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An Y, Glavatskikh M, Lim J, Wang X, Norris-Drouin J, Hardy PB, Leisner TM, Pearce KH, Kireev D. Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot. RESEARCH SQUARE 2023:rs.3.rs-3197490. [PMID: 37645935 PMCID: PMC10462244 DOI: 10.21203/rs.3.rs-3197490/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify. Novel therapeutic targets are often hard-to-drug proteins, such as messengers or transcription factors. Computational strategies arise as a promising solution to expedite drug discovery for unconventional therapeutic targets. FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments. The seeded fragments can then inform either (i) de novo design of 3D ligand structures or (ii) ultra-large-scale virtual screening of commercially available compounds. Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising but ligand-orphan drug target implicated in triple negative breast cancer. The signaling function of CIB1 relies on protein-protein interactions and its structure does not feature any natural ligand-binding pocket. FRASE-based virtual screening identified the first small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depleted cells.
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Affiliation(s)
- Yi An
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Marta Glavatskikh
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Jiwoong Lim
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Xiaowen Wang
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
- Chemistry department, University of Missouri, Columbia, Columbia, MO, 65211
| | - Jacqueline Norris-Drouin
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - P. Brian Hardy
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Tina M. Leisner
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Kenneth H. Pearce
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Dmitri Kireev
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
- Chemistry department, University of Missouri, Columbia, Columbia, MO, 65211
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2
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Li C, Xiao J, Wu S, Liu L, Zeng X, Zhao Q, Zhang Z. Clinical application of serum-based proteomics technology in human tumor research. Anal Biochem 2023; 663:115031. [PMID: 36580994 DOI: 10.1016/j.ab.2022.115031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 12/24/2022] [Indexed: 12/27/2022]
Abstract
The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.
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Affiliation(s)
- Chen Li
- Department of Pathology, The First Affiliated Hospital of University of South China, Hunan, Hengyang, 421001, Hunan Province, China
| | - Juan Xiao
- Department of Otorhinolaryngology, The Second Affiliated Hospital of University of South China, Hunan, Hengyang, 421001, Hunan Province, China
| | - Shihua Wu
- Department of Pathology, The Second Hospital of Shaoyang College, Hunan, Shaoyang, 422000, Hunan Province, China
| | - Lu Liu
- Department of Pathology, The First Affiliated Hospital of University of South China, Hunan, Hengyang, 421001, Hunan Province, China
| | - Xuemei Zeng
- Cancer Research Institute of Hengyang Medical College, University of South China, Key Laboratory of Cancer Cellular and Molecular Pathology in Hunan Province, Hunan, Hengyang, 421001, China
| | - Qiang Zhao
- Department of Pathology, The First Affiliated Hospital of University of South China, Hunan, Hengyang, 421001, Hunan Province, China.
| | - Zhiwei Zhang
- Department of Pathology, The First Affiliated Hospital of University of South China, Hunan, Hengyang, 421001, Hunan Province, China; Cancer Research Institute of Hengyang Medical College, University of South China, Key Laboratory of Cancer Cellular and Molecular Pathology in Hunan Province, Hunan, Hengyang, 421001, China.
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3
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Immunopeptidome of hepatocytes isolated from patients with HBV infection and hepatocellular carcinoma. JHEP Rep 2022; 4:100576. [PMID: 36185575 PMCID: PMC9523389 DOI: 10.1016/j.jhepr.2022.100576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/28/2022] [Accepted: 08/16/2022] [Indexed: 01/01/2023] Open
Abstract
Background & Aims Antigen-specific immunotherapy is a promising strategy to treat HBV infection and hepatocellular carcinoma (HCC). To facilitate killing of malignant and/or infected hepatocytes, it is vital to know which T cell targets are presented by human leucocyte antigen (HLA)-I complexes on patient-derived hepatocytes. Here, we aimed to reveal the hepatocyte-specific HLA-I peptidome with emphasis on peptides derived from HBV proteins and tumour-associated antigens (TAA) to guide development of antigen-specific immunotherapy. Methods Primary human hepatocytes were isolated with high purity from (HBV-infected) non-tumour and HCC tissues using a newly designed perfusion-free procedure. Hepatocyte-derived HLA-bound peptides were identified by unbiased mass spectrometry (MS), after which source proteins were subjected to Gene Ontology and pathway analysis. HBV antigen and TAA-derived HLA peptides were searched for using targeted MS, and a selection of peptides was tested for immunogenicity. Results Using unbiased data-dependent acquisition (DDA), we acquired a high-quality HLA-I peptidome of 2 × 105 peptides that contained 8 HBV-derived peptides and 14 peptides from 8 known HCC-associated TAA that were exclusive to tumours. Of these, 3 HBV- and 12 TAA-derived HLA peptides were detected by targeted MS in the sample they were originally identified in by DDA. Moreover, 