Is a gene-centric human proteome project the best way for proteomics to serve biology?

A high-stringency blueprint of the human proteome

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .

The UniProtKB guide to the human proteome

Advances in high-throughput and advanced technologies allow researchers to routinely perform whole genome and proteome analysis. For this purpose, they need high-quality resources providing comprehensive gene and protein sets for their organisms of interest. Using the example of the human proteome, we will describe the content of a complete proteome in the UniProt Knowledgebase (UniProtKB). We will show how manual expert curation of UniProtKB/Swiss-Prot is complemented by expert-driven automatic annotation to build a comprehensive, high-quality and traceable resource. We will also illustrate how the complexity of the human proteome is captured and structured in UniProtKB.

Database URL: www.uniprot.org

Getting intimate with trypsin, the leading protease in proteomics

Nowadays, mass spectrometry-based proteomics is carried out primarily in a bottom-up fashion, with peptides obtained after proteolytic digest of a whole proteome lysate as the primary analytes instead of the proteins themselves. This experimental setup crucially relies on a protease to digest an abundant and complex protein mixture into a far more complex peptide mixture. Full knowledge of the working mechanism and specificity of the used proteases is therefore crucial, both for the digestion step itself as well as for the downstream identification and quantification of the (fragmentation) mass spectra acquired for the peptides in the mixture. Targeted protein analysis through selected reaction monitoring, a relative newcomer in the specific field of mass spectrometry-based proteomics, even requires a priori understanding of protease behavior for the proteins of interest. Because of the rapidly increasing popularity of proteomics as an analytical tool in the life sciences, there is now a renewed demand for detailed knowledge on trypsin, the workhorse protease in proteomics. This review addresses this need and provides an overview on the structure and working mechanism of trypsin, followed by a critical analysis of its cleavage behavior, typically simply accepted to occur exclusively yet consistently after Arg and Lys, unless they are followed by a Pro. In this context, shortcomings in our ability to understand and predict the behavior of trypsin will be highlighted, along with the downstream implications. Furthermore, an analysis is carried out on the inherent shortcomings of trypsin with regard to whole proteome analysis, and alternative approaches will be presented that can alleviate these issues. Finally, some reflections on the future of trypsin as the workhorse protease in mass spectrometry-based proteomics will be provided.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

A cell-based approach to the human proteome project

The general scope of a project to determine the protein molecules that comprise the cells within the human body is framed. By focusing on protein primary structure as expressed in specific cell types, this concept for a cell-based version of the Human Proteome Project (CB-HPP) is crafted in a manner analogous to the Human Genome Project while recognizing that cells provide a primary context in which to define a proteome. Several activities flow from this articulation of the HPP, which enables the definition of clear milestones and deliverables. The CB-HPP highlights major gaps in our knowledge regarding cell heterogeneity and protein isoforms, and calls for development of technology that is capable of defining all human cell types and their proteomes. The main activities will involve mapping and sorting cell types combined with the application of beyond the state-of-the art in protein mass spectrometry.

Comparative ranking of human chromosomes based on post-genomic data

The goal of the Human Proteome Project (HPP) is to fully characterize the 21,000 human protein-coding genes with respect to the estimated two million proteins they encode. As such, the HPP aims to create a comprehensive, detailed resource to help elucidate protein functions and to advance medical treatment. Similarly to the Human Genome Project (HGP), the HPP chose a chromosome-centric approach, assigning different chromosomes to different countries. Here we introduce a scoring method for chromosome ranking based on several characteristics, including relevance to health problems, existing published knowledge, and current transcriptome and proteome coverage. The score of each chromosome was computed as a weighted combination of indexes reflecting the aforementioned characteristics. The approach is tailored to the chromosome-centric HPP (C-HPP), and is advantageous in that it takes into account currently available information. We ranked the human chromosomes using the proposed score, and observed that Chr Y, Chr 13, and Chr 18 were top-ranked, whereas the scores of Chr 19, Chr 11, and Chr 17 were comparatively low. For Chr 18, selected for the Russian part of C-HPP, about 25% of the encoded genes were associated with diseases, including cancers and neurodegenerative and psychiatric diseases, as well as type 1 diabetes and essential hypertension. This ranking approach could easily be adapted to prioritize research for other sets of genes, such as metabolic pathways and functional categories.

Gene-centric view on the human proteome project: the example of the Russian roadmap for chromosome 18

During the 2010 Human Proteome Organization Congress in Sydney, a gene-centric approach emerged as a feasible and tractable scaffold for assemblage of the Human Proteome Project. Bringing the gene-centric principle into practice, a roadmap for the 18th chromosome was drafted, postulating the limited sensitivity of analytical methods, as a serious bottleneck in proteomics. In the context of the sensitivity problem, we refer to the "copy number of protein molecules" as a measurable assessment of protein abundance. The roadmap is focused on the development of technology to attain the low- and ultralow -"copied" portion of the proteome. Roadmap merges the genomic, transcriptomic and proteomic levels to identify the majority of 285 proteins from 18th chromosome - master proteins. Master protein is the primary translation of the coding sequence and resembling at least one of the known isoforms, coded by the gene. The executive phase of the roadmap includes the expansion of the study of the master proteins with alternate splicing, single amino acid polymorphisms (SAPs) and post-translational modifications. In implementing the roadmap, Russian scientists are expecting to establish proteomic technologies for integrating MS and atomic force microscopy (AFM). These technologies are anticipated to unlock the value of new biomarkers at a detection limit of 10(-18) M, i.e. 1 protein copy per 1 μL of plasma. The roadmap plan is posted at www.proteome.ru/en/roadmap/ and a forum for discussion of the document is supported.