Systems biology approach reveals genome to phenome correlation in type 2 diabetes

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AIM

Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.

METHODS

Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach.

RESULTS

These three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity.

CONCLUSION

Predictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

Exploring lipodystrophy gene expression in adipocytes: unveiling insights into the pathogenesis of insulin resistance, type 2 diabetes, and clustering diseases (metabolic syndrome) in Asian Indians

Background

Studying the molecular mechanisms of lipodystrophy can provide valuable insights into the pathophysiology of insulin resistance (IR), type 2 diabetes (T2D), and other clustering diseases [metabolic syndrome (MetS)] and its underlying adipocentric disease (MetS disease).

Methods

A high-confidence lipodystrophy gene panel comprising 50 genes was created, and their expressions were measured in the visceral and subcutaneous (both peripheral and abdominal) adipose depots of MetS and non-MetS individuals at a tertiary care medical facility.

Results

Most lipodystrophy genes showed significant downregulation in MetS individuals compared to non-MetS individuals in both subcutaneous and visceral depots. In the abdominal compartment, all the genes showed relatively higher expression in visceral depot as compared to their subcutaneous counterpart, and this difference narrowed with increasing severity of MetS. Their expression level shows an inverse correlation with T2D, MetS, and HOMA-IR and with other T2D-related intermediate traits. Results also demonstrated that individualization of MetS patients could be done based on adipose tissue expression of just 12 genes.

Conclusion

Adipose tissue expression of lipodystrophy genes shows an association with MetS and its intermediate phenotypic traits. Mutations of these genes are known to cause congenital lipodystrophy syndromes, whereas their altered expression in adipose tissue contributes to the pathogenesis of IR, T2D, and MetS.

Identification of autophagy-related genes in diabetic foot ulcer based on bioinformatic analysis

Diabetic foot ulcer (DFU) complications involve autophagy dysregulation. This study aimed to identify autophagy-related bioindicators in DFU. Differentially expressed genes (DEGs) between DFU and healthy samples were analysed from the Gene Expression Omnibus (GEO) datasets, GSE7014 and GSE29221. The roles of autophagy-related DEGs were investigated using protein-protein interaction (PPI) networks, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Gene Ontology (GO) enrichment, and Gene Set Enrichment Analysis (GSEA). Immune cell infiltration's correlation with these DEGs was also assessed. From the Human Autophagy Database (HADB), 232 autophagy-related genes (ARGs) were identified, with an intersection of 17 key DEGs between GSE7014 and GSE29221. These genes are involved in pathways like autophagy-animal, NOD-like receptor signalling, and apoptosis. In the protein network, epidermal growth factor receptor (EGFR) and phosphatase and tensin homologue (PTEN) showed significant interactions with ARGs. Survival analysis indicated the prognostic importance of calpain 2 (CAPN2), integrin subunit beta 1 (ITGB1), and vesicle-associated membrane protein 3 (VAMP3). Lower immune scores were observed in the type 2 diabetes mellitus (DM2) group than in controls. Autophagy and ARGs significantly influence DFU pathophysiology.

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.

Identification and verification of ferroptosis-related genes in diabetic foot using bioinformatics analysis

Ferroptosis is a novel form of cell death that plays a key role in several diseases, including inflammation and tumours; however, the role of ferroptosis-related genes in diabetic foot remains unclear. Herein, diabetic foot-related genes were downloaded from the Gene Expression Omnibus and the ferroptosis database (FerrDb). The least absolute shrinkage and selection operator regression algorithm was used to construct a related risk model, and differentially expressed genes were analysed through immune infiltration. Finally, we identified relevant core genes through a protein-protein interaction network, subsequently verified using immunohistochemistry. Comprehensive analysis showed 198 genes that were differentially expressed during ferroptosis. Based on functional enrichment analysis, these genes were primarily involved in cell response, chemical stimulation, and autophagy. Using the CIBERSORT algorithm, we calculated the immune infiltration of 22 different types of immune cells in diabetic foot and normal tissues. The protein-protein interaction network identified the hub gene TP53, and according to immunohistochemistry, the expression of TP53 was high in diabetic foot tissues but low in normal tissues. Accordingly, we identified the ferroptosis-related gene TP53 in the diabetic foot, which may play a key role in the pathogenesis of diabetic foot and could be used as a potential biomarker.

Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing

BACKGROUND

The exact mechanisms of type 2 diabetes mellitus (T2DM) remain largely unknown. We intended to authenticate critical genes linked to T2DM progression by tandem single-cell sequencing and general transcriptome sequencing data.

METHODS

T2DM single-cell RNA-sequencing data were submitted by the Gene Expression Omnibus (GEO) database and ArrayExpress (EBI), from which gene expression matrices were retrieved. The common cell clusters and representative marker genes were ascertained by principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), CellMarker, and FindMarkers in two datasets (GSE86469 and GSE81608). T2DM-related differentially expressed marker genes were defined by intersection analysis of marker genes and GSE86468-differentially expressed genes. Receiver operating characteristic (ROC) curves were utilized to assign representative marker genes with diagnostic values by GSE86468, GSE29226 and external validation GSE29221, and their prospective target compounds were forecasted by PubChem. Besides, the R package clusterProfiler-based functional annotation was designed to unveil the intrinsic mechanisms of the target genes. At last, western blot was used to validate the alternation of CDKN1C and DLK1 expression in primary pancreatic islet cells cultured with or without 30mM glucose.

RESULTS

Three common cell clusters were authenticated in two independent T2DM single-cell sequencing data, covering neurons, epithelial cells, and smooth muscle cells. Functional ensemble analysis disclosed an intimate association of these cell clusters with peptide/insulin secretion and pancreatic development. Pseudo-temporal trajectory analysis indicated that almost all epithelial and smooth muscle cells were of neuron origin. We characterized CDKN1C and DLK1, which were notably upregulated in T2DM samples, with satisfactory availability in recognizing three representative marker genes in non-diabetic and T2DM samples, and they were also robustly interlinked with the clinical characteristics of patients. Western blot also demonstrated that, compared with control group, the expression of CDKN1C and DLK1 were increased in primary pancreatic islet cells cultured with 30 mM glucose for 48 h. Additionally, PubChem projected 11 and 21 potential compounds for CDKN1C and DLK1, respectively.

CONCLUSION

It is desirable that the emergence of the 2 critical genes indicated (CDKN1C and DLK1) could be catalysts for the investigation of the mechanisms of T2DM progression and the exploitation of innovative therapies.

Integrated bioinformatics analyses identifying potential biomarkers for type 2 diabetes mellitus and breast cancer: In SIK1-ness and health

The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1β). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1β and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association.

Proteomic Analysis of Skeletal Muscle and White Adipose Tissue after Aerobic Exercise Training in High Fat Diet Induced Obese Mice

Obesity is associated with excessive fat accumulation in adipose tissue and other organs, such as skeletal muscle, whereas aerobic exercise (AE) plays an important role in managing obesity through profound protein regulation. Our study aimed to investigate the impact of AE on proteomic changes in both the skeletal muscle and the epididymal fat pad (EFP) of high-fat-diet-induced obese mice. Bioinformatic analyses were performed on differentially regulated proteins using gene ontology enrichment analysis and ingenuity pathway analysis. Eight weeks of AE significantly reduced body weight, increased the serum FNDC5 level, and improved the homeostatic model assessment of insulin resistance. A high-fat diet caused alterations in a subset of proteins involved in the sirtuin signaling pathway and the production of reactive oxygen species in both skeletal muscle and EFP, leading to insulin resistance, mitochondrial dysfunction, and inflammation. On the other hand, AE upregulated skeletal muscle proteins (NDUFB5, NDUFS2, NDUFS7, ETFD, FRDA, and MKNK1) that enhance mitochondrial function and insulin sensitivity. Additionally, the upregulation of LDHC and PRKACA and the downregulation of CTBP1 in EFP can promote the browning of white adipose tissue with the involvement of FNDC5/irisin in the canonical pathway. Our study provides insights into AE-induced molecular responses and may help further develop exercise-mimicking therapeutic targets.

Comprehensive analysis of key m5C modification-related genes in type 2 diabetes

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear.

Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.

Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells.

Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.

Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks

An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.

Microbiota and adipocyte mitochondrial damage in type 2 diabetes are linked by Mmp12+ macrophages

Microbiota contribute to the induction of type 2 diabetes by high-fat/high-sugar (HFHS) diet, but which organs/pathways are impacted by microbiota remain unknown. Using multiorgan network and transkingdom analyses, we found that microbiota-dependent impairment of OXPHOS/mitochondria in white adipose tissue (WAT) plays a primary role in regulating systemic glucose metabolism. The follow-up analysis established that Mmp12+ macrophages link microbiota-dependent inflammation and OXPHOS damage in WAT. Moreover, the molecular signature of Mmp12+ macrophages in WAT was associated with insulin resistance in obese patients. Next, we tested the functional effects of MMP12 and found that Mmp12 genetic deficiency or MMP12 inhibition improved glucose metabolism in conventional, but not in germ-free mice. MMP12 treatment induced insulin resistance in adipocytes. TLR2-ligands present in Oscillibacter valericigenes bacteria, which are expanded by HFHS, induce Mmp12 in WAT macrophages in a MYD88-ATF3-dependent manner. Thus, HFHS induces Mmp12+ macrophages and MMP12, representing a microbiota-dependent bridge between inflammation and mitochondrial damage in WAT and causing insulin resistance.

Polygonatum sibiricum polysaccharides (PSP) improve the palmitic acid (PA)-induced inhibition of survival, inflammation, and glucose uptake in skeletal muscle cells

Polygonatum sibiricum polysaccharides (PSP) can decrease the levels of fasting blood glucose, total cholesterol, and triglyceride (TG) in hyperlipidemic and diabetic animals. It can also reduce inflammatory cytokines and promote glucose uptake in adipocytes. However, the underlying molecular mechanisms of PSP in improving insulin resistance (IR) in skeletal muscle remain unclear. In this study, palmitic acid (PA) induced an IR model in L6 myotubes. After treatment, cell proliferation was measured using the CCK8. miR-340-3p, glucose transporter 4 (GLUT-4), and interleukin-1 receptor-associated kinase 3 (IRAK3) expression was measured by qRT-PCR. IRAK3 protein levels were measured by Western blotting. Glucose in the cell supernatant, TG concentration in L6 myotubes, and the levels of IL-1β, IL-6, and TNF-α were measured by an ELISA. We found that cell survival, glucose uptake, and GLUT-4 expression in L6 myotubes were significantly suppressed, while lipid accumulation and inflammatory factor levels were enhanced by PA stimulation. Furthermore, PSP treatment markedly alleviated these effects. Interestingly, PSP also significantly reduced the upregulated expression of miR-340-3p in the L6 myotube model of IR. Furthermore, overexpression of miR-340-3p reversed the beneficial effects of PSP in the same IR model. miR-340-3p can bind to the 3′-untranslated regions of IRAK3. Additionally, PA treatment inhibited IRAK3 expression, whereas PSP treatment enhanced IRAK3 expression in L6 myotubes. Additionally, miR-340-3p also inhibited IRAK3 expression in L6 myotubes. Taken together, PSP improved inflammation and glucose uptake in PA-treated L6 myotubes by regulating miR-340-3p/IRAK3, suggesting that PSP may be suitable as a novel therapeutic agent for IR.

