Human Proteome Project Research Articles

Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence.

The current catalogue of the human proteome is not yet complete, as experimental proteomics evidence is still elusive for a group of proteins known as the missing proteins. The Human Proteome Project (HPP) has been successfully using technology and bioinformatic resources to improve the characterization of such challenging proteins. In this manuscript, we propose a pipeline starting with the mining of the PRIDE database to select a group of data sets potentially enriched in missing proteins that are subsequently analyzed for protein identification with a method based on the statistical analysis of proteotypic peptides. Spermatozoa and the HEK293 cell line were found to be a promising source of missing proteins and clearly merit further attention in future studies. After the analysis of the selected samples, we found 342 PSMs, suggesting the presence of 97 missing proteins in human spermatozoa or the HEK293 cell line, while only 36 missing proteins were potentially detected in the retina, frontal cortex, aorta thoracica, or placenta. The functional analysis of the missing proteins detected confirmed their tissue specificity, and the validation of a selected set of peptides using targeted proteomics (SRM/MRM assays) further supports the utility of the proposed pipeline. As illustrative examples, DNAH3 and TEPP in spermatozoa, and UNCX and ATAD3C in HEK293 cells were some of the more robust and remarkable identifications in this study. We provide evidence indicating the relevance to carefully analyze the ever-increasing MS/MS data available from PRIDE and other repositories as sources for missing proteins detection in specific biological matrices as revealed for HEK293 cells.

Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate.

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Deep Coverage Proteomics Identifies More Low-Abundance Missing Proteins in Human Testis Tissue with Q-Exactive HF Mass Spectrometer.

Since 2012, missing proteins (MPs) investigation has been one of the critical missions of Chromosome-Centric Human Proteome Project (C-HPP) through various biochemical strategies. On the basis of our previous testis MPs study, faster scanning and higher resolution mass-spectrometry-based proteomics might be conducive to MPs exploration, especially for low-abundance proteins. In this study, Q-Exactive HF (HF) was used to survey proteins from the same testis tissues separated by two separating methods (tricine- and glycine-SDS-PAGE), as previously described. A total of 8526 proteins were identified, of which more low-abundance proteins were uniquely detected in HF data but not in our previous LTQ Orbitrap Velos (Velos) reanalysis data. Further transcriptomics analysis showed that these uniquely identified proteins by HF also had lower expression at the mRNA level. Of the 81 total identified MPs, 74 and 39 proteins were listed as MPs in HF and Velos data sets, respectively. Among the above MPs, 47 proteins (43 neXtProt PE2 and 4 PE3) were ranked as confirmed MPs after verifying with the stringent spectra match and isobaric and single amino acid variants filtering. Functional investigation of these 47 MPs revealed that 11 MPs were testis-specific proteins and 7 MPs were involved in spermatogenesis process. Therefore, we concluded that higher scanning speed and resolution of HF might be factors for improving the low-abundance MP identification in future C-HPP studies. All mass-spectrometry data from this study have been deposited in the ProteomeXchange with identifier PXD004092.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1.

Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing 15 essential points followed by two pages of expanded description of each. Here we present an overview of the guidelines and provide an in-depth description of each of the 15 elements to facilitate understanding of the intentions and rationale behind the guidelines, for both authors and reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories, how error analysis should be presented, and how detection of novel proteins should be supported with additional confirmatory evidence. These guidelines, developed by the HPP community, are presented to the broader scientific community for further discussion.

Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update.

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.

Phosphoproteome Characterization of Human Colorectal Cancer SW620 Cell-Derived Exosomes and New Phosphosite Discovery for C-HPP.

Identification of all phosphorylation forms of known proteins is a major goal of the Chromosome-Centric Human Proteome Project (C-HPP). Recent studies have found that certain phosphoproteins can be encapsulated in exosomes and function as key regulators in tumor microenvironment, but no deep coverage phosphoproteome of human exosomes has been reported to date, which makes the exosome a potential source for the new phosphosite discovery. In this study, we performed highly optimized MS analyses on the exosomal and cellular proteins isolated from human colorectal cancer SW620 cells. With stringent data quality control, 313 phosphoproteins with 1091 phosphosites were confidently identified from the SW620 exosome, from which 202 new phosphosites were detected. Exosomal phosphoproteins were significantly enriched in the 11q12.1-13.5 region of chromosome 11 and had a remarkably high level of tyrosine-phosphorylated proteins (6.4%), which were functionally relevant to ephrin signaling pathway-directed cytoskeleton remodeling. In conclusion, we here report the first high-coverage phosphoproteome of human cell-secreted exosomes, which leads to the identification of new phosphosites for C-HPP. Our findings provide insights into the exosomal phosphoprotein systems that help to understand the signaling language being delivered by exosomes in cell-cell communications. The mass spectrometry proteomics data have been deposited to the ProteomeXchange consortium with the data set identifier PXD004079, and iProX database (accession number: IPX00076800).

