A cross-species analysis method to analyze animal models' similarity to human's disease state

Abstract

BackgroundAnimal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism.ResultsWe developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery.We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning.ConclusionsWe developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.

Translation of Pre-Clinical Studies into Successful Clinical Trials for Alzheimer’s Disease: What are the Roadblocks and How Can They Be Overcome?1

“…Longitudinal animal studies, characterized by long-term follow up on the nature leading to lack of evidence for epigenetic of exposure to drugs, nutrition, biotherapeutics or other noninfluence pathogenesis of AD pharmacological interventions can provide crucial evidence to delineate the causal factors in disease pathogenesis of several genes related to AD and their interrelated activities at cellular levels play a unique and significant role in pathogenesis of disease in mouse models and patients, and these pose a serious challenge for experimental neurologists [159,[161][162][163].…”

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

“…AMD, refers to pathological changes in the central area of the retina, is the most important cause of irreversible visual loss in elderly populations, and is considered as a complex disease having multiple epistatic interactions [8]. The AMD data set contains 103,611 SNPs genotyped by 96 cases and 50 controls, which has been widely used as a benchmark data set [2, 8, 9, 12, 13, 15, 16, 29, 36, 37]. …”

Current transcriptomics in pig immunity research

“…Besides cross-platform meta-analyses, also cross-species meta-analyses are very promising, and R packages are freely available to conduct them (Kuhn et al 2008;Kristiansson et al 2013). However, until now, the main goal of these cross-species expression comparisons is often to employ model organisms for human diagnostic or therapeutic research (Yu et al 2012;Grigoryev et al 2013). Kapetanovic and colleagues show how the clustering software BioLayout Express 3D (BE3D) can be used to visualize inter-species expression comparisons ).…”