Distinctive gene expression profiles in Balb/3T3 cells exposed to low dose cobalt nanoparticles, microparticles and ions: potential nanotoxicological relevance.

Size-dependent characteristics of novel engineered nanomaterials might result in unforeseen biological responses and toxicity. To address this issue, we used cDNA microarray analysis (13443 genes) coupled with bioinformatics and functional gene annotation studies to investigate the transcriptional profiles of Balb/3T3 cells exposed to a low dose (1 μM) of cobalt nanoparticles (CoNP), microparticles (CoMP) and ions (Co2+). CoNP, CoMP and Co2+ affected 124, 91 and 80 genes, respectively. Hierarchical clustering revealed two main gene clusters, one up-regulated, mainly after Co2+, the other down-regulated, mainly after CoNP and CoMP. The significant Gene Ontology (GO) terms included oxygen binding and transport and hemoglobin binding for Co2+, while the GOs of CoMP and CoNP were related to nucleus and intracellular components. Pathway analysis highlighted: i) mitochondrial dysfunction for Co2+, ii) signaling, activation of innate immunity, and apoptosis for CoNP, and iii) cell metabolism, G1/S cell cycle checkpoint regulation and signaling for CoMP. Unlike ions, particles affected toxicologically-relevant pathways implicated in carcinogenesis and inflammation.

Interactive data analysis and clustering of genomic data.

In this work a new clustering approach is used to explore a well- known dataset [Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., et al. (2002). Molecular biology of the cell: Vol. 13. Identification of genes periodically expressed in the human cell cycle and their expression in tumors (pp. 1977-2000)] of time dependent gene expression profiles in human cell cycle. The approach followed by us is realized with a multi-step procedure: after preprocessing, parameters are chosen by using data sub sampling and stability measures; for any used model, several different clustering solutions are obtained by random initialization and are selected basing on a similarity measure and a figure of merit; finally the selected solutions are tuned by evaluating a reliability measure. Three different models for clustering, K-means, Self-organizing Maps and Probabilistic Principal Surfaces are compared. Comparative analysis is carried out by considering: similarity between best solutions obtained through the three methods, absolute distortion value and validation through the use of Gene Ontology (GO) annotations. The GO annotations are used to give significance to the obtained clusters and to compare the results with those obtained in the work cited above.

A multi-step approach to time series analysis and gene expression clustering.

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.