Functional genomics: technological challenges and opportunities

Environmental health research in the post-genome era: new fields, new challenges, and new opportunities

The human genome sequence provides researchers with a genetic framework to eventually understand the relationships of gene-environment interactions. This wealth of information has led to the birth of several related areas of research, including proteomics, functional genomics, pharmacogenomics, and toxicogenomics. Developing techniques such as DNA/protein microarrays, small-interfering RNA (siRNA) applications, two-dimensional gel electrophoresis, and mass spectrometry in conjunction with advanced analysis software and the availability of Internet databases offers a powerful set of tools to investigate an individual's response to specific stimuli. This review summarizes these emerging scientific fields and techniques focusing specifically on their applications to the complexities of gene-environment interactions and their potential role in environ-mental biosecurity.

Development of physiological regulatory systems: altering the timing of crucial events

There is currently tremendous interest in how the physiology of individual animals changes and develops during ontogeny. One of the key areas is the extent to which the timing and/or rate of physiological development is fixed within an individual and to what extent can it be altered. We propose that plasticity in the timing of the onset of a particular physiological regulatory system during an individuals development be referred to as physiological heterokairy (to clearly distinguish this phenomenon from physiological heterochrony, which is an evolutionary pattern), and we marshal evidence for three different patterns of heterokairy: 1. altering relative position in the physiological itinerary; 2. altering overall rate of development per se and; 3. a combination of 1 and 2. Using these patterns as a starting point, we develop a framework for investigating physiological heterokairy which takes cognizance of the facts that multiple components of each regulatory system could appear at different times and multiple regulatory systems could come 'on-line' at different times. We finish by placing physiological heterokairy in the wider context of its ecological and evolutionary implications and its relationship to physiological genomics and heterochrony.

Gene expression profiling. Methods and clinical applications in oncology

The advent of microarray technology undoubtedly will have great impact on the medical field during the next decade. This article discusses different genomic technologies, statistical methods for data analysis, and clinical applications of microarrays. Emphasis is devoted to integration of microarrays into the field of pediatric oncology.

Feature (gene) selection in gene expression-based tumor classification

There is increasing interest in changing the emphasis of tumor classification from morphologic to molecular. Gene expression profiles may offer more information than morphology and provide an alternative to morphology-based tumor classification systems. Gene selection involves a search for gene subsets that are able to discriminate tumor tissue from normal tissue, and may have either clear biological interpretation or some implication in the molecular mechanism of the tumorigenesis. Gene selection is a fundamental issue in gene expression-based tumor classification. In the formation of a discriminant rule, the number of genes is large relative to the number of tissue samples. Too many genes can harm the performance of the tumor classification system and increase the cost as well. In this report, we discuss criteria and illustrate techniques for reducing the number of genes and selecting an optimal (or near optimal) subset of genes from an initial set of genes for tumor classification. The practical advantages of gene selection over other methods of reducing the dimensionality (e.g., principal components), include its simplicity, future cost savings, and higher likelihood of being adopted in a clinical setting. We analyze the expression profiles of 2000 genes in 22 normal and 40 colon tumor tissues, 5776 sequences in 14 human mammary epithelial cells and 13 breast tumors, and 6817 genes in 47 acute lymphoblastic leukemia and 25 acute myeloid leukemia samples. Through these three examples, we show that using 2 or 3 genes can achieve more than 90% accuracy of classification. This result implies that after initial investigation of tumor classification using microarrays, a small number of selected genes may be used as biomarkers for tumor classification, or may have some relevance in tumor development and serve as a potential drug target. In this report we also show that stepwise Fisher's linear discriminant function is a practicable method for gene expression-based tumor classification.

Recursive partitioning for tumor classification with gene expression microarray data

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.

What will be the role of pharmacogenetics in evaluating drug safety and minimising adverse effects?

In the US, adverse drug reactions (ADRs) rank between the fourth to sixth leading cause of death, ahead of pneumonia and diabetes mellitus. An important reason for the high incidence of serious and fatal ADRs is that the existing drug development paradigms do not generate adequate information on the mechanistic sources of marked variability in pharmacokinetics and pharmacodynamics of new therapeutic candidates, precluding treatments from being tailored for individual patients. Pharmacogenetics is the study of the hereditary basis of person-to-person variations in drug response. The focus of pharmacogenetic investigations has traditionally been unusual and extreme drug responses resulting from a single gene effect. The Human Genome Project and recent advancements in molecular genetics now present an unprecedented opportunity to study all genes in the human genome, including genes for drug metabolism, drug targets and postreceptor second messenger machinery, in relation to variability in drug safety and efficacy. In addition to sequence variations in the genome, high throughput and genome-wide transcript profiling for differentially regulated mRNA species before and during drug treatment will serve as important tools to uncover novel mechanisms of drug action. Pharmacogenetic-guided drug discovery and development represent a departure from the conventional approach which markets drugs for broad patient populations, rather than smaller groups of patients in whom drugs may work more optimally. Pharmacogenetics provides a rational framework to minimise the uncertainty in outcome of drug therapy and clinical trials and thereby should significantly reduce the risk of drug toxicity.