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Du J, Luo H, Ye S, Zhang H, Zheng Z, Liu K. Unraveling IFI44L's biofunction in human disease. Front Oncol 2024; 14:1436576. [PMID: 39737399 PMCID: PMC11682996 DOI: 10.3389/fonc.2024.1436576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 11/26/2024] [Indexed: 01/01/2025] Open
Abstract
Interferon-induced protein 44-like (IFI44L) is regarded as an immune-related gene and is a member of interferon-stimulated genes (ISGs). They participate in network transduction, and its own epigenetic modifications, apoptosis, cell-matrix formation, and many other pathways in tumors, autoimmune diseases, and viral infections. The current review provides a comprehensive overview of the onset and biological mechanisms of IFI44L and its potential clinical applications in malignant tumors and non-neoplastic diseases.
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Yu X, Wu Z, Zhang N. Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers. Mol Med 2024; 30:215. [PMID: 39543487 PMCID: PMC11562697 DOI: 10.1186/s10020-024-00955-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Accepted: 10/08/2024] [Indexed: 11/17/2024] Open
Abstract
BACKGROUND To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU). METHODS Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level. RESULTS Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages. CONCLUSION The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.
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Affiliation(s)
- Xin Yu
- Pediatric Oncology of the First Hospital of Jilin University, Changchun, 130021, China
| | - Zhuo Wu
- Mircrosurgery Department of PLA General Hospital, Beijing, 100853, China
| | - Nan Zhang
- Burn Department of the First Hospital of Jilin University, No. 1 Xinmin Street, Chaoyang District, Changchun, 130021, Jilin Province, China.
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Matboli M, Al-Amodi HS, Khaled A, Khaled R, Roushdy MMS, Ali M, Diab GI, Elnagar MF, Elmansy RA, TAhmed HH, Ahmed EME, Elzoghby DMA, M.Kamel HF, Farag MF, ELsawi HA, Farid LM, Abouelkhair MB, Habib EK, Fikry H, Saleh LA, Aboughaleb IH. Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats. Front Endocrinol (Lausanne) 2024; 15:1384984. [PMID: 38854687 PMCID: PMC11157016 DOI: 10.3389/fendo.2024.1384984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/03/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.
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Affiliation(s)
- Marwa Matboli
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Hiba S. Al-Amodi
- Biochemistry Department, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Abdelrahman Khaled
- Bioinformatics Group, Center of Informatics Sciences (CIS), School of Information Technology and Computer Sciences, Nile University, Giza, Egypt
| | - Radwa Khaled
- Biotechnology/Biomolecular Chemistry Department, Faculty of Science, Cairo University, Cairo, Egypt
- Medicinal Biochemistry and Molecular Biology Department, Modern University for Technology and Information, Cairo, Egypt
| | - Marian M. S. Roushdy
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Marwa Ali
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | | | | | - Rasha A. Elmansy
- Anatomy Unit, Department of Basic Medical Sciences, College of Medicine and Medical Sciences, Qassim University, Buraydah, Saudi Arabia
- Department of Anatomy and Cell Biology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Hagir H. TAhmed
- Anatomy Unit, Department of Basic Medical Sciences, College of Medicine and Medical Sciences, AlNeelain University, Khartoum, Sudan
| | - Enshrah M. E. Ahmed
- Pathology Unit, Department of Basic Medical Sciences, College of Medicine and Medical Sciences, Gassim University, Buraydah, Saudi Arabia
| | | | - Hala F. M.Kamel
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
- Biochemistry Department, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Mohamed F. Farag
- Medical Physiology Department, Armed Forces College of Medicine, Cairo, Egypt
| | - Hind A. ELsawi
- Department of Internal Medicine, Badr University in Cairo, Badr, Egypt
| | - Laila M. Farid
- Pathology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | | | - Eman K. Habib
- Department of Anatomy and Cell Biology, Faculty of Medicine, Galala University, Attaka, Suez Governorate, Egypt
| | - Heba Fikry
- Department of Histology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Lobna A. Saleh
- Department of Clinical Pharmacology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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Wang L, Yang F, Ye J, Zhang L, Jiang X. Insight into the role of IRF7 in skin and connective tissue diseases. Exp Dermatol 2024; 33:e15083. [PMID: 38794808 DOI: 10.1111/exd.15083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 03/15/2024] [Accepted: 04/08/2024] [Indexed: 05/26/2024]
Abstract
