Expression profile analysis of mycotoxin-related genes in cartilage with endemic osteochondropathy kashin-beck disease

<p>Abstract</p> <p>Background</p> <p>Kashin-Beck Disease (KBD) is an endemic osteochondropathy. Mycotoxins are believed to play an important role in the pathogenesis of KBD. Because the molecular mechanism of mycotoxin-induced cartilage lesions remains unclear, there is not effective treatment for KBD now. To identify key genes involved in the mycotoxin-induced cartilage lesions, we compared the expression profiles of mycotoxin-related genes (MRG) between KBD cartilage and healthy cartilage.</p> <p>Methods</p> <p>Total RNA was isolated from cartilage samples, following by being amplified, labeled and hybridized to Agilent human whole genome microarray chip. qRT-PCR was conducted to validate the microarray data. 1,167 MRG were derived from the environmentally related genomic database Toxicogenomics. The microarray data of MRG was subjected to single gene and gene ontology (GO) expression analysis for identifying differently expressed genes and GO.</p> <p>Results</p> <p>We identified 7 up-regulated MRG and 2 down-regulated MRG in KBD cartilage, involved in collagen, apoptosis, metabolism and growth & development. GO expression analysis found that 4 apoptosis-related GO and 5 growth & development-related GO were significantly up-regulated in KBD cartilage.</p> <p>Conclusions</p> <p>Based on the results of previous and our studies, we suggest that mycotoxins might contribute to the development of KBD through dysfunction of MRG involved in collagen, apoptosis and growth & development in cartilage.</p

Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment