standard error of the mean. (d) Scanning electron microscopy (SEM) image of HA alone (100×). (e) SEM image of the precipitated HA particles in the 3D construct (200×). (f) SEM image of HA particles precipitated to one side of the construct with bone cells and ECM on and around them (400×). (g) % Weight of elements using energy-dispersive spectroscopy (EDS). HA powder was used as a positive control to confirm composition. The main components in HA are phosphorous and calcium. Samples containing HA showed significantly elevated levels of these two elements compared to samples without HA. (g) HA powder with % weight (below) showing high levels of calcium and phosphorous. (h) Sample containing HA shows high levels of calcium and phosphorous. (i) Sample with no HA shows little to no calcium and phosphorous. Full article ">Figure 5
Alizarin Red staining of 3D constructs shows significant differences in calcium deposition. (a) Bone cell constructs from the C1 condition stained with Alizarin Red; (b) bone cell constructs from the C4 condition stained with Alizarin Red; (c) bone cell constructs from the C7 condition stained with Alizarin Red. One-way ANOVA, p = 0.0015 (Tukey multiple comparisons C1 vs. C4; ** p = 0.0046; C1 vs. C7, * p = 0.03; and C4 vs. C7, ** p = 0.0014). Images were taken using the EVOS Cell Imaging System. n = 2/group. Error bars represent the standard deviation. Full article ">

15 pages, 2763 KiB

Open AccessArticle

Association Between Synovial NTN4 Expression and Pain Scores, and Its Effects on Fibroblasts and Sensory Neurons in End-Stage Knee Osteoarthritis

by Ayumi Tsukada, Yui Uekusa, Etsuro Ohta, Akito Hattori, Manabu Mukai, Dai Iwase, Jun Aikawa, Yoshihisa Ohashi, Gen Inoue, Masashi Takaso and Kentaro Uchida

Cells 2025, 14(6), 395; https://doi.org/10.3390/cells14060395 (registering DOI) - 8 Mar 2025

Viewed by 43

Abstract

Osteoarthritis (OA) is a chronic joint disease marked by synovial inflammation, cartilage degradation, and persistent pain. Although Netrin-4 (NTN4) has been implicated in pain modulation in rheumatoid arthritis (RA), its role in OA pain remains less understood. Previous research has documented that NTN4 [...] Read more.

Osteoarthritis (OA) is a chronic joint disease marked by synovial inflammation, cartilage degradation, and persistent pain. Although Netrin-4 (NTN4) has been implicated in pain modulation in rheumatoid arthritis (RA), its role in OA pain remains less understood. Previous research has documented that NTN4 promotes axonal growth in rodent-derived neurons; however, its effects on human sensory neurons are yet to be fully explored. NTN4 also plays a multifactorial role in various non-neuronal cells, such as endothelial cells, tumor cells, and stromal cells. Nevertheless, its specific impact on synovial fibroblasts, which are key components of the synovium and have been linked to OA pain, is still unclear. This study examined the correlation between NTN4 expression levels and pain severity in OA, specifically investigating its effects on human iPSC-derived sensory neurons (iPSC-SNs) and synovial fibroblasts from OA patients. Our findings indicate a positive correlation between synovial NTN4 expression and pain severity. Recombinant human Netrin-4 (rh-NTN4) was also shown to enhance neurite outgrowth in human iPSC-SNs, suggesting a potential role in neuronal sensitization. Additionally, rh-NTN4 stimulated the production of pro-inflammatory cytokines (IL-6, IL-8) and chemokines (CXCL1, CXCL6, CXCL8) in synovium-derived fibroblastic cells, implicating it in synovial inflammation. Collectively, these results suggest that NTN4 may contribute to KOA pathology by promoting synovial inflammation and potentially sensitizing sensory neurons, thereby influencing the mechanisms of underlying pain. Full article

(This article belongs to the Special Issue Molecular Mechanisms of Neuropathic Pain)

