Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years

We celebrate the 10th anniversary of the launch of the HUPO Human Proteome Project (HPP) and its major milestone of confident detection of at least one protein from each of 90% of the predicted protein-coding genes, based on the output of the entire proteomics community. The Human Genome Project reached a similar decadal milestone 20 years ago. The HPP has engaged proteomics teams around the world, strongly influenced data-sharing, enhanced quality assurance, and issued stringent guidelines for claims of detecting previously "missing proteins." This invited perspective complements papers on "A High-Stringency Blueprint of the Human Proteome" and "The Human Proteome Reaches a Major Milestone" in special issues of Nature Communications and Journal of Proteome Research, respectively, released in conjunction with the October 2020 virtual HUPO Congress and its celebration of the 10th anniversary of the HUPO HPP.

MloDisDB: a manually curated database of the relations between membraneless organelles and diseases

Cells are compartmentalized by numerous membrane-bounded organelles and membraneless organelles (MLOs) to ensure temporal and spatial regulation of various biological processes. A number of MLOs, such as nucleoli, nuclear speckles and stress granules, exist as liquid droplets within the cells and arise from the condensation of proteins and RNAs via liquid-liquid phase separation (LLPS). By concentrating certain proteins and RNAs, MLOs accelerate biochemical reactions and protect cells during stress, and dysfunction of MLOs is associated with various pathological processes. With the development in this field, more and more relations between the MLOs and diseases have been described; however, these results have not been made available in a centralized resource. Herein, we build MloDisDB, a database which aims to gather the relations between MLOs and diseases from dispersed literature. In addition, the relations between LLPS and diseases were included as well. Currently, MloDisDB contains 771 curated entries from 607 publications; each entry in MloDisDB contains detailed information about the MLO, the disease and the functional factor in the relation. Furthermore, an efficient and user-friendly interface for users to search, browse and download all entries was provided. MloDisDB is the first comprehensive database of the relations between MLOs and diseases so far, and the database is freely accessible at http://mlodis.phasep.pro/.

Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma

BACKGROUND

We have verified a mass spectrometry (MS)-based targeted proteomics signature for the detection of malignant pleural mesothelioma (MPM) from the blood.

METHODS

A seven-peptide biomarker MPM signature by targeted proteomics in serum was identified in a previous independent study. Here, we have verified the predictive accuracy of a reduced version of that signature, now composed of six-peptide biomarkers. We have applied liquid chromatography-selected reaction monitoring (LC-SRM), also known as multiple-reaction monitoring (MRM), for the investigation of 402 serum samples from 213 patients with MPM and 189 cancer-free asbestos-exposed donors from the United States, Australia, and Europe.

RESULTS

Each of the biomarkers composing the signature was independently informative, with no apparent functional or physical relation to each other. The multiplexing possibility offered by MS proteomics allowed their integration into a single signature with a higher discriminating capacity than that of the single biomarkers alone. The strategy allowed in this way to increase their potential utility for clinical decisions. The signature discriminated patients with MPM and asbestos-exposed donors with AUC of 0.738. For early-stage MPM, AUC was 0.765. This signature was also prognostic, and Kaplan-Meier analysis showed a significant difference between high- and low-risk groups with an HR of 1.659 (95% CI, 1.075-2.562; P = 0.021).

CONCLUSIONS

Targeted proteomics allowed the development of a multianalyte signature with diagnostic and prognostic potential for MPM from the blood.

IMPACT

The proteomic signature represents an additional diagnostic approach for informing clinical decisions for patients at risk for MPM.

