Human Proteomic Data Research Articles

3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data

BackgroundIntegrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.Results3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.Conclusions3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.

Fast Multi-blind Modification Search through Tandem Mass Spectrometry

With great biological interest in post-translational modifications (PTMs), various approaches have been introduced to identify PTMs using MS/MS. Recent developments for PTM identification have focused on an unrestrictive approach that searches MS/MS spectra for all known and possibly even unknown types of PTMs at once. However, the resulting expanded search space requires much longer search time and also increases the number of false positives (incorrect identifications) and false negatives (missed true identifications), thus creating a bottleneck in high throughput analysis. Here we introduce MODa, a novel "multi-blind" spectral alignment algorithm that allows for fast unrestrictive PTM searches with no limitation on the number of modifications per peptide while featuring over an order of magnitude speedup in relation to existing approaches. We demonstrate the sensitivity of MODa on human shotgun proteomics data where it reveals multiple mutations, a wide range of modifications (including glycosylation), and evidence for several putative novel modifications. Based on the reported findings, we argue that the efficiency and sensitivity of MODa make it the first unrestrictive search tool with the potential to fully replace conventional restrictive identification of proteomics mass spectrometry data.

Characterization of the Liver Tissue Interstitial Fluid (TIF) Proteome Indicates Potential for Application in Liver Disease Biomarker Discovery

Tissue interstitial fluid (TIF) forms the interface between circulating body fluids and intracellular fluid. Pathological alterations of liver cells could be reflected in TIF, making it a promising source of liver disease biomarkers. Mouse liver TIF was extracted, separated by SDS-PAGE, analyzed by linear ion trap mass spectrometer, and 1450 proteins were identified. These proteins may be secreted, shed from membrane vesicles, or represent cellular breakdown products. They show different profiling patterns, quantities, and possibly modification/cleavage of intracellular proteins. The high solubility and even distribution of liver TIF supports its suitability for proteome analysis. Comparison of mouse liver TIF data with liver tissue and plasma proteome data identified major proteins that might be released from liver to plasma and serve as blood biomarkers of liver origin. This result was partially supported by comparison of human liver TIF data with human liver and plasma proteome data. Paired TIFs from tumor and nontumor liver tissues of a hepatocellular carcinoma patient were analyzed and the profile of subtracted differential proteins supports the potential for biomarker discovery in TIF. This study is the first analysis of the liver TIF proteome and provides a foundation for further application of TIF in liver disease biomarker discovery.

First Insight into the Human Liver Proteome from PROTEOMESKY-LIVERHu 1.0, a Publicly Available Database

Herein, we report proteome and transcriptome profiles of the human adult liver and present an initial analysis. Overall, the human liver proteome (HLP) data set comprises 6788 identified proteins with at least two peptides matches at 95% confidence, including 3721 proteins newly identified in liver. The human liver transcriptome (HLT) data set consists of 11 205 expressed genes. The HLP is the largest proteome data set for a human organ and is the first direct association between a proteome and its transcriptome derived from the same sample. Although it is hard to approach complete coverage of the HLP currently, several conclusions based on this data set are clearly reached: (1) The 5816 protein-encoding genes (PEGs) represented by the HLP and the 11 104 PEGs represented in the HLT have been identified from 20 070 PEGs in IPI Human v3.07 and 19 478 PEGs in the integrated human transcriptome database, respectively. (2) The patterns of chromosomal distribution of the genes corresponding to the HLP are highly consistent with those of the HLT. Some chromosomal regions, such as 16p13.3, 19q13.31, 19q13.42, and Xq28, exhibit particularly high densities of liver-specific genes, which perform the important functions related to normal physiology or/and pathology in this organ. (3) The HLP spans 6 orders of magnitude in relative protein abundance and 78% of the proteins fall in the middle of this range. Of newly identified liver proteins, 82.5% are of low abundance. (4) Proteins involving in metabolism, transport, and coagulation and those containing active domains for metabolism, transport, and biosynthesis are significantly enriched in liver. (5) All 94 metabolic pathways in KEGG are touched to different extent. Of which, for 48 pathways, particularly those involved in metabolism of carbohydrates and amino acids, more than 80% of the component proteins have been detected. The liver-specific pathways, such as those participating in metabolism of bile acid and bilirubin and in biotransformation, are identified with remarkably high coverage. A total of 31 members of the cytochrome P450 family are identified, four of which have been observed for the first time in human liver. (6) Transport proteins involved in energy metabolism and secretion of both protein and bile acid are highly abundant. Three ion channels are described for the first time in liver. (7) The 800 proteins related to signal transduction and primarily involved in cellular recognition, localization, communication, and inflammation are present in the HLP data set. Insulin and adipocytokine pathways, which are involved in the regulation of glucose and fatty acids, are highly covered. (8) Transcription factors (309 in total) have been recognized at relatively low detection rates and abundance; however, transcription factors regulating gene expression related to transport, metabolism, and biosynthesis are detected at relatively higher coverage and the protein products of their target genes (100 in total), such as metabolic enzymes and plasma proteins, are also identified. (9) The overlap between the human liver and plasma proteomes is particularly noteworthy in the coagulation/anticoagulation/fibrinolysis and complement system. There is a significantly positive linear correlation between the abundance of coagulator proteins in liver and plasma.

