Genome wide analysis of gene expression changes in skin from patients with type 2 diabetes

Research advances of MAL family members in tumorigenesis and tumor progression (Review)

The myelin and lymphocyte protein (MAL) family is a novel gene family first identified and characterized in 2002. This family is comprised of seven members, including MAL, MAL2, plasmolipin, MALL, myeloid differentiation‑associated marker (MYADM), MYADML2 and CMTM8, which are located on different chromosomes. In addition to exhibiting extensive activity during transcytosis, the MAL family plays a vital role in the neurological, digestive, respiratory, genitourinary and other physiological systems. Furthermore, the intimate association between MAL and the pathogenesis, progression and metastasis of malignancies, attributable to several mechanisms such as DNA methylation has also been elucidated. In the present review, an overview of the structural and functional properties of the MAL family and the latest research findings regarding the relationship between several MAL members and various cancers is provided. Furthermore, the potential clinical and scientific significance of MAL is discussed and directions for future research are summarized.

LncRNAs associated with oxidative stress in diabetic wound healing: Regulatory mechanisms and application prospects

Diabetes is a group of chronic diseases with blood glucose imbalance, and long-term hyperglycaemia causes sustained damage to various organs of the body, resulting in vascular lesions, neuropathy and impaired wound healing. Diabetic wound formation involves a variety of complex mechanisms, and they are characterized by a persistent chronic inflammatory response, degradation of angiogenesis and imbalance of extracellular matrix regulation, all of which are related to oxidative stress. Additionally, repair and healing of diabetic wounds require the participation of a variety of cells, cytokines, genes, and other factors, which together constitute a complex biological regulatory network. Recent studies have shown that long noncoding RNAs (lncRNAs) can be involved in the regulation of several key biological pathways and cellular functions demonstrating their critical role in diabetic wound healing. LncRNAs are a major family of RNAs with limited or no protein-coding function. Numerous studies have recently reported a strong link between oxidative stress and lncRNAs. Given that both lncRNAs and oxidative stress have been identified as potential drivers of diabetic wound healing, their link in diabetic wound healing can be inferred. However, the specific mechanism of oxidative stress related to lncRNAs in diabetic wound healing is still unclear, and elucidating the functions of lncRNAs in these processes remains a major challenge. This article reviews the mechanisms of lncRNAs related to oxidative stress in several stages of diabetic wound healing and discusses diagnostic and treatment potential of lncRNAs to treat diabetic wounds by improving oxidative stress, as well as the challenges of using lncRNAs for this purpose. It is hoped that these results will provide new targets and strategies for the diagnosis and treatment of impaired wound healing in diabetic patients.

Prediction of Weight Loss in Filipino Americans to Decrease Risk for Type 2 Diabetes: Using Multi-Dimensional Data (Preprint)

BACKGROUND

Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy.

OBJECTIVE

The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D.

METHODS

The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss.

RESULTS

Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]).

CONCLUSIONS

Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

Free fatty acids, glucose, and insulin in type 2 diabetes mellitus

Xu et al used the HOMA2 model to estimate the β-cell function and insulin resistance levels in an individual from simultaneously measured fasting plasma glucose and fasting plasma insulin levels. This method is based on the assumption that the glucose-insulin axis is central for the metabolic activities, which led to type 2 diabetes. However, significant downregulation of both the NKX2-1 gene and the TPD52L3 gene force an increase in the release of free fatty acids (FFAs) into the blood circulation, which leads to a marked reduction in membrane flexibility. These data favor a FFA-glucose-insulin axis. The authors are invited to extend their study with the introduction of the saturation index (number of carbon-carbon double bonds per 100 fatty-acyl chains), as observed in erythrocytes.

Homo Sapiens (Hsa)-microRNA (miR)-6727-5p Contributes to the Impact of High-Density Lipoproteins on Fibroblast Wound Healing In Vitro

Chronic, non-healing wounds are a significant cause of global morbidity and mortality, and strategies to improve delayed wound closure represent an unmet clinical need. High-density lipoproteins (HDL) can enhance wound healing, but exploitation of this finding is challenging due to the complexity and instability of these heterogeneous lipoproteins. The responsiveness of primary human neonatal keratinocytes, and neonatal and human dermal fibroblasts (HDF) to HDL was confirmed by cholesterol efflux, but promotion of ‘scrape’ wound healing occurred only in primary human neonatal (HDFn) and adult fibroblasts (HDFa). Treatment of human fibroblasts with HDL induced multiple changes in the expression of small non-coding microRNA sequences, determined by microchip array, including hsa-miR-6727-5p. Intriguingly, levels of hsa-miR-6727-5p increased in HDFn, but decreased in HDFa, after exposure to HDL. Delivery of a hsa-miR-6727-5p mimic elicited repression of different target genes in HDFn (ZNF584) and HDFa (EDEM3, KRAS), and promoted wound closure in HDFn. By contrast, a hsa-miR-6727-5p inhibitor promoted wound closure in HDFa. We conclude that HDL treatment exerts distinct effects on the expression of hsa-miR-6727-5p in neonatal and adult fibroblasts, and that this is a sequence which plays differential roles in wound healing in these cell types, but cannot replicate the myriad effects of HDL.