Overexpression of E3 ubiquitin ligase Cbl attenuates endothelial dysfunction in diabetes mellitus by inhibiting the JAK2/STAT4 signaling and Runx3-mediated H3K4me3

BACKGROUND

Diabetes mellitus (DM), a most common chronic disease, is featured with impaired endothelial function and bioavailability of nitric oxide (NO), while E3 ubiquitin ligase appears to alleviate endothelial dysfunction as a promising option for DM treatment. Herein, we aimed to determine whether E3 ubiquitin ligase casitas B-lineage lymphoma (Cbl) alleviates endothelial dysfunction in DM rats by JAK2/STAT4 pathway.

METHODS

A rat model of DM was developed through intraperitoneal injection of streptozotocin, followed by collection of aortic tissues to determine the expression of Cbl, JAK2, runt-related transcription factor 3 (Runx3) and STAT4. Human umbilical vein endothelial cells (HUVECs) were cultured in high glucose (HG) condition to induce DM as an in vitro model. With gain- and loss-function method, we assessed the aberrantly expressed Cb1 on endothelial dysfunction, NO production and apoptosis of HUVECs.

RESULTS

Cbl was reduced in DM rat tissues and HG-induced HUVECs, where JAK2, Runx3 and STAT4 were elevated. It was found that overexpression of Cbl alleviated endothelial dysfunction by increasing NO production and restoring vasodilation and suppressing apoptosis of HUVECs. Mechanistically, Cb1 enhanced JAK2 ubiquitination and decreased JAK2 and STAT4 expression, where STAT4 improved Runx3 expression by regulating histone H3 lysine 4 trimethylation level. Overexpression of JAK2 and STAT4, or Runx3 increased apoptosis of HUVECs, abrogating the effect of Cb1 on endothelial function.

CONCLUSION

In conclusion, Cbl alleviates endothelial dysfunction by inactivation of the JAK2/STAT4 pathway and inhibition of Runx3 expression in DM. These evidence might underlie novel Cbl-based treatment against DM in the future.

Elucidate multidimensionality of type 1 diabetes mellitus heterogeneity by multifaceted information

Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein-protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.

Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1

Cisplatin is a DNA-damaging chemotherapeutic agent used for treating cancer. Based on cDNA dataset analysis, we investigated how cisplatin modified gene expression and observed cisplatin-induced dysregulation and system-level variations relating to insulin resistance and type 2 diabetes mellitus (T2DM). T2DM is a multifactorial disease affecting 462 million people in the world, and drug-induced T2DM is a serious issue. To understand this etiology, we designed an integrative, system-level study to identify associations between cisplatin-induced differentially expressed genes (DEGs) and T2DM. From a list of differential expressed genes, cisplatin downregulated the cyclin-dependent kinase inhibitor 1 (CDKN1A), tumor necrosis factor (FAS), and sestrin-1 (SESN1) genes responsible for modifying signaling pathways, including the p53, JAK-STAT, FOXO, MAPK, mTOR, P13-AKT, Toll-like receptor (TLR), adipocytokine, and insulin signaling pathways. These enriched pathways were expressively associated with the disease. We observed significant gene signatures, including SMAD3, IRS, PDK1, PRKAA1, AKT, SOS, RAS, GRB2, MEK1/2, and ERK, interacting with source genes. This study revealed the value of system genetics for identifying the cisplatin-induced genetic variants responsible for the progression of T2DM. Also, by cross-validating gene expression data for T2DM islets, we found that downregulating IRS and PRK families is critical in insulin and T2DM signaling pathways. Cisplatin, by inhibiting CDKN1A, FAS, and SESN1, promotes IRS and PRK activity in a similar way to rosiglitazone (a popular drug used for T2DM treatment). Our integrative, network-based approach can help in understanding the drug-induced pathophysiological mechanisms of diabetes.

qPCR Analysis Reveals Association of Differential Expression of SRR, NFKB1, and PDE4B Genes With Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a heterogeneous, metabolic, and chronic condition affecting vast numbers of the world's population. The related variables and T2DM associations have not been fully understood due to their diverse nature. However, functional genomics can facilitate understanding of the disease. This information will be useful in drug design, advanced diagnostic, and prognostic markers.

AIM

To understand the genetic causes of T2DM, this study was designed to identify the differentially expressed genes (DEGs) of the disease.

METHODS

We investigated 20 publicly available disease-specific cDNA datasets from Gene Expression Omnibus (GEO) containing several attributes including gene symbols and clone identifiers, GenBank accession numbers, and phenotypic feature coordinates. We analyzed an integrated system-level framework involving Gene Ontology (GO), protein motifs and co-expression analysis, pathway enrichment, and transcriptional factors to reveal the biological information of genes. A co-expression network was studied to highlight the genes that showed a coordinated expression pattern across a group of samples. The DEGs were validated by quantitative PCR (qPCR) to analyze the expression levels of case and control samples (50 each) using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as the reference gene.

