Dataset Information

Multiplexed surrogate analysis of glycotransferase activity in whole biospecimens.

ABSTRACT: Dysregulated glycotransferase enzymes in cancer cells produce aberrant glycans--some of which can help facilitate metastases. Within a cell, individual glycotransferases promiscuously help to construct dozens of unique glycan structures, making it difficult to comprehensively track their activity in biospecimens--especially where they are absent or inactive. Here, we describe an approach to deconstruct glycans in whole biospecimens then analytically pool together resulting monosaccharide-and-linkage-specific degradation products ("glycan nodes") that directly represent the activities of specific glycotransferases. To implement this concept, a reproducible, relative quantitation-based glycan methylation analysis methodology was developed that simultaneously captures information from N-, O-, and lipid linked glycans and is compatible with whole biofluids and homogenized tissues; in total, over 30 different glycan nodes are detectable per gas chromatography-mass spectrometry (GC-MS) run. Numerous nonliver organ cancers are known to induce the production of abnormally glycosylated serum proteins. Thus, following analytical validation, in blood plasma, the technique was applied to a group of 59 lung cancer patient plasma samples and age/gender/smoking-status-matched non-neoplastic controls from the Lung Cancer in Central and Eastern Europe (CEE) study to gauge the clinical utility of the approach toward the detection of lung cancer. Ten smoking-independent glycan node ratios were found that detect lung cancer with individual receiver operating characteristic (ROC) c-statistics ranging from 0.76 to 0.88. Two glycan nodes provided novel evidence for altered ST6Gal-I and GnT-IV glycotransferase activities in lung cancer patients. In summary, a conceptually novel approach to the analysis of glycans in unfractionated human biospecimens has been developed that, upon clinical validation for specific applications, may provide diagnostic and/or predictive information in glycan-altering diseases.

SUBMITTER: Borges CR

PROVIDER: S-EPMC3650733 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Multiplexed surrogate analysis of glycotransferase activity in whole biospecimens.

Borges Chad R CR Rehder Douglas S DS Boffetta Paolo P

Analytical chemistry 20130215 5

Dysregulated glycotransferase enzymes in cancer cells produce aberrant glycans--some of which can help facilitate metastases. Within a cell, individual glycotransferases promiscuously help to construct dozens of unique glycan structures, making it difficult to comprehensively track their activity in biospecimens--especially where they are absent or inactive. Here, we describe an approach to deconstruct glycans in whole biospecimens then analytically pool together resulting monosaccharide-and-lin ...[more]

PMID: 23368525

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Project description:High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (108, 107, 106, 105 and 104 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 106 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 106 bacteria per sample for robust and reproducible microbiota analysis.

Dataset Information

Multiplexed surrogate analysis of glycotransferase activity in whole biospecimens.

Publications

Multiplexed surrogate analysis of glycotransferase activity in whole biospecimens.

Similar Datasets

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