| This thesis presents an interdisciplinary research involved in analytical chemistry and chemical biology. Glycosylation enrichment methods, mass spectrometry techniques and bioinformatics tools have been combined, for the goal revealing glycosylation information in important biological samples accurately and efficiently.Glycosylation is one of the most important and universal protein post-translational modifications (PTMs), while glycoproteomics is becoming a significant field in the post-proteomics era. The development of enrichment methods and bio-mass spectrometry (MS) techniques has been facilitating glycoproteomic researches; glycosylation information has been revealed from various samples. Nevertheless, there is always room for methodological development in glycoproteomics, e.g. development and improvement of glycan-related enrichment methods and MS techniques. Meanwhile, with the increasing output of MS data, efforts from bioinformatics have been made on glycoproteomics, to assist or even replace time-consuming manual interpretation. However, because of the complexity of protein glycosylation, it is still full of chances and challenges in this field:many important samples need interpretation, and automatic analysis of intact glycopeptides is at the initial stage.The major contributions of this work are as follows:(1) N-glycoproteins of normal human liver have been profiled for the first time, and the large dataset, which is not only a valuable supplement for normal human liver proteome, but also the basis of meaningful bioinformatic analysis.(2) An internationally advanced platform for intact glycopeptide interpretation and revealing (GRIP) has been established; it newly introduced experiment-based de-glycopeptide and glycan datasets to form a sample-friendly glycopeptide database, which have achieved high-throughput glycopeptide analysis for human serum.(3) Two strategies novelly combining sequential multi-enzymatic approach and glycan-related enriching method have been developed, assisting glycoprotein sequence coverage improvement and accurate glycosite/glycopeptide identification.(4) First comparison of different?-elimination-based methods for O-glycosylation study on practical sample has been carried out; features of our results could be useful references for the application and development of the methodology.This thesis consists of four parts which are summarized as follows:Part 1. Introduction:a brief and comprehensive introduction of protein glycosylation and glycoproteomics-related fields.As one of the most important and universal protein PTMs, glycosylation takes part in many physiological processes. The alteration of glycoprotein amount and glycan structure probably indicates pathological changes, while many FDA approved drugs are glycoproteins. There are four major types of protein glycosylation; the two mostly studied are N-linked and O-linked glycosylation. Although it is still a tough job, glycoproteomics has been largely facilitated by the development of different technologies, e.g. glycan-related enrichment methods, MS techniques. Meanwhile, bioinformatics on glycoproteomics is another related field rapidly developing but just in the start. Generally, glycosylation study now is still full of chances and challenges.Part 2. High-throughput N-glycosite Studies:two sections included (1) normal human liver N-glycoproteome profiling; (2) sequential multi-enzymatic assisted and high specific glycoprotein/site identification.(1) As the largest human organ, liver plays vital roles especially in metabolism. Biological functions and related disease of liver have always been paid close attention to. Till 2010, Chinese human liver proteome project (CNHLPP) has identified over six thousand proteins from normal human liver. However, because of the high complexity and the wide dynamic range of proteins in practical samples, revealing of low-abundance proteins is still a tough and on-working task. PTM-enriching could act not only as a targeted approach, but also as an efficient way to reduce sample complexity, so probably revealing supplementary information. Here, for the study of normal human liver N-glycoproteome, we have combined two enrichment methods (hydrazide chemistry and hydrophilic affinity) and two MS dissociation methods (collision-induced dissociation and electron-transfer dissociation), and obtained the largest human liver N-glycoproteome dataset, containing 915 N-glycoproteins and 1,786 N-glycosites. The dataset is not only a valuable supplement for normal human liver proteome (382 newly identified proteins), but also important basis of further researches in the field.