Interferons (IFNs) are signalling proteins primarily involved in initiating innate immune responses against pathogens and promoting the maturation of immune cells. Interferon Regulatory Factor 7 (IRF7) plays a pivotal role in the IFNs signalling pathway. The activation process of IRF7 is incited by exogenous or abnormal nucleic acids, which is followed by the identification via pattern recognition receptors (PRRs) and the ensuing signalling cascades. Upon activation, IRF7 modulates the expression of both IFNs and inflammatory gene regulation. As a multifunctional transcription factor, IRF7 is mainly expressed in immune cells, yet its presence is also detected in keratinocytes, fibroblasts, and various dermal cell types. In these cells, IRF7 is critical for skin immunity, inflammation, and fibrosis. IRF7 dysregulation may lead to autoimmune and inflammatory skin conditions, including systemic scleroderma (SSc), systemic lupus erythematosus (SLE), Atopic dermatitis (AD) and Psoriasis. This comprehensive review aims to extensively elucidate the role of IRF7 and its signalling pathways in immune cells and keratinocytes, highlighting its significance in skin-related and connective tissue diseases.
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Affiliation(s)
- Lian Wang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Fengjuan Yang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Ye
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Lu Zhang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xian Jiang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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Du G, Chen J, Zhu X, Zhu Z. Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot. iScience 2024; 27:109094. [PMID: 38439964 PMCID: PMC10910239 DOI: 10.1016/j.isci.2024.109094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/08/2023] [Accepted: 01/30/2024] [Indexed: 03/06/2024] Open
Abstract
The role of transforming growth factor β (TGF-β) in inflammation and immune response is established, but the mechanism of TGF-β signaling pathway-related genes (TRGs) in diabetic foot ulcer (DFU) is not fully understood. We aimed to investigate the contribution of TRGs in the identification, molecular categorization, and immune infiltration of DFU through bioinformatics analysis. TGF-β signaling pathway is activated in DFU. 33 TRGs were upregulated. Regression analysis revealed TGFBR1 and TGFB1 as significant differential expression core genes, validated by quantitative real-time PCR. The diagnostic model with core genes had high clinical validity (AUC = 0.909). Core gene expression was associated with immune cell infiltration. A total of 5672 genes showed differential expression in TGF-related patterns, with differences in biological functions and immune infiltration. TGF-β signaling pathway may be critical in DFU development.
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Affiliation(s)
- Guanggang Du
- Department of Burn and Wound Repair, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Jie Chen
- Department of Burn and Wound Repair, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Xuezhu Zhu
- Department of Burn and Wound Repair, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Zongdong Zhu
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
- Department of Orthopaedics, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
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Cheng Y, Ren L, Niyazi A, Sheng L, Zhao Y. Identification of potential immunologic resilience in the healing process of diabetic foot ulcers. Int Wound J 2024; 21:e14465. [PMID: 37926487 PMCID: PMC10898407 DOI: 10.1111/iwj.14465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/15/2023] [Indexed: 11/07/2023] Open
Abstract
Diabetic foot ulcers (DFUs) are one of the most common and challenging complications of diabetes, yet our understanding of their pathogenesis remains limited. We collected gene expression data of DFU patients from public databases. Bioinformatics tools were applied for systematic analysis, including the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA) and enrichment analysis. We further used single-cell RNA sequencing to identify the distribution of different cell populations in DFU. Finally, key results were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and flow cytometry. We identified 217 DEGs between ulcerated and healthy skin, and 37 DEGs between healing ulcers and ulcers. WGCNA revealed that the cyan module had the highest positive correlation with healthy skin and negative correlation with ulcers. The black module had the highest negative correlation with healthy skin and positive correlation with ulcers. Enrichment analysis showed that the genes in the cyan module were mainly associated with complement and coagulation cascades, while the genes in the black module were mainly associated with the IL-17 signalling pathway. In addition, CD8 T cells were significantly lower in ulcers than in healthy and healing ulcers. By comparing marker genes of CD8 T cells, we identified key genes in the cyan and black modules and validated their expression using RT-qPCR. The proportion of CD8 T cells was increased in healing ulcers. Flow cytometry detected increased levels of CD8 T, B and natural killer cells in healing ulcers. CD8 T cells and related key genes play an important role in the healing process of DFU. The results of this study provide a new perspective for understanding the pathogenesis and treatment of DFU.
