Systems biology for identifying liver toxicity pathways

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

Regulatory Forum Opinion Piece: Carcinogen Risk Assessment: The Move from Screens to Science

Throughout the last 50 years, the paradigm for carcinogenicity assessment has depended on lifetime bioassays in rodents. Since 1997, the International Conference on Harmonisation (ICH) S1B has permitted the use of a 2-year rodent bioassay (usually in the rat) and an alternative, genetically modified mouse model to support cancer risk assessment of pharmaceuticals. Since its introduction, it has become apparent that many of the stated advantages of the 6-month Tg mouse bioassay have, in actual fact, not been realized, and the concern exists that an albeit imperfect, 2-year mouse bioassay has been replaced by a similarly imperfect 6-month equivalent. This essay argues strongly that model systems, using cancer as the end point, should be discontinued, and that the recent initiatives, from the Organization for Economic Cooperation and Development and Institute of Peace and Conflict Studies, on "mode of action," "adverse outcome pathways," and "human relevance framework" should be embraced as being risk assessments based upon the available science. The recent suggested revisions to the ICH S1 guidelines, utilizing carcinogenicity assessment documents, go some way to developing a science-based risk assessment that does not depend almost entirely on a single, imperfect, cancer-based end point in nonrelevant animal species.

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data

Motivation: Eukaryotic gene expression (GE) is subjected to precisely coordinated multi-layer controls, across the levels of epigenetic, transcriptional and post-transcriptional regulations. Recently, the emerging multi-dimensional genomic dataset has provided unprecedented opportunities to study the cross-layer regulatory interplay. In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME). However, suitable analysis methods for such data are currently sparse.

Results: In this article, we introduced a sparse Multi-Block Partial Least Squares (sMBPLS) regression method to identify multi-dimensional regulatory modules from this new type of data. A multi-dimensional regulatory module contains sets of regulatory factors from different layers that are likely to jointly contribute to a local ‘gene expression factory’. We demonstrated the performance of our method on the simulated data as well as on The Cancer Genomic Atlas Ovarian Cancer datasets including the CNV, DM, ME and GE data measured on 230 samples. We showed that majority of identified modules have significant functional and transcriptional enrichment, higher than that observed in modules identified using only a single type of genomic data. Our network analysis of the modules revealed that the CNV, DM and microRNA can have coupled impact on expression of important oncogenes and tumor suppressor genes.

Availability and implementation: The source code implemented by MATLAB is freely available at: http://zhoulab.usc.edu/sMBPLS/.

Contact:xjzhou@usc.edu

Supplementary information:Supplementary material are available at Bioinformatics online.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.