(2) Outstanding specificity of hydrazide chemistry (usually above 90%) means the capability to reduce sample complexity and also reliable N-glycosite identification. However, due to its high specificity, usually glycoprotein identification can only rely on a limited number of de-glycopeptides. Those identified glycoproteins have low sequence coverage, and some are single-peptide-hit identification likely. A novel two-step protease digestion and glycopeptide capture approach has been developed. Through controllable release, separate identification and combined interpretation of non-glycopeptides (newly introduced LT-peptides) and traditional de-glycopeptides (DG-peptide), the approach could not only achieve routine N-glycosite identification, but also provide further proofs of N-glycosites and increase glycoprotein sequence coverage. The approach has been successfully applied to cell lysate. Without sacrificing enrichment specificity, glycoproteins got improved sequence coverage with increase even up to 350%(averagely 79.4%), and DG-peptide-revealed N-glycosites got further confirmation by related LT-peptides.Part 3. High-throughput N-glycopeptide Studies:two sections included (1) One-pipeline approach achieving glycoprotein identification and obtaining intact glycopeptide information; (2) Glycopeptide revealing and interpretation platform (GRIP).(1) Analysis of intact glycopeptides largely depends on glycosylation enrichment; traditionally, after peptide-level enrichment, protein identification and glycopeptides interpretation would be in two separated flow paths:one carries out de-glycosylation, the other keeps glycopeptides intact. A novel one-pipeline approach has been developed. Without de-glycosylation, this approach has been demonstrated to achieve glycoprotein identification and obtain intact glycosylation information after peptide-level enrichment. The proposed workflow has two enrichment steps plus two proteolytic processes:enriched glycoproteins were digested by Lys-C, and then enriched again and secondly digested by trypsin. In the resulting mixture, with a reasonable complexity, intact glycopeptides could be preserved and utilized informatively for glycosylation analysis, and non-glycopeptides for protein identification. In both standard protein mixture tests and real sample analysis, the resulting glycopeptides and non-glycopeptides were proved to play their expected roles, thus more confident protein glycosylation information was obtained.(2) High-throughput intact glycopeptides analysis is one of the most difficult tasks in glycoproteomics. Several reasons, e.g. high complexity of glycosylation, relatively low abundance and special physiochemical properties of glycopeptides, make the study on intact glycopeptides difficult. Enriching methods have not achieved a stable high specific separation for intact glycoprotein/peptides. Till now, there has not been a widely used routine for glycopeptide analysis, as that for N-glycosites. Bioinformatics tools targeting glycopeptides have been developed, but existing tools usually could not endure complex samples, or would provide too many results to choose. In our glycopeptide revealing and interpretation platform (GRIP), we introduced experiment-based de-glycopeptide and glycan datasets to form a sample-friendly glycopeptide database, and used novel algorithm designed for glycopeptides "sequence tag" searching and composition interpretation. GRIP has achieved high-throughput glycopeptide analysis for human serum:3,091 spectra (1% false positive rate), corresponding to 1,020 different glycopeptides were identified; micro-heterogeneity of glycosylation was also revealed in the result. Analysis of those glycopeptides by another MS fragmentation technique HCD (higher-energy collision-induced dissociation) proved the accuracy of aforementioned results.Part 4.?-elimination Methodology Based O-glycosylation Studies:comparative studies different?-elimination methods on practical sample for O-glycosite and O-glycan identification.Because there is neither consensus sequence for O-glycosylation, nor a universal O-glycosidase which could function the way as peptide-N-glycosidase F (PNGase F) do in de-N-glycosylation, recognition of O-glycosylation sites (O-glycosites) is still challenging. Recent investigations have worked on mild P-elimination/addition methods which could remove O-glycans while preserve peptide backbones, but most of those studies focused on samples with low complexity (e.g. synthetic peptides, purified proteins) rather than complex samples. Here, for the first time, we applied three different?-elimination/addition approaches, to explore the O-glycosite profile of human serum. Surprisingly quite different results were obtained from different approaches, though they were based on similar mechanism. Totally,157 O-glycosites from 96 proteins were revealed, only four of them were identified from two or more approaches; the number of detected modification on Ser was higher than that on Thr (61% vs.39%); glycosites identified from the same proteins showed close appearances. Meanwhile, reductive?-elimination of O-glycans from serum sample was also performed from both protein and peptide levels. MS analysis showed the former favored glycans with lower molecular weight, while the latter favored glycans in the higher mass range. |
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