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Affiliation(s)
- Yifeng Cheng
- Department of BurnsThe First Affiliated Hospital of Xinjiang Medical UniversityXinjiangChina
| | - Lei Ren
- Department of BurnsThe First Affiliated Hospital of Xinjiang Medical UniversityXinjiangChina
| | - Aihemaitijiang Niyazi
- Department of BurnsThe First Affiliated Hospital of Xinjiang Medical UniversityXinjiangChina
| | - Li Sheng
- Department of BurnsThe First Affiliated Hospital of Xinjiang Medical UniversityXinjiangChina
| | - Yang Zhao
- Department of BurnsThe First Affiliated Hospital of Xinjiang Medical UniversityXinjiangChina
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Shi H, Yuan X, Yang X, Huang R, Fan W, Liu G. A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration. BMC Genomics 2024; 25:125. [PMID: 38287255 PMCID: PMC10826017 DOI: 10.1186/s12864-024-10038-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/22/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis. METHODS We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset. RESULTS In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929). CONCLUSION This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.
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Affiliation(s)
- Hongshuo Shi
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Yuan
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiao Yang
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Renyan Huang
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Weijing Fan
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Guobin Liu
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Guangming Traditional Chinese Medicine Hospital Pudong New Area, Shanghai, China.
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Jin Y, Huang Y, Zeng G, Hu J, Li M, Tian M, Lei T, Huang R. Advanced glycation end products regulate macrophage apoptosis and influence the healing of diabetic foot wound through miR-361-3p/CSF1R and PI3K/AKT pathway. Heliyon 2024; 10:e24598. [PMID: 38312602 PMCID: PMC10835292 DOI: 10.1016/j.heliyon.2024.e24598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
Background Diabetic foot ulcers (DFUs) are a severe complication of diabetes. Persistent inflammation and impaired vascularization present considerable challenges in tissue wound healing. The aim of this study was to identify the crucial regulators of DFU wound healing and investigate their specific mechanisms in DFU. Methods DFU RNA sequencing data were obtained to identify crucial feature genes. The expression levels of the feature genes and their corresponding microRNAs (miRNAs) were verified in clinical samples. Subsequently, the expression of CD68 was determined in DFU and non-diabetic foot skin samples. RAW 264.7 cells were treated with advanced glycation end products (AGEs) to determine their viability and apoptosis. Finally, the roles of the selected crucial genes and their corresponding miRNAs were investigated using in vitro experiments and a mouse model of diabetes. Results Bioinformatic analysis showed that five crucial feature genes (CORO1A, CSF1R, CTSH, NFE2L3, and SLC16A10) were associated with DFU wound healing. The expression validation showed that miR-361-3p-CSF1R had a significant negative correlation and was thus selected for further experiments. AGEs significantly inhibited the viability of RAW 264.7 cells and enhanced their apoptosis; furthermore, the AGEs significantly downregulated CSF1R and increased miR-361-3p levels compared with the control cells. Additionally, inhibition of miR-361-3p decreased the cell apoptosis caused by AGEs and increased the levels of p-AKT/AKT and p-PI3K/PI3K, whereas CSF1R knockdown reversed the effects of miR-361-3p. In vivo experiments showed that miR-361-3p inhibition promoted wound healing in diabetic mice and regulated PI3K/AKT levels. Conclusions AGEs may regulate macrophage apoptosis via the miR-361-3p/CSF1R axis and PI3K/AKT pathway, thereby influencing DFU wound healing.