2 HBV- and 2 TAA-derived HLA peptides were detected in samples in which no identification was made using unbiased MS. Finally, immunogenicity was demonstrated for 5 HBV-derived and 3 TAA-derived peptides. Conclusions We present a first HLA-I immunopeptidome of isolated primary human hepatocytes, devoid of immune cells. Identified HBV-derived and TAA-derived peptides directly aid development of antigen-specific immunotherapy for chronic HBV infection and HCC. The described methodology can also be applied to personalise immunotherapeutic treatment of liver diseases in general. Lay summary Immunotherapy that aims to induce immune responses against a virus or tumour is a promising novel treatment option to treat chronic HBV infection and liver cancer. For the design of successful therapy, it is essential to know which fragments (i.e. peptides) of virus-derived and tumour-specific proteins are presented to the T cells of the immune system by diseased liver cells and are thus good targets for immunotherapy. Here, we have isolated liver cells from patients who have chronic HBV infection and/or liver cancer, analysed what peptides are presented by these cells, and assessed which peptides are able to drive immune responses. We developed a perfusion-free method to isolate primary hepatocytes that are depleted of immune cells. We derived a large-scale unbiased hepatocyte HLA ligandome from patients with HBV and/or HCC. The ligandome included peptides derived from HBV proteins and tumour-associated antigens (TAA). Using a targeted MS regime, the detection sensitivity of several HBV and TAA-derived peptides could be increased. Immunogenicity was demonstrated for a selection of TAA- and HBV-derived HLA peptides.
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Key Words
- Antigen presentation
- Cancer germline antigen
- Cancer testis antigen
- DDA, data-dependent acquisition
- GO, Gene Ontology
- HBV, Hepatitis B virus
- HCC, hepatocellular carcinoma
- HLA
- HLA, human leucocyte antigen
- IEDB, Immune Epitope Database
- IFNγ, interferon γ
- IP, immunoprecipitation
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- LSEC, liver sinusoidal cell
- Liver cancer
- MHC
- MS, mass spectrometry
- PBMCs, peripheral blood mononuclear cells
- PRM, parallel reaction monitoring
- Peptidome
- Pol, polymerase
- T cell epitope
- TAA, tumour-associated antigen
- Viral hepatitis
- cHBV, chronic HBV
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4
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Omenn GS, Lane L, Overall CM, Pineau C, Packer NH, Cristea IM, Lindskog C, Weintraub ST, Orchard S, Roehrl MHA, Nice E, Liu S, Bandeira N, Chen YJ, Guo T, Aebersold R, Moritz RL, Deutsch EW. The 2022 Report on the Human Proteome from the HUPO Human Proteome Project. J Proteome Res 2022; 22:1024-1042. [PMID: 36318223 PMCID: PMC10081950 DOI: 10.1021/acs.jproteome.2c00498] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan48109, United States.,Institute for Systems Biology, Seattle, Washington98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015Lausanne, Switzerland
| | | | - Charles Pineau
- French Institute of Health and Medical Research, 35042RENNESCedexFrance
| | - Nicolle H Packer
- Macquarie University, Sydney, New South Wales2109, Australia.,Griffith University's Institute for Glycomics, Sydney, New South Wales2109, Australia
| | | | | | - Susan T Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas78229-3900, United States
| | - Sandra Orchard
- EMBL-EBI, Hinxton, CambridgeshireCB10 1SD, United Kingdom
| | - Michael H A Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York10065, United States
| | - Edouard Nice
- Monash University, ClaytonVictoria3800, Australia
| | - Siqi Liu
- BGI Group, Shenzhen518083, P. R. China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California92093, United States
| | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei11529, Taiwan
| | - Tiannan Guo
- Westlake University Guomics Laboratory of Big Proteomic Data, Hangzhou310024, ZhejiangProvinceP. R. China
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington98109, United States
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5
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A Bioinformatics Approach to Mine the Microbial Proteomic Profile of COVID-19 Mass Spectrometry Data. Appl Microbiol 2022. [DOI: 10.3390/applmicrobiol2010010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mass spectrometry (MS) is one of the key technologies used in proteomics. The majority of studies carried out using proteomics have focused on identifying proteins in biological samples such as human plasma to pin down prognostic or diagnostic biomarkers associated with particular conditions or diseases. This study aims to quantify microbial (viral and bacterial) proteins in healthy human plasma. MS data of healthy human plasma were searched against the complete proteomes of all available viruses and bacteria. With this baseline established, the same strategy was applied to characterize the metaproteomic profile of different SARS-CoV-2 disease stages in the plasma of patients. Two SARS-CoV-2 proteins were detected with a high confidence and could serve as the early markers of SARS-CoV-2 infection. The complete bacterial and viral protein content in SARS-CoV-2 samples was compared for the different disease stages. The number of viral proteins was found to increase significantly with the progression of the infection, at the expense of bacterial proteins. This strategy can be extended to aid in the development of early diagnostic tests for other infectious diseases based on the presence of microbial biomarkers in human plasma samples.