The Human Skeletal Muscle Proteome Project: a reappraisal of the current literature.

Skeletal muscle is a large organ that accounts for up to half the total mass of the human body. A progressive decline in muscle mass and strength occurs with ageing and in some individuals configures the syndrome of ‘sarcopenia’, a condition that impairs mobility, challenges autonomy, and is a risk factor for mortality. The mechanisms leading to sarcopenia as well as myopathies are still little understood. The Human Skeletal Muscle Proteome Project was initiated with the aim to characterize muscle proteins and how they change with ageing and disease. We conducted an extensive review of the literature and analysed publically available protein databases. A systematic search of peer‐reviewed studies was performed using PubMed. Search terms included ‘human’, ‘skeletal muscle’, ‘proteome’, ‘proteomic(s)’, and ‘mass spectrometry’, ‘liquid chromatography‐mass spectrometry (LC‐MS/MS)’. A catalogue of 5431 non‐redundant muscle proteins identified by mass spectrometry‐based proteomics from 38 peer‐reviewed scientific publications from 2002 to November 2015 was created. We also developed a nosology system for the classification of muscle proteins based on localization and function. Such inventory of proteins should serve as a useful background reference for future research on changes in muscle proteome assessed by quantitative mass spectrometry‐based proteomic approaches that occur with ageing and diseases. This classification and compilation of the human skeletal muscle proteome can be used for the identification and quantification of proteins in skeletal muscle to discover new mechanisms for sarcopenia and specific muscle diseases that can be targeted for the prevention and treatment.

Structural Bioinformatics Analysis On Diabetes Associated Proteins

The Human Diabetes Proteome Project (HDPP) mentioned more than 1000 diabetes associated proteins. 400 diabetes associated proteins whose structure is not analyzed yet are selected from the database. Each proteins structure is analyzed via prediction tools in order to reveal structural similarity with IAPP (Islet Amyloid Polypeptide) whose role is well characterized in diabetes. Through similarity analysis between proteins and in region of disulfide bridge formation, we aimed to find similar possible misfolding patterns in other diabetes associated proteins. Result indicates that Calcitonin Gene Related Peptide I is the only proteins who shows high structural and conformational similarity with IAPP. We believe that one of the possible enrolments of the protein Calcitonin Gene Related Peptide 1 to the diabetes can be similar conformational changes due to disulfide bridge formation break. Finally we propose that the accumulation of misfolded Calcitonin Gene Related Peptide 1 is similar to other protein based conformational diseases. Further structural analyses needs to be performed to confirm these results.

Integrated GlycoProteome Analyzer (I-GPA) for Automated Identification and Quantitation of Site-Specific N-Glycosylation.

Human glycoproteins exhibit enormous heterogeneity at each N-glycosite, but few studies have attempted to globally characterize the site-specific structural features. We have developed Integrated GlycoProteome Analyzer (I-GPA) including mapping system for complex N-glycoproteomes, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. Using an N-glycopeptide database that we constructed, we created novel scoring algorithms with decoy glycopeptides, where 95 N-glycopeptides from standard α1-acid glycoprotein were identified with 0% false positives, giving the same results as manual validation. Additionally automated label-free quantitation method was first developed that utilizes the combined intensity of top three isotope peaks at three highest MS spectral points. The efficiency of I-GPA was demonstrated by automatically identifying 619 site-specific N-glycopeptides with FDR ≤ 1%, and simultaneously quantifying 598 N-glycopeptides, from human plasma samples that are known to contain highly glycosylated proteins. Thus, I-GPA platform could make a major breakthrough in high-throughput mapping of complex N-glycoproteomes, which can be applied to biomarker discovery and ongoing global human proteome project.

Towards a functional definition of the mitochondrial human proteome

The mitochondrial human proteome project (mt-HPP) was initiated by the Italian HPP group as a part of both the chromosome-centric initiative (C-HPP) and the ⿿biology and disease driven⿿ initiative (B/D-HPP). In recent years several reports highlighted how mitochondrial biology and disease are regulated by specific interactions with non-mitochondrial proteins. Thus, it is of great relevance to extend our present view of the mitochondrial proteome not only to those proteins that are encoded by or transported to mitochondria, but also to their interactors that take part in mitochondria functionality. Here, we propose a graphical representation of the functional mitochondrial proteome by retrieving mitochondrial proteins from the NeXtProt database and adding to the network their interactors as annotated in the IntAct database. Notably, the network may represent a reference to map all the proteins that are currently being identified in mitochondrial proteomics studies.

