Preclinical study of diabetic foot ulcers: From pathogenesis to vivo/vitro models and clinical therapeutic transformation

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis

BACKGROUND

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU.

METHODS

In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses.

RESULTS

In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways.

CONCLUSIONS

PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

Animal Models of Type 2 Diabetes Complications: A Review

Diabetes mellitus is a multifactorial metabolic disease, of which type 2 diabetes (T2D) is one of the most common. The complications of diabetes are far more harmful than diabetes itself. Type 2 diabetes complications include diabetic nephropathy (DN), diabetic heart disease, diabetic foot ulcers (DFU), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (DR) et al. Many animal models have been developed to study the pathogenesis of T2D and discover an effective strategy to treat its consequences. In this sense, it is crucial to choose the right animal model for the corresponding diabetic complication. This paper summarizes and classifies the animal modeling approaches to T2D complications and provides a comprehensive review of their advantages and disadvantages. It is hopeful that this paper will provide theoretical support for animal trials of diabetic complications.

Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning

Background

A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.

Methods

This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.

Results

This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.

Conclusion

This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.

Preclinical study of diabetic foot ulcers: From pathogenesis to vivo/vitro models and clinical therapeutic transformation

Diabetic foot ulcer (DFU), a common intractable chronic complication of diabetes mellitus (DM), has a prevalence of up to 25%, with more than 17% of the affected patients at risk of amputation or even death. Vascular risk factors, including vascular stenosis or occlusion, dyslipidemia, impaired neurosensory and motor function, and skin infection caused by trauma, all increase the risk of DFU in patients with diabetes. Therefore, diabetic foot is not a single pathogenesis. Preclinical studies have contributed greatly to the pathogenesis determination and efficacy evaluation of DFU. Many therapeutic tools are currently being investigated using DFU animal models for effective clinical translation. However, preclinical animal models that completely mimic the pathogenesis of DFU remain unexplored. Therefore, in this review, the preparation methods and evaluation criteria of DFU animal models with three major pathological mechanisms: neuropathy, angiopathy and DFU infection were discussed in detail. And the advantages and disadvantages of various DFU animal models for clinical sign simulation. Furthermore, the current status of vitro models of DFU and some preclinical studies have been transformed into clinical treatment programs, such as medical dressings, growth factor therapy, 3D bioprinting and pre-vascularization, Traditional Chinese Medicine treatment. However, because of the complexity of the pathological mechanism of DFU, the clinical transformation of DFU model still faces many challenges. We need to further optimize the existing preclinical studies of DFU to provide an effective animal platform for the future study of pathophysiology and clinical treatment of DFU.

Thou shall not heal: Overcoming the non-healing behaviour of diabetic foot ulcers by engineering the inflammatory microenvironment

Diabetic foot ulcers (DFUs) are a devastating complication for diabetic patients that have debilitating effects and can ultimately lead to limb amputation. Healthy wounds progress through the phases of healing leading to tissue regeneration and restoration of the barrier function of the skin. In contrast, in diabetic patients dysregulation of these phases leads to chronic, non-healing wounds. In particular, unresolved inflammation in the DFU microenvironment has been identified as a key facet of chronic wounds in hyperglyceamic patients, as DFUs fail to progress beyond the inflammatory phase and towards resolution. Thus, control over and modulation of the inflammatory response is a promising therapeutic avenue for DFU treatment. This review discusses the current state-of-the art regarding control of the inflammatory response in the DFU microenvironment, with a specific focus on the development of biomaterials-based delivery strategies and their cargos to direct tissue regeneration in the DFU microenvironment.

Mouse models of diabetes-related ulcers: a systematic review and network meta-analysis

BACKGROUND

Diabetic foot ulcers (DFUs) are a common complication of diabetes, associated with important morbidity. Appropriate animal models of DFUs may improve drug development, and subsequently the success rate of clinical trials. However, while many models have been proposed, they are extremely heterogeneous, and no standard has emerged. We thus propose a systematic review with a network meta-analysis (NMA) to gather direct and indirect evidence, and compare the different mouse models of diabetes-related ulcers.

METHODS

The systematic search was performed in Pubmed and Embase. The main outcomes were wound size measurement at days 3, 7, 11 and 15 (±1 day). The risk of bias and methodological quality of all included studies was assessed by using the Systematic Review Center for Laboratory animal Experimentation (SYRCLE) risk of bias tool. Meta-regressions were done on prespecified variables, including mouse strain, type of ulcer, sex, age, and use of a splint.

FINDINGS

We included 295 studies. Among all models, only db/db, ob/ob, streptozotocin (STZ), and STZ + high fat diet mice showed a significantly delayed wound healing, compared with controls, at each time point. Age, sex and ulcer type had influence on wound healing, although not at all time points.

INTERPRETATION

In conclusion, the db/db model is associated with the largest delay in wound healing The STZ model also exhibits significantly decreased wound healing. STZ + high fat diet and ob/ob mice may also be relevant models of diabetes-related ulcers, although the results rely on a more limited number of studies.

FUNDING

This work was funded by the Agence Nationale de la Recherche (grant ANR-18-CE17-0017).