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6
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Schulze S, Pohlschroder M. Proteomic Sample Preparation and Data Analysis in Line with the Archaeal Proteome Project. Methods Mol Biol 2022; 2522:287-300. [PMID: 36125757 DOI: 10.1007/978-1-0716-2445-6_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Despite the ecological, evolutionary and economical significance of archaea, key aspects of their cell biology, metabolic pathways, and adaptations to a wide spectrum of environmental conditions, remain to be elucidated. Proteomics allows for the system-wide analysis of proteins, their changes in abundance between different conditions, as well as their post-translational modifications, providing detailed insights into the function of proteins and archaeal cell biology. In this chapter, we describe a sample preparation and mass spectrometric analysis workflow that has been designed for Haloferax volcanii but can be applied to a broad range of archaeal species. Furthermore, proteomics experiments provide a wealth of data that is invaluable to various disciplines. Therefore, we previously initiated the Archaeal Proteome Project (ArcPP), a community project that combines the analysis of multiple datasets with expert knowledge in various fields of archaeal research. The corresponding bioinformatic analysis, allowing for the integration of new proteomics data into the ArcPP, as well as the interactive exploration of ArcPP results is also presented here. In combination, these protocols facilitate an optimized, detailed and collaborative approach to archaeal proteomics.
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Affiliation(s)
- Stefan Schulze
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Bu F, Cheng Q, Zhang Y, Zhang X, Yan K, Liu F, Li Z, Lu X, Ren Y, Liu S. Discovery of Missing Proteins from an Aneuploidy Cell Line Using a Proteogenomic Approach. J Proteome Res 2021; 20:5329-5339. [PMID: 34748338 DOI: 10.1021/acs.jproteome.1c00772] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
With the steadfast development of proteomic technology, the number of missing proteins (MPs) has been continuously shrinking, with approximately 1470 MPs that have not been explored yet. Due to this phenomenon, the discovery of MPs has been increasingly more difficult and elusive. In order to face this challenge, we have hypothesized that a stable aneuploid cell line with increased chromosomes serves as a useful material for assisting MP exploration. Ker-CT cell line with trisomy at chromosome 5 and 20 was selected for this purpose. With a combination strategy of RNA-Seq and LC-MS/MS, a total of 22 178 transcripts and 8846 proteins were identified in Ker-CT. Although the transcripts corresponding to 15 and 15 MP genes located at chromosome 5 and 20 were detected, none of the MPs were found in Ker-CT. Surprisingly, 3 MPs containing at least two unique non-nest peptides of length ≥9 amino acids were identified in Ker-CT, whose genes are located on chromosome 3 and 10, respectively. Furthermore, the 3 MPs were verified using the method of parallel reaction monitoring (PRM). These results suggest that the abnormal status of chromosomes may not only impact the expression of the corresponding genes in trisomy chromosomes, but also influence that of other chromosomes, which benefits MP discovery. The data obtained in this study are available via ProteomeXchange (PXD028647) and PeptideAtlas (PASS01700), respectively.
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Affiliation(s)
- Fanyu Bu
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China.,Department of BGI Education, School of Life Sciences, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
| | - Qingqiu Cheng
- Clinical Laboratory Center of Dongguan Eighth People's Hospital, Dongguan 523325, China
| | - Yuxing Zhang
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China.,Department of BGI Education, School of Life Sciences, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
| | - Xia Zhang
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China.,Department of BGI Education, School of Life Sciences, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
| | - Keqiang Yan
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China.,Department of BGI Education, School of Life Sciences, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
| | - Frank Liu
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Zelong Li
- Biological Resource Center of Plants, Animals and Microorganisms, China National Gene Bank, BGI-Shenzhen, Guangdong 518120, China
| | - Xiaomei Lu
- Clinical Laboratory Center of Dongguan Eighth People's Hospital, Dongguan 523325, China
| | - Yan Ren
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Siqi Liu
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China.,Department of BGI Education, School of Life Sciences, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
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8
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Roy AL, Wilder EL, Anderson JM. Validation of antibodies: Lessons learned from the Common Fund Protein Capture Reagents Program. SCIENCE ADVANCES 2021; 7:eabl7148. [PMID: 34757791 PMCID: PMC8580312 DOI: 10.1126/sciadv.abl7148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
Large-scale generation of protein capture reagents remains a technical challenge, but their generation is just the beginning. Validation is a critical, iterative process that yields different results for different uses. Independent, community-based validation offers the possibility of transparent data sharing, with use case–specific results made broadly available. This type of resource, which can grow as new validation data are obtained for an expanding group of reagents, provides a community resource that should accompany future reagent-generating efforts. To address a pressing need for antibodies or other reagents that recognize human proteins, the National Institutes of Health Common Fund launched the Protein Capture Reagents Program in 2010 as a pilot to target human transcription factors. Here, we describe lessons learned from this program concerning generation and validation of research reagents, which we believe are generally applicable for future research endeavors working in a similar space.
