Angiotensin-converting enzyme genotype and late respiratory complications of mustard gas exposure

<p>Abstract</p> <p>Background</p> <p>Exposure to mustard gas frequently results in long-term respiratory complications. However the factors which drive the development and progression of these complications remain unclear. The Renin Angiotensin System (RAS) has been implicated in lung inflammatory and fibrotic responses. Genetic variation within the gene coding for the Angiotensin Converting Enzyme (ACE), specifically the Insertion/Deletion polymorphism (I/D), is associated with variable levels of ACE and with the severity of several acute and chronic respiratory diseases. We hypothesized that the ACE genotype might influence the severity of late respiratory complications of mustard gas exposure.</p> <p>Methods</p> <p>208 Kurdish patients who had suffered high exposure to mustard gas, as defined by cutaneous lesions at initial assessment, in Sardasht, Iran on June 29 1987, underwent clinical examination, spirometric evaluation and ACE Insertion/Deletion genotyping in September 2005.</p> <p>Results</p> <p>ACE genotype was determined in 207 subjects. As a continuous variable, FEV₁% predicted tended to be higher in association with the D allele 68.03 ± 20.5%, 69.4 ± 21.4% and 74.8 ± 20.1% for II, ID and DD genotypes respectively. Median FEV₁% predicted was 73 and this was taken as a cut off between groups defined as having better or worse lung function. The ACE DD genotype was overrepresented in the better spirometry group (Chi²4.9 p = 0.03). Increasing age at the time of exposure was associated with reduced FEV₁%predicted (p = 0.001), whereas gender was not (p = 0.43).</p> <p>Conclusion</p> <p>The ACE D allele is associated with higher FEV₁% predicted when assessed 18 years after high exposure to mustard gas.</p

Mouse models of neurodegenerative disease: preclinical imaging and neurovascular component.

Neurodegenerative diseases represent great challenges for basic science and clinical medicine because of their prevalence, pathologies, lack of mechanism-based treatments, and impacts on individuals. Translational research might contribute to the study of neurodegenerative diseases. The mouse has become a key model for studying disease mechanisms that might recapitulate in part some aspects of the corresponding human diseases. Neurode- generative disorders are very complicated and multifacto- rial. This has to be taken in account when testing drugs. Most of the drugs screening in mice are very di cult to be interpretated and often useless. Mouse models could be condiderated a ‘pathway models’, rather than as models for the whole complicated construct that makes a human disease. Non-invasive in vivo imaging in mice has gained increasing interest in preclinical research in the last years thanks to the availability of high-resolution single-photon emission computed tomography (SPECT), positron emission tomography (PET), high eld Magnetic resonance, Optical Imaging scanners and of highly speci c contrast agents. Behavioral test are useful tool to characterize di erent ani- mal models of neurodegenerative pathology. Furthermore, many authors have observed vascular pathological features associated to the di erent neurodegenerative disorders. Aim of this review is to focus on the di erent existing animal models of neurodegenerative disorders, describe behavioral tests and preclinical imaging techniques used for diagnose and describe the vascular pathological features associated to these diseases

Epistasis analysis with global transcriptional phenotypes

Classical epistasis analysis can determine the order of function of genes in pathways using morphological, biochemical and other phenotypes. It requires knowledge of the pathway's phenotypic output and a variety of experimental expertise and so is unsuitable for genome-scale analysis. Here we used microarray profiles of mutants as phenotypes for epistasis analysis. Considering genes that regulate activity of protein kinase A in Dictyostelium, we identified known and unknown epistatic relationships and reconstructed a genetic network with microarray phenotypes alone. This work shows that microarray data can provide a uniform, quantitative tool for large-scale genetic network analysis