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.

Project description:The high incidence of pre-eclampsia, affecting 5-10% of pregnancies, makes it a major health problem. Early detection of pre-eclampsia, before the appearance of the first clinical symptoms, is therefore an urgent need, since it would allow the early intervention. Identification of plasma/serum biomarkers would pave the way to develop new strategies for the diagnosis of pre-eclampsia. Liquid biopsy emerges as a promising source of protein biomarkers that circumvent some of the inherent challenges of proteome wide analysis of plasma and serum. Besides, purified exosomes have the added interest of being communication vehicles between cells and tissues both in physiological and pathological processes. Methods We compared the protein abundance in purified exosomes from three different cohorts of control and preeclamptic serum samples, obtained around the sixth month and at the end of pregnancy. To this end, a shotgun label-free proteomics analysis was conducted and the relevant differential proteins were then validated by targeted MRM. Results An exosome purification method was developed that yielded highly enriched preparations, as indicated by the detection of a large number of exosomal markers. Likewise, the presence of specific pregnancy protein markers suggested that a very significant percentage of purified exosomes come from tissues related to pregnancy. Shotgun quantitative proteomic analysis allowed us to identify 10, 114 and 98 differentially regulated proteins in the three studied cohorts, with a high degree of concordance among them. Functional analysis suggests that these proteins participate in biological processes closely related to pre-eclampsia, such as angiogenesis, inflammation or cell migration. Differential abundance of 66 proteins were validated by MRM. Finally, we show the impact of the pre-eclampsia associated exosomes in the proteome of a cellular model, compared to normal exosomes. Conclusions We identified and validated differential proteins in liquid biopsy of patients that open new possibilities for the early diagnostic of pre-eclampsia. Additionally, the functional impact of the different proteome composition of purified pre-eclamptic exosomes in target cells provide new information to further understand changes on embryo-maternal interactions and therefore the pathogenesis of this disease.

Project description:Pre-eclampsia is one of the most serious pregnancy-associated disorders, and is defined by hypertension (systolic blood pressure higher than 140mmHg or diastolic blood pressure higher than 90mmHg) with proteinuria (more than 0.3g/day). It is not a simple complication of pregnancy, but is rather a syndrome of multiple organ failure involving the liver, kidney, and lung, as well as coagulatory and neural systems. There is now an emerging consensus that pre-eclampsia is a complex polygenetic trait in which maternal and fetal genes, as well as environmental factors, are involved, although the precise mechanism of the disorder has remained elusive. In this study, we have performed gene expression profiling to further elucidate the mechanisms underlying the development of the pre-eclamptic condition. We thus compared the expression profiles in placentas from women who underwent a normal pregnancy and from women suffering from severe pre-eclampsia. In addition, we compared the expression data between the early and late onset forms of pre-eclampsia and obtained a number of potential new prognostic biomarkers for this disease. Experiment Overall Design: Placental biopsies were obtained during Caesarean section from both normotensive patients and from those with pre-eclampsia (n = 14) (early onset type; earlier than 31 weeks gestation, n = 6 and late onset type; 31 weeks gestation or later, n = 8). Pre-eclampsia was defined as a blood pressure of higher than 160/110 mmHg, with proteinuria of more than 2g in a 24 hour collection. The expression profiles of approximately 47,669 genes were analyzed using a Whole Human Genome Oligo Microarray Kit (Agilent Technologies). A total of 10 placentas from women with pre-eclampsia and four from normal subjects were used in the hybridizations. Reverse transcription labeling and hybridization was performed using the protocol recommended by the manufacturer. The microarray experiments were then carried out using competitive hybridization experiments with Cy3 and Cy5-labeled targets; one with test placental RNAs from patients or normal control subjects, and another using pooled normal placental RNA as a template control for normalization. The glass slides were scanned using an Agilent G2565BA microarray scanner. Scanned images were then analyzed using Feature Extraction software. The average signal intensities were corrected for median background intensity and transferred with GenBank descriptors to a Microsoft Excel data spreadsheet (Microsoft, Redmond, WA). Data analysis was performed using Genespring software version 6.1 (Silicon Genetics, Redwood City, CA). After intensity dependent normalization (Lowess), the expression levels relative to the control were calculated as a ratio, and the expression profiles were then compared between each pre-eclamptic or normal sample.

Dataset Information

Homo sapiens

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure