sscMap: An extensible Java application for connecting small-molecule drugs using gene-expression signatures

Background
Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures.

Results
This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.

Conclusion
The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap

A novel method for isolation of human lung T cells from lung resection tissue reveals increased expression of GAPDH and CXCR6

Lung T lymphocytes are important in pulmonary immunity and inflammation. it has been difficult to study these cells due to contamination with other cell types, mainly alveolar macrophages. We have developed a novel method for isolating lung T cells from lung resection tissue, using a combination of approaches. Firstly the lung tissue was finely chopped and filtered through a nylon mesh. Lymphocytic cells were enriched by Percoll density centrifugation and the T cells purified using human CD3 microbeads, resulting in 90.5% +/- 1.9% (n = 11) pure lymphocytes. The T cell yield from the crude cell preparation was 10.8 +/- 2.1% and viability, calculated using propidium iodide (PI) staining and trypan blue, was typically over 95%. The purification process did not affect expression of CD69 or CD103, nor was there a difference in the proportion of CD4 and CD8 cells between the starting population and the purified cells. Microarray analysis and real time RT-PCR revealed upregulation of GAPDH and CXCR6 of the lung T cells as compared to blood-derived T cells. This technique highly enriches lung T cells to allow detailed investigation of the biology of these cells. (C) 2008 Elsevier B.V. All rights reserved.

Novel genomic methods for drug discovery and mechanism-based toxicological assessment

Genomics encompasses a range of powerful technologies that can be applied at all levels of gene expression, from transcription to mRNA translation. Collectively, these technologies have great potential for improving drug discovery, both target and molecule recognition, and development. In this article we review the current and potential future status of established and novel genomic methods within drug discovery.

A simple and robust method for connecting small-molecule drugs using gene-expression signatures

Background

Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Using a suitably robust algorithm these signatures can potentially be used to connect molecules with similar pharmacological or toxicological properties by gene expression profile. Lamb et al first proposed the Connectivity Map [Lamb et al (2006), Science 313, 1929–1935] to make successful connections among small molecules, genes, and diseases using genomic signatures.

Results

Here we have built on the principles of the Connectivity Map to present a simpler and more robust method for the construction of reference gene-expression profiles and for the connection scoring scheme, which importantly allows the valuation of statistical significance of all the connections observed. We tested the new method with two randomly generated gene signatures and three experimentally derived gene signatures (for HDAC inhibitors, estrogens, and immunosuppressive drugs, respectively). Our testing with this method indicates that it achieves a higher level of specificity and sensitivity and so advances the original method.

Conclusion

The method presented here not only offers more principled statistical procedures for testing connections, but more importantly it provides effective safeguard against false connections at the same time achieving increased sensitivity. With its robust performance, the method has potential use in the drug development pipeline for the early recognition of pharmacological and toxicological properties in chemicals and new drug candidates, and also more broadly in other 'omics sciences.

Association of gene expression with sequential proliferation, differentiation and tumor formation in murine skin

Differential gene expression in two established initiation and promotion skin carcinogenesis models during promotion and tumor formation was determined by microarray technology with the purpose of distinguishing the genes more associated with neoplastic transformation from those linked with proliferation and differentiation. The first model utilized dimethylbenz[a]anthracene initiation and 12-O-tetradecanoylphorbol 13-acetate (TPA) promotion in the FVB/N mouse, and the second TPA promotion of the Tg.Ac mouse, which is endogenously initiated by virtue of an activated Ha-ras transgene. Comparison of gene expression profiles across the two models identified genes whose altered expression was associated with papilloma formation rather than TPA-induced proliferation and differentiation. DMBA suppressed TPA-induced differentiation which allowed identification of those genes associated more specifically with differentiation rather than proliferation. EASE (Expression Analysis Systemic Explorer) indicated a correlation between muscle-associated genes and skin differentiation, whereas genes involved with protein biosynthesis were strongly correlated with proliferation. For verification the altered expression of selected genes were confirmed by RT-PCR; Carbonic anhydrase 2, Thioredoxin 1 and Glutathione S-transferase omega 1 associated with papilloma formation and Enolase 3, Cystatin 6 and Filaggrin associated with TPA-induced proliferation and differentiation. In situ analysis located the papillomas Glutathione S-transferase omega 1 expression to the proliferating areas of the papillomas. Thus we have identified profiles of differential gene expression associated with the tumorigenesis and promotion stages for skin carcinogenesis in the mouse.

Effect of pooling samples on the efficiency of comparative studies using microarrays

Motivation: Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large.

A statistical framework for the design of microarray experiments and effective detection of differential gene expression

Motivation: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment.

Application of connectivity mapping in predictive toxicology based on gene-expression similarity.

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.