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Affiliation(s)
- Ananda L. Roy
- Office of Strategic Coordination, National Institutes of Health, Bethesda, MD 20892, USA
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth L. Wilder
- Office of Strategic Coordination, National Institutes of Health, Bethesda, MD 20892, USA
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
| | - James M. Anderson
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
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9
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van Wijk KJ, Leppert T, Sun Q, Boguraev SS, Sun Z, Mendoza L, Deutsch EW. The Arabidopsis PeptideAtlas: Harnessing worldwide proteomics data to create a comprehensive community proteomics resource. THE PLANT CELL 2021; 33:3421-3453. [PMID: 34411258 PMCID: PMC8566204 DOI: 10.1093/plcell/koab211] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/13/2021] [Indexed: 05/02/2023]
Abstract
We developed a resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis thaliana proteome, such as the significance of protein splice forms and post-translational modifications (PTMs), or simply to obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) data collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical, and biological metadata. Nearly 40 million out of ∼143 million MS/MS (tandem MS) spectra were matched to the reference genome Araport11, identifying ∼0.5 million unique peptides and 17,858 uniquely identified proteins (only isoform per gene) at the highest confidence level (false discovery rate 0.0004; 2 non-nested peptides ≥9 amino acid each), assigned canonical proteins, and 3,543 lower-confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified that were generated from pseudogenes, alternative start, stops, and/or splice variants, and small Open Reading Frames; these features should be considered when updating the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB, and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS/MS data.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, USA
- Authors for correspondence: (K.J.V.W.), (E.W.D.)
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, USA
| | - Sascha S Boguraev
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, USA
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
- Authors for correspondence: (K.J.V.W.), (E.W.D.)
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10
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Richards AM. Proteomic probing for markers and mechanisms in heart failure. Cardiovasc Res 2021; 117:2137-2139. [PMID: 34375389 DOI: 10.1093/cvr/cvab258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Indexed: 11/12/2022] Open
Affiliation(s)
- A Mark Richards
- Christchurch Heart Institute, University of Otago, Dunedin, New Zealand.,Cardiovascular Research Institute, National University of Singapore, Singapore, Singapore
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11
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Richards AM, Pemberton CJ. Urinary peptides in heart failure - the need for care with pees and cues. Eur J Heart Fail 2021; 23:1888-1890. [PMID: 34118184 DOI: 10.1002/ejhf.2269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 06/10/2021] [Indexed: 11/07/2022] Open
Affiliation(s)
- A Mark Richards
- Christchurch Heart Institute, University of Otago, Dunedin, New Zealand
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12
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Tonry C, McDonald K, Ledwidge M, Hernandez B, Glezeva N, Rooney C, Morrissey B, Pennington SR, Baugh JA, Watson CJ. Multiplexed measurement of candidate blood protein biomarkers of heart failure. ESC Heart Fail 2021; 8:2248-2258. [PMID: 33779078 PMCID: PMC8120401 DOI: 10.1002/ehf2.13320] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 02/01/2021] [Accepted: 03/12/2021] [Indexed: 12/13/2022] Open
Abstract
AIMS There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients. METHODS AND RESULTS Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001]. CONCLUSIONS This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.
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Affiliation(s)
- Claire Tonry
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Rd, Belfast, BT9 7BL, UK
| | - Ken McDonald
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Mark Ledwidge
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Belinda Hernandez
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Nadezhda Glezeva
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Cathy Rooney
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Brian Morrissey
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Stephen R Pennington
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - John A Baugh
- UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
| | - Chris J Watson
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Rd, Belfast, BT9 7BL, UK.,UCD Conway Institute, School of Medicine, University College Dublin, Dublin, Ireland
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13
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Digre A, Lindskog C. The Human Protein Atlas-Spatial localization of the human proteome in health and disease. Protein Sci 2021; 30:218-233. [PMID: 33146890 PMCID: PMC7737765 DOI: 10.1002/pro.3987] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/29/2020] [Accepted: 10/30/2020] [Indexed: 12/11/2022]
Abstract
For a complete understanding of a system's processes and each protein's role in health and disease, it is essential to study protein expression with a spatial resolution, as the exact location of proteins at tissue, cellular, or subcellular levels is tightly linked to protein function. The Human Protein Atlas (HPA) project is a large-scale initiative aiming at mapping the entire human proteome using antibody-based proteomics and integration of various other omics technologies. The publicly available knowledge resource www.proteinatlas.org is one of the world's most visited biological databases and has been extensively updated during the last few years. The current version is divided into six main sections, each focusing on particular aspects of the human proteome: (a) the Tissue Atlas showing the distribution of proteins across all major tissues and organs in the human body; (b) the Cell Atlas showing the subcellular localization of proteins in single cells; (c) the Pathology Atlas showing the impact of protein levels on survival of patients with cancer; (d) the Blood Atlas showing the expression profiles of blood cells and actively secreted proteins; (e) the Brain Atlas showing the distribution of proteins in human, mouse, and pig brain; and (f) the Metabolic Atlas showing the involvement of proteins in human metabolism. The HPA constitutes an important resource for further understanding of human biology, and the publicly available datasets hold much promise for integration with other emerging efforts focusing on single cell analyses, both at transcriptomic and proteomic level.
