Project description:Gene expression patterns in the brain are strongly influenced by the severity of physiological stress at death. This agonal effect, if not well controlled, can lead to spurious findings in case-control comparisons. While many recent studies match samples by tissue pH and clinically recorded agonal conditions, we found that these commonly used indicators were sometimes at odds with observed stress-related patterns of gene expression, and that matching by these criteria still sometimes results in identifying differences between cases and controls that are primarily driven by residual agonal effects. This problem is analogous to the one in genetic studies, where race and ethnicity are often imprecise proxies for complex environmental and genetic factors. We developed an Agonal Stress Rating (ASR) system that evaluates each sampleâs degree of stress based on gene expression data, and used ASRs in post hoc sample matching or covariate analysis. While we found that gene expression patterns are generally correlated across different regions of the same brain, we also found strong region-region differences in empirical ASRs in many subjects that are likely due to inter-individual variabilities in local structure or function, resulting in region-specific vulnerability to agonal stress. Variation of agonal stress from one region of the brain to another differs between individuals, revealing a new level of complexity for gene expression studies of brain tissues. The Agonal Stress Ratings provide a direct assessment of the regulatory responses to agonal stress in individual samples, and allow a strong control of this important confounder. Our strategy is analogous to sample matching by inferred ancestral proportions in genetic association studies to control subtle confounding by ancestry. Keywords: Agonal Stress Rating comparison We examined the relationship between the Agonal Stress Ratings (ASRs) and conventional pre hoc indicators such as pH and clinically derived Agonal Factor Scores (AFS), compared the stress ratings across six brain regions in up t0 126 samples, and assessed the performance of different sample matching strategies.

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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