Month: April 2020

Whether TNF inhibition has divergent effects on key gene networks in these three diseases. Over the past decade, investigators have turned to microarray analytic techniques of peripheral blood cells and target tissues to examine cross-sectional and longitudinal gene expression. From these studies, several fundamental insights emerged. First, the molecular network in the immune mediated inflammatory disorders is far more complex than expected. Second, cross-sectional differential gene expression is much lower in blood cells and in specific cell lineages compared to whole tissues. Third, gene expression signatures in blood cells and synovial biopsies are heterogeneous and very patient-specific. Fourth, to date, no pre-GS-5734 AbMole treatment gene expression profile in blood or tissue can accurately and reliably predict response to anti-TNF therapy in any of these three diseases. Despite these caveats, microarray studies in autoimmune disorders reveal shared perturbations of common cellular processes, particularly apoptosis, regulation of cytokines and T cell activation. Taken together, microarray studies reveal a complex, heterogeneous immune inflammatory response in the immune mediated inflammatory diseases yet common signatures, as outlined above, are characteristic of specific autoimmune diseases. Given the marked effects of TNF inhibition on patient reported outcomes, systemic inflammation and tissue remodeling in RA, PsA and Ps, genomic analysis of cells and tissues before and after treatment has the potential to unveil pivotal overlapping and disease-specific transcriptional events in disease pathogenesis. The immune mediated inflammatory diseases RA, PsA and Ps have divergent phenotypes but share several pathologic features that include overproduction of TNF and other cytokines, along with cellular proliferation and tissue remodeling. They also have overlapping metabolic and cardiovascular comorbidities and demonstrate, on average, good to excellent responses to antiTNF agents. We hypothesized that the genomic response to antiTNF agents in these three diseases would be distinct and would vary in the different cell types and target tissues. To our knowledge, this is the first study of differential gene expression in discrete populations of monocytes and immune cells that also examined expression in target tissues following treatment with IFX. Moreover, the response in RA was clustered around the 2 week time point whereas the most notable effect on gene expression in PsA and Ps was at 10 weeks. In Ps biopsies, a large number of genes were dramatically down-regulated by IFX at 2 weeks but the gene expression profile in skin from patients with excellent clinical responses was still significantly different from uninvolved skin.

It has been shown that even classically-used controls can differ in abundance across different sample types or even by sample handling methods. For example, Gapdh was found to be less stable over time in FFPE breast tumour samples by qRT-PCR whereas it was deemed a suitable reference gene for use in lung tumour FFPE samples. In a proteomic analysis, multiple species of GAPDH were identified within human platelet samples; of these, the most abundant of species was highly variable across both age and sex. This indicates that particular effort must be made when validating loading controls for western blot, as different antibodies may target different species. Exposure to TCDD has been shown to have a dramatically different effect on transcriptomic regulation across various animal models. This has been shown to result from ligand activation of the AHR by TCDD-binding while the degree of toxicity is directly related to the Ahr-genotype within rodents. While studies into the specific transcriptomic changes responsible for overall toxicity are still ongoing, progress has been made in the identification of candidate lists within various animal models, including strains of rats and mice. However, as toxicity likely results from subsequent changes in the proteome, further studies are required to verify which of these candidate genes are Remdesivir GS-5734 concomitantly altered at the protein level. While validation of reference genes for RNA quantitation in various mouse models has been completed, there is no reason to expect similar results to be obtained at the level of the proteome. Here, we have evaluated the protein abundance of 7 reference genes for use in toxico-proteomic analyses of TCDD-induced toxicity within a wide range of mouse models. In particular, we have assessed the effect of TCDD exposure on protein abundance within mouse models of various strains, Ahr-genotype and sex across both a timecourse and dose-response approach. Protein abundance was assessed by quantitative western blot analysis and each candidate’s suitability as a reference control was evaluated using 3 analysis methods: 1) the fold-difference in protein content from basal levels, 2) the NormFinder algorithm, which is an assessment of target stability and 3) the ability of each candidate to reduce instability of the others. As TCDD is known to have a significant impact on transcriptional regulation, and has been shown to affect the proteome, the protein abundance of our candidates was first assessed using biologically similar animals that were treated with either TCDD or corn oil alone. HPRT was identified as the protein least affected by TCDD while EEF1A1 and SDHA showed significant variability across multiple experimental conditions.