Human Proteinpedia: a unified discovery resource for proteomics research

Sharing proteomic data with the biomedical community through a unified proteomic resource, especially in the context of individual proteins, is a challenging prospect. We have developed a community portal, designated as Human Proteinpedia (http://www.humanproteinpedia.org/), for sharing both unpublished and published human proteomic data through the use of a distributed annotation system designed specifically for this purpose. This system allows laboratories to contribute and maintain protein annotations, which are also mapped to the corresponding proteins through the Human Protein Reference Database (HPRD; http://www.hprd.org/). Thus, it is possible to visualize data pertaining to experimentally validated posttranslational modifications (PTMs), protein isoforms, protein–protein interactions (PPIs), tissue expression, expression in cell lines, subcellular localization and enzyme substrates in the context of individual proteins. With enthusiastic participation of the proteomics community, the past 15 months have witnessed data contributions from more than 75 labs around the world including 2710 distinct experiments, >1.9 million peptides, >4.8 million MS/MS spectra, 150 368 protein expression annotations, 17 410 PTMs, 34 624 PPIs and 2906 subcellular localization annotations. Human Proteinpedia should serve as an integrated platform to store, integrate and disseminate such proteomic data and is inching towards evolving into a unified human proteomics resource.

Human Proteinpedia as a Resource for Clinical Proteomics

Clinical proteomics is an emerging field that deals with the use of proteomic technologies for medical applications. With a major objective of identifying proteins involved in pathological processes and as potential biomarkers, this field is already gaining momentum. Consequently, clinical proteomics data are being generated at a rapid pace, although mechanisms of sharing such data with the biomedical community lag far behind. Most of these data are either provided as supplementary information through journal web sites or directly made available by the authors through their own web resources. Integration of these data within a single resource that displays information in the context of individual proteins is likely to enhance the use of proteomic data in biomedical research. Human Proteinpedia is one such portal that unifies human proteomic data under a single banner. The goal of this resource is to ultimately capture and integrate all proteomic data obtained from individual studies on normal and diseased tissues. We anticipate that harnessing of these data will help prioritize experiments related to protein targets and also permit meta-analysis to uncover molecular signatures of disease. Finally, we encourage all biomedical investigators to maximize dissemination of their valuable proteomic data to rest of the community by active participation in existing repositories such as Human Proteinpedia.