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Relationship between NTN4 expression and osteoarthritis pathology. Full article ">Figure 2
Expression of NTN4 and its receptors in iPSC-derived sensory neurons (iPSC-SNs) and synovial fibroblasts. Box-and-whisker plots showing the relative expression levels of (A) NTN4, (B) UNC5B, and (C) NEO1 in fibroblasts (Fb), normalized to the expression levels in iPSC-derived sensory neurons (iPSC-SNs), which are set as 1. An asterisk (*) indicates statistical significance with p-values less than 0.05, demonstrating differences between fibroblast and iPSC-SN expression levels. Full article ">Figure 3
Effect of rh-NTN4 on neurite outgrowth in human iPSC-derived sensory neurons (A–C). Optical microscopy images at day 4 after rh-NTN4 stimulation: (A) vehicle-treated cells, (B) cells treated with 50 ng/mL rh-NTN4, (C) cells treated with 500 ng/mL rh-NTN4. (D) Quantification of neurite length in vehicle-treated, 50 ng/mL rh-NTN4-treated, and 500 ng/mL rh-NTN4-treated cells at day 4. Data are presented as mean ± SE. (E–G) Fluorescence microscopy images at day 14 after rh-NTN4 stimulation: (E) vehicle-treated cells, (F) cells treated with 50 ng/mL rh-NTN4, (G) cells treated with 500 ng/mL rh-NTN4. (H) Quantification of neurite length in vehicle-treated, 50 ng/mL rh-NTN4-treated, and 500 ng/mL rh-NTN4-treated cells at day 14. Data are presented as mean ± SE. * indicates significant differences between groups (p < 0.05). (I–K) Neurite length per neuron in the (E–G) images was measured using NeuronJ, an ImageJ plug-in. Full article ">Figure 4
qPCR analysis of vehicle and recombinant Netrin-4 treated fibroblastic cells derived from the synovium. Relative expression levels of (A) NTN4, (B) UNC5B, (C) NEO1, (D) MMP1, (E) MMP3, (F) MMP13, (G) VCAM1, (H) CXCL1, (I) CXCL6, (J) IL6, and (K) IL8 following rh-NTN4 treatment compared to vehicle control is presented using box-and-whisker plots. These plots depict the median, quartiles, and range of each dataset. Statistical significances between groups are clearly indicated with lines, and asterisks (*) denote p-values less than 0.05, highlighting statistically significant differences. Full article ">Figure 5
ELISA analysis of cell supernatant in vehicle- and recombinant Netrin-4-treated fibroblastic cells derived from the synovium. Concentrations of (A) MMP-1, (B) MMP-3, (C) MMP-13, (D) CXCL1, (E) CXCL6, (F) IL-6, and (G) IL-8 in cell supernatant are presented in box-and-whisker plots, showing the median, 25th and 75th percentiles, and range. * p < 0.05. Full article ">Figure 6
Flow cytometric analysis of vehicle- and recombinant Netrin-4-treated fibroblastic cells derived from the synovium. (A–C): Dot plot analysis of fibroblastic cells treated with vehicle (A), 50 ng/mL (B), and 500 ng/mL (C) recombinant human Netrin-4 (rh-NTN4). (D) Mean fluorescence intensity of vehicle- and rhNTN4-treated fibroblastic cells displayed as box-and-whisker plots. * indicates significant differences (p < 0.05) vs. vehicle. Full article ">

14 pages, 2001 KiB

Open AccessArticle

Mechanism of β-Catenin in Pulmonary Fibrosis Following SARS-CoV-2 Infection

by Min Jiang, Jiaqi Hou, Qianqian Chai, Shihao Yin and Qian Liu

Cells 2025, 14(6), 394; https://doi.org/10.3390/cells14060394 - 7 Mar 2025

Viewed by 65

Abstract

Pulmonary fibrosis due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the leading cause of death in patients with COVID-19. β-catenin, a key molecule in the Wnt/β-catenin signaling pathway, has been shown to be involved in the development of pulmonary fibrosis [...] Read more.

Pulmonary fibrosis due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the leading cause of death in patients with COVID-19. β-catenin, a key molecule in the Wnt/β-catenin signaling pathway, has been shown to be involved in the development of pulmonary fibrosis (e.g., idiopathic pulmonary fibrosis, silicosis). In this study, we developed a SARS-CoV-2-infected A549-hACE2 cell model to evaluate the efficacy of the A549-hACE2 monoclonal cell line against SARS-CoV-2 infection. The A549-hACE2 cells were then subjected to either knockdown or overexpression of the effector β-catenin, and the modified cells were subsequently infected with SARS-CoV-2. Additionally, we employed transcriptomics and raw letter analysis approaches to investigate other potential effects of β-catenin on SARS-CoV-2 infection. We successfully established a model of cellular fibrosis induced by SARS-CoV-2 infection in lung-derived cells. This model can be utilized to investigate the molecular biological mechanisms and cellular signaling pathways associated with virus-induced lung fibrosis. The results of our mechanistic studies indicate that β-catenin plays a significant role in lung fibrosis resulting from SARS-CoV-2 infection. Furthermore, the inhibition of β-catenin mitigated the accumulation of mesenchymal stroma in A549-hACE2 cells. Additionally, β-catenin knockdown was found to facilitate multi-pathway crosstalk following SARS-CoV-2 infection. The fact that β-catenin overexpression did not exacerbate cellular fibrosis may be attributed to the activation of PPP2R2B. Full article

