Differential expression of MUC genes in endometrial and cervical tissues and tumors

BACKGROUND: Mucin glycoprotein's are major components of mucus and are considered an important class of tumor associated antigens. The objective of this study was to investigate the expression of human MUC genes (MUC1, MUC2, MUC5B, MUC5AC and MUC8) in human endometrium and cervix, and to compare and quantitate the expression of MUC genes in normal and cancerous tissues. METHODS: Slot blot techniques were used to study the MUC gene expression and quantitation. RESULTS: Of the five-mucin genes studied, MUC1, MUC5B and MUC8 showed high expression levels in the normal and cancerous endometrial and cervical tissues, MUC2 and MUC5AC showed considerably lower expression. Statistically, higher levels of MUC1, MUC5B and MUC8 were observed in endometrial adenocarcinomas compared to normal tissues. In contrast, only MUC1 levels increased with no significant changes in expression of MUC5B and MUC8 in cervical tumors over normal cervical tissues. CONCLUSION: Endometrial tumors showed increased expression of MUC1, MUC5B and MUC8 over normal tissues. Only MUC1 appears to be increase, in cervical tumors. All the studied tissues showed high and consistent expression of MUC8 mRNA. Low to neglible levels of MUC2 and MUC5AC were observed in all studied endometrial and cervical tissues

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

<p>Abstract</p> <p>Background</p> <p>One-dimensional ¹H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.</p> <p>Results</p> <p>We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of ¹H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.</p> <p>Conclusions</p> <p>MetaboHunter is a freely accessible, easy to use and user friendly ¹H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.</p> <p>Availability</p> <p><url>http://www.nrcbioinformatics.ca/metabohunter/</url></p

Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometry-driven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets