Recent Advances in the Chromosome-Centric Human Proteome Project: Missing Proteins in the Spot Light

TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data

Translation is a key regulatory step, linking transcriptome and proteome. Two major methods of translatome investigations are RNC-seq (sequencing of translating mRNA) and Ribo-seq (ribosome profiling). To facilitate the investigation of translation, we built a comprehensive database TranslatomeDB (http://www.translatomedb.net/) which provides collection and integrated analysis of published and user-generated translatome sequencing data. The current version includes 2453 Ribo-seq, 10 RNC-seq and their 1394 corresponding mRNA-seq datasets in 13 species. The database emphasizes the analysis functions in addition to the dataset collections. Differential gene expression (DGE) analysis can be performed between any two datasets of same species and type, both on transcriptome and translatome levels. The translation indices translation ratios, elongation velocity index and translational efficiency can be calculated to quantitatively evaluate translational initiation efficiency and elongation velocity, respectively. All datasets were analyzed using a unified, robust, accurate and experimentally-verifiable pipeline based on the FANSe3 mapping algorithm and edgeR for DGE analyzes. TranslatomeDB also allows users to upload their own datasets and utilize the identical unified pipeline to analyze their data. We believe that our TranslatomeDB is a comprehensive platform and knowledgebase on translatome and proteome research, releasing the biologists from complex searching, analyzing and comparing huge sequencing data without needing local computational power.

Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function

An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the "parts list" alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 "dark proteins" of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional characterization of large numbers of dark proteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to characterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.

Utilizing glycine N-methyltransferasegene knockout mice as a model for identification of missing proteins in hepatocellular carcinoma

Glycine N-methyltransferase is a tumor suppressor gene for hepatocellular carcinoma, which can activate DNA methylation by inducing the S-adenosylmethionine to S-adenosylhomocystine. Previous studies have indicated that the expression of Glycine N-methyltransferase is inhibited in hepatocellular carcinoma. To confirm and identify missing proteins, the pathologic analysis of the tumor-bearing mice will provide critical histologic information. Such a mouse model is applied as a screening tool for hepatocellular carcinoma as well as a strategy for missing protein discovery. In this study we designed an analysis platform using the human proteome atlas to compare the possible missing proteins to human whole chromosomes. This will integrate the information from animal studies to establish an optimal technique in the missing protein biomarker discovery.

The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and >1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.

Systematic Proteogenomic Approach To Exploring a Novel Function for NHERF1 in Human Reproductive Disorder: Lessons for Exploring Missing Proteins

One of the major goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to fill the knowledge gaps between human genomic information and the corresponding proteomic information. These gaps are due to "missing" proteins (MPs)-predicted proteins with insufficient evidence from mass spectrometry (MS), biochemical, structural, or antibody analyses-that currently account for 2579 of the 19587 predicted human proteins (neXtProt, 2017-01). We address some of the lessons learned from the inconsistent annotations of missing proteins in databases (DB) and demonstrate a systematic proteogenomic approach designed to explore a potential new function of a known protein. To illustrate a cautious and strategic approach for characterization of novel function in vitro and in vivo, we present the case of Na(+)/H(+) exchange regulatory cofactor 1 (NHERF1/SLC9A3R1, located at chromosome 17q25.1; hereafter NHERF1), which was mistakenly labeled as an MP in one DB (Global Proteome Machine Database; GPMDB, 2011-09 release) but was well known in another public DB and in the literature. As a first step, NHERF1 was determined by MS and immunoblotting for its molecular identity. We next investigated the potential new function of NHERF1 by carrying out the quantitative MS profiling of placental trophoblasts (PXD004723) and functional study of cytotrophoblast JEG-3 cells. We found that NHERF1 was associated with trophoblast differentiation and motility. To validate this newly found cellular function of NHERF1, we used the Caenorhabditis elegans mutant of nrfl-1 (a nematode ortholog of NHERF1), which exhibits a protruding vulva (Pvl) and egg-laying-defective phenotype, and performed genetic complementation work. The nrfl-1 mutant was almost fully rescued by the transfection of the recombinant transgenic construct that contained human NHERF1. These results suggest that NHERF1 could have a previously unknown function in pregnancy and in the development of human embryos. Our study outlines a stepwise experimental platform to explore new functions of ambiguously denoted candidate proteins and scrutinizes the mandated DB search for the selection of MPs to study in the future.