Knowledge discovery in genetics of diabetes in Iran, a roadmap for future researches

Purpose

The pathogenesis of diabetes is considered polygenic as a result of complex interactions between genetic/epigenetic and environmental factors. This review intended to evaluate the scientometric and knowledge gap of diabetes genetics researches conducted in Iran as a case of developing countries, and drawn up a roadmap for future studies.

Methods

We searched Scopus and PubMed databases from January 2015 until December 2019 using the keywords: (diabetes OR diabetic) AND (Iran). All publications were reviewed by two experts and after choosing relevant articles, they were categorized based on the subject, level of evidence, study design, publication year, and type of genetic studies.

Results

Of 10,540 records, 428 articles were met the inclusion criteria. Generally, the number of researches about diabetes genetics rose since 2015. Case-control/cross-sectional and animal studies were the common types of study design and based on the subject, the most frequent researches were about genetic factors involved in diabetes development (38%). Briefly, the top seven genes that were evaluated for T2DM were TCF7L2, APOAII, FTO, PON1, ADIPOQ, MTHFR, and PPARG respectively, and also, CTL4 for T1DM. miR-21, miR-155, and miR-375 respectively were the most micro-RNAs that were evaluated. Furthermore, there were six studies about lncRNAs.

Discussion and Conclusion

Investigation about the genetic of diabetes is progressed although there are some limitations like non-homogenous data from Iran, heterogeneity of ethnicity, and rationale of studies. Compared to the previous analysis in Iran, still, GWAS and large-scale studies are required to achieve better policies for manage and control of diabetes disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00838-8.

High-fat diet-associated cognitive decline: Is zinc finger protein 1 (ZPR1) the molecular connection?

Zinc finger protein 1 (ZPR1) is required for cellular replication and viability. Recently, ZPR1 variant rs964184 has been repeatedly linked to high plasma triglyceride levels, metabolic syndrome, type 2 diabetes mellitus (T2DM), and nonalcoholic fatty liver disease (NAFLD), suggesting its involvement in lipid metabolism. This article attempts to explain how ZPR1 contributes to the mechanism of high-fat diet-associated cognitive decline through three premises: i) high-fat diet results in cognitive decline, ii) ZPR1 deficiency also results in cognitive decline, and iii) high-fat diet results in ZPR1 deficiency. Therefore, ZPR1 has the potential to be the connection between high-fat diet and cognitive decline. The two modalities of cognitive decline caused by low concentrations of ZPR1 are reduced brain-derived growth factor (BDNF) synthesis and neuron death, both occurring in the hippocampus. Downregulation of ZPR1 may lead to decreased synthesis of BDNF due to reduced concentrations of peroxisome proliferator-activated receptor-gamma (PPAR-γ), tropomyosin receptor kinase B (Trk B), and cAMP response element-binding protein (CREB), resulting in reduced ability to form and retain long-term memory as well as reduced neuroplasticity. Likewise, low concentrations of ZPR1 facilitate neuron death by producing lower amount of spinal motor neuron (SMN) protein, causing genomic instability, activating mixed-lineage protein kinase 3 (MLK3), mitogen-activated protein kinase 7 (MKK7), and c-Jun N-terminal kinase 3 (JNK3) signal cascade, and ultimately resulting in the activation of Caspase 3.

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ZPR1 plays an important role in cell division and viability.

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ZPR1 variant rs964184 has been linked to dysregulation of lipid metabolism.

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High-fat diet can reduce ZPR1 to induce hippocampal neuronal death.

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High-fat diet correlates with cognitive decline.

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ZPR1 may mediate high-fat diet-induced cognitive decline.

Noncoding RNAs and RNA-binding proteins in diabetic wound healing

Poor wound healing is a common complication in diabetic patients. It often leads to intractable infections and lower limb amputations and is associated with cardiovascular morbidity and mortality. NcRNAs, which can regulate gene expression, have emerged as important regulators of various physiological processes. Herein, we summarize the diverse roles of ncRNAs in the key stages of diabetic wound healing, including inflammation, angiogenesis, re-epithelialization, and extracellular matrix remodeling. Meanwhile, the potential use of ncRNAs as novel therapeutic targets for wound healing in diabetic patients is also discussed. In addition, we summarize the role of RNA-binding proteins (RBPs) in the regulation of gene expression and signaling pathways during skin repair, which may provide opportunities for therapeutic intervention for this potentially devastating disease. However, so far, research on the modulated drug based on ncRNAs that lead to significantly altered gene expression in diabetic patients is scarce. We have compiled some drugs that may be able to modulate ncRNAs, which significantly regulate the gene expression in diabetic patients.