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Significant upregulation of β-catenin, its downstream signaling molecules, and ECM components in A549-hACE2 cells following SARS-CoV-2 infection. (A) IFA of N Protein of SARS-CoV-2 to detect efficiency of infection at various time points. 200× magnification for all fields of view. (B) WB image of β-catenin, c-Myc, cyclin D1, α-SMA, and Vimentin following A549-hACE2 infection with SARS-CoV-2. (C) qPCR of β-catenin, c-Myc, cyclin D1, α-SMA, Vimentin, and COL3A1 following A549-hACE2 infection with SARS-CoV-2. A p-value < 0.05 (denoted by “*”) was considered statistically significant. Differences with p-value < 0.01 are denoted by “**”, while differences with p-value < 0.005 are denoted by “***”. Non-significant differences are denoted by “ns”. Full article ">Figure 2
Inhibition of β-catenin alleviated fibrosis following SARS-CoV-2 infection in A549-hACE2 cells, while overexpression of β-catenin did not exacerbate fibrosis. (A) Knockdown efficiency test of CTNNB1. (B) Detection efficiency of SARS-CoV-2 infection. 200× magnification for all fields of view. (C) WB image of c-Myc, cyclin D1, α-SMA, Vimentin, and FN1 following A549-hACE2-shCTNNB1 infection with SARS-CoV-2. (D) qPCR of c-myc, cyclin D1, a-SMA, Vimentin, COL3A1, and FN1 following A549-shACE2 infection with SARS-CoV-2. (E) Overexpression efficiency test of CTNNB1. (F) Detection efficiency of SARS-CoV-2 infection. 200× magnification for all fields of view. (G) WB image of c-Myc, cyclin D1, α-SMA, Vimentin, and FN1 following A549-hACE2-CTNNB1 infection with SARS-CoV-2. (H) qPCR of c-myc, cyclin D1, a-SMA, Vimentin, COL3A1, and FN1 following A549-ACE2 infection with SARS-CoV-2. A p-value < 0.05 (denoted by “*”) was considered statistically significant. Differences with p-value < 0.01 are denoted by “**”. Non-significant differences are denoted by “ns”. Full article ">Figure 3
(A,B) KEGG pathway and network of common DEGs of NC+SARS-2/NC+Mock and shC+SARS-2/NC+SARS-2. Inhibition of β-catenin induces crosstalk among multiple signaling pathways following SARS-CoV-2 infection. (C,D) Cellular component and biological process of common DEGs of NC+SARS-2/NC+Mock and shC+SARS-2/NC+SARS-2. GO enrichment analysis confirmed that the inhibition of β-catenin altered components, including collagen-containing ECM components and fibrinogen complexes, in A549-hACE2 cells following SARS-CoV-2 infection. (E,F) Volcanic plot and expression clustering of DEGs of eF+ SARS-2/PNCF+ SARS-2. Clustering and visualization of 45 DEGs revealed that PPP2R2B was upregulated in the CTNNB1 overexpression group. Full article ">

24 pages, 6292 KiB

Open AccessArticle

Role of Galactosylceramide Metabolism in Satellite Glial Cell Dysfunction and Neuron–Glia Interactions in Painful Diabetic Peripheral Neuropathy

by Xin Xu, Yue Zhang, Shuo Li, Chenlong Liao, Xiaosheng Yang and Wenchuan Zhang

Cells 2025, 14(6), 393; https://doi.org/10.3390/cells14060393 - 7 Mar 2025

Viewed by 49

Abstract

Diabetic peripheral neuropathy (DPN) is a prevalent and disabling complication of diabetes, with painful diabetic peripheral neuropathy (PDPN) being its most severe subtype due to chronic pain and resistance to treatment. Satellite glial cells (SGCs), critical for maintaining dorsal root ganglion (DRG) homeostasis, [...] Read more.

Diabetic peripheral neuropathy (DPN) is a prevalent and disabling complication of diabetes, with painful diabetic peripheral neuropathy (PDPN) being its most severe subtype due to chronic pain and resistance to treatment. Satellite glial cells (SGCs), critical for maintaining dorsal root ganglion (DRG) homeostasis, undergo significant structural and functional changes under pathological conditions. This study investigated the role of galactosylceramide (GalCer), a key sphingolipid, in SGC dysfunction and neuron–glia interactions during DPN progression. Using a rat model of PDPN, we employed single-cell RNA sequencing (scRNA-seq), targeted mass spectrometry, and immunofluorescence analysis. The PDPN group exhibited transcriptional activation and structural reorganization of SGCs, characterized by increased SGC abundance and glial activation, evidenced by elevated Gfap expression. Functional enrichment analyses revealed disruptions in sphingolipid metabolism, including marked reductions in GalCer levels. Subclustering identified vulnerable SGC subsets, such as Cluster a, with dysregulated lipid metabolism. The depletion of GalCer impaired SGC-neuron communication, destabilizing DRG homeostasis and amplifying neurodegeneration and neuropathic pain. These findings demonstrate that GalCer depletion is a central mediator of SGC dysfunction in PDPN, disrupting neuron–glia interactions and exacerbating neuropathic pain. This study provides novel insights into the molecular mechanisms of DPN progression and identifies GalCer metabolism as a potential therapeutic target. Full article