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Affiliation(s)
- Andreas Digre
- Department of Immunology, Genetics and PathologyRudbeck Laboratory, Uppsala UniversityUppsalaSweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and PathologyRudbeck Laboratory, Uppsala UniversityUppsalaSweden
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14
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Rehiman SH, Lim SM, Lim FT, Chin AV, Tan MP, Kamaruzzaman SB, Ramasamy K, Abdul Majeed AB. Fibrinogen isoforms as potential blood-based biomarkers of Alzheimer's disease using a proteomics approach. Int J Neurosci 2020; 132:1014-1025. [PMID: 33280461 DOI: 10.1080/00207454.2020.1860038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Objective: Alzheimer's disease (AD), the commonest form of dementia which is characterized by progressive decline in cognitive function, can only be definitively diagnosed after death. Although biomarkers may aid diagnosis, currently available AD biomarkers, which are predominantly based on cerebrospinal fluid and neuroimaging facilities, are either invasive or costly. Blood-based biomarkers for AD diagnosis are highly sought after due to its practicality at the clinic. This study was undertaken to determine the differential protein expression in plasma amongst Malaysian AD, mild cognitive impairment (MCI) and non-AD individuals. Methods: A proteomic approach which utilized two-dimensional differential in gel electrophoresis (2 D DIGE) was performed for blood samples from 15 AD, 14 MCI and 15 non-AD individuals. Results: Mass spectrometry (MS)-based protein identification via MALDI ToF/ToF showed that fibrinogen-β-chain (spot 64) and fibrinogen-γ-chain (spot 91) with differential expression ratio >1.5 were significantly upregulated (p < 0.05) in AD patients when compared to non-AD individuals. Further data analysis using Pearson correlation found that the upregulated fibrinogen-γ-chain was weakly but significantly (p < 0.05) and inversely correlated with cognitive decline. Conclusion: Fibrinogen isoforms may play important roles in the vascular pathology of AD as well as neuroinflammation. As such, fibrinogen appears to be a promising blood-based biomarker for AD. Further validation of the present findings in larger population is now warranted.
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Affiliation(s)
- Siti Hajar Rehiman
- Collaborative Drug Discovery Research (CDDR) and Brain Degeneration and Therapeutics Research Group, Faculty of Pharmacy, University Teknologi MARA (UiTM) Cawangan Selangor, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
| | - Siong Meng Lim
- Collaborative Drug Discovery Research (CDDR) and Brain Degeneration and Therapeutics Research Group, Faculty of Pharmacy, University Teknologi MARA (UiTM) Cawangan Selangor, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
| | - Fei Tieng Lim
- Collaborative Drug Discovery Research (CDDR) and Brain Degeneration and Therapeutics Research Group, Faculty of Pharmacy, University Teknologi MARA (UiTM) Cawangan Selangor, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
| | - Ai-Vyrn Chin
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Maw Pin Tan
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Shahrul Bahyah Kamaruzzaman
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kalavathy Ramasamy
- Collaborative Drug Discovery Research (CDDR) and Brain Degeneration and Therapeutics Research Group, Faculty of Pharmacy, University Teknologi MARA (UiTM) Cawangan Selangor, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
| | - Abu Bakar Abdul Majeed
- Collaborative Drug Discovery Research (CDDR) and Brain Degeneration and Therapeutics Research Group, Faculty of Pharmacy, University Teknologi MARA (UiTM) Cawangan Selangor, Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
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15
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González-Gomariz J, Serrano G, Tilve-Álvarez CM, Corrales FJ, Guruceaga E, Segura V. UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm. J Proteome Res 2020; 19:4795-4807. [PMID: 33155801 DOI: 10.1021/acs.jproteome.0c00364] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a "guilt-by-association" analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder.proteored.org) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available (https://github.com/ubioinformat/UPEfinder).