RESULTS

From the list of 50 DEGs, we ranked three T2DM-related genes (p < 0.05): SRR, NFKB1, and PDE4B. The enriched terms revealed a significant functional role in amino acid metabolism, signal transduction, transmembrane and intracellular transport, and other vital biological functions. DMBX1, TAL1, ZFP161, NFIC (66.7%), and NR1H4 (33.3%) are transcriptional factors associated with the regulatory mechanism. We found substantial enrichment of insulin signaling and other T2DM-related pathways, such as valine, leucine and isoleucine biosynthesis, serine and threonine metabolism, adipocytokine signaling pathway, P13K/Akt pathway, and Hedgehog signaling pathway. The expression profiles of these DEGs verified by qPCR showed a substantial level of twofold change (FC) expression (2-ΔΔCT) in the genes SRR (FC ≤ 0.12), NFKB1 (FC ≤ 1.09), and PDE4B (FC ≤ 0.9) compared to controls (FC ≥ 1.6). The downregulated expression of these genes is associated with pathophysiological development and metabolic disorders.

CONCLUSION

This study would help to modulate the therapeutic strategies for T2DM and could speed up drug discovery outcomes.

The Transcriptomic Evidence on the Role of Abdominal Visceral vs. Subcutaneous Adipose Tissue in the Pathophysiology of Diabetes in Asian Indians Indicates the Involvement of Both

The roles of abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) in the molecular pathogenesis type-2 diabetics (T2D) among Asian Indians showing a "thin fat" phenotype largely remains obscure. In this study, we generated transcription profiles in biopsies of these adipose depots obtained during surgery in 19 diabetics (M: F ratio, 8:11) and 16 (M: F ratio 5:11) age- and BMI-matched non-diabetics. Gene set enrichment analysis (GSEA) was used for comparing transcription profile and showed that 19 gene sets, enriching inflammation and immune system-related pathways, were upregulated in diabetics with F.D.R. <25% and >25%, respectively, in VAT and SAT. Moreover, 13 out of the 19 significantly enriched pathways in VAT were among the top 20 pathways in SAT. On comparison of VAT vs. SAT among diabetics, none of the gene sets were found significant at F.D.R. <25%. The Weighted Gene Correlation Analysis (WGCNA) analysis of the correlation between measures of average gene expression and overall connectivity between VAT and SAT was significantly positive. Several modules of co-expressed genes in both the depots showed a bidirectional correlation with various diabetes-related intermediate phenotypic traits. They enriched several diabetes pathogenicity marker pathways, such as inflammation, adipogenesis, etc. It is concluded that, in Asian Indians, diabetes pathology inflicts similar molecular alternations in VAT and SAT, which are more intense in the former. Both adipose depots possibly play a role in the pathophysiology of T2D, and whether it is protective or pathogenic also depends on the nature of modules of co-expressed genes contained in them.

Expression Profile of SARS-CoV-2 Host Receptors in Human Pancreatic Islets Revealed Upregulation of ACE2 in Diabetic Donors

Cellular entry of SARS-CoV-2 is thought to occur through the binding of viral spike S1 protein to ACE2. The entry process involves priming of the S protein by TMPRSS2 and ADAM17, which collectively mediate the binding and promote ACE2 shedding. In this study, microarray and RNA-sequencing (RNA-seq) expression data were utilized to profile the expression pattern of ACE2, ADAM17, and TMPRSS2 in type 2 diabetic (T2D) and non-diabetic human pancreatic islets. Our data show that pancreatic islets express all three receptors irrespective of diabetes status. The expression of ACE2 was significantly increased in diabetic/hyperglycemic islets compared to non-diabetic/normoglycemic. Islets from female donors showed higher ACE2 expression compared to males; the expression of ADAM17 and TMPRSS2 was not affected by gender. The expression of the three receptors was statistically similar in young (≤40 years old) versus old (≥60 years old) donors. Obese (BMI > 30) donors have significantly higher expression levels of ADAM17 and TMPRSS2 relative to those from non-obese donors (BMI < 25). TMPRSS2 expression correlated positively with HbA1c and negatively with age, while ADAM17 and TMPRSS2 correlated positively with BMI. The expression of the three receptors was statistically similar in muscle and subcutaneous adipose tissues obtained from diabetic and nondiabetic donors. Lastly, ACE2 expression was higher in sorted pancreatic β-cell relative to other endocrine cells. In conclusion, ACE2 expression is increased in diabetic human islets. More studies are required to investigate whether variations of ACE2 expression could explain the severity of COVID-19 infection-related symptoms between diabetics and non-diabetic patients.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10^-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10^-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10^-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10^-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

Functional Interactomes of Genes Showing Association with Type-2 Diabetes and Its Intermediate Phenotypic Traits Point towards Adipo-Centric Mechanisms in Its Pathophysiology