OmixLitMiner: A Bioinformatics Tool for Prioritizing Biological Leads from 'Omics Data Using Literature Retrieval and Data Mining

Proteomics and genomics discovery experiments generate increasingly large result tables, necessitating more researcher time to convert the biological data into new knowledge. Literature review is an important step in this process and can be tedious for large scale experiments. An informed and strategic decision about which biomolecule targets should be pursued for follow-up experiments thus remains a considerable challenge. To streamline and formalise this process of literature retrieval and analysis of discovery based 'omics data and as a decision-facilitating support tool for follow-up experiments we present OmixLitMiner, a package written in the computational language R. The tool automates the retrieval of literature from PubMed based on UniProt protein identifiers, gene names and their synonyms, combined with user defined contextual keyword search (i.e., gene ontology based). The search strategy is programmed to allow either strict or more lenient literature retrieval and the outputs are assigned to three categories describing how well characterized a regulated gene or protein is. The category helps to meet a decision, regarding which gene/protein follow-up experiments may be performed for gaining new knowledge and to exclude following already known biomarkers. We demonstrate the tool's usefulness in this retrospective study assessing three cancer proteomics and one cancer genomics publication. Using the tool, we were able to corroborate most of the decisions in these papers as well as detect additional biomolecule leads that may be valuable for future research.

Mining the Proteome Associated with Rheumatic and Autoimmune Diseases

A steady increase in the incidence of osteoarthritis and other rheumatic diseases has been observed in recent decades, including autoimmune conditions such as rheumatoid arthritis, spondyloarthropathies, systemic lupus erythematosus, systemic sclerosis, and Sjögren's syndrome. Rheumatic and autoimmune diseases (RADs) are characterized by the inflammation of joints, muscles, or other connective tissues. In addition to often experiencing debilitating mobility and pain, RAD patients are also at a higher risk of suffering comorbidities such as cardiovascular or infectious events. Given the socioeconomic impact of RADs, broad research efforts have been dedicated to these diseases worldwide. In the present work, we applied literature mining platforms to identify "popular" proteins closely related to RADs. The platform is based on publicly available literature. The results not only will enable the systematic prioritization of candidates to perform targeted proteomics studies but also may lead to a greater insight into the key pathogenic processes of these disorders.

Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project

The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.

New advances in extracting and learning from protein-protein interactions within unstructured biomedical text data

Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

The Human Brain Proteome Project: Biological and Technological Challenges

Brain proteomics has become a method of choice that allows zooming-in where neuropathophysiological alterations are taking place, detecting protein mediators that might eventually be measured in cerebrospinal fluid (CSF) as potential neuropathologically derived biomarkers. Following this hypothesis, mass spectrometry-based neuroproteomics has emerged as a powerful approach to profile neural proteomes derived from brain structures and CSF in order to map the extensive protein catalog of the human brain. This chapter provides a historical perspective on the Human Brain Proteome Project (HBPP), some recommendation to the experimental design in neuroproteomic projects, and a brief description of relevant technological and computational innovations that are emerging in the neurobiology field thanks to the proteomics community. Importantly, this chapter highlights recent discoveries from the biology- and disease-oriented branch of the HBPP (B/D-HBPP) focused on spatiotemporal proteomic characterizations of mouse models of neurodegenerative diseases, elucidation of proteostatic networks in different types of dementia, the characterization of unresolved clinical phenotypes, and the discovery of novel biomarker candidates in CSF.

Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project

The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the Journal of Proteome Research, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.

Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity

Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic. We describe here a method to identify and rank protein-topic relationships by calculating the semantic similarity between a protein and a query term in the biomerical literature while adjusting for the impact and immediacy of associated research articles. We term the calculated metric the weighted copublication distance (WCD) and show that it compares well to related approaches in predicting benchmark protein lists in multiple biological processes. We used WCD to extract prioritized "popular proteins" across multiple cell types, subanatomical regions, and standardized vocabularies containing over 20 000 human disease terms. The collection of protein-disease associations across the resulting human "diseasome" supports data analytical workflows to perform reverse protein-to-disease queries and functional annotation of experimental protein lists. We envision that the described improvement to the popular proteins strategy will be useful for annotating protein lists and guiding method development efforts as well as generating new hypotheses on understudied disease proteins using bibliometric information.

Biology/Disease-Driven Initiative on Protein-Aggregation Diseases of the Human Proteome Project: Goals and Progress to Date

The Biology/Disease-driven (B/D) working groups of the Human Proteome Project are alliances of research groups aimed at developing or improving proteomic tools to support specific biological or disease-related research areas. Here, we describe the activities and progress to date of the B/D working group focused on protein aggregation diseases (PADs). PADs are characterized by the intra- or extracellular accumulation of aggregated proteins and include devastating diseases such as Parkinson's and Alzheimer's disease and systemic amyloidosis. The PAD B/D working group aims for the development of proteomic assays for the quantification of aggregation-prone proteins involved in PADs to support basic and clinical research on PADs. Because the proteins in PADs undergo aberrant conformational changes, a goal is to quantitatively resolve altered protein structures and aggregation states in complex biological specimens. We have developed protein-extraction protocols and a set of mass spectrometric (MS) methods that enable the detection and quantification of proteins involved in the systemic and localized amyloidosis and the probing of aberrant protein conformational transitions in cell and tissue extracts. In several studies, we have demonstrated the potential of MS-based proteomics approaches for specific and sensitive clinical diagnoses and for the subtyping of PADs. The developed methods have been detailed in both protocol papers and manuscripts describing applications to facilitate implementation by nonspecialized laboratories, and assay coordinates are shared through public repositories and databases. Clinicians actively involved in the PAD working group support the transfer to clinical practice of the developed methods, such as assays to quantify specific disease-related proteins and their fragments in biofluids and multiplexed MS-based methods for the diagnosis and typing of systemic amyloidosis. We believe that the increasing availability of tools to precisely measure proteins involved in PADs will positively impact research on the molecular bases of these diseases and support early disease diagnosis and a more-confident subtyping.

Liver cancer-associated changes to the proteome: what deserves clinical focus?

INTRODUCTION

Hepatocellular carcinoma (HCC) is recognized as the fifth most common neoplasm and currently represents the second leading form of cancer-related death worldwide. Despite great progress has been done in the understanding of its pathogenesis, HCC represents a heavy societal and economic burden as most patients are still diagnosed at advanced stages and the 5-year survival rate remain below 20%. Early detection and revolutionary therapies that rely on the discovery of new molecular biomarkers and therapeutic targets are therefore urgently needed to develop precision medicine strategies for a more efficient management of patients. Areas covered: This review intends to comprehensively analyse the proteomics-based research conducted in the last few years to address some of the principal still open riddles in HCC biology, based on the identification of molecular drivers of tumor progression and metastasis. Expert commentary: The technical advances in mass spectrometry experienced in the last decade have significantly improved the analytical capacity of proteome wide studies. Large-scale protein and protein variant (post-translational modifications) identification and quantification have allowed detailed dissections of molecular mechanisms underlying HCC progression and are already paving the way for the identification of clinically relevant proteins and the development of their use on patient care.

A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project

Targeted metabolomics and biochemical studies complement the ongoing investigations led by the Human Proteome Organization (HUPO) Biology/Disease-Driven Human Proteome Project (B/D-HPP). However, it is challenging to identify and prioritize metabolite and chemical targets. Literature-mining-based approaches have been proposed for target proteomics studies, but text mining methods for metabolite and chemical prioritization are hindered by a large number of synonyms and nonstandardized names of each entity. In this study, we developed a cloud-based literature mining and summarization platform that maps metabolites and chemicals in the literature to unique identifiers and summarizes the copublication trends of metabolites/chemicals and B/D-HPP topics using Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores. We successfully prioritized metabolites and chemicals associated with the B/D-HPP targeted fields and validated the results by checking against expert-curated associations and enrichment analyses. Compared with existing algorithms, our system achieved better precision and recall in retrieving chemicals related to B/D-HPP focused areas. Our cloud-based platform enables queries on all biological terms in multiple species, which will contribute to B/D-HPP and targeted metabolomics/chemical studies.