Bioinformatic Analysis Reveals Hub Immune-Related Genes of Diabetic Foot Ulcers

Unraveling IFI44L's biofunction in human disease

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.

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers

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.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

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.

Insight into the role of IRF7 in skin and connective tissue diseases

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.

Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot

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.

Identification of potential immunologic resilience in the healing process of diabetic foot ulcers

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.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

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.

Advanced glycation end products regulate macrophage apoptosis and influence the healing of diabetic foot wound through miR-361-3p/CSF1R and PI3K/AKT pathway

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.

Role of M1 macrophages in diabetic foot ulcers and related immune regulatory mechanisms

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.

Characterisation of key biomarkers in diabetic ulcers via systems bioinformatics

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.

Mini Review Therapeutic Strategies Targeting for Biofilm and Bone Infections

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.