The pathogenic mechanisms causing type 2 diabetes (T2D) are still poorly understood; a greater awareness of its causation can lead to the development of newer and better antidiabetic drugs. In this study, we used a network-based approach to assess the cellular processes associated with protein–protein interaction subnetworks of glycemic traits—HOMA-β and HOMA-IR. Their subnetworks were further analyzed in terms of their overlap with the differentially expressed genes (DEGs) in pancreatic, muscle, and adipose tissue in diabetics. We found several DEGs in these tissues showing an overlap with the HOMA-β subnetwork, suggesting a role of these tissues in β-cell failure. Many genes in the HOMA-IR subnetwork too showed an overlap with the HOMA-β subnetwork. For understanding the functional theme of these subnetworks, a pathway-to-pathway complementary network analysis was done, which identified various adipose biology-related pathways, containing genes involved in both insulin secretion and action. In conclusion, network analysis of genes showing an association between T2D and its intermediate phenotypic traits suggests their potential role in beta cell failure. These genes enriched the adipo-centric pathways and were expressed in both pancreatic and adipose tissue and, therefore, might be one of the potential targets for future antidiabetic treatment.

Molecular Mechanism of the Effect of Huanglian Jiedu Decoction on Type 2 Diabetes Mellitus Based on Network Pharmacology and Molecular Docking

BACKGROUND

Huanglian Jiedu Decoction (HLJDD) is a Traditional Chinese Medicine (TCM) formula comprising four herbal medicines. This decoction has long been used in China for clinically treating T2DM. However, the molecular mechanism of HLJDD treat for T2DM is still not fully known. Hence, this study was designed to reveal the synergistic mechanism of HLJDD formula in the treatment of T2DM by using network pharmacology method and molecular docking.

METHODS

Retrieving and screening of active components of different herbs in HLJDD and corresponding T2DM-related target genes across multiple databases. Subsequently, STRING and Cytoscape were applied to analysis and construct PPI network. In addition, cluster and topological analysis were employed for the analysis of PPI networks. Then, the GO and KEGG enrichment analysis were performed by using ClueGO tool. Finally, the differentially expressed analysis was used to verify whether the expression of key target genes in T2DM and non-T2DM samples was statistically significant, and the binding capacity between active components and key targets was validated by molecular docking using AutoDock.

RESULTS

There are 65 active components involved in 197 T2DM-related targets that are identified in HLJDD formula. What is more, 39 key targets (AKT1, IL-6, FOS, VEGFA, CASP3, etc.) and 3 clusters were obtained after topological and cluster analysis. Further, GO and KEGG analysis showed that HLJDD may play an important role in treating T2DM and its complications by synergistically regulating many biological processes and pathways which participated in signaling transduction, inflammatory response, apoptotic process, and vascular processes. Differentially expressed analysis showed that AKT1, IL-6, and FOS were upregulated in T2DM samples and a significant between sample differential expression. These results were validated by molecular docking, which identified 5 high-affinity active components in HLJDD, including quercetin, wogonin, baicalein, kaempferol, and oroxylin A.

CONCLUSION

Our research firstly revealed the basic pharmacological effects and relevant mechanisms of the HLJDD in the treatment of T2DM and its complications. The prediction results might facilitate the development of HLJDD or its active compounds as alternative therapy for T2DM. However, more pharmacological experiments should be performed for verification.

The common pathophysiologic threads between Asian Indian diabetic's 'Thin Fat Phenotype' and partial lipodystrophy: the peripheral adipose tissue transcriptomic evidences

T2D is a complex disease with poorly understood mechanisms. In Asian Indians, it is associated with “thin fat” phenotype which resembles with partial lipodystrophy. We hypothesized that disturbed expression of lipodystrophy genes might play a role in T2D pathogenesis. Therefore, we attempted to establish a link between these two diseases by studying the overlap between the network of lipodystrophy genes and the differentially expressed genes (DEGs) in the peripheral subcutaneous adipose tissue of Asian Indians diabetics. We found that 16, out of 138 lipodystrophy genes were differentially regulated in diabetics and around 18% overlap between their network and the DEGs; the expression level of lipodystrophy genes showed an association with disease-related intermediate phenotypic traits among diabetics but not in the control group. We also attempted to individualize the diabetic patients based on ±2 fold altered expression of lipodystrophy genes as compared to their average expression in the control group. In conclusion, significant overlap exists between some of the lipodystrophy genes and their network with DEGs in the peripheral adipose tissue in diabetics. They possibly play a role in the pathogenesis of diabetes and individualization of diabetics is possible based on their altered expression in their peripheral adipose tissue.

Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

BACKGROUND The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. MATERIAL AND METHODS The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. RESULTS Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. CONCLUSIONS DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.

Transcriptome profiling reveals association of peripheral adipose tissue pathology with type-2 diabetes in Asian Indians

Type 2 diabetes (T2D) is a complex disease with an elusive link between its molecular aetiology and clinical presentation. Although, the role of visceral adipose tissue in insulin-resistance and T2D is known, limited information is available on the role of peripheral-subcutaneous adipose tissue especially in Asian Indians. In this microarray-based study of diabetic and normal glucose tolerant Asian Indians, we generated the transcriptome of their thigh adipose tissue and analyzed differentially expressed genes (DEGs) using weighted gene co-expression network analysis; further we identified perturbed pathways implicated by these DEGs in relevant co-expression modules. We also attempted to link these pathways with known aspects of T2D pathophysiology in terms of their association with some of their intermediate traits, namely; adipocyte size, HOMA-B, HOMA-R, Hb1Ac, insulin, glucose-level, TNF-α, IL-6, VLDLs, LDLs, HDLs, and NEFAs. It was observed that several modules of co-expressed genes show an association with diabetes and some of its intermediate phenotypic traits mentioned above. Therefore, these findings suggest a role of peripheral subcutaneous adipose tissue in the pathophsiology of T2D in Asian Indians. Additionally, our study indicated that the peripheral subcutaneous adipose tissue in diabetics shows pathologic changes characterized by adipocyte hypertrophy and up-regulation of inflammation-related pathways.

