In an era of unprecedented accumulation of biomedical data; our understanding of the mechanisms of the development of complex diseases is greatly by the performance of high-throughput; experiments and their subsequent analyses at various levels that range from single genes to biological pathways, modules, and networks. Genome-wide transcriptomic profiling has instrumental in providing accurate representations of the expression; programs in homeostasis and disease as well as before and after pharmaceutical interventions.
A new computational framework has revealed key differences between four rheumatoid arthritis medications and their impact on biological pathways in mice. Niki Karagianni of Biomedcode Hellas SA, Greece; colleagues presents their new approach and findings in PLOS Computational Biology. People with rheumatoid arthritis often receive medications that target and inhibit Tumor-Necrosis Factor (TNF); a protein involved in the painful and damaging inflammation characteristic of the disease.
Model of chronic inflammatory polyarthritis
While several anti-TNF drugs are used widely with comparable clinical success; the details of their different molecular effects on biological processes have unclear. To fill this gap, Karagianni and colleagues employed a mouse model of chronic inflammatory polyarthritis mice that express the human TNF and develop symptoms and signatures that closely mirror the human form of the disease.
Therefore the diseased mice received treatment with one of four anti-TNF drugs (Remicade, Cimzia, Humira, or Enbrel); or for comparison, none of the drugs. So the researchers then compared them to healthy. After treatment, the researchers collected joint tissue from all the mice and analyzed their transcriptomes the complete set of messenger RNA molecules in the tissues; which indicates which genes are on or off.
Drugs affect gene expression
Then, they applied a series of computational steps to the transcriptome data in order to compare the effects of the four different drugs..But the analysis revealed previously unknown differences in the way the four drugs affect gene expression in diseased mice. Some of these differences found for genes directly involved in arthritis, but many found in non-arthritis-related genes; such as genes involved in cardiovascular disease and other conditions that may occur alongside arthritis.
“Perhaps the most important result to come out of our study is a large number of down-regulated genes in the diseased animals, which are associated with functions and pathways that were until recently largely overlooked,” says study co-author Christoforos Nikolaou. “These could provide additional insight into arthritis pathology mechanisms.” The new computational framework developed for this study could repurpose for detailed comparisons of other drugs in other diseases. To help facilitate this, the researchers are working to organize the system into an automated; standalone package.