Project description:White blood cells (WBCs) express tens of thousands of genes. Their expression levels are modified by genetic and external factors. In further study we found that gene expression profiles of these cellls can serve as surrogate markers for monitoring exercise and training load.The purpose of the present study was to investigate the effects of acute exercise on gene expression profiles (GEPs) of PBMCs and T-lymphocytes in order to re-detect the patterns in subpopulations. The use of WBC subpopulations is necessary because of the well known subpopulation shifts that occur during physical activities. Three male probands performed an exhaustive treadmill test (ET) at 80% of their VO2max. PBMCs were isolated using BD Vacutainer CPT tubes containig Ficoll. T-lymphocytes were isolated from the PBMCs via rosetting technology. Gene expression profiles were measured using the Affymetrix GeneChipÂ® technology. After scaling, normalisation, and filtering groupwise and pairwise comparisons of gene expression intensities were performed. We found that sorting increases the detection sensitivity and enables the researcher to observe regulation that is hidden in heterogenous populations. We therefore suggest not to work on mixed nbut instead on sorted cells when performing gene expression analyses. Experiment Overall Design: Three healthy male probands executed an exhaustive treadmill test (80% VO2max) until individual exhaustion. Blood samples (16ml) were drawn before and one hour past the tests. PBMCs were isolated using the Ficoll density centrifugation methode. T-lymphocytes were isolated by adding rosetting anibodies zo the cells before centrifugation. Cells were lysed using Trizol and worked up with Qiagen RNeasy Micro columns. RNA was processed and hybridised on U133A 2.0 Affymetrix GeneChips. Samples were grouped and gene expression changes were detected via multiple algorithms included in GeneSpring 7.2 (Agilent). Experiment Overall Design: The four groups contained three samples related to the cell type "PBMC" or "T-cell" and "before exercise" or "1hour past exercise". TTest, GOs and KEGG classification, principal component analysis and hierarchical clstering were used to scan for gene expression profiles induced through the different exercise conditions.

Project description:Transcriptional profiling of Homo sapiens inflammatory skin diseases (whole skin biospies): Psoriasis (Pso), vs Atopic Dermatitis (AD) vs Lichen planus (Li), vs Contact Eczema (KE), vs Healthy control (KO) In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders. We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases. These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation. In recent years, different genes and proteins have been highlighted as potential biomarkers for psoriasis, one of the most common inflammatory skin diseases worldwide. However, most of these markers are not psoriasis-specific but also found in other inflammatory disorders. We performed an unsupervised cluster analysis of gene expression profiles in 150 psoriasis patients and other inflammatory skin diseases (atopic dermatitis, lichen planus, contact eczema, and healthy controls). We identified a cluster of IL-17/TNFα-associated genes specifically expressed in psoriasis, among which IL-36γ was the most outstanding marker. In subsequent immunohistological analyses IL-36γ was confirmed to be expressed in psoriasis lesions only. IL-36γ peripheral blood serum levels were found to be closely associated with disease activity, and they decreased after anti-TNFα-treatment. Furthermore, IL-36γ immunohistochemistry was found to be a helpful marker in the histological differential diagnosis between psoriasis and eczema in diagnostically challenging cases. These features highlight IL-36γ as a valuable biomarker in psoriasis patients, both for diagnostic purposes and measurement of disease activity during the clinical course. Furthermore, IL-36γ might also provide a future drug target, due to its potential amplifier role in TNFα- and IL-17 pathways in psoriatic skin inflammation.

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Homo sapiens

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