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Affiliation(s)
| | - Guillermo Serrano
- Bioinformatics Platform, CIMA University of Navarra, Pamplona E-31008, Spain
| | - Carlos M Tilve-Álvarez
- Fundación Profesor Nóvoa-Santos, Instituto de Investigación Biomédica da Coruña, Coruña E-15006, Spain
| | - Fernando J Corrales
- Proteomics Unit, National Center for Biotechnology, CSIC, Madrid E-28049, Spain
| | - Elizabeth Guruceaga
- IdiSNA, Navarra Institute for Health Research, Pamplona E-31008, Spain.,Bioinformatics Platform, CIMA University of Navarra, Pamplona E-31008, Spain
| | - Victor Segura
- Tracasa Instrumental, Sarriguren E-31621, Spain.,Sección de Ingeniería del Dato, Dirección General de Telecomunicaciones y Digitalización, Gobierno de Navarra, Sarriguren E-31621, Spain
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16
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Zhang Y, Zhang K, Bu F, Hao P, Yang H, Liu S, Ren Y. D283 Med, a Cell Line Derived from Peritoneal Metastatic Medulloblastoma: A Good Choice for Missing Protein Discovery. J Proteome Res 2020; 19:4857-4866. [DOI: 10.1021/acs.jproteome.0c00743] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Yuanliang Zhang
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
- BGI-Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Keren Zhang
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
- BGI-Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Fanyu Bu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, China
| | - Piliang Hao
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Huanming Yang
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Siqi Liu
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
- BGI-Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Yan Ren
- BGI-Shenzhen, Beishan Industrial Zone 11th Building, Yantian District, Shenzhen, Guangdong 518083, China
- BGI-Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
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17
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Chua XY, Aballo T, Elnemer W, Tran M, Salomon A. Quantitative Interactomics of Lck-TurboID in Living Human T Cells Unveils T Cell Receptor Stimulation-Induced Proximal Lck Interactors. J Proteome Res 2020; 20:715-726. [PMID: 33185455 DOI: 10.1021/acs.jproteome.0c00616] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
While Lck has been widely recognized to play a pivotal role in the initiation of the T cell receptor (TCR) signaling pathway, an understanding of the precise regulation of Lck in T cells upon TCR activation remains elusive. Investigation of protein-protein interaction (PPI) using proximity labeling techniques such as TurboID has the potential to provide valuable molecular insights into Lck regulatory networks. By expressing Lck-TurboID in Jurkat T cells, we have uncovered a dynamic, short-range Lck protein interaction network upon 30 min of TCR stimulation. In this novel application of TurboID, we detected 27 early signaling-induced Lck-proximal interactors in living T cells, including known and novel Lck interactors, validating the discovery power of this tool. Our results revealed previously unappreciated Lck PPI which may be associated with cytoskeletal rearrangement, ubiquitination of TCR signaling proteins, activation of the mitogen-activated protein kinase cascade, coalescence of the LAT signalosome, and formation of the immunological synapse. In this study, we demonstrated for the first time in immune cells and for the kinase Lck that TurboID can be utilized to unveil PPI dynamics in living cells at a time scale consistent with early TCR signaling. Data are available via ProteomeXchange with identifier PXD020759.
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Affiliation(s)
- Xien Yu Chua
- Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, Rhode Island, United States
| | - Timothy Aballo
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, United States
| | - William Elnemer
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, United States
| | - Melanie Tran
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, United States
| | - Arthur Salomon
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, United States
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18
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Lachén-Montes M, Mendizuri N, Ausín K, Pérez-Mediavilla A, Azkargorta M, Iloro I, Elortza F, Kondo H, Ohigashi I, Ferrer I, de la Torre R, Robledo P, Fernández-Irigoyen J, Santamaría E. Smelling the Dark Proteome: Functional Characterization of PITH Domain-Containing Protein 1 (C1orf128) in Olfactory Metabolism. J Proteome Res 2020; 19:4826-4843. [PMID: 33185454 DOI: 10.1021/acs.jproteome.0c00452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The Human Proteome Project (HPP) consortium aims to functionally characterize the dark proteome. On the basis of the relevance of olfaction in early neurodegeneration, we have analyzed the dark proteome using data mining in public resources and omics data sets derived from the human olfactory system. Multiple dark proteins localize at synaptic terminals and may be involved in amyloidopathies such as Alzheimer's disease (AD). We have characterized the dark PITH domain-containing protein 1 (PITHD1) in olfactory metabolism using bioinformatics, proteomics, in vitro and in vivo studies, and neuropathology. PITHD1-/- mice exhibit olfactory bulb (OB) proteome changes related to synaptic transmission, cognition, and memory. OB PITHD1 expression increases with age in wild-type (WT) mice and decreases in Tg2576 AD mice at late stages. The analysis across 6 neurological disorders reveals that olfactory tract (OT) PITHD1 is specifically upregulated in human AD. Stimulation of olfactory neuroepithelial (ON) cells with PITHD1 alters the ON phosphoproteome, modifies the proliferation rate, and induces a pro-inflammatory phenotype. This workflow applied by the Spanish C-HPP and Human Brain Proteome Project (HBPP) teams across the ON-OB-OT axis can be adapted as a guidance to decipher functional features of dark proteins. Data are available via ProteomeXchange with identifiers PXD018784 and PXD021634.
