Comprehensive native glycan profiling with isomer separation and quantitation for the discovery of cancer biomarkers

Glycosylation is highly sensitive to the biochemical environment and has been implicated in many diseases including cancer. Glycan compositional profiling of human serum with mass spectrometry has already identified potential biomarkers for several types of cancer and diseases; however, composition alone does not fully describe glycan stereo-and regioisomeric diversity. The vast structural heterogeneity of glycans presents a formidable analytical challenge. We have developed a method to identify and quantify isomeric native glycans using nanoflow liquid chromatography (nano-LC)/mass spectrometry. A microfluidic chip packed with graphitized carbon was used to chromatographically separate the glycans. To determine the utility of this method for structure-specific biomarker discovery, we analyzed serum samples from two groups of prostate cancer patients with different prognoses. More than 300 N-glycan species (including isomeric structures) were identified, corresponding to over 100 N-glycan compositions. Statistical tests established significant differences in glycan abundances between patient groups. This method provides comprehensive, selective, and quantitative glycan profiling

Transcriptome Profiling of Bovine Milk Oligosaccharide Metabolism Genes Using RNA-Sequencing

This study examines the genes coding for enzymes involved in bovine milk oligosaccharide metabolism by comparing the oligosaccharide profiles with the expressions of glycosylation-related genes. Fresh milk samples (n = 32) were collected from four Holstein and Jersey cows at days 1, 15, 90 and 250 of lactation and free milk oligosaccharide profiles were analyzed. RNA was extracted from milk somatic cells at days 15 and 250 of lactation (n = 12) and gene expression analysis was conducted by RNA-Sequencing. A list was created of 121 glycosylation-related genes involved in oligosaccharide metabolism pathways in bovine by analyzing the oligosaccharide profiles and performing an extensive literature search. No significant differences were observed in either oligosaccharide profiles or expressions of glycosylation-related genes between Holstein and Jersey cows. The highest concentrations of free oligosaccharides were observed in the colostrum samples and a sharp decrease was observed in the concentration of free oligosaccharides on day 15, followed by progressive decrease on days 90 and 250. Ninety-two glycosylation-related genes were expressed in milk somatic cells. Most of these genes exhibited higher expression in day 250 samples indicating increases in net glycosylation-related metabolism in spite of decreases in free milk oligosaccharides in late lactation milk. Even though fucosylated free oligosaccharides were not identified, gene expression indicated the likely presence of fucosylated oligosaccharides in bovine milk. Fucosidase genes were expressed in milk and a possible explanation for not detecting fucosylated free oligosaccharides is the degradation of large fucosylated free oligosaccharides by the fucosidases. Detailed characterization of enzymes encoded by the 92 glycosylation-related genes identified in this study will provide the basic knowledge for metabolic network analysis of oligosaccharides in mammalian milk. These candidate genes will guide the design of a targeted breeding strategy to optimize the content of beneficial oligosaccharides in bovine milk

Computational methods for the identification and quantification of microbial organisms in metagenomes

Metagenomics allows analyzing genomic material taken directly from the environment. In contrast to classical genomics, no purification of single organisms is performed and therefore the extracted genomic material reflects the composition of the original microbial community. The possible applications of metagenomics are manifold and the field has become increasingly popular due to the recent improvements in sequencing technologies. One of the most fundamental challenges in metagenomics is the identification and quantification of organisms in a sample, called taxonomic profiling. In this work, we present approaches to the following current problems in taxonomic profiling: First, differentiation between closely related organisms in metagenomic samples is still challenging. Second, the identification of novel organisms in metagenomic samples poses problems to current taxonomic profiling methods, especially when there is no suitable reference genome available. The contribution of this thesis comprises three major projects. First, we introduce the Genome Abundance Similarity Correction (GASiC) algorithm, a method that allows differentiating between and quantifying highly similar microbial organisms in a metagenomic sample. The method first estimates the similarities between the available reference genomes with a simulation approach. Based on the similarities, GASiC corrects the observed abundances of each reference genome using a non-negative lasso approach. In several experiments we showed that the abundance estimates are highly accurate and reduce the error compared to current approaches by 5% to 60%. The approach was also successfully applied to metaproteomics. In the second project, we developed a statistical framework to fit mixtures of discrete distribution functions to the histograms of sequencing coverage depth after mapping metagenomic reads to reference genomes. We tailored a family of distributions for this particular application and modified the expectation-maximization algorithm to also fit discrete distributions when maximum likelihood estimation of the distribution parameters is not directly possible. The most important application of our framework is the genome validity score that measures how suitable a reference genome is for a particular (metagenomic) dataset. In the third project, we developed a taxonomic profiling tool, called MicrobeGPS. In contrast to previous approaches, MicrobeGPS identifies and characterizes organisms in a metagenome even if there are no suitable reference genomes available. Distances to existing reference genomes are measured with the genome validity score and allow the user to spot organisms for which the available reference genomes are insufficient. We demonstrated on gold standard and real metagenomic data that our approach is more accurate than other existing methods, provides more meaningful results, and handles complex microbial communities. Taken together, these three projects enhance the current repertoire of computational methods for taxonomic profiling and enable the simultaneous quantification of highly related organisms and the identification and characterization of unknown organisms in complex metagenomic datasets

I. question. Why are you a Catholique? [electronic resource] : The answer (enlarged in this second edition) follows. II. Question. But why are you a Protestant? An answer attempted (in vain.) Permissu Superiorum.

By Serenus Cressy.Imprint conjectured by Wing.Reproduction of the original in the British Library.Wing (2nd ed., 1994)Electronic reproduction