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Establishment of the diabetic neuropathy model and group stratification. (A) Blood glucose levels in control (N, n = 6) and STZ-treated (n = 15) rats over 28 days. STZ-treated rats developed significant hyperglycemia from Day 3 onward compared to the control group. (B) Mechanical sensitivity, measured as the 50% paw withdrawal threshold (PWT), was assessed in control (n = 5) and STZ-treated diabetic rats, which were further stratified into diabetic non-allodynic (DM, n = 5) and diabetic allodynic (PDPN, n = 5) groups based on their pain responses. Significant differences in PWT were observed in the PDPN group compared to the DM and control groups starting from Day 14, demonstrating variability in pain sensitivity among STZ-treated rats. (C) Thermal sensitivity assessment using the Hargreaves test. The PDPN group showed significantly reduced withdrawal latency (WL) in response to radiant heat compared to the DM and control groups, indicating thermal hyperalgesia. Heat hyperalgesia latency (HHL) was significantly increased in the PDPN group, further confirming heightened thermal sensitivity. Data are presented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001, NS: not significant. Full article ">Figure 2
Comprehensive Analysis of Cell Types and Gene Expression in DRG Tissues. (A) Schematic illustration of the experimental workflow. The diagram shows the process of collecting dorsal root ganglion (DRG) tissues from rats, followed by cell dissociation, single-cell RNA sequencing, and subsequent data analysis including clustering and visualization of different cell populations. (B) t-SNE plot and cell count bar graph of different cell types identified in DRG tissues. The t-SNE plot shows the clustering of different cell types, including SGCs (Satellite Glial Cells), neurons, Schwann cells, vascular endothelial cells (VECs), macrophage, and proliferating satellite glial cells (PSGC). The bar graph displays the number of cells in each cluster, with SGCs (1688, 29.8%), neurons (2734, 48.3%), Schwann cells (876, 15.5%), VECs (273, 4.8%), macrophage (49, 0.9%), and PSGC (38, 0.7%). (C) Dot plot showing the expression of selected marker genes across different cell types. The size of the dots represents the percentage of cells expressing the gene, and the color intensity indicates the average expression level. (D) t-SNE plot and cell count bar graph showing the distribution of all DRG cells across three groups (Control, DM, PDPN). The t-SNE plot visualizes the clustering of DRG cells from different conditions. The bar graph displays the total number of DRG cells identified in each group, with Control (1869 cells, 33%), DM (1271 cells, 22.5%), and PDPN groups (2516 cells, 44.5%). (E) t-SNE plot and bar graph illustrating the number of detected genes per cell across different DRG cell populations. The t-SNE plot visualizes the distribution of DRG cells based on the number of genes detected per cell, with darker shades representing a higher number of detected genes. The bar graph on the right quantifies the average number of detected genes per cell within each DRG cell type. SGCs exhibited the highest average number of detected genes (1989.3 per cell), followed by neurons (1065.4), Schwann cells (971.6), VECs (1045.3), macrophages (896.3), and PSGC (1268.9). Full article ">Figure 3
Analysis of SGC cells and Their Associated Pathways. (A) t-SNE plot of SGC cells color-coded by three groups (Control, DM, PDPN). The middle bar graph displays the total SGC cell counts for each group, with 395 cells (23.4%) in the Control, 294 cells (17.4%) in the DM, and 997 cells (59.2%) in the PDPN group. The right bar graph shows the proportion of SGC cells relative to the total DRG cell population in each group, with SGC accounting for 21.1% of DRG cells in the Control, 23.1% in the DM, and 39.6% in the PDPN group. (B) CellChat interaction network illustrating the weight of interactions among different cell types. The network highlights interactions between SGCs cells and other cell types such as Schwann cells, neurons, vascular endothelial cells (VECs), macrophage, and proliferating satellite glial cells (PSGC); Edge thickness represents the interaction frequency, with SGCs exhibiting strong interactions with neurons (top panel). The bar graph on the bottom quantifies the interaction strength between each cell type and neurons, showing that SGCs have the highest communication strength (0.4204), followed by PSGC (0.3547), neurons (0.1120), Schwann cells (0.3352), VECs (0.2625), and macrophages (0.0001). (C) Volcano plot of differentially expressed genes (DEGs) in SGCs across the three experimental groups. Upregulated genes in the Control vs. DM, Control vs. PDPN, and DM vs. PDPN comparisons are shown in red, while downregulated genes are in blue. (D) Enrichment analysis of differentially expressed genes. The left panel shows the GO enrichment results, and the right panel shows KEGG pathway enrichment results. The bar colors in the GO enrichment chart represent the −log10 adjusted p-values, and the sizes of the dots in the KEGG enrichment chart indicate the gene counts within each pathway, with colors representing the −log10 adjusted p-values. Full article ">Figure 4
