Xinning Jiang

Glycoproteomics Analysis of Human Liver Tissue by Combination of Multiple Enzyme Digestion and Hydrazide Chemistry

The study of protein glycosylation has lagged far behind the progress of current proteomics because of the enormous complexity, wide dynamic range distribution and low stoichiometric modification of glycoprotein. Solid phase extraction of tryptic N-glycopeptides by hydrazide chemistry is becoming a popular protocol for the analysis of N-glycoproteome. However, in silico digestion of proteins in human proteome database by trypsin indicates that a significant percentage of tryptic N-glycopeptides is not in the preferred detection mass range of shotgun proteomics approach, that is, from 800 to 3500 Da. And the quite big size of glycan groups may block trypsin to access the K, R residues near N-glycosites for digestion, which will result in generation of big glycopeptides. Thus many N-glycosites could not be localized if only trypsin was used to digest proteins. Herein, we describe a comprehensive way to analyze the N-glycoproteome of human liver tissue by combination of hydrazide chemistry method and multiple enzyme digestion. The lysate of human liver tissue was digested with three proteases, that is, trypsin, pepsin and thermolysin, with different specificities, separately. Use of trypsin alone resulted in identification of 622 N-glycosites, while using pepsin and thermolysin resulted in identification of 317 additional N-glycosites. Among the 317 additional N-glycosites, 98 (30.9%) could not be identified by trypsin in theory because the corresponding in silico tryptic peptides are either too small or too big to detect in mass spectrometer. This study clearly demonstrated that the coverage of N-glycosites could be significantly increased due to the adoption of multiple enzyme digestion. A total number of 939 N-glycosites were identified confidently, covering 523 noredundant glycoproteins from human liver tissue, which leads to the establishment of the largest data set of glycoproteome from human liver up to now.

Specific Phosphopeptide Enrichment with Immobilized Titanium Ion Affinity Chromatography Adsorbent for Phosphoproteome Analysis

The elucidation of protein post-translational modifications, such as phosphorylation, remains a challenging analytical task for proteomic studies. Since many of the proteins targeted for phosphorylation are low in abundance and phosphorylation is typically substoichiometric, a prerequisite for their identification is the specific enrichment of phosphopeptide prior to mass spectrometric analysis. Here, we presented a new method termed as immobilized titanium ion affinity chromatography (Ti (4+)-IMAC) for enriching phosphopeptides. A phosphate polymer, which was prepared by direct polymerization of monomers containing phosphate groups, was applied to immobilize Ti (4+) through the chelating interaction between phosphate groups on the polymer and Ti (4+). The resulting Ti (4+)-IMAC resin specifically isolates phosphopeptides from a digest mixture of standard phosphoproteins and nonphosphoprotein (BSA) in a ratio as low as 1:500. Ti (4+)-IMAC was further applied for phosphoproteome analysis of mouse liver. We also compared Ti (4+)-IMAC to other enrichment methods including Fe (3+)-IMAC, Zr (4+)-IMAC, TiO 2 and ZrO 2, and demonstrate superior selectivity and efficiency of Ti (4+)-IMAC for the isolation and enrichment of phosphopeptides. The high specificity and efficiency of phosphopeptide enrichment by Ti (4+)-IMAC mainly resulted from the flexibility of immobilized titanium ion with spacer arm linked to polymer beads as well as the specific interaction between immobilized titanium ion and phosphate group on phosphopeptides.

Reversed-Phase-Reversed-Phase Liquid Chromatography Approach with High Orthogonality for Multidimensional Separation of Phosphopeptides

Protein phosphorylation regulates a series of important biological processes in eukaryotes. However, the phosphorylation sites found up to now are far below than that actually exists in proteins due to the extreme complexity of the proteome sample. Here a new reversed-phase-reversed-phase liquid chromatography (RP-RPLC) approach was developed for multidimensional separation of phosphopeptides. In this approach, a large number of fractions were collected from the first dimensional RPLC separation at high pH. And then these fractions were pooled every two fractions with equal time interval, one from the early eluted section and another one from the later eluted section. The pooled fractions were finally submitted to RPLC-tandem mass spectrometry (MS/MS) analysis at low pH. It was found the resulting 2D separation was highly orthogonal and yielded more than 30% phosphopeptide identifications over the conventional RP-RPLC approach. This study provides a powerful approach for efficient separation of phosphopeptides and global phosphorylation analysis, where the orthogonality of 2D separation is greatly improved and the first dimensional separation is of high resolution.

Preparation of monodisperse immobilized Ti4+ affinity chromatography microspheres for specific enrichment of phosphopeptides

This study presented an approach to prepare monodisperse immobilized Ti(4+) affinity chromatography (Ti(4+)-IMAC) microspheres for specific enrichment of phosphopeptides in phosphoproteome analysis. Monodisperse polystyrene seed microspheres with a diameter of ca. 4.8mum were first prepared by a dispersion polymerization method. Monodisperse microspheres with a diameter of ca. 13mum were prepared using the seed microspheres by a single-step swelling and polymerization method. Ti(4+) ion was immobilized after chemical modification of the microspheres with phosphonate groups. The specificity of the Ti(4+)-IMAC microspheres to phosphopeptides was demonstrated by selective enrichment of phosphopeptides from mixture of tryptic digests of alpha-casein and bovine serum albumin (BSA) at molar ratio of 1 to 500 by MALDI-TOF MS analysis. The sensitivity of detection for phosphopeptides determined by MALDI-TOF MS was as low as 5fmol for standard tryptic digest of beta-casein. The Ti(4+)-IMAC microspheres were compared with commercial Fe(3+)-IMAC adsorbent and homemade Zr(4+)-IMAC microspheres for enrichment of phosphopeptides. The phosphopeptides and non-phosphopeptides identified by Fe(3+)-IMAC, Zr(4+)-IMAC and Ti(4+)-IMAC methods were 26, 114, 127 and 181, 11, 11 respectively for the same tryptic digest samples. The results indicated that the Ti(4+)-IMAC had the best performance for enrichment of phosphopeptides.