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Affiliation(s)
- Yongzhi Jin
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Yi Huang
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Guang Zeng
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Junsheng Hu
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Mengfan Li
- Department of General Surgery, LiQun Hospital, Shanghai, 200333, China
| | - Ming Tian
- Shanghai Burn Institute, Department of Burn, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - Tao Lei
- Department of Endocrinology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Rong Huang
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
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Li Y, Li X, Ju S, Li W, Zhou S, Wang G, Cai Y, Dong Z. Role of M1 macrophages in diabetic foot ulcers and related immune regulatory mechanisms. Front Pharmacol 2023; 13:1098041. [PMID: 36699091 PMCID: PMC9868553 DOI: 10.3389/fphar.2022.1098041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/14/2022] [Indexed: 01/11/2023] Open
Abstract
Objectives: Diabetes foot ulcers (DFUs) are characterized by immune infiltration of M1 macrophages observed in foot skin, in which immune-associated genes (IRGs) play a prominent role. The precise expression of IRGs as well as any possible regulatory mechanisms that could be present in DFUs is yet unknown. Methods: The sequencing data of single-cell RNA (scRNA) in the foot skin of patients with DFUs were analyzed, screening out the cluster marker genes of foot skin obtained from the ImmPort database. IRG activity was assessed with the AUCell software package. The IRGs of DFUs were explored by analyzing the batch sequencing dataset of DFU skin tissue. HumanTFDB was adopted to identify relevant regulatory transcription factors (TFs). The STRING dataset was used to build the main TF protein-protein interaction networks. WB and immunofluorescence methods were used to verify M1 macrophage-related immune regulators. Results: There were 16 clusters found: SMC1, fibro, t-lympho, he fibro, vasendo, baselkera, diffkera, SMC2, M1 macro, M2 macro, sweet/seba, B-Lympho, Melanio, lymphendo, plasma, and Schwann. M1 and M2 macrophages both had considerably higher AUC ratings than patients with DFUs compared to other sub-populations of cells. The proportion of M1 macrophages was the highest in the non-healing group. According to scRNA analysis and batch sequencing data by GO and KEGG, DEGs were enriched in immune response. Some 106 M1 macro-IRGs were finally identified and 25 transcription factors were revealed as associated with IRG expression. The PPI network indicated NFE2L2, REL, ETV6, MAF, and NF1B as central transcription factors. Conclusion: Based on the bio-informatics analysis of scRNA and high-throughput sequencing data, we concluded that M1 macrophages may serve as the influencing factor of DFUs' non-union. In addition, NFE2L2 could be involved in the regulation of IRG expression within M1 macrophages.
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Affiliation(s)
- Yao Li
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China
| | - Xiaoyan Li
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China
| | - Shuai Ju
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China
| | - Wenqiang Li
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China
| | - Siyuan Zhou
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China,Shanghai Medical College, Fudan University, Shanghai, China
| | - Guili Wang
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China,Shanghai Medical College, Fudan University, Shanghai, China
| | - Yunmin Cai
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China
| | - Zhihui Dong
- Jinshan Hospital, Fudan University, Shanghai, China,Zhongshan Diabetic foot Multidisciplinary Diagnosis and Treatment Center and Jinshan Operation Center, Shanghai, China,Zhongshan Hospital, Fudan University, Shanghai, China,*Correspondence: Zhihui Dong,
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Zhang Z, Zhang Y, Yang D, Luo Y, Luo Y, Ru Y, Song J, Fei X, Chen Y, Li B, Jiang J, Kuai L. Characterisation of key biomarkers in diabetic ulcers via systems bioinformatics. Int Wound J 2022; 20:529-542. [PMID: 36181454 PMCID: PMC9885479 DOI: 10.1111/iwj.13900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/02/2022] [Accepted: 07/04/2022] [Indexed: 02/03/2023] Open
Abstract
Diabetic ulcers (DUs) are characterised by a high incidence and disability rate. However, its pathogenesis remains elusive. Thus, a deep understanding of the underlying mechanisms for the pathogenesis of DUs has vital implications. The weighted gene co-expression network analysis was performed on the main data from the Gene Expression Omnibus database. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were adopted to analyse the potential biological function of the most relevant module. Furthermore, we utilised CytoHubba and protein-protein interaction network to identify the hub genes. Finally, the hub genes were validated by animal experiments in diabetic ulcer mice models. The expression of genes from the turquoise module was found to be strongly related to DUs. GO terms, KEGG analysis showed that biological functions are closely related to immune response. The hub genes included IFI35, IFIT2, MX2, OASL, RSAD2, and XAF1, which were higher in wounds of DUs mice than that in normal lesions. Additionally, we also demonstrated that the expression of hub genes was correlated with the immune response using immune checkpoint, immune cell infiltration, and immune scores. These data suggests that IFI35, IFIT2, MX2, OASL, RSAD2, and XAF1 are crucial for DUs.