Subcluster Analysis of SGC Cells and Interaction Networks. (A) t-SNE plot showing the clustering of SGCs (Satellite Glial Cells) subclusters labeled as a, b, c, and d. The bar graph on the right shows the cell counts for each subcluster: Cluster a (706 cells, 41.9%), Cluster b (705 cells, 41.8%), Cluster c (143 cells, 8.5%), and Cluster d (132 cells, 7.8%). (B) t-SNE plot colored by the number of genes expressed in each SGC cell, highlighting the distribution of cells within the t-SNE space. The color intensity represents the number of genes expressed in each cell. The bar graph on the right quantifies the average number of detected genes per cell across different cell populations, showing that Cluster a exhibits the highest transcriptional activity (1806 genes per cell), followed by Cluster b (1014.2), Cluster c (1364.9) and Cluster d (1102.9). (C) t-SNE plot of SGC subclusters across experimental groups (Control, DM, PDPN). The bar graph on the right displays the number of SGC cells in each subcluster for each group: Control, DM, and PDPN. (D) Heatmap showing GO enrichment analysis for each SGC subcluster. The heatmap displays the significant GO terms associated with each subcluster, highlighting pathways related to sphingolipid metabolism and fatty acid biosynthesis. (E) CellChat interaction network within SGC subclusters. The network diagram illustrates the interactions among subclusters a, b, c, and d; Edge thickness represents the interaction frequency, with SGCs exhibiting strong interactions with neurons (left panel). The bar graph on the right quantifies the strength of the interactions between each cell type, showing that Cluster a has the highest relative communication strength with other cell clusters, Cluster b (1.19), Cluster c (1.53), and Cluster d (1.42). (F) CellChat interaction network between SGC subclusters and other cell types, including neurons, Schwann cells, vascular endothelial cells (VECs), macrophage, and proliferating satellite glial cells (PSGC). The network diagram shows the interactions, with line thickness representing the interaction strength. The bar graph on the right quantifies the interaction strength between each cell type and neurons, showing that Cluster a has the highest relative communication strength with neurons (1.5256). (G) Dot plot displaying the expression of specific ligand-receptor pairs across different cell types. The size of the dots represents the percentage of cells expressing the ligand-receptor pair, and the color intensity indicates the average expression level, highlighting the ligand-receptor pairs of Cluster a and neurons. Full article ">Figure 5
Quantification of GalCer levels in DRG samples using targeted mass spectrometry. (A) Standard curve for GalCer quantification, generated using external standards with concentrations ranging from 0 to 3.5 × 102 ng/mL. The curve demonstrates a linear relationship with the equation f(x) = 14951.7 × x + 0 and R2 = 1, indicating high precision and reliability. The quantification was based on the ion transition m/z = 826.60 > 646.60. (B) Quantification of GalCer levels (% of sample mass) in the Control, DM, and PDPN groups (n = 5 samples per group). GalCer levels were significantly reduced in the PDPN group compared to the Control and DM groups. (C) Western blot analysis of Ugt8 protein expression in DRG samples from Control, DM, and PDPN groups (n = 2 samples per group). The right panel shows the quantification of Ugt8 protein levels normalized to Gapdh. (D) qPCR analysis of Ugt8 mRNA expression in DRG samples from Control, DM, and PDPN groups (n = 5 samples per group). Data are presented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001, NS: not significant. Full article ">Figure 6
Structural and functional changes in SGC–neuron units and Gfap expression in DRG across Control, DM, and PDPN groups. (A) Low-power electron micrograph illustrating the structure of neuron–satellite glial cell (SGC) units in a dorsal root ganglion (DRG). Neurons are labeled as N1–N6, and SGCs are colored in blue. The widened area in the SGCs surrounding N3 contains the cell’s nucleus. ct, connective tissue space; v, blood vessels. Scale bar, 10 µm. Adapted from Hanani M. (2020) [<a href="#B8-cells-14-00393" class="html-bibr">8</a>]. (B) Immunofluorescence images of DRG tissue from Control, DM, and PDPN groups, showing DAPI (blue), Gfap (red), and Ugt8 (green). White arrows indicate neuronal nuclei, characterized by their large size and faint appearance, while yellow arrows indicate SGC nuclei, which are smaller and brighter. Scale bars, 10 μm. (C) Quantification of the number of SGC nuclei surrounding each neuron. The Control group showed 2.15 ± 1.01 SGCs per neuron, the DM group showed 2.05 ± 1.20, and the PDPN group exhibited a significant increase to 5.00 ± 1.26. The middle panel quantifies Gfap expression, which was significantly elevated in the PDPN group compared to the Control and DM groups. The right panel quantifies Ugt8 expression, showing a significant reduction in the PDPN group. (D) Western blot analysis of Gfap protein expression in DRG samples from Control, DM, and PDPN groups (n = 3 samples per group). The right panel shows the quantification of Gfap protein levels normalized to Gapdh. (E) qPCR analysis of Gfap mRNA expression in DRG samples from Control, DM, and PDPN groups (n = 5 samples per group). Data are presented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001, NS: not significant. Full article ">

