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We have used a supervised classification approach to systematically mine a large microarray database derived from livers of compound-treated rats. Thirty-four distinct signatures (classifiers) for pharmacological and toxicological end points can be identified. Just 200 genes are sufficient to classify these end points. Signatures were enriched in xenobiotic and immune response genes and contain un-annotated genes, indicating that not all key genes in the liver xenobiotic responses have been characterized. Many signatures with equal classification capabilities but with no gene in common can be derived for the same phenotypic end point. The analysis of the union of all genes present in these signatures can reveal the underlying biology of that end point as illustrated here using liver fibrosis signatures. Our approach using the whole genome and a diverse set of compounds allows a comprehensive view of most pharmacological and toxicological questions and is applicable to other situations such as disease and development.

SummaryThe minimum structural requirements for peptide interactions with major histocompatibility complex (MHC) class II molecules and with T cell receptors (TCKs) were examined. In this report we show that substituting alanines at all but five amino acids in the myelin basic protein (MBP) peptide Ac1-11 does not alter its ability to bind Act"A/3" (MHC class II molecules), to stimulate specific T cells and, surprisingly, to induce experimental autoimmune encephalomyelitis (EAE) in (PL/J x SJL/J)F1 mice. Most other amino acid side chains in the Ac1-11 peptide are essentially irrelevant for T cell stimulation and for disease induction. Further analysis revealed that binding to Aoz"A/~ occurred with a peptide that consists mainly of ahnines and only three of the original residues of Ac1-11. Moreover, when used as a coimmunogen with MBP Acl-ll, this peptide inhibited EAE. The finding that a specific in vivo response can be generated by a peptide containing only five native residues provides evidence that disease-inducing TCRs recognize only a very short sequence of the MHC-bound peptide.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as “rewards” for the class-of-interest) while others have a negative contribution (act as “penalties”) to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class

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