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Affiliation(s)
- Zhan Zhang
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Ying Zhang
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Dan Yang
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Yue Luo
- Department of Integrated TCM and Western Medicine, Shanghai Skin Disease HospitalTongji UniversityShanghaiChina
| | - Ying Luo
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Yi Ru
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Jiankun Song
- Department of Integrated TCM and Western Medicine, Shanghai Skin Disease HospitalTongji UniversityShanghaiChina
| | - Xiaoya Fei
- Department of Integrated TCM and Western Medicine, Shanghai Skin Disease HospitalTongji UniversityShanghaiChina
| | - Yiran Chen
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
| | - Bin Li
- Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina,Department of Integrated TCM and Western Medicine, Shanghai Skin Disease HospitalTongji UniversityShanghaiChina
| | - Jingsi Jiang
- Department of Skin and Cosmetics Research, Shanghai Skin Disease HospitalTongji UniversityShanghaiChina
| | - Le Kuai
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina,Institute of DermatologyShanghai Academy of Traditional Chinese MedicineShanghaiChina
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11
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Wu S, Wu B, Liu Y, Deng S, Lei L, Zhang H. Mini Review Therapeutic Strategies Targeting for Biofilm and Bone Infections. Front Microbiol 2022; 13:936285. [PMID: 35774451 PMCID: PMC9238355 DOI: 10.3389/fmicb.2022.936285] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/25/2022] [Indexed: 12/21/2022] Open
Abstract
Bone infection results in a complex inflammatory response and bone destruction. A broad spectrum of bacterial species has been involved for jaw osteomyelitis, hematogenous osteomyelitis, vertebral osteomyelitis or diabetes mellitus, such as Staphylococcus aureus (S. aureus), coagulase-negative Staphylococcus species, and aerobic gram-negative bacilli. S. aureus is the major pathogenic bacterium for osteomyelitis, which results in a complex inflammatory response and bone destruction. Although various antibiotics have been applied for bone infection, the emergence of drug resistance and biofilm formation significantly decrease the effectiveness of those agents. In combination with gram-positive aerobes, gram-negative aerobes and anaerobes functionally equivalent pathogroups interact synergistically, developing as pathogenic biofilms and causing recurrent infections. The adhesion of biofilms to bone promotes bone destruction and protects bacteria from antimicrobial agent stress and host immune system infiltration. Moreover, bone is characterized by low permeability and reduced blood flow, further hindering the therapeutic effect for bone infections. To minimize systemic toxicity and enhance antibacterial effectiveness, therapeutic strategies targeting on biofilm and bone infection can serve as a promising modality. Herein, we focus on biofilm and bone infection eradication with targeting therapeutic strategies. We summarize recent targeting moieties on biofilm and bone infection with peptide-, nucleic acid-, bacteriophage-, CaP- and turnover homeostasis-based strategies. The antibacterial and antibiofilm mechanisms of those therapeutic strategies include increasing antibacterial agents’ accumulation by bone specific affinity, specific recognition of phage-bacteria, inhibition biofilm formation in transcription level. As chronic inflammation induced by infection can trigger osteoclast activation and inhibit osteoblast functioning, we additionally expand the potential applications of turnover homeostasis-based therapeutic strategies on biofilm or infection related immunity homeostasis for host-bacteria. Based on this review, we expect to provide useful insights of targeting therapeutic efficacy for biofilm and bone infection eradication.
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Affiliation(s)
- Shizhou Wu
- Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Binjie Wu
- Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- West China School of Public Health, Sichuan University, Chengdu, China
| | - Shu Deng
- Boston University Henry M. Goldman School of Dental Medicine, Boston, MA, United States
| | - Lei Lei
- West China Hospital of Stomatology, Sichuan University, Chengdu, China
- *Correspondence: Lei Lei,
| | - Hui Zhang
- Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Hui Zhang,
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