Direct molecular fishing in molecular partners investigation in protein–protein and protein–peptide interactions

An original experimental method of direct molecular fishing has been developed for identification of potential partners of protein–protein and protein–peptide interactions. It is based on combination of surface plasmon resonance technology (SPR), size exclusion and affinity chromatography and mass spectrometric identification of proteins (LC-MS/MS). Previously, we demonstrated applicability of this method for protein interactomics using experimental model system, as well as in the pilot study in the frame of the Human Proteome Project (HPP). In the present paper, this method was successfully applied to identify possible molecular partners of 7 target proteins encoded by genes of 18 chromosome (also in the frame of the HPP). Fishing on the affinity sorbents with immobilized target proteins as ligands was carried out using total lysate of human liver tissue as well as pooled sets of fractions (individual for each bait-protein) obtained by means of a combination of size exclusion chromatography and SPR analysis for the presence of potential prey-proteins in each fraction. As a result we obtained lists of possible molecular partners of all 7 proteins and performed a comparative evaluation of direct fishing specificity for these target proteins. Direct molecular fishing was also successfully used for search of potential protein partners interacting with different isoforms of amyloid-beta peptide, playing a key role in the development of Alzheimer’s disease. The synthetic peptides that are analogues of the metal-binding domain isoforms of beta-amyloid were used as molecular baits and the fishing was performed in various fractions of immortalized human neural cells. As a result, 13 potential partner proteins were identified in the cytosol fraction of the cells by fishing on amyloid-beta peptide (1-16).

Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome.

No field in science and medicine today remains untouched by Big Data, and psychiatry is no exception. Proteomics is a Big Data technology and a next generation biomarker, supporting novel system diagnostics and therapeutics in psychiatry. Proteomics technology is, in fact, much older than genomics and dates to the 1970s, well before the launch of the international Human Genome Project. While the genome has long been framed as the master or "elite" executive molecule in cell biology, the proteome by contrast is humble. Yet the proteome is critical for life-it ensures the daily functioning of cells and whole organisms. In short, proteins are the blue-collar workers of biology, the down-to-earth molecules that we cannot live without. Since 2010, proteomics has found renewed meaning and international attention with the launch of the Human Proteome Project and the growing interest in Big Data technologies such as proteomics. This article presents an interdisciplinary technology foresight analysis and conceptualizes the terms "environtome" and "social proteome". We define "environtome" as the entire complement of elements external to the human host, from microbiome, ambient temperature and weather conditions to government innovation policies, stock market dynamics, human values, political power and social norms that collectively shape the human host spatially and temporally. The "social proteome" is the subset of the environtome that influences the transition of proteomics technology to innovative applications in society. The social proteome encompasses, for example, new reimbursement schemes and business innovation models for proteomics diagnostics that depart from the "once-a-life-time" genotypic tests and the anticipated hype attendant to context and time sensitive proteomics tests. Building on the "nesting principle" for governance of complex systems as discussed by Elinor Ostrom, we propose here a 3-tiered organizational architecture for Big Data science such as proteomics. The proposed nested governance structure is comprised of (a) scientists, (b) ethicists, and (c) scholars in the nascent field of "ethics-of-ethics", and aims to cultivate a robust social proteome for personalized medicine. Ostrom often noted that such nested governance designs offer assurance that political power embedded in innovation processes is distributed evenly and is not concentrated disproportionately in a single overbearing stakeholder or person. We agree with this assessment and conclude by underscoring the synergistic value of social and biological proteomes to realize the full potentials of proteomics science for personalized medicine in psychiatry in the present era of Big Data.

Proteogenomic Study beyond Chromosome 9: New Insight into Expressed Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues.

This is a report of a human proteome project (HPP) related to chromosome 9 (Chr 9). To reveal missing proteins and undiscovered features in proteogenomes, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based identification and characterization were conducted on five pairs of lung adenocarcinoma tumors and adjacent nontumor tissues. Before our previous Chromosome-Centric Human Proteome Project (C-HPP) special issue, there were 170 remaining missing proteins on Chr 9 (neXtProt 2013.09.26 rel.); 133 remain at present (neXtProt 2015.04.28 rel.). In the proteomics study, we found two missing protein candidates that require follow-up work and one unrevealed protein across all chromosomes. RNA-seq analysis detected RNA expression for four nonsynonymous (NS) single nucleotide polymorphisms (SNPs) (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and three synonymous SNPs (in CDH17, CST1, and HNF1A) in all five tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data and by searching the proteomics data from the same sample, we identified four missense mutations in four genes (LTF, HDLBP, TF, and HBD). Two of these mutations were found in tumor samples but not in paired normal tissues. In summary, our proteogenomic study of human primary lung tumor tissues detected additional and revealed novel missense mutations and synonymous SNP signatures, some of which are specific to lung cancers. Data from mass spectrometry have been deposited in the ProteomeXchange with the identifier PXD002523.

Chromosome-Based Proteomic Study for Identifying Novel Protein Variants from Human Hippocampal Tissue Using Customized neXtProt and GENCODE Databases.

