Dataset Information

System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

ABSTRACT:

Background

Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies.

Objective

In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology.

Method

We used 'R', an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D.

Result

Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology.

Conclusion

Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.

SUBMITTER: Saxena A

PROVIDER: S-EPMC5476948 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

Saxena Aditya A Sachin Kumar K Bhatia Ashok Kumar AK

Current genomics 20170601 3

<h4>Background</h4>Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and hete ...[more]

PMID: 28659725

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Project description:BackgroundWith the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft.ResultsThe simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels.ConclusionWe present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the identified genes and pathways may lead to better understanding of CAN at the molecular level.

Project description:Inflammation, decreased levels of circulating endothelial nitric oxide (eNO) and brain-derived neurotrophic factor (BDNF), altered activity of hypothalamic neurotransmitters (including serotonin and vagal tone) and gut hormones, increased concentrations of free radicals, and imbalance in the levels of bioactive lipids and their pro- and anti-inflammatory metabolites have been suggested to play a role in diabetes mellitus (DM). Type 1 diabetes mellitus (type 1 DM) is due to autoimmune destruction of pancreatic β cells because of enhanced production of IL-6 and tumor necrosis factor-α (TNF-α) and other pro-inflammatory cytokines released by immunocytes infiltrating the pancreas in response to unknown exogenous and endogenous toxin(s). On the other hand, type 2 DM is due to increased peripheral insulin resistance secondary to enhanced production of IL-6 and TNF-α in response to high-fat and/or calorie-rich diet (rich in saturated and trans fats). Type 2 DM is also associated with significant alterations in the production and action of hypothalamic neurotransmitters, eNO, BDNF, free radicals, gut hormones, and vagus nerve activity. Thus, type 1 DM is because of excess production of pro-inflammatory cytokines close to β cells, whereas type 2 DM is due to excess of pro-inflammatory cytokines in the systemic circulation. Hence, methods designed to suppress excess production of pro-inflammatory cytokines may form a new approach to prevent both type 1 and type 2 DM. Roux-en-Y gastric bypass and similar surgeries ameliorate type 2 DM, partly by restoring to normal: gut hormones, hypothalamic neurotransmitters, eNO, vagal activity, gut microbiota, bioactive lipids, BDNF production in the gut and hypothalamus, concentrations of cytokines and free radicals that results in resetting glucose-stimulated insulin production by pancreatic β cells. Our recent studies suggested that bioactive lipids, such as arachidonic acid, eicosapentaneoic acid, and docosahexaenoic acid (which are unsaturated fatty acids) and their anti-inflammatory metabolites: lipoxin A4, resolvins, protectins, and maresins, may have antidiabetic actions. These bioactive lipids have anti-inflammatory actions, enhance eNO, BDNF production, restore hypothalamic dysfunction, enhance vagal tone, modulate production and action of ghrelin, leptin and adiponectin, and influence gut microbiota that may explain their antidiabetic action. These pieces of evidence suggest that methods designed to selectively deliver bioactive lipids to pancreatic β cells, gut, liver, and muscle may prevent type 1 and type 2 DM.

Dataset Information

System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

Background

Objective

Method

Result

Conclusion

Publications

System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

Similar Datasets

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