Evolutionary-conserved gene expression response profiles across mammalian tissues

No population left behind: Improving paediatric drug safety using informatics and systems biology

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Global regulatory architecture of human, mouse and rat tissue transcriptomes

Background

Predicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species.

Results

Here, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species.

Conclusion

The results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species.

A gene family-based method for interspecies comparisons of sequencing-based transcriptomes and its use in environmental adaptation analysis

We describe a new method for sequencing-based cross-species transcriptome comparisons and define a new metric for evaluating gene expression across species using protein-coding families as units of comparison. Using this measure transcriptomes from different species were evaluated by mapping them to gene families and integrating the mapping results with expression data. Statistical tests were applied to the transcriptome evaluation results to identify differentially expressed families. A Perl program named Pro-Diff was compiled to implement this method. To evaluate the method and provide an example of its use, two liver EST transcriptomes from two closely related fish that live in different temperature zones were compared. One EST library was from a recent sequencing project of Dissosticus mawsoni, a fish that lives in cold Antarctic sea waters, while the other was newly sequenced data (available at: http://www.fishgenome.org/polarbank/) from Notothenia angustata, a species that lives in temperate near-shore water of southern New Zealand. Results from the comparison were consistent with results inferred from phenotype differences and also with our previously published Gene Ontology-based method. The Pro-Diff program and operation manual can be downloaded from: http://www.fishgenome.org/download/Prodiff.rar.

Construction and use of gene expression covariation matrix

BACKGROUND

One essential step in the massive analysis of transcriptomic profiles is the calculation of the correlation coefficient, a value used to select pairs of genes with similar or inverse transcriptional profiles across a large fraction of the biological conditions examined. Until now, the choice between the two available methods for calculating the coefficient has been dictated mainly by technological considerations. Specifically, in analyses based on double-channel techniques, researchers have been required to use covariation correlation, i.e. the correlation between gene expression changes measured between several pairs of biological conditions, expressed for example as fold-change. In contrast, in analyses of single-channel techniques scientists have been restricted to the use of coexpression correlation, i.e. correlation between gene expression levels. To our knowledge, nobody has ever examined the possible benefits of using covariation instead of coexpression in massive analyses of single channel microarray results.

RESULTS

We describe here how single-channel techniques can be treated like double-channel techniques and used to generate both gene expression changes and covariation measures. We also present a new method that allows the calculation of both positive and negative correlation coefficients between genes. First, we perform systematic comparisons between two given biological conditions and classify, for each comparison, genes as increased (I), decreased (D), or not changed (N). As a result, the original series of n gene expression level measures assigned to each gene is replaced by an ordered string of n(n-1)/2 symbols, e.g. IDDNNIDID....DNNNNNNID, with the length of the string corresponding to the number of comparisons. In a second step, positive and negative covariation matrices (CVM) are constructed by calculating statistically significant positive or negative correlation scores for any pair of genes by comparing their strings of symbols.

CONCLUSION

This new method, applied to four different large data sets, has allowed us to construct distinct covariation matrices with similar properties. We have also developed a technique to translate these covariation networks into graphical 3D representations and found that the local assignation of the probe sets was conserved across the four chip set models used which encompass three different species (humans, mice, and rats). The application of adapted clustering methods succeeded in delineating six conserved functional regions that we characterized using Gene Ontology information.

William's syndrome: gene expression is related to parental origin and regional coordinate control

William's syndrome (WS) features a spectrum of neurocognitive and behavioral abnormalities due to a rare 1.5 MB deletion that includes about 24-28 genes on chromosome band 7q11.23. Study of the expression of these genes from the single normal copy provides an opportunity to elucidate the genetic and epigenetic controls on these genes as well as their roles in both WS and normal brain development and function. We used quantitative RT-PCR to determine the transcriptional level of 14 WS gene markers in a cohort of 77 persons with WS and 48 normal controls. Results reported here: (1) show that the expression of the genes deleted in WS is decreased in some but not all cases, (2) demonstrate that the parental origin of the deletion contributes to the level of expression of GTF2I independently of age and gender and (3) indicate that the correlation of expression between GTF2I and some other genes in the WS region differs in WS subjects and normal controls, which in turn points toward a regulatory role for this gene. Interspecies comparisons suggest GTF2I may play a key role in normal brain development.