Systematic Analysis of Protein Phosphorylation Networks From Phosphoproteomic Data

In eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes.

Phosphoproteome analysis of human liver tissue by long-gradient nanoflow LC coupled with multiple stage MS analysis

Reversible protein phosphorylation plays a critical role in liver development and function. Comprehensively cataloging the phosphoproteins and their phosphorylation sites in human liver tissue will facilitate the understanding of physiological and pathological mechanisms of liver. Owing to lacking of efficient approach to fractionate phosphopeptides, nanoflow‐RPLC with long‐gradient elution was applied to reduce the complexity of the phosphopeptides in this study. Two approaches were performed to further improve the coverage of phosphoproteome analysis of human liver tissue. In one approach, ten‐replicated long‐gradient LC‐MS/MS runs were performed to analyze the enriched phosphopeptides, which resulted in the localization of 1080 phosphorylation sites from 495 proteins. In another approach, proteins from liver tissue were first fractionated by SDS‐PAGE and then long‐gradient LC‐MS/MS analysis was performed to analyze the phosphopeptides derived from each fraction, which resulted in the localization of 1786 phosphorylation sites from 911 proteins. The two approaches showed the complementation in phosphoproteome analysis of human liver tissue. Combining the results of the two approaches, identification of 2225 nonredundant phosphorylation sites from 1023 proteins was obtained. The confidence of phosphopeptide identifications was strictly controlled with false discovery rate (FDR)≤1% by a MS2/MS3 target‐decoy database search approach. Among the localized 2225 phosphorylated sites, as many as 70.07% (1559 phosphorylated sites) were also reported by others, which confirmed the high confidence of the sites determined in this study. Considering the data acquired from low accuracy mass spectrometer and processed by a conservative MS2/MS3 target‐decoy approach, the number of localized phosphorylation sites obtained for human liver tissue in this study is quite impressive.

Perspectives of Comprehensive Phosphoproteome Analysis Using Shotgun Strategy

Protein phosphorylation is a ubiquitous post-translational modification that regulates almost all cellular processes. The analysis of protein phosphorylation is challenging due to the high dynamic range and low abundance natures of the analyte. Shotgun based proteomics has emerged as a very useful platform to achieve a comprehensive phosphoproteome analysis in considerable depth. In the past few years, significant breakthroughs on the large scale phosphorylation analysis have been witnessed along with the great development of related technologies. The combination of effective enrichment materials, refined analysis workflows, new type of powerful mass spectrometers, and sophisticated bioinformatic tools greatly boost the performance of comprehensive phosphoproteome analysis. In this Perspective, we briefly reviewed recent technological developments on the enrichment materials, prefractionation workflows, and different mass spectrometry fragmentation modes as well as software tools for phosphoproteome identification and quantification. Then, we described the current challenges and potential directions for the future of comprehensive phosphoproteome analysis. We also provide perspectives on how to further improve the performance of related analysis methods and technologies.

Automatic Validation of Phosphopeptide Identifications by the MS2/MS3 Target-Decoy Search Strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.

Automation of nanoflow liquid chromatography-tandem mass spectrometry for proteome analysis by using a strong cation exchange trap column.

An approach was developed to automate sample introduction for nanoflow LC‐MS/MS (μLC‐MS/MS) analysis using a strong cation exchange (SCX) trap column. The system consisted of a 100 μm id×2 cm SCX trap column and a 75 μm id×12 cm C18 RP analytical column. During the sample loading step, the flow passing through the SCX trap column was directed to waste for loading a large volume of sample at high flow rate. Then the peptides bound on the SCX trap column were eluted onto the RP analytical column by a high salt buffer followed by RP chromatographic separation of the peptides at nanoliter flow rate. It was observed that higher performance of separation could be achieved with the system using SCX trap column than with the system using C18 trap column. The high proteomic coverage using this approach was demonstrated in the analysis of tryptic digest of BSA and yeast cell lysate. In addition, this system was also applied to two‐dimensional separation of tryptic digest of human hepatocellular carcinoma cell line SMMC‐7721 for large scale proteome analysis. This system was fully automated and required minimum changes on current μLC‐MS/MS system. This system represented a promising platform for routine proteome analysis.

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics.

BackgroundIn proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.ResultsIn this study, we implemented a machine learning approach known as predictive genetic algorithm (GA) for the optimization of filtering criteria to maximize the number of identified peptides at fixed false-discovery rate (FDR) for SEQUEST database searching. As the FDR was directly determined by decoy database search scheme, the GA based optimization approach did not require any pre-knowledge on the characteristics of the data set, which represented significant advantages over statistical approaches such as PeptideProphet. Compared with PeptideProphet, the GA based approach can achieve similar performance in distinguishing true from false assignment with only 1/10 of the processing time. Moreover, the GA based approach can be easily extended to process other database search results as it did not rely on any assumption on the data.ConclusionOur results indicated that filtering criteria should be optimized individually for different samples. The new developed software using GA provides a convenient and fast way to create tailored optimal criteria for different proteome samples to improve proteome coverage.