21 pages, 12742 KiB

Open AccessArticle

Adaptive Thermogenesis and Lipid Metabolism Modulation in Inguinal and Perirenal Adipose Tissues of Hezuo Pigs in Response to Low-Temperature Exposure

by Yao Li, Hai-Xia Shi, Jie Li, Hong Du, Rui Jia, Yu-Hao Liang, Xiao-Yu Huang, Xiao-Li Gao, Shuang-Bao Gun and Qiao-Li Yang

Cells 2025, 14(6), 392; https://doi.org/10.3390/cells14060392 - 7 Mar 2025

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Abstract

In mammals, exposure to low temperatures induces white adipose tissue (WAT) browning and alters lipid metabolism to promote thermogenesis, thereby maintaining body temperature. However, this response varies across different adipose depots. In this study, Hezuo pigs were exposed to either room temperature (23 [...] Read more.

In mammals, exposure to low temperatures induces white adipose tissue (WAT) browning and alters lipid metabolism to promote thermogenesis, thereby maintaining body temperature. However, this response varies across different adipose depots. In this study, Hezuo pigs were exposed to either room temperature (23 ± 2 °C) or low temperature (−15 ± 2 °C) for periods of 12 h, 24 h, 48 h, 5 d, 10 d, and 15 d. Inguinal fat (IF) and perirenal fat (PF) were collected and analyzed using hematoxylin and eosin (HE) staining, transmission electron microscopy, RT-qPCR, and RNA-seq. Following cryoexposure, our results demonstrated a significant increase in adipocyte number and a corresponding decrease in cross-sectional area in both IF and PF groups from 24 h to 10 d. While adipocyte numbers were elevated at 12 h and 15 d, these changes were not statistically significant. Moreover, lipid droplets and mitochondria were more abundant, and the mRNA expression levels of thermogenic genes UCP3 and PGC-1α were significantly higher compared to the control group during the 24 h-10 d cold exposure period. No significant changes were observed in the other groups. RNA-seq data indicated that the lipid metabolism of IF and PF peaked on day 5 of low-temperature treatment. In IF tissue, lipid metabolism is mainly regulated by genes such as FABP4, WNT10B, PCK1, PLIN1, LEPR, and ADIPOQ. These genes are involved in the classical lipid metabolism pathway and provide energy for cold adaptation. In contrast, in PF tissue, genes like ATP5F1A, ATP5PO, SDHB, NDUFS8, SDHA, and COX5A play roles within the neurodegenerative disease pathway, and PF tissue has a positive impact on the process related to degenerative diseases. Further investigation is needed to clarify the functions of these candidate genes in lipid metabolism in Hezuo pigs and to explore the genetic mechanisms underlying the cold-resistance traits in local pig populations. Full article

(This article belongs to the Special Issue Second Edition of Advances in Adipose Tissue Biology)

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16 pages, 3213 KiB

Open AccessArticle

Epigallocatechin Gallate Promotes Cuproptosis via the MTF1/ATP7B Axis in Hepatocellular Carcinoma

by Yuhan Fu, Lirui Hou, Kai Han, Chong Zhao, Hongbo Hu and Shutao Yin

Cells 2025, 14(6), 391; https://doi.org/10.3390/cells14060391 - 7 Mar 2025

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Abstract

Background: Cuproptosis is a form of copper-dependent non-apoptotic cell death. Cancer cells that prefer to use aerobic glycolysis for energy generation are commonly insensitive to cuproptosis, which hinders its application for cancer treatment. Epigallocatechin gallate (EGCG) possesses diverse pharmacological activities. However, the association [...] Read more.

