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.

Catchment and in-stream influences on metal concentration and ochre deposit density in upland streams, Northern Ireland

Metal concentrations from stream waters in two geological blocks in Northern Ireland were compared to determine the contributions of catchment characteristics and in-stream conditions. One block is composed of metamorphosed schist and unconsolidated glacial drift with peat or peaty podzol (mainly humic) soils, while the other block consists of tertiary basalt with brown earth and gley soils. Water samples were collected from 52 stream sites and analysed for Fe, Mn and Al as well as a range of other chemical determinands known to affect metal solubility. Densities of metal-rich ochre deposit were determined for stream bed stone samples. Higher conductivities and concentrations of bicarbonate, alkalinity, Ca and Mg occurred on basalt than on schist. Despite higher Fe and Mn oxide concentrations in basalt-derived non-humic soils, stream water concentrations were much lower and ochre deposit densities only one third of those on schist overlain by humic soils. Neither rock nor soil type predicted Al concentrations, but pH and dissolved oxygen did. Peat-generated acidity and the limited acid neutralising capacity of base-poor metamorphosed schist have resulted in elevated concentrations of metals and ochre deposit in surface waters.

Discrimination of honey of different floral origins by a combination of various chemical parameters

Abstract Honey is a high value food commodity with recognized nutraceutical properties. A primary driver of the value of honey is its floral origin. The feasibility of applying multivariate data analysis to various chemical parameters for the discrimination of honeys was explored. This approach was applied to four authentic honeys with different floral origins (rata, kamahi, clover and manuka) obtained from producers in New Zealand. Results from elemental profiling, stable isotope analysis, metabolomics (UPLC-QToF MS), and NIR, FT-IR, and Raman spectroscopic fingerprinting were analyzed. Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine which technique or combination of techniques provided the best classification and prediction abilities. Good prediction values were achieved using metabolite data (for all four honeys, Q² = 0.52; for manuka and clover, Q² = 0.76) and the trace element/isotopic data (for manuka and clover, Q² = 0.65), while the other chemical parameters showed promise when combined (for manuka and clover, Q² = 0.43).