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

Exploring High-Throughput Immunoassays for Biomarker Validation in Rheumatic Diseases in the Context of the Human Proteome Project

Rheumatic diseases are high prevalence pathologies with different etiology and evolution and low sensitivity in clinical diagnosis. Therefore, it is necessary to develop an early diagnosis method which allows personalized treatment, depending on the specific pathology. The biology/disease initiative, at Human Proteome Project, is an integrative approach to identify relevant proteins in the human proteome associated with pathologies. A previously reported literature data mining analysis, which identified proteins related to osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PSA) was used to establish a systematic prioritization of potential biomarkers candidates for further evaluation by functional proteomics studies. The aim was to study the protein profile of serum samples from patients with rheumatic diseases such as OA, RA, and PSA. To achieve this goal, customized antibody microarrays (containing 151 antibodies targeting 121 specific proteins) were used to identify biomarkers related to early and specific diagnosis in a screening of 960 serum samples (nondepleted) (OA, n = 480; RA, n = 192; PSA, n = 288). This functional proteomics screening has allowed the determination of a panel (30 serum proteins) as potential biomarkers for these rheumatic diseases, displaying receiver operating characteristics curves with area under the curve values of 80-90%.

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

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.

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

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.

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

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.