Background: Cuproptosis is a form of copper-dependent non-apoptotic cell death. Cancer cells that prefer to use aerobic glycolysis for energy generation are commonly insensitive to cuproptosis, which hinders its application for cancer treatment. Epigallocatechin gallate (EGCG) possesses diverse pharmacological activities. However, the association between EGCG and cuproptosis has not been studied. Methods: The cell viability, proliferation, and cuproptosis-related protein levels were detected to investigate whether EGCG enhances the sensitivity of HCC cells to cuproptosis. The intracellular copper level, related copper metabolism proteins, and gene expression were detected to explore the mechanisms. In addition, a nude mouse xenograft model was established to determine the effects of EGCG on cuproptosis in tumor tissues. Results: The combination of EGCG and copper ionophores significantly enhanced the mortality of HCC cells and heightened the sensitivity of HCC cells to cuproptosis. There was a notable reduction in the expression of copper export protein copper-transporting P-type ATPase (ATP7B). EGCG effectively suppressed metal regulatory transcription factor (MTF1) expression and subsequently hindered the transcriptional regulation of ATP7B. EGCG also facilitated the intratumoral accumulation of copper and augmented susceptibility to cuproptosis in vivo. Conclusions: EGCG can increase the sensitivity of hepatocellular carcinoma cells to cuproptosis by promoting intracellular copper accumulation through the MTF1/ATP7B axis. Full article

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ECGG enhanced copper ionophore-mediated hepatocellular carcinoma cell death in vitro. (A,B) Cell viability of HepG2 (A) and SMMC-7721 (B) after gradient concentrations of ES 24 h treatment with DMSO or 100 μM EGCG was measured with crystal violet staining. (C,D) The cell death rate and quantification of 100 μM EGCG combined with 30 nM ES or 3 μM 8-HQ treatment of HepG2 and SMMC-7721 cells for 24 h using flow cytometry. (E,F) Colony formation after treatment with 50 μM EGCG combined with 20 nM ES or 2 μM 8-HQ in HepG2. For (A–F), media were supplemented with 2 μM CuCl2. (The data are presented as the mean ± standard deviation. n = 3, *** p < 0.001.). Full article ">Figure 2
EGCG combined with copper ionophores promotes hepatocellular carcinoma cell cuproptosis. (A) Diagram of apoptosis, necroptosis, ferroptosis, and pyroptosis mechanisms. (B) Heatmap of cell viability after 100 μM EGCG combined with 30 nM ES or 3 μM 8-HQ treatment for 24 h with 20 μM Z-Vad-fmk, 20 μM DPQ, 10 μM Ac-FLTD-cmk, 10 μM Nec-1, 10 μM NSA, 10 μM Fer-1, 10 μM DFO, and 10 μM autophagy inhibitor CQ. (C–F) Expression of HSP70 in liver cancer cells treated with 100 μM EGCG combined with 30 nM ES and 3 μM 8-HQ for 24 h. (G) DLAT protein aggregation was analyzed using immunofluorescence (green, DLAT; blue, DAPI). White scale bars on full tiles are 10 μm. (H,I) Western blotting detection of DLAT protein aggregation in liver cancer cells. For (B–I), media were supplemented with 2 μM CuCl2. (The data are presented as the mean ± standard deviation. n = 3, ** p < 0.01, and *** p < 0.001.). Full article ">Figure 3
EGCG inhibited ATP7B expression and increased intracellular copper accumulation. (A) Copper levels were assessed via ICP-MS in HepG2 cells treated with 30 nM ES with or without 100 μM EGCG for 18 h (n = 3). (B) Representative images of copper fluorescence in HepG2 cells treated with or without drugs for 18 h (red, copper; blue, DAPI). White scale bars on full tiled images are 100 μm. (C–H) ATP7B and CTR1 protein expression in HepG2 and SMMC-7721 cells treated with or without drugs for 18 h (n= 3). For A-H, media were supplemented with 2 μM CuCl2. The data are presented as the mean ± standard deviation. ** p < 0.01, *** p < 0.001, and ns indicates no significant difference. Full article ">Figure 4
EGCG regulates ATP7B transcription through MTF1. (A–C) Cell viability (n = 3) (A), ATP7B expression (B), and quantification (n = 3) (C) after pretreatment with 50 nM BafA1 and 10 μM MG132 for 6 h followed by drug treatment for 18 h. (D) Relative mRNA levels of genes of CTR1 and ATP7B in HepG2 cells after 12 h of drug treatment (n = 3). (E) A correlation analysis was performed to evaluate the association between MTF1 and ATP7B using GEPIA2, (F–I) MTF1 expression levels in HCC cells treated with drugs after 12 h. The concentration of EGCG was 100 μM, ES was 30 nM, and 8-HQ was 3 μM. For (A–I), media were supplemented with 2 μM CuCl2. (The data are presented as the mean ± standard deviation. * p < 0.05, *** p < 0.001, and ns indicates no significant difference.). Full article ">Figure 5
ECGG promoted cuproptosis in liver cancer in vivo. (A) Establishment of a xenograft model in nude mice and an experimental schematic diagram (n = 6). (B) Tumor growth curve. (C) Photographs of tumors. (D) Tumor weight. (E) Body weight of mice. (F) Tumor copper levels. (G,H) Protein content of tumor tissues. (The data are expressed as mean ± standard deviation, * p < 0.05, ** p < 0.01, *** p < 0.001 and ns indicates no significant difference.). Full article ">

