Constructing regulatory networks to identify biomarkers for insulin resistance

Transcriptional Profiling and Biological Pathway(s) Analysis of Type 2 Diabetes Mellitus in a Pakistani Population

The epidemic of type 2 diabetes mellitus (T2DM) is an important global health concern. Our earlier epidemiological investigation in Pakistan prompted us to conduct a molecular investigation to decipher the differential genetic pathways of this health condition in relation to non-diabetic controls. Our microarray studies of global gene expression were conducted on the Affymetrix platform using Human Genome U133 Plus 2.0 Array along with Ingenuity Pathway Analysis (IPA) to associate the affected genes with their canonical pathways. High-throughput qRT-PCR TaqMan Low Density Array (TLDA) was performed to validate the selected differentially expressed genes of our interest, viz., ARNT, LEPR, MYC, RRAD, CYP2D6, TP53, APOC1, APOC2, CYP1B1, SLC2A13, and SLC33A1 using a small population validation sample (n = 15 cases and their corresponding matched controls). Overall, our small pilot study revealed a discrete gene expression profile in cases compared to controls. The disease pathways included: Insulin Receptor Signaling, Type II Diabetes Mellitus Signaling, Apoptosis Signaling, Aryl Hydrocarbon Receptor Signaling, p53 Signaling, Mitochondrial Dysfunction, Chronic Myeloid Leukemia Signaling, Parkinson's Signaling, Molecular Mechanism of Cancer, and Cell Cycle G1/S Checkpoint Regulation, GABA Receptor Signaling, Neuroinflammation Signaling Pathway, Dopamine Receptor Signaling, Sirtuin Signaling Pathway, Oxidative Phosphorylation, LXR/RXR Activation, and Mitochondrial Dysfunction, strongly consistent with the evidence from epidemiological studies. These gene fingerprints could lead to the development of biomarkers for the identification of subgroups at high risk for future disease well ahead of time, before the actual disease becomes visible.

Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases

Background

The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases.

Material/Methods

IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein–protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively.

Results

A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes.

Conclusions

The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.

Transient postnatal over nutrition induces long-term alterations in cardiac NLRP3-inflammasome pathway

BACKGROUND AND AIMS

The prevalence of obesity is increasing worldwide at an alarming rate. Altered early nutrition, in particular postnatal overfeeding (PNOF), is a risk factor for impaired cardiac function in adulthood. In the understanding of the initiation or progression of heart diseases, NLRP3 inflammasome and non-coding RNAs have been proposed as key players. In this context, the aim of this study was to decipher the role of NLRP3 inflammasome and its post transcriptional control by micro-RNAs in the regulation of cardiac metabolic function induced by PNOF in mice.

METHODS AND RESULTS

Based on a model of mice exposed to PNOF through litter size reduction, we observed increased cardiac protein expression levels of NLRP3 and ETS-1 associated with alterations in insulin signaling. Additionally, miR-193b levels were down-regulated in the adult hearts of overfed animals. In a cardiomyocyte cell line, transfection with miR-193b induced down-regulation of ETS-1 and NLRP3 and improved insulin signaling.

CONCLUSIONS

These findings suggest that the miR-193b could be involved in cardiac phenotypic changes observed in adulthood induced by PNOF likely through the regulation of ETS-1 and NLRP3 expression, and through this of insulin signaling.

Pharmacogenomics of Sulfonylureas Response in Relation to rs7754840 Polymorphisms in Cyclin-Dependent Kinase 5 Regulatory Subunit-associated Protein 1-like (CDKAL1) Gene in Iranian Type 2 Diabetes Patients

Background:

Sulfonylureas are important drugs of choice for treatment of type 2 diabetes mellitus (T2DM). It is suggested that differential response to sulfonylureas from T2DM patients is under influence of single nucleotide polymorphisms in some of the target genes. In spite of favorable therapeutic effects, sulfonylureas are associated with some adverse side effects such as microvascular complications and stroke, especially in older patients. Therefore, for T2DM patients who are getting less benefit, sulfonylureas should be avoided. Cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like (CDKAL1) gene variation is reported to be associated with sulfonylureas effectiveness. Due to the inconsistency of available data regarding association of rs7754840 in CDKAL1 gene with sulfonylureas response in T2DM patients, the present study is conducted.

Materials and Methods:

Fifty-one diabetic patients sensitive to sulfonylureas and 51 patients resistant to sulfonylureas treatment were recruited to this study. After extraction of DNA from patients' peripheral blood samples, rs7754840 single-nucleotide polymorphism was genotyped by polymerase chain reaction-restriction fragment length polymorphism assay using MaeII (Tail) restriction enzyme.

Results:

Frequency of G allele in resistant group was more than sensitive group (71, 6% vs. 57, 8%). Regression analysis was shown significant association between GG genotype and higher risk of resistance to sulfonylureas treatment (odds ratio = 2.250 [95% confidential intervals: 1.010–5.012]; P = 0.046).

Conclusion:

Our data confirmed that genotypes of rs7754840 are significantly associated with sulfonylureas treatment response. rs7754840 in CDKAL1 gene in combination with other clinicopathological findings would help to move towards personalized therapy of T2DM patients.

Building a glaucoma interaction network using a text mining approach

Background

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease.

Results

A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx.

Conclusions

This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0096-2) contains supplementary material, which is available to authorized users.

MicroRNAs in obesity-associated disorders

The emergence of a worldwide obesity epidemic has dramatically increased the prevalence of insulin resistance and metabolic syndrome, predisposing individuals to a greater risk for the development of non-alcoholic fatty liver disease, type II diabetes and atherosclerotic cardiovascular diseases. Current available pharmacological interventions combined with diet and exercise-based managements are still poorly effective for weight management, likely in part due to an incomplete understanding of regulatory mechanisms and pathways contributing to the systemic metabolic abnormalities under disturbed energy homeostasis. MicroRNAs, small non-coding RNAs that regulate posttranscriptional gene expression, have been increasingly described to influence shifts in metabolic pathways under various obesity-related disease settings. Here we review recent discoveries of the mechanistic role that microRNAs play in regulating metabolic functions in liver and adipose tissues involved in obesity associated disorders, and briefly discusses the potential candidates that are being pursued as viable therapeutic targets.