Tracing the origin of functional and conserved domains in the human proteome: implications for protein evolution at the modular level

BackgroundThe functional repertoire of the human proteome is an incremental collection of functions accomplished by protein domains evolved along the Homo sapiens lineage. Therefore, knowledge on the origin of these functionalities provides a better understanding of the domain and protein evolution in human. The lack of proper comprehension about such origin has impelled us to study the evolutionary origin of human proteome in a unique way as detailed in this study.ResultsThis study reports a unique approach for understanding the evolution of human proteome by tracing the origin of its constituting domains hierarchically, along the Homo sapiens lineage. The uniqueness of this method lies in subtractive searching of functional and conserved domains in the human proteome resulting in higher efficiency of detecting their origins. From these analyses the nature of protein evolution and trends in domain evolution can be observed in the context of the entire human proteome data. The method adopted here also helps delineate the degree of divergence of functional families occurred during the course of evolution.ConclusionThis approach to trace the evolutionary origin of functional domains in the human proteome facilitates better understanding of their functional versatility as well as provides insights into the functionality of hypothetical proteins present in the human proteome. This work elucidates the origin of functional and conserved domains in human proteins, their distribution along the Homo sapiens lineage, occurrence frequency of different domain combinations and proteome-wide patterns of their distribution, providing insights into the evolutionary solution to the increased complexity of the human proteome.

Characterization of plasma membrane proteins from ovarian cancer cells using mass spectrometry.

To determine how the repertoire of plasma membrane proteins change with disease state, specifically related to cancer, several methods for preparation of plasma membrane proteins were evaluated. Cultured cells derived from stage IV ovarian tumors were grown to 90% confluence and harvested in buffer containing CHAPS detergent. This preparation was centrifuged at low speed to remove insoluble cellular debris resulting in a crude homogenate. Glycosylated proteins in the crude homogenate were selectively enriched using lectin affinity chromatography. The crude homogenate and the lectin purified sample were prepared for mass spectrometric evaluation. The general procedure for protein identification began with trypsin digestion of protein fractions followed by separation by reversed phase liquid chromatography that was coupled directly to a conventional tandem mass spectrometer (i.e. LCQ ion trap). Mass and fragmentation data for the peptides were searched against a human proteome data base using the informatics program SEQUEST. Using this procedure 398 proteins were identified with high confidence, including receptors, membrane-associated ligands, proteases, phosphatases, as well as structural and adhesion proteins. Results indicate that lectin chromatography provides a select subset of proteins and that the number and quality of the identifications improve as does the confidence of the protein identifications for this subset. These results represent the first step in development of methods to separate and successfully identify plasma membrane proteins from advanced ovarian cancer cells. Further characterization of plasma membrane proteins will contribute to our understanding of the mechanisms underlying progression of this deadly disease and may lead to new targeted interventions as well as new biomarkers for diagnosis.

Profiling Core Proteomes of Human Cell Lines by One-dimensional PAGE and Liquid Chromatography-Tandem Mass Spectrometry

Protein expression profiles vary considerably between human cell lines and tissues, which is in part a reflection of their specialized roles within an organism. It is of considerable practical use to establish which proteins constitute the primary components of the respective proteomes. When compiled into databases, such information can facilitate the assessment of selectivity and specificity of a wide range of proteomic experiments. Here we describe the major constituents of proteomes of six human immortalized cell lines. By employing a combination of one-dimensional SDS-PAGE and nanocapillary liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified up to 1785 non-redundant cytoplasmic and nuclear proteins from a single cell line using 50 and 30 microg of total protein from the corresponding fractions. Up to 38 proteins could be identified from a single band in one liquid chromatography-MS/MS experiment. When combined with systematic gridding of gel lanes into 48 slices, a dynamic range for protein identification of approximately 1:2000 can be envisaged for this approach. Identified proteins range from 4-553 kDa in size, cover the pI range between 3.4 and 12.8, and include 255 proteins with predicted transmembrane domains. Repeated analysis of peptides derived from the same gel band showed that the reproducibility of nanocapillary liquid chromatography-MS/MS of such complex mixtures is about 60-70% suggesting that a particular analytical experiment would need to be repeated about three times to arrive at a representative estimate of the set of highly abundant proteins in a given proteome. Given its technical simplicity, sensitivity, and wealth of generated information, we have adopted this experimental approach to characterize every cell line and tissue that is the subject of experimentation in our laboratory. The combined dataset for the six cell lines consists of 2341 non-redundant human proteins and thus constitutes one of the largest collections of human proteomic data published to date.