Systems Genomics of Thigh Adipose Tissue From Asian Indian Type-2 Diabetics Revealed Distinct Protein Interaction Hubs

We performed a systematic analysis of genes implicated in thigh subcutaneous adipose tissue of Asian Indian Type 2 Diabetes Mellitus (AIT2DM) and created a phenome-interactome network. This analysis was performed on 60 subjects specific to limb thigh fat by integrating phenotypic traits and similarity scores associated with AIT2DM. Using a phenotypic attribute, a contextual neighbor was identified across all the traits, viz. body mass index (BMI) statistics, adipocyte size, lipid parameters, homeostatic model assessment- insulin resistance (HOMA-IR), HOMA-ß. In this work, we have attempted to characterize transcription signatures using the phenome-interactome maps where each of the traits under study including the intermediary phenotypes has a distinct set of genes forming the hubs. Furthermore, we have identified various clinical, biochemical, and radiological parameters which show significant correlation with distinct hubs. We observed a number of novel pathways and genes including those that are non-coding RNAs implicated in AIT2DM.We showed that they appear to be associated with pathways, viz. tyrosine kinase JAK2, NOTCH thereby recruiting signaling molecules such as STAT5 and Src family kinases on the cell surface regulated them and our analyses comprising significant hubs suggest that thigh subcutaneous adipose tissue plays a role in pathophysiology of AIT2DM.

Inflammation, metaflammation and immunometabolic disorders

Proper regulation and management of energy, substrate diversity and quantity, as well as macromolecular synthesis and breakdown processes, are fundamental to cellular and organismal survival and are paramount to health. Cellular and multicellular organization are defended by the immune response, a robust and critical system through which self is distinguished from non-self, pathogenic signals are recognized and eliminated, and tissue homeostasis is safeguarded. Many layers of evolutionarily conserved interactions occur between immune response and metabolism. Proper maintenance of this delicate balance is crucial for health and has important implications for many pathological states such as obesity, diabetes, and other chronic non-communicable diseases.

Computational analyses of type 2 diabetes-associated loci identified by genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) of type 2 diabetes (T2D) have discovered a number of loci that contribute to susceptibility to the disease. Future challenges include elucidation of functional mechanisms through which these GWAS-identified loci modulate T2D disease risk. The aim of the present study was to comprehensively characterize T2D associated single nucleotide polymorphisms (SNPs) and genes through computational approaches.

METHODS

Computational biology approaches used in the present study included comparative genomic analyses and functional annotation using GWAS3D and RegulomeDB, investigation of the effects of T2D-associated SNPs on miRNA binding and protein phosphorylation, and gene ontology, pathway enrichment, protein-protein interaction (PPI) networks and functional module analysis of T2D-associated genes from previously published GWAS.

RESULTS

Computational analysis identified a number of T2D GWAS-associated SNPs that were located at protein binding sites, including CCCTC-binding factor (CTCF), E1A binding protein p300 (EP300), hepatocyte nuclear factor 4alpha (HNF4A), transcription factor 7 like 2 (TCF7L2), forkhead box A1 (FOXA1) and A2 (FOXA2), and potentially affected the binding of miRNAs and protein phosphorylation. Pathway enrichment analysis confirmed two well-known maturity onset diabetes of the young and T2D pathways, whereas PPI network analysis identified highly interconnected "hub" genes, such as TCF7L2, melatonin receptor 1B (MTNR1B), and solute carrier family 30 (zinc transporter), member 8 (SLC30A8), that created two tight subnetworks.

CONCLUSIONS

The results provide objectives and clues for future experimental studies and further insights into the molecular pathogenesis of T2D.

Serum microRNA profiling and bioinformatics analysis of patients with type 2 diabetes mellitus in a Chinese population

Type 2 diabetes mellitus (T2DM) is characterized by islet β-cell dysfunction and insulin resistance, which leads to an inability to maintain blood glucose homeostasis. Circulating microRNAs (miRNAs) have been suggested as novel biomarkers for T2DM prediction or disease progression. However, miRNAs and their roles in the pathogenesis of T2DM remain to be fully elucidated. In the present study, the serum miRNA expression profiles of T2DM patients in Chinese cohorts were examined. Total RNA was extracted from serum samples of 10 patients with T2DM and five healthy controls, and these was used in reverse-transcription‑quantitative polymerase chain reaction analysis with the Exiqon PCR system of 384 serum/plasma miRNAs. A total of seven miRNAs were differentially expressed between the two groups (fold change >3 or <0.33; P<0.05). The serum expression levels of miR‑455‑5p, miR‑454‑3p, miR‑144‑3p and miR‑96‑5p were higher in patients with T2DM, compared with those of healthy subjects, however, the levels of miR‑409‑3p, miR‑665 and miR‑766‑3p were lower. Hierarchical cluster analysis indicated that it was possible to separate patients with T2DM and control individuals into their own similar categories by these differential miRNAs. Target prediction showed that 97 T2DM candidate genes were potentially modulated by these seven miRNAs. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that 24 pathways were enriched for these genes, and the majority of these pathways were enriched for the targets of induced and repressed miRNAs, among which insulin, adipocytokine and T2DM pathways, and several cancer‑associated pathways have been previously associated with T2DM. In conclusion, the present study demonstrated that serum miRNAs may be novel biomarkers for T2DM and provided novel insights into the pathogenesis of T2DM.

Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus

Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis.

Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation.

Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets.

Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes.

Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies

MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets. Results indicate that Subpathway-GMir is more effective in identifying phenotype-associated metabolic pathways than other methods and our results are reproducible and robust. Subpathway-GMir provides a flexible platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles that miRNAs play in a variety of diseases. The Subpathway-GMir method has been implemented as a freely available R package.

Prediction of scaffold proteins based on protein interaction and domain architectures

Background

Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively.

Results

From a total of 10,419 basic scaffold protein candidates in protein interactomes, we classified them into three classes according to the structural evidences for scaffolding, such as domain architectures, domain interactions and protein complexes. Finally, we could define 2716 highly reliable scaffold protein candidates and their characterized functional features. To assess the accuracy of our prediction, the gold standard positive and negative data sets were constructed. We prepared 158 gold standard positive data and 844 gold standard negative data based on the functional information from Gene Ontology consortium. The precision, sensitivity and specificity of our testing was 80.3, 51.0, and 98.5 % respectively. Through the function enrichment analysis of highly reliable scaffold proteins, we could confirm the significantly enriched functions that are related to scaffold protein binding. We also identified functional association between scaffold proteins and their recruited proteins. Furthermore, we checked that the disease association of scaffold proteins is higher than kinases.

Conclusions

In conclusion, we could predict larger volume of scaffold proteins and analyzed their functional characteristics. Deeper understandings about the roles of scaffold proteins from this study will provide a higher opportunity to find therapeutic or engineering applications of scaffold proteins using their functional characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1079-5) contains supplementary material, which is available to authorized users.

Bioenergetic Analysis of Single Pancreatic β-Cells Indicates an Impaired Metabolic Signature in Type 2 Diabetic Subjects

Impaired activation of mitochondrial energy metabolism by glucose has been demonstrated in type 2 diabetic β-cells. The cause of this dysfunction is unknown. The aim of this study was to identify segments of energy metabolism with normal or with altered function in human type 2 diabetes mellitus. The mitochondrial membrane potential (ΔψM), and its response to glucose, is the main driver of mitochondrial ATP synthesis and is hence a central mediator of glucose-induced insulin secretion, but its quantitative determination in β-cells from human donors has not been attempted, due to limitations in assay technology. Here, novel fluorescence microscopic assays are exploited to quantify ΔψM and its response to glucose and other secretagogues in β-cells of dispersed pancreatic islet cells from 4 normal and 3 type 2 diabetic organ donors. Mitochondrial volume densities and the magnitude of ΔψM in low glucose were not consistently altered in diabetic β-cells. However, ΔψM was consistently less responsive to elevation of glucose concentration, whereas the decreased response was not observed with metabolizable secretagogue mixtures that feed directly into the tricarboxylic acid cycle. Single-cell analysis of the heterogeneous responses to metabolizable secretagogues indicated no dysfunction in relaying ΔψM hyperpolarization to plasma membrane potential depolarization in diabetic β-cells. ΔψM of diabetic β-cells was distinctly responsive to acute inhibition of ATP synthesis during glucose stimulation. It is concluded that the mechanistic deficit in glucose-induced insulin secretion and mitochondrial hyperpolarization of diabetic human β-cells is located upstream of the tricarboxylic acid cycle and manifests in dampening the control of ΔψM by glucose metabolism.

Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets

Understanding distinct gene expression patterns of normal adult and developing fetal human pancreatic α- and β-cells is crucial for developing stem cell therapies, islet regeneration strategies, and therapies designed to increase β-cell function in patients with diabetes (type 1 or 2). Toward that end, we have developed methods to highly purify α-, β-, and δ-cells from human fetal and adult pancreata by intracellular staining for the cell-specific hormone content, sorting the subpopulations by flow cytometry, and, using next-generation RNA sequencing, we report the detailed transcriptomes of fetal and adult α- and β-cells. We observed that human islet composition was not influenced by age, sex, or BMI, and transcripts for inflammatory gene products were noted in fetal β-cells. In addition, within highly purified adult glucagon-expressing α-cells, we observed surprisingly high insulin mRNA expression, but not insulin protein expression. This transcriptome analysis from highly purified islet α- and β-cell subsets from fetal and adult pancreata offers clear implications for strategies that seek to increase insulin expression in type 1 and type 2 diabetes.

Genetics, genomics and personalized medicine in Type 2 diabetes: a perspective on the Arab region

Type 2 diabetes (T2D) is a wide-spread, chronic metabolic disorder, affecting millions of people worldwide. The epidemic of diabetes has placed a huge strain on public health, longevity and economy. T2D occurs as a result of both genetic and environmental factors and is heterogeneous in its presentation across individuals. This review gives an overview of the genetic variations identified by genome-wide association studies which predispose individuals to T2D and those which are responsible for variable drug response across patients, and the necessity to adopt a personalized approach to diabetes management. We also include a perspective on diabetes in Arabs, given the high incidence of T2D and consanguineous marriages, and the need to understand associated genetic components in this vulnerable population.

