Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis

Identification and Validation of Immune-Related Genes Diagnostic for Progression of Atherosclerosis and Diabetes

Background

Atherosclerosis and type 2 diabetes mellitus contribute to a large part of cardiovascular events, but the underlying mechanism remains unclear. In this study, we focused on identifying the linking genes of the diagnostic biomarkers and effective therapeutic targets associated with these two diseases.

Methods

The transcriptomic datasets of atherosclerosis and type 2 diabetes mellitus were obtained from the GEO database. Differentially expressed genes analysis was performed by R studio software, and differential analysis including functional enrichment, therapeutic small molecular agents prediction, and protein-protein interaction analysis were applied to the common shared differentially expressed genes. Hub genes were identified and further validated using an independent dataset and clinical samples. Furthermore, we measured the expression correlations, immune cell infiltration, and diagnostic capability of the three key genes.

Results

We screened out 28 up-regulated and six down-regulated common shared differentially expressed genes. Functional enrichment analysis showed that cytokines and immune activation were involved in the development of these two diseases. Six small molecules with the highest absolute enrichment value were identified. Three critical genes (CD4, PLEK, and THY1) were further validated both in validation sets and clinical samples. The gene correlation analysis showed that CD4 was strongly positively correlated with PLEK, and ROC curves confirmed the good discriminatory capacity of CD4 and PLEK in two diseases.

We have established the co-expression network between atherosclerosis lesions progressions and type 2 diabetes mellitus, and identified CD4 and PLEK as key genes in the two diseases, which may facilitate both development of diagnosis and therapeutic strategies.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network

Background

Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods

Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results

Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions

We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00240-1.

Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.

Integrated analysis of transcriptome profiling of lncRNAs and mRNAs in livers of type 2 diabetes mellitus

Long noncoding RNAs (lncRNAs) influence the progression of almost all human diseases, but the participation of lncRNAs in type 2 diabetes mellitus (T2DM) has not been fully elucidated. The present study aimed to systematically compare the transcriptome profiling of lncRNAs and mRNAs in livers between T2DM patients and controls, to identify key genes associated with T2DM pathogenesis, and to predict the underlying molecular mechanisms. As a result, a total of 1,512 differentially expressed (DE) lncRNAs and 1,923 DE mRNAs were identified through microarray analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that multiple metabolic processes were dysregulated such as small molecule, organic acid, lipid and branched chain amino acid metabolism. Protein-protein interaction network was constructed and 10 hub mRNAs were identified, including EHHADH, ATM, ACOX1, PIK3R1, EGFR, UQCRFS1, HMGCL, UQCRC2, NDUFS3 and F2. RT-qPCR was conducted to verify the validity of microarray results. Then, coding-noncoding co-expression network and competing endogenous RNA (ceRNA) network were analyzed to predict the lncRNA-mRNA and lncRNA-miRNA-mRNA regulatory patterns. Subsequently, 10 key intermediating miRNAs in ceRNA networks with a node degree > 80 were identified, including hsa-miR-5692a, hsa-miR-12136, hsa-miR-5680, hsa-miR-1305, hsa-miR-6833-5p, hsa-miR-7159-5p, hsa-miR-548as-3p, hsa-miR-6873-3p, hsa-miR-1290 and hsa-miR-4768-5p. In conclusion, the present study evaluated the transcriptome profiling of lncRNAs and mRNAs in livers from T2DM patients, with a value for understanding the molecular mechanism of disease pathogenesis and identifying effective biomarkers in clinical diagnosis.

The role of Wnt pathway in obesity induced inflammation and diabetes: a review

Diabetes has become a major killer worldwide and at present, millions are affected by it. Being a chronic disease it increases the risk of other diseases ranging from pulmonary disorders to soft tissue infections. The loss of insulin-producing capacity of the pancreatic β-cells is the main reason for the development of the disease. Obesity is a major complication that can give rise to several other diseases such as cancer, diabetes, etc. Visceral adiposity is one of the major factors that play a role in the development of insulin resistance. Obesity causes a chronic low-grade inflammation in the tissues that further increases the chances of developing diabetes. Several pathways have been associated with the development of diabetes due to inflammation caused by obesity. The Wnt pathway is one such candidate pathway that is found to have a controlling effect on the development of insulin resistance. Moreover, the pathway has also been linked to obesity and inflammation. This review aims to find a connection between obesity, inflammation, and diabetes by taking the wnt pathway as the connecting link.