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Affiliation(s)
- Mercedes Lachén-Montes
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Naroa Mendizuri
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Karina Ausín
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Alberto Pérez-Mediavilla
- IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain.,Neurobiology of Alzheimer's Disease, Department of Biochemistry, Center for Applied Medical Research (CIMA), Neurosciences Division, University of Navarra, 31008 Pamplona, Spain
| | - Mikel Azkargorta
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Ibon Iloro
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Felix Elortza
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Hiroyuki Kondo
- Division of Experimental Immunology, Institute of Advanced Medical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Izumi Ohigashi
- Division of Experimental Immunology, Institute of Advanced Medical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Isidre Ferrer
- Bellvitge Biomedical Research Institute (IDIBELL), 08908 Hospitalet de Llobregat, Spain.,CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 28029 Madrid, Spain.,Department of Pathology and Experimental Therapeutics, University of Barcelona, 08908 Hospitalet de Llobregat, Spain.,Institute of Neurosciences, University of Barcelona, 08007 Barcelona, Spain
| | - Rafael de la Torre
- Integrative Pharmacology and Systems Neuroscience Research Group, Neurosciences Research Program, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain.,Department of Experimental and Health Sciences, Pompeu Fabra University (CEXS-UPF), 08002 Barcelona, Spain.,School of Medicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.,CIBER de Fisiopatología de la Obesidad y Nutrición (CB06/03), CIBEROBN, 28029 Madrid, Spain
| | - Patricia Robledo
- Integrative Pharmacology and Systems Neuroscience Research Group, Neurosciences Research Program, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain.,Department of Experimental and Health Sciences, Pompeu Fabra University (CEXS-UPF), 08002 Barcelona, Spain
| | - Joaquín Fernández-Irigoyen
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
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19
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Wu S, Sun J, Wang X, Xu F, Chi H, Li Y, Zhong B, Xie Y, Yan Z, Chang L, Wang D, He F, Wu J, Zhang Y, Xu P. Open-pFind Verified Four Missing Proteins from Multi-Tissues. J Proteome Res 2020; 19:4808-4814. [PMID: 33172275 DOI: 10.1021/acs.jproteome.0c00370] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The Chromosome-Centric Human Proteome Project (C-HPP) was launched in 2012 to perfect the annotation of human protein existence by identifying stronger evidence of the expression of missing proteins (MPs) at the protein level. After an 8 year effort all over the world, the number of MPs in the neXtProt database significantly decreased from 5511 (2012-02-24) to 1899 (2020-01-17). It is now more difficult to provide confident evidence of the remaining MPs because of their specific characteristics, including low abundance, low molecular weight, unexpected modifications, transmembrane structure, tissue-expression specificity, and so on. A higher resolution mass spectrometry (MS) interpretation engine might provide an opportunity to identify these buried MPs in complex samples by the combination with multi-tissue large-scale proteomics. In this study, open-pFind was used to dig MPs from 20 pairs of healthy human tissues by Wang et al. ( Mol. Syst. Biol. 2019, 15 (2), e8503) combined with our large-scale testis data set digested by three enzymes (Glu-C, Lys-C, and trypsin) with specificity for different amino acid residues ( J. Proteme Res. 2019, 18 (12), 4189-4196). A total of 1 535 536 peptides with 17 283 477 peptide-spectrum matches (PSMs) were mapped to 14 279 protein entries at a false discovery rate of <1% at the PSM, peptide, and protein levels. A total of 103 MP candidates were identified, among which 86 candidates had more unique peptide numbers compared with our single testis tissue. After rigorous screening, manual checks, peptide synthesis, and matching with documented peptides from PeptideAtlas, we validated four MPs, P0C7T8 (duodenum and small intestine), Q8WWZ4 (stomach and rectum), Q8IV35 (fallopian tube), and O14921 (tonsil), at the protein level. All MS raw files have been deposited to the ProteomeXchange with identifier PXD021391.