The goal of the Chromosome-Centric Human Proteome Project (C-HPP) is to fully provide proteomic information from each human chromosome, including novel proteoforms, such as novel protein-coding variants expressed from noncoding genomic regions, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). In the 144 LC/MS/MS raw files from human hippocampal tissues of control, epilepsy, and Alzheimer's disease, we identified the novel proteoforms with a workflow including integrated proteomic pipeline using three different search engines, MASCOT, SEQUEST, and MS-GF+. With a <1% false discovery rate (FDR) at the protein level, the 11 detected peptides mapped to four translated long noncoding RNA variants against the customized databases of GENCODE lncRNA, which also mapped to coding-proteins at different chromosomal sites. We also identified four novel ASVs against the customized databases of GENCODE transcript. The target peptides from the variants were validated by tandem MS fragmentation pattern from their corresponding synthetic peptides. Additionally, a total of 128 SAAVs paired with their wild-type peptides were identified with FDR <1% at the peptide level using a customized database from neXtProt including nonsynonymous single nucleotide polymorphism (nsSNP) information. Among these results, several novel variants related in neuro-degenerative disease were identified using the workflow that could be applicable to C-HPP studies. All raw files used in this study were deposited in ProteomeXchange (PXD000395).

Positional proteomics in the era of the human proteome project on the doorstep of precision medicine

Proteolytic processing is a pervasive and irreversible post-translational modification that expands the protein universe by generating new proteoforms (protein isoforms). Unlike signal peptide or prodomain removal, protease-generated proteoforms can rarely be predicted from gene sequences. Positional proteomic techniques that enrich for N- or C-terminal peptides from proteomes are indispensable for a comprehensive understanding of a protein's function in biological environments since protease cleavage frequently results in altered protein activity and localization. Proteases often process other proteases and protease inhibitors which perturbs proteolytic networks and potentiates the initial cleavage event to affect other molecular networks and cellular processes in physiological and pathological conditions. This review is aimed at researchers with a keen interest in state of the art systems level positional proteomic approaches that: (i) enable the study of complex protease-protease, protease-inhibitor and protease-substrate crosstalk and networks; (ii) allow the identification of proteolytic signatures as candidate disease biomarkers; and (iii) are expected to fill the Human Proteome Project missing proteins gap. We predict that these methodologies will be an integral part of emerging precision medicine initiatives that aim to customize healthcare, converting reactive medicine into a personalized and proactive approach, improving clinical care and maximizing patient health and wellbeing, while decreasing health costs by eliminating ineffective therapies, trial-and-error prescribing, and adverse drug effects. Such initiatives require quantitative and functional proteome profiling and dynamic disease biomarkers in addition to current pharmacogenomics approaches. With proteases at the pathogenic center of many diseases, high-throughput protein termini identification techniques such as TAILS (Terminal Amine Isotopic Labeling of Substrates) and COFRADIC (COmbined FRActional DIagonal Chromatography) will be fundamental for individual and comprehensive assessment of health and disease.

Integrated Proteomic and Genomic Analysis of Gastric Cancer Patient Tissues.

V-erb-b2 erythroblastic leukemia viral oncogene homologue 2, known as ERBB2, is an important oncogene in the development of certain cancers. It can form a heterodimer with other epidermal growth factor receptor family members and activate kinase-mediated downstream signaling pathways. ERBB2 gene is located on chromosome 17 and is amplified in a subset of cancers, such as breast, gastric, and colon cancer. Of particular interest to the Chromosome-Centric Human Proteome Project (C-HPP) initiative is the amplification mechanism that typically results in overexpression of a set of genes adjacent to ERBB2, which provides evidence of a linkage between gene location and expression. In this report we studied patient samples from ERBB2-positive together with adjacent control nontumor tissues. In addition, non-ERBB2-expressing patient samples were selected as comparison to study the effect of expression of this oncogene. We detected 196 proteins in ERBB2-positive patient tumor samples that had minimal overlap (29 proteins) with the non-ERBB2 tumor samples. Interaction and pathway analysis identified extracellular signal regulated kinase (ERK) cascade and actin polymerization and actinmyosin assembly contraction as pathways of importance in ERBB2+ and ERBB2- gastric cancer samples, respectively. The raw data files are deposited at ProteomeXchange (identifier: PXD002674) as well as GPMDB.

Interaction of Human Kidney and Urine Proteome Project (HKUPP) in the Human Proteome Project

Interaction of Human Kidney and Urine Proteome Project (HKUPP) in the Human Proteome Project

HPP に向けての HKUPP の取り組み

HPP に向けての HKUPP の取り組み

HKUPP: Human Kidney and Urine Proteome Project

HKUPP: Human Kidney and Urine Proteome Project

Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project.

Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ~15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.