13 pages, 1831 KiB

Open AccessArticle

Chloroquine Causes Aging-like Changes in Diaphragm Neuromuscular Junction Morphology in Mice

by Chloe I. Gulbronson, Sepideh Jahanian, Heather M. Gransee, Gary C. Sieck and Carlos B. Mantilla

Cells 2025, 14(6), 390; https://doi.org/10.3390/cells14060390 - 7 Mar 2025

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Abstract

Autophagy impairments have been implicated in various aging conditions. Previous studies in cervical motor neurons show an age-dependent increase in the key autophagy proteins LC3 and p62, reflecting autophagy impairment and autophagosome accumulation. Chloroquine is commonly used to inhibit autophagy by preventing autophagosome–lysosome [...] Read more.

Autophagy impairments have been implicated in various aging conditions. Previous studies in cervical motor neurons show an age-dependent increase in the key autophagy proteins LC3 and p62, reflecting autophagy impairment and autophagosome accumulation. Chloroquine is commonly used to inhibit autophagy by preventing autophagosome–lysosome fusion and may thus emulate the effects of aging on the neuromuscular system. Indeed, acute chloroquine administration in old mice decreases maximal transdiaphragmatic pressure generation, consistent with aging effects. We hypothesized that chloroquine alters diaphragm muscle neuromuscular junction (NMJ) morphology and increases denervation. Adult male and female C57BL/6 × 129J mice between 5 and 8 months of age were used to examine diaphragm muscle NMJ morphology and denervation following daily intraperitoneal injections of chloroquine (10 mg/kg/d) or vehicle for 7 days. The motor end-plates and pre-synaptic terminals were fluorescently labeled with α-bungarotoxin and anti-synaptophysin, respectively. Confocal microscopy was used to assess pre- and post-synaptic morphology and denervation. At diaphragm NMJs, chloroquine treatment decreased pre-synaptic volume by 12% compared to the vehicle (p < 0.05), with no change in post-synaptic volume. Chloroquine treatment increased the proportion of partially denervated NMJs by 2.7-fold compared to vehicle treatment (p < 0.05). The morphological changes observed were similar to those previously reported in the diaphragm muscles of 18-month-old mice. These findings highlight the importance of autophagy in the maintenance of the structural properties at adult NMJs in vivo. Full article

(This article belongs to the Special Issue Experimental Systems to Model Aging Processes)

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24 pages, 4533 KiB

Open AccessArticle

Anti-Tumor Effects of Cecropin A and Drosocin Incorporated into Macrophage-like Cells Against Hematopoietic Tumors in Drosophila mxc Mutants

by Marina Hirata, Tadashi Nomura and Yoshihiro H. Inoue

Cells 2025, 14(6), 389; https://doi.org/10.3390/cells14060389 - 7 Mar 2025

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Abstract

Five major antimicrobial peptides (AMPs) in Drosophila are induced in multiple sex combs (mxc) mutant larvae harboring lymph gland (LG) tumors, and they exhibit anti-tumor effects. The effects of other well-known AMPs, Cecropin A and Drosocin, remain unexplored. We investigated the [...] Read more.

Five major antimicrobial peptides (AMPs) in Drosophila are induced in multiple sex combs (mxc) mutant larvae harboring lymph gland (LG) tumors, and they exhibit anti-tumor effects. The effects of other well-known AMPs, Cecropin A and Drosocin, remain unexplored. We investigated the tumor-elimination mechanism of these AMPs. A half-dose reduction in either the Toll or Imd gene reduced the induction of these AMPs and enhanced tumor growth in mxc^mbn1 mutant larvae, indicating that their anti-tumor effects depend on the innate immune pathway. Overexpression of these AMPs in the fat body suppressed tumor growth without affecting cell proliferation. Apoptosis was promoted in the mutant but not in normal LGs. Conversely, knockdown of them inhibited apoptosis and enhanced tumor growth; therefore, they inhibit LG tumor growth by inducing apoptosis. The AMPs from the fat body were incorporated into the hemocytes of mutant but not normal larvae. Another AMP, Drosomycin, was taken up via phagocytosis factors. Enhanced phosphatidylserine signals were observed on the tumor surface. Inhibition of the signals exposed on the cell surface enhanced tumor growth. AMPs may target phosphatidylserine in tumors to induce apoptosis and execute their tumor-specific effects. AMPs could be beneficial anti-cancer drugs with minimal side effects for clinical development. Full article

(This article belongs to the Special Issue Drosophila as a Model for Understanding Human Disease)

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