Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P04155 (pS2)
 1,234 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Asthma is a chronic inflammatory disease of the airways that is accompanied by goblet cell metaplasia and mucus hypersecretion. Trefoil factor family (TFF) peptides represent major secretory products of the respiratory tract and are synthesized together with mucins. In the murine lung, TFF2 is mainly expressed, whereas TFF1 transcripts represent only a minor species. TFF peptides are well known for their motogenic and anti-apoptotic effects, and they modulate the inflammatory response of bronchial epithelial cells. Here, an established mouse model of asthma was investigated (i.e., exposure to Aspergillus fumigatus [AF] antigens). RT-PCR analysis of lung tissue showed elevated levels particularly of TFF1 transcripts in AF-sensitized/challenged animals. In contrast, transcripts encoding Clara cell secretory protein (CCSP/CC10) were strongly diminished in these animals. For comparison, the expression of the goblet cell secretory granule marker mCLCA3/Gob-5, the mucins Muc1-Muc6 and Muc19, and the secretoglobins ScgB3A1 and ScgB3A2, as well as the mammalian ependymin-related gene MERP2, were monitored. Immunohistochemistry localized TFF1 mainly in cells with a mixed phenotype (e.g., TFF1-positive cells stain with the lectin wheat germ agglutinin (WGA), which recognizes mucins characteristic of goblet cells). In addition, these cells express CCSP/CC10, a Clara cell marker. When compared with mucins or CCSP/CC10, TFF1 was stored in a different population of secretory granules localized at the more basolateral portion of these cells. Thus, the results presented indicate for the first time that allergen exposure leads to the trans-differentiation of Clara cells toward a TFF1-expressing mucous phenotype.
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PMID:Induced trefoil factor family 1 expression by trans-differentiating Clara cells in a murine asthma model. 1699 Jun 15

Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.
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PMID:Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis. 2708