The Link between Type 2 Diabetes Mellitus and the Polymorphisms of Glutathione-Metabolizing Genes Suggests a New Hypothesis Explaining Disease Initiation and Progression

The present study investigated whether type 2 diabetes (T2D) is associated with polymorphisms of genes encoding glutathione-metabolizing enzymes such as glutathione synthetase (GSS) and gamma-glutamyl transferase 7 (GGT7). A total of 3198 unrelated Russian subjects including 1572 T2D patients and 1626 healthy subjects were enrolled. Single nucleotide polymorphisms (SNPs) of the GSS and GGT7 genes were genotyped using the MassArray-4 system. We found that the GSS and GGT7 gene polymorphisms alone and in combinations are associated with T2D risk regardless of sex, age, and body mass index, as well as correlated with plasma glutathione, hydrogen peroxide, and fasting blood glucose levels. Polymorphisms of GSS (rs13041792) and GGT7 (rs6119534 and rs11546155) genes were associated with the tissue-specific expression of genes involved in unfolded protein response and the regulation of proteostasis. Transcriptome-wide association analysis has shown that the pancreatic expression of some of these genes such as EDEM2, MYH7B, MAP1LC3A, and CPNE1 is linked to the genetic risk of T2D. A comprehensive analysis of the data allowed proposing a new hypothesis for the etiology of type 2 diabetes that endogenous glutathione deficiency might be a key condition responsible for the impaired folding of proinsulin which triggered an unfolded protein response, ultimately leading to beta-cell apoptosis and disease development.

Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data

Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.

Identification of Common Pathogenetic Processes between Schizophrenia and Diabetes Mellitus by Systems Biology Analysis

Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive symptoms (i.e., psychosis) and negative symptoms (such as apathy, anhedonia, and poverty of speech). Epidemiological data show a high likelihood of early onset of type 2 diabetes mellitus (T2DM) in SCZ patients. However, the molecular processes that could explain the epidemiological association between SCZ and T2DM have not yet been characterized. Therefore, in the present study, we aimed to identify underlying common molecular pathogenetic processes and pathways between SCZ and T2DM. To this aim, we analyzed peripheral blood mononuclear cell (PBMC) transcriptomic data from SCZ and T2DM patients, and we detected 28 differentially expressed genes (DEGs) commonly modulated between SCZ and T2DM. Inflammatory-associated processes and membrane trafficking pathways as common biological processes were found to be in common between SCZ and T2DM. Analysis of the putative transcription factors involved in the regulation of the DEGs revealed that STAT1 (Signal Transducer and Activator of Transcription 1), RELA (v-rel reticuloendotheliosis viral oncogene homolog A (avian)), NFKB1 (Nuclear Factor Kappa B Subunit 1), and ERG (ETS-related gene) are involved in the expression of common DEGs in SCZ and T2DM. In conclusion, we provide core molecular signatures and pathways that are shared between SCZ and T2DM, which may contribute to the epidemiological association between them.

Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Simple Summary

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In the past few years, studies on the gene expression changes of dairy cow mammary glands were only based on transcriptome comparisons between two lactation stages. Few studies focused on the relationships between gene expression of the dairy mammary gland and lactation stage or milk composition in a lactation cycle. In this study, we detected milk yield and composition in a lactation cycle. For the first time, we constructed a gene coexpression network using WGCNA on the basis of 18 gene expression profiles during six stages of a lactation cycle by transcriptome sequencing, generating 10 specific modules. Genes in each module were performed with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Module–trait relationship analysis showed a series of potential candidates related to milk yield and composition. The current study provides an important theoretical basis for the further molecular breeding of dairy cows.

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Identification of Three Significant Genes Associated with Immune Cells Infiltration in Dysfunctional Adipose Tissue-Induced Insulin-Resistance of Obese Patients via Comprehensive Bioinformatics Analysis

BACKGROUND

Low-grade chronic inflammation in dysfunctional adipose tissue links obesity with insulin resistance through the activation of tissue-infiltrating immune cells. Numerous studies have reported on the pathogenesis of insulin-resistance. However, few studies focused on genes from genomic database. In this study, we would like to explore the correlation of genes and immune cells infiltration in adipose tissue via comprehensive bioinformatics analyses and experimental validation in mice and human adipose tissue.

METHODS

Gene Expression Omnibus (GEO) datasets (GSE27951, GSE55200, and GSE26637) of insulin-resistant individuals or type 2 diabetes patients and normal controls were downloaded to get differently expressed genes (DEGs), and GO and KEGG pathway analyses were performed. Subsequently, we integrated DEGs from three datasets and constructed commonly expressed DEGs' PPI net-works across datasets. Center regulating module of DEGs and hub genes were screened through MCODE and cytoHubba in Cytoscape. Three most significant hub genes were further analyzed by GSEA analysis. Moreover, we verified the predicted hub genes by performing RT qPCR analysis in animals and human samples. Besides, the relative fraction of 22 immune cell types in adipose tissue was detected by using the deconvolution algorithm of CIBERSORT (Cell Type Identification by Estimating Relative Subsets of RNA Transcripts). Furthermore, based on the significantly changed types of immune cells, we performed correlation analysis between hub genes and immune cells. And, we performed immunohistochemistry and immunofluorescence analysis to verify that the hub genes were associated with adipose tissue macrophages (ATM).

RESULTS

Thirty DEGs were commonly expressed across three datasets, most of which were upregulated. DEGs mainly participated in the process of multiple immune cells' infiltration. In protein-protein interaction network, we identified CSF1R, C1QC, and TYROBP as hub genes. GSEA analysis suggested high expression of the three hub genes was correlated with immune cells functional pathway's activation. Immune cell infiltration and correlation analysis revealed that there were significant positive correlations between TYROBP and M0 macrophages, CSF1R and M0 macrophages, Plasma cells, and CD8 T cells. Finally, hub genes were associated with ATMs infiltration by experimental verification.

CONCLUSIONS

This article revealed that CSF1R, C1QC, and TYROBP were potential hub genes associated with immune cells' infiltration and the function of proinflammation, especially adipose tissue macrophages, in the progression of obesity-induced diabetes or insulin-resistance.

Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

PURPOSE OF REVIEW

Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020.

RECENT FINDINGS

Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.

In silico prediction and validation of potential therapeutic genes in pancreatic β-cells associated with type 2 diabetes

Diabetes mellitus is becoming a major health burden worldwide. Pancreatic β-cell death is a characteristic of type 2 diabetes (T2D), but the underlying mechanisms of pancreatic β-cell death remain unknown. Therefore, the aim of the present study was to identify potential targets in the pancreatic islet of T2D. The GSE20966 dataset was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using the GEO2R tool. The Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analysis of DEGs were further assessed using the Database for Annotation, Visualization and Integrated Discovery. Furthermore, protein-protein interaction (PPI) networks were constructed for the up- and downregulated genes using STRING databases and were then visualized with Cytoscape. The body weight, fasting blood glucose (FBG), pancreatic index and biochemistry parameters were measured in db/db mice. Moreover, the morphology of the pancreas was detected by hematoxylin and eosin staining, and hub genes were assessed using reverse transcription-quantitative PCR (RT-qPCR) and western blot analysis. In total, 570 DEGs were screened, including 376 upregulated and 194 downregulated genes, which were associated with 'complement activation, classical pathway', 'proteolysis', 'complement activation' and 'pancreatic secretion pathway'. It was found that the body weight, FBG, alanine aminotransferase, aspartate aminotransferase, total cholesterol, triglycerides, blood urea nitrogen, creatinine, fasting serum insulin, glucagon and low-density lipoprotein cholesterol levels were significantly higher in db/db mice, while high-density lipoprotein cholesterol levels and the pancreatic index were significantly decreased. Furthermore, albumin, interleukin-8, CD44, C-C motif chemokine ligand 2, hepatocyte growth factor, cystic fibrosis transmembrane conductance regulator, histone cluster 1 H2B family member n, mitogen-activated protein kinase 11 and neurotrophic receptor tyrosine kinase 2 were identified as hub genes in PPI network. RT-qPCR and western blotting results demonstrated the same expression trend in hub genes as found by the bioinformatics analysis. Therefore, the present study identified a series of hub genes involved in the progression of pancreatic β-cell, which may help to develop effective therapeutic strategy for T2D.

Probiotic mediated NF-κB regulation for prospective management of type 2 diabetes

Diabetes and other lifestyle disorders have been recognized as the leading cause of morbidity and mortality globally. Nuclear factor kappa B (NF-κB) is a major factor involved in the early pathobiology of diabetes and studies reveal that hyperglycemic conditions in body leads to NF-κB mediated activation of several cytokines, chemokines and inflammatory molecules. NF-κB family comprises of certain DNA-binding protein factors that elicit the transcription of pro-inflammatory molecules. Various studies have identified NF-κB as a promising target for diabetic management. Probiotics have been proposed as bio-therapeutic agents for treatment of inflammatory disorders and many other chronic clinical stages. The precise mechanisms by which probiotics acts is yet to be fully understood, however research findings have indicated their role in NF-κB modulation. The current review highlights NF-κB as a bio-therapeutic target for probable management of type 2 diabetes through probiotic intervention.

Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis.

MATERIALS AND METHODS

To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).

RESULTS

In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine-cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway.

CONCLUSION

Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.

Revealing the Interactions Between Diabetes, Diabetes-Related Diseases, and Cancers Based on the Network Connectivity of Their Related Genes

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein-protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov-Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, "respiratory system development" and "regulation of mRNA metabolic process" are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.