Whole transcriptome RNA-seq reveals key regulatory factors involved in type 2 diabetes pathology in peripheral fat of Asian Indians

Circulating microRNA profiling identifies microRNAs linked to prediabetes associated with alcohol dependence syndrome

BACKGROUND

MicroRNAs are abundant in serum and have emerged as important regulators of gene expression, implicating them in a wide range of diseases. The purpose of this study was to discover and validate serum miRNAs in prediabetes associated with alcohol dependence syndrome (ADS).

METHOD

Serum samples from ADS patients with or without prediabetes and normoglycemic controls were subjected to microarray. Validation of identified candidate miRNAs was performed by RT-qPCR. Additionally, GO and KEGG pathway analyses were carried out to uncover target genes anticipated to be controlled by the candidate miRNAs.

RESULTS

Notably, 198, and 172 miRNAs were differentially expressed in ADS-patients with or without prediabetes compared to healthy controls, and 7 miRNAs in ADS-patients with prediabetes compared to ADS-normoglycemic patients, respectively. Furthermore, hsa-miR-320b and hsa-miR-3135b were differentially expressed exclusively in ADS-patients with prediabetes, and this was further validated. Interestingly, GO and KEGG pathway analysis revealed that genes predicted to be modulated by the candidates were considerably enriched in numerous diabetes-related biological processes and pathways.

CONCLUSION

Our findings revealed that ADS-patients with or without prediabetes have different sets of miRNAs compared to normoglycemic healthy subjects. We propose serum hsa-miR-320b and hsa-miR-3135b as potential biomarkers for the diagnosis of prediabetes in ADS-patients.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches

Background

Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.

Objectives

In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.

Methods

An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.

Results

Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.

Conclusion

A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Exploration of the common pathogenic link between COVID-19 and diabetic foot ulcers: An in silico approach

Background and Aims

The Coronavirus Disease-19 (COVID-19) is posing an ongoing threat to human health. Patients of diabetic foot ulcer (DFU) are susceptible to COVID-19-induced adverse outcomes. Nevertheless, investigations into their mutual molecular mechanisms have been limited to date. In the present work, we tried to uncover the shared pathogenesis and regulatory gene targets of COVID-19 and DFU.

Methods

In this study, we chose GSE161281 as the COVID-19 data set, which contained severe acute respiratory syndrome coronavirus 2 infected human induced embryonic stem cell-derived peripheral neurons (n = 2) with uninfected controls (n = 2). The GSE134431 designated as the DFU data set, comprising full-thickness DFU (n = 13) and diabetic foot skin (n = 8) samples from diabetic patients. The differential expressed genes (DEGs) were identified from GSE161281 and GSE134431, and the common DEGs between COVID-19 and DFU were extracted. Multifactor regulatory network and co-expression network of the common DEGs were analyzed, along with candidate drug prediction.

Results

Altogether, six common DEGs (dickkopf-related protein 1 [DKK1], serine proteinase inhibitor A3 [SERPINA3], ras homolog family member D [RHOD], myelin protein zero like 3 [MPZL3], Claudin-11 [CLDN11], and epidermal growth factor receptor pathway substrate 8-like 1 [EPS8L1]) were found between COVID-19 and DFU. Functional analyses indicated that pathways of apoptotic and Wnt signaling may contribute to progression of COVID-19. Gene co-expression network implied the shared pathways of immune regulation and cytokine response participated collectively in the development of DFU and COVID-19. A multifactor regulatory network was constructed integrating the corresponding microRNAs (miRNAs) and transcription factors. Additionally, we proposed potential drug objects for the combined therapy.

Conclusion

Our study revealed the shared molecular mechanisms underlying COVID-19 and DFU. The identified pivotal targets and common pathways can provide new perspectives for further research and assist the development of management strategies in patients of DFU complicated with COVID-19.

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.

Comparative co-expression analysis of RNA-Seq transcriptome revealing key genes, miRNA and transcription factor in distinct metabolic pathways in diabetic nerve, eye, and kidney disease

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies

Type 2 diabetes mellitus (T2DM) is one of the world’s leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Lipidomics in diabetes

PURPOSE OF REVIEW

Multiple studies have shown a strong association between lipids and diabetes. These are usually described through the effects of cholesterol content of lipid particles and in particular low-density lipoprotein. However, lipoprotein particles contain other components, such as phospholipids and more complex lipid species, such as ceramides and sphingolipids. Ceramides, such as sphingolipids are also produced intracellularly and have signalling actions in regulating cell metabolism including effects on inflammation, and potentially have a mechanistic role in the development of insulin resistance.

RECENT FINDINGS

Recently, techniques have been developed to analyse detailed molecular profiles of lipid particles - lipidomics. Proteomics has confirmed the different proteins associated with different particles but far less is known about the relationship of individual lipid species with diabetes and cardiovascular risk. A number of studies have now shown that the plasma lipidome, and in particular, ceramides and sphingolipids may predict the development of diabetes.

SUMMARY

Lipidomics had identified ceramides and sphingolipids as potential mediators of cellular dysfunction in diabetes. Further work is required to ascertain whether they have clinical utility.