Network Cluster Analysis of Protein-Protein Interaction Network-Identified Biomarker for Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a complex disease that is caused by an impairment in the secretion of β-cell insulin and by a peripheral resistance to insulin. Most patients suffering from T2DM and from obesity exhibit insulin resistance in the muscles, liver, and fat, resulting in a reduced response of these tissues to insulin. In healthy individuals, pancreatic islet β-cells secrete insulin to regulate the increase in blood glucose levels. Once these β-cells fail to function, T2DM develops. Despite the progress achieved in this field in recent years, the genetic causes for insulin resistance and for T2DM have not yet been fully discovered. The present study aims to characterize T2DM by comparing its gene expression with that of normal controls, as well as to identify biomarkers for early T2DM. Gene expression profiles were downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified for type 2 diabetes. Furthermore, functional analyses were conducted for the gene ontology and for the pathway enrichment. In total, 781 DEGs were identified in the T2DM samples relative to healthy controls. These genes were found to be involved in several biological processes, including cell communication, cell proliferation, cell shape, and apoptosis. We constructed a protein-protein interaction (PPI) network, and the clusters in the PPI were analyzed by using ClusterONE. Six functional genes that may play important roles in the initiation of T2DM were identified within the network.

The pancreatic β-cell transcriptome and integrated-omics

PURPOSE OF REVIEW

β Cells represent one of many cell types in heterogeneous pancreatic islets and play the central role in maintaining glucose homeostasis, such that disrupting β-cell function leads to diabetes. This review summarizes the methods for isolating and characterizing β cells, and describes integrated 'omics' approaches used to define the β cell by its transcriptome and proteome.

RECENT FINDINGS

RNA sequencing and mass spectrometry-based protein identification have now identified RNA and protein profiles for mouse and human pancreatic islets and β cells, and for β-cell lines. Recent publications have outlined these profiles and, more importantly, have begun to assign the presence or absence of specific genes and regulatory molecules to β-cell function and dysfunction. Overall, researchers have focused on understanding the pathophysiology of diabetes by connecting genome, transcriptome, proteome, and regulatory RNA profiles with findings from genome-wide association studies.

SUMMARY

Studies employing these relatively new techniques promise to identify specific genes or regulatory RNAs with altered expression as β-cell function begins to deteriorate in the spiral toward the development of diabetes. The ultimate goal is to identify the potential therapeutic targets to prevent β-cell dysfunction and thereby better treat the individual with diabetes.

VIDEO ABSTRACT

http://links.lww.com/COE/A5.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.

Epistatic effects of multiple receptor genes on pathophysiology of asthma - its limits and potential for clinical application

To date, genome-wide association studies (GWAS) permit a comprehensive scan of the genome in an unbiased manner, with high sensitivity, and thereby have the potential to identify candidate genes for the prevalence or development of multifactorial diseases such as bronchial asthma. However, most studies have only managed to explain a small additional percentage of hereditability estimates, and often fail to show consistent results among studies despite large sample sizes. Epistasis is defined as the interaction between multiple different genes affecting phenotypes. By applying epistatic analysis to clinical genetic research, we can analyze interactions among more than 2 molecules (genes) considering the whole system of the human body, illuminating dynamic molecular mechanisms. An increasing number of genetic studies have investigated epistatic effects on the risk for development of asthma. The present review highlights a concept of epistasis to overcome traditional genetic studies in humans and provides an update of evidence on epistatic effects on asthma. Furthermore, we review concerns regarding recent trends in epistatic analyses from the perspective of clinical physicians. These concerns include biological plausibility of genes identified by computational statistics, and definition of the diagnostic label of 'physician-diagnosed asthma'. In terms of these issues, further application of epistatic analysis will prompt identification of susceptibility of diseases and lead to the development of a new generation of pharmacological strategies to treat asthma.

Biobanking across the phenome - at the center of chronic disease research

Background

Recognized public health relevant risk factors such as obesity, physical inactivity, smoking or air pollution are common to many non-communicable diseases (NCDs). NCDs cluster and co-morbidities increase in parallel to age. Pleiotropic genes and genetic variants have been identified by genome-wide association studies (GWAS) linking NCD entities hitherto thought to be distant in etiology. These different lines of evidence suggest that NCD disease mechanisms are in part shared.

Discussion

Identification of common exogenous and endogenous risk patterns may promote efficient prevention, an urgent need in the light of the global NCD epidemic. The prerequisite to investigate causal risk patterns including biologic, genetic and environmental factors across different NCDs are well characterized cohorts with associated biobanks. Prospectively collected data and biospecimen from subjects of various age, sociodemographic, and cultural groups, both healthy and affected by one or more NCD, are essential for exploring biologic mechanisms and susceptibilities interlinking different environmental and lifestyle exposures, co-morbidities, as well as cellular senescence and aging. A paradigm shift in the research activities can currently be observed, moving from focused investigations on the effect of a single risk factor on an isolated health outcome to a more comprehensive assessment of risk patterns and a broader phenome approach. Though important methodological and analytical challenges need to be resolved, the ongoing international efforts to establish large-scale population-based biobank cohorts are a critical basis for moving NCD disease etiology forward.

Summary

Future epidemiologic and public health research should aim at sustaining a comprehensive systems view on health and disease. The political and public discussions about the utilitarian aspect of investing in and contributing to cohort and biobank research are essential and are indirectly linked to the achievement of public health programs effectively addressing the global NCD epidemic.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”