Long non-coding RNAs in diabetic wound healing: Current research and clinical relevance

Diabetic wounds are a protracted complication of diabetes mainly characterised by chronic inflammation, obstruction of epithelialization, damaged blood vessels and collagen production (maturation), as well as neuropathy. As a non‐coding RNA (ncRNA) that lack coding potential, long non‐coding RNAs (lncRNAs) have recently been reported to play a salient role in diabetic wound healing. Here, this review summarises the roles of lncRNAs in the pathology and treatments of diabetic wounds, providing references for its potential clinical diagnostic criteria or therapeutic targets in the future.

Polyplex-Loaded Hydrogels for Local Gene Delivery to Human Dermal Fibroblasts

Impaired cutaneous healing leading to chronic wounds affects between 2 and 6% of the total population in most developed countries and it places a substantial burden on healthcare budgets. Current treatments involving antibiotic dressings and mechanical debridement are often not effective, causing severe pain, emotional distress, and social isolation in patients for years or even decades, ultimately resulting in limb amputation. Alternatively, gene therapy (such as mRNA therapies) has emerged as a viable option to promote wound healing through modulation of gene expression. However, protecting the genetic cargo from degradation and efficient transfection into primary cells remain significant challenges in the push to clinical translation. Another limiting aspect of current therapies is the lack of sustained release of drugs to match the therapeutic window. Herein, we have developed an injectable, biodegradable and cytocompatible hydrogel-based wound dressing that delivers poly(β-amino ester)s (pBAEs) nanoparticles in a sustained manner over a range of therapeutic windows. We also demonstrate that pBAE nanoparticles, successfully used in previous in vivo studies, protect the mRNA load and efficiently transfect human dermal fibroblasts upon sustained release from the hydrogel wound dressing. This prototype wound dressing technology can enable the development of novel gene therapies for the treatment of chronic wounds.

Emerging Roles of Long Non-Coding RNAs in Diabetic Foot Ulcers

Diabetes mellitus is one of the most widespread metabolic diseases in the world, and diabetic foot ulcer (DFU), as one of its chronic complications, not only causes a large amount of physiological and psychological pain to patients but also places a tremendous burden on the entire economy and society. Despite significant advances in knowledge on the mechanism and in the treatment of DFU, clinical practice is still not satisfactory, and our understanding of its cellular and molecular pathogenesis is far from complete. Fortunately, progress in studying the roles of long non-coding RNAs (lncRNAs), which play important regulatory roles in the expression of genes at multiple levels, suggests that we can apply them in the early diagnosis and potential targeted intervention of DFU. In this review, we briefly summarize the current knowledge regarding the functional roles and potential mechanisms of reported lncRNAs in regulating DFU.

Integrated Bioinformatics-Based Identification of Potential Diagnostic Biomarkers Associated with Diabetic Foot Ulcer Development

The present study was designed to detect possible biomarkers associated with diabetic foot ulcer (DFU) incidence in an effort to develop novel treatments for this condition. The GSE7014 and GSE29221 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, after which differentially expressed genes (DEGs) were identified between DFU and healthy samples. These DEGs were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. In total, 1192 DEGs were identified in the GSE7014 dataset (900 upregulated, 292 downregulated), while 1177 were identified in the GSE29221 dataset (257 upregulated, 919 downregulated). GO analyses revealed these DEGs to be significantly enriched in biological processes including sarcomere organization, muscle filament sliding, and the regulation of cardiac conduction, molecular functions including structural constituent of muscle, protein binding, and calcium ion binding, and cellular components including Z disc, myosin filament, and M band. These DEGs were also enriched in the adrenergic signaling in cardiomyoctes, dilated cardiomyopathy, and tight junction KEGG pathways. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with DFU development.

Plasmolipin and Its Role in Cell Processes

The mechanisms involved in the origin and development of malignant and neurodegenerative diseases are an important area of modern biomedicine. A crucial task is to identify new molecular markers that are associated with rearrangements of intracellular signaling and can be used for prognosis and the development of effective treatment approaches. The proteolipid plasmolipin (PLLP) is a possible marker. PLLP is a main component of the myelin sheath and plays an important role in the development and normal function of the nervous system. PLLP is involved in intracellular transport, lipid raft formation, and Notch signaling. PLLP is presumably involved in various disorders, such as cancer, schizophrenia, Alzheimer's disease, and type 2 diabetes mellitus. PLLP and its homologs were identified as possible virus entry receptors. The review summarizes the data on the PLLP structure, normal functions, and role in diseases.

Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response

Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients' quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.

Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis

OBJECTIVE

To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA).

METHODS

The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate < 0.05 and a log2 fold change ≥ 0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes.

RESULTS

Fourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally, HLA-DRB1, LRP1, and MMP2 were identified as key genes.

CONCLUSION

This was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease.