Heat shock proteins in leukaemia cell differentiation and cell death

When HL60 cells were induced to differentiate to granulocyte-like cells with the agents N-methylformamide and tunicamycin an concentrations marginally below those which were cytotoxic, there was a decrease in the synthesis of the glucose- regulated proteins which preceded the expression of markers of a differentiated phenotype. There was a transient increase in the amount of hsp70 after 36 hours in NMF treated cells but in differentiated cells negligible amounts were detected. Inducers which were known to modulate hsp70 such as azetadine carboxylic acid did not induce differentiation suggesting early changes in the endoplasmic reticulum may be involved in the commitment to terminal differentiation of HL60 cells. These changes in group synthesis were not observed when K562 human chronic myelogenous leukemia cells were induced to differentiate to erythroid-like cells but there was a comparable increase in amounts of hsp70. When cells were treated with concentrations of drugs which brought about a loss in cell viability there was an early increase in the amount of hsp70 protein in the absence of any increase in synthesis. HL60 cells were treated with NMF (225mM), Adriamycin (1μM), or CB3717 (5μM) and there was an increase in the amounts of hsp70, in the absence of any new synthesis, which preceded any loss of membrane integrity and any significant changes in cell cycle but was concomitant with a later loss in viability of > 50% and a loss in proliferative potential. The amounts of hsp70 in the cell after treatment with any of the drugs was comparable to that obtained after a heat shock. Following a heat shock hsp70 was translocated from the cytoplasm to the nucleus, but treatment with toxic concentrations of drug caused hsp70 to remain localised in the cytoplasm. Changes in hsp70 turn-over was observed after a heat shock compared to NMF-treated cells. Morphological studies suggested that cells that had been treated with NMF and CB3717 were undergoing necrosis whereas the Adriamycin cells showed characteristics that were indicative of apoptosis. The data supports the hypothesis that an increase in amounts of hsp70 is an early marker of cell death.

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.