Detergent-Insoluble Proteome Analysis Revealed Aberrantly Aggregated Proteins in Human Preeclampsia Placentas

Preeclampsia (PE) is a placenta disease, featured by hypertension, proteinuria, and other multiorgan dysfunctions, and its etiology is unclear. We and others have shown that intensive endoplasmic reticulum (ER) stress and unfolded protein response (UPR) occur in the PE placenta. In this study, we isolated detergent-insoluble proteins (DIPs) from human placenta tissues, which were enriched with protein aggregates, to characterize the placenta UPR in PE. With data-independent acquisition (DIA) mass spectrometry, we identified 2066 DIPs across all normal (n = 10) and PE (n = 10) placenta samples, among which 110 and 108 DIPs were significantly up- and down-regulated in PE, respectively. Per clustering analysis, differential DIPs could generally distinguish PE from normal placentas. We verified the MS quantitation of endoglin and vimentin by immunoblotting. In addition, we observed that PE placenta tissues have remarkably more endoglin in the cytoplasm. Furthermore, we found that DIPs were evenly distributed across different chromosomes and could be enriched in diversified gene ontology terms, while differential DIPs avoided to distribute on X-chromosome. Significantly up-regulated DIPs in PE were focused on the top functions of lipid metabolism, while 23 of these DIPs could form the top network regulating cellular movement, development, growth, and proliferation. Our results implicate that human PE placentas have disease-relevant differential DIPs, which reflect aberrantly aggregated proteins of placental tissues. The mass spectrometry proteomics data have been deposited to ProteomeXchange consortium with the data set identifier PXD006654, and iProX database (accession number: IPX0000948000).

Progress and pitfalls in finding the 'missing proteins' from the human proteome map

INTRODUCTION

The Human Proteome Project was launched with two main goals: the comprehensive and systematic definition of the human proteome map and the development of ready to use analytical tools to measure relevant proteins in their biological context in health and disease. Despite the great progress in this endeavour, there is still a group of reluctant proteins with no, or scarce, experimental evidence supporting their existence. These are called the 'missing proteins' and represent one of the biggest challenges to complete the human proteome map. Areas covered: This review focuses on the description of the missing proteome based on the HUPO standards, the analysis of the reasons explaining the difficulty of detecting missing proteins and the strategies currently used in the search for missing proteins. The present and future of the quest for the missing proteins is critically revised hereafter. Expert commentary: An overarching multidisciplinary effort is currently being done under the HUPO umbrella to allow completion of the human proteome map. It is expected that the detection of missing proteins will grow in the coming years since the methods and the best tissue/cell type sample for their search are already on the table.

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.

Why are they missing? : Bioinformatics characterization of missing human proteins

NeXtProt is a web-based protein knowledge platform that supports research on human proteins. NeXtProt (release 2015-04-28) lists 20,060 proteins, among them, 3373 canonical proteins (16.8%) lack credible experimental evidence at protein level (PE2:PE5). Therefore, they are considered as "missing proteins". A comprehensive bioinformatic workflow has been proposed to analyze these "missing" proteins. The aims of current study were to analyze physicochemical properties, existence and distribution of the tryptic cleavage sites, and to pinpoint the signature peptides of the missing proteins. Our findings showed that 23.7% of missing proteins were hydrophobic proteins possessing transmembrane domains (TMD). Also, forty missing entries generate tryptic peptides were either out of mass detection range (>30aa) or mapped to different proteins (<9aa). Additionally, 21% of missing entries didn't generate any unique tryptic peptides. In silico endopeptidase combination strategy increased the possibility of missing proteins identification. Coherently, using both mature protein database and signal peptidome database could be a promising option to identify some missing proteins by targeting their unique N-terminal tryptic peptide from mature protein database and or C-terminus tryptic peptide from signal peptidome database. In conclusion, Identification of missing protein requires additional consideration during sample preparation, extraction, digestion and data analysis to increase its incidence of identification.

Genic insights from integrated human proteomics in GeneCards

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite’s next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein–RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome.

Database URL: http://www.genecards.org/