Regulation of beta-cell viability and gene expression by distinct agonist fragments of adiponectin

Obesity is an established risk factor for type 2 diabetes. Activation of the adiponectin receptors has a clear role in improving insulin resistance although conflicting evidence exists for its effects on pancreatic beta-cells. Previous reports have identified both adiponectin receptors (ADR-1 and ADR-2) in the beta-cell. Recent evidence has suggested that two distinct regions of the adiponectin molecule, the globular domain and a small N-terminal region, have agonist properties. This study investigates the effects of two agonist regions of adiponectin on insulin secretion, gene expression, cell viability and cell signalling in the rat beta-cell line BRIN-BD11, as well as investigating the expression levels of adiponectin receptors (ADRs) in these cells. Cells were treated with globular adiponectin and adiponectin (15-36) +/-leptin to investigate cell viability, expression of key beta-cell genes and ERK1/2 activation. Both globular adiponectin and adiponectin (15-36) caused significant ERK1/2 dependent increases in cell viability. Leptin co-incubation attenuated adiponectin (15-36) but not globular adiponectin induced cell viability. Globular adiponectin, but not adiponectin (15-36), caused a significant 450% increase in PDX-1 expression and a 45% decrease in LPL expression. ADR-1 was expressed at a higher level than ADR-2, and ADR mRNA levels were differentially regulated by non-esterified fatty acids and peroxisome-proliferator-activated receptor agonists. These data provide evidence of roles for two distinct adiponectin agonist domains in the beta-cell and confirm the potentially important role of adiponectin receptor agonism in maintaining beta-cell mass.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.