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Affiliation(s)
- Shujia Wu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Jinshuai Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Xi Wang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing 100080, China
| | - Feng Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing 100080, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Bowen Zhong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Yuping Xie
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Zhonghua Yan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Dongxue Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Junzhu Wu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Ping Xu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
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20
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Sivertsson Å, Lindström E, Oksvold P, Katona B, Hikmet F, Vuu J, Gustavsson J, Sjöstedt E, von Feilitzen K, Kampf C, Schwenk JM, Uhlén M, Lindskog C. Enhanced Validation of Antibodies Enables the Discovery of Missing Proteins. J Proteome Res 2020; 19:4766-4781. [PMID: 33170010 PMCID: PMC7723238 DOI: 10.1021/acs.jproteome.0c00486] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
![]()
The localization of proteins at a
tissue- or cell-type-specific
level is tightly linked to the protein function. To better understand
each
protein’s role in cellular systems, spatial information constitutes
an important complement to quantitative data. The standard methods
for determining the spatial distribution of proteins in single cells
of complex tissue samples make use of antibodies. For a stringent
analysis of the human proteome, we used orthogonal methods and independent
antibodies to validate 5981 antibodies that show the expression of
3775 human proteins across all major human tissues. This enhanced
validation uncovered 56 proteins corresponding to the group of “missing
proteins” and 171 proteins of unknown function. The presented
strategy will facilitate further discussions around criteria for evidence
of protein existence based on immunohistochemistry and serves as a
useful guide to identify candidate proteins for integrative studies
with quantitative proteomics methods.
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Affiliation(s)
- Åsa Sivertsson
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Emil Lindström
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Per Oksvold
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Borbala Katona
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Feria Hikmet
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Jimmy Vuu
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Jonas Gustavsson
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
| | - Evelina Sjöstedt
- Department of Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Caroline Kampf
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden.,Atlas Antibodies AB, 16869 Bromma, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 17121 Stockholm, Sweden.,Department of Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, 75185 Uppsala, Sweden
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21
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Omenn GS, Lane L, Overall CM, Cristea IM, Corrales FJ, Lindskog C, Paik YK, Van Eyk JE, Liu S, Pennington SR, Snyder MP, Baker MS, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project. J Proteome Res 2020; 19:4735-4746. [PMID: 32931287 DOI: 10.1021/acs.jproteome.0c00485] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States.,Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | | | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | | | | | - Mark S Baker
- Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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22
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Adhikari S, Nice EC, Deutsch EW, Lane L, Omenn GS, Pennington SR, Paik YK, Overall CM, Corrales FJ, Cristea IM, Van Eyk JE, Uhlén M, Lindskog C, Chan DW, Bairoch A, Waddington JC, Justice JL, LaBaer J, Rodriguez H, He F, Kostrzewa M, Ping P, Gundry RL, Stewart P, Srivastava S, Srivastava S, Nogueira FCS, Domont GB, Vandenbrouck Y, Lam MPY, Wennersten S, Vizcaino JA, Wilkins M, Schwenk JM, Lundberg E, Bandeira N, Marko-Varga G, Weintraub ST, Pineau C, Kusebauch U, Moritz RL, Ahn SB, Palmblad M, Snyder MP, Aebersold R, Baker MS. A high-stringency blueprint of the human proteome. Nat Commun 2020; 11:5301. [PMID: 33067450 PMCID: PMC7568584 DOI: 10.1038/s41467-020-19045-9] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Subash Adhikari
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
| | - Edouard C Nice
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
- Faculty of Medicine, Nursing and Health Sciences, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
| | - Eric W Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Lydie Lane
- Faculty of Medicine, SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, University of Geneva, CMU, Michel-Servet 1, 1211, Geneva, Switzerland
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA
| | - Stephen R Pennington
- UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, University College Dublin, Dublin, Ireland
| | - Young-Ki Paik
- Yonsei Proteome Research Center, 50 Yonsei-ro, Sudaemoon-ku, Seoul, 120-749, South Korea
| | | | - Fernando J Corrales
- Functional Proteomics Laboratory, Centro Nacional de Biotecnología-CSIC, Proteored-ISCIII, 28049, Madrid, Spain
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jennifer E Van Eyk
- Cedars Sinai Medical Center, Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Los Angeles, CA, 90048, USA
| | - Mathias Uhlén
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Cecilia Lindskog
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, 75185, Uppsala, Sweden
| | - Daniel W Chan
- Department of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21224, USA
| | - Amos Bairoch
- Faculty of Medicine, SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, University of Geneva, CMU, Michel-Servet 1, 1211, Geneva, Switzerland
| | - James C Waddington
- UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, University College Dublin, Dublin, Ireland
| | - Joshua L Justice
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Joshua LaBaer
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Markus Kostrzewa
- Bruker Daltonik GmbH, Microbiology and Diagnostics, Fahrenheitstrasse, 428359, Bremen, Germany
| | - Peipei Ping
- Cardiac Proteomics and Signaling Laboratory, Department of Physiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Rebekah L Gundry
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Peter Stewart
- Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | | | - Sudhir Srivastava
- Cancer Biomarkers Research Branch, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Suite 5E136, Rockville, MD, 20852, USA
| | - Fabio C S Nogueira
- Proteomics Unit and Laboratory of Proteomics, Institute of Chemistry, Federal University of Rio de Janeiro, Av Athos da Silveria Ramos, 149, 21941-909, Rio de Janeiro, RJ, Brazil
| | - Gilberto B Domont
- Proteomics Unit and Laboratory of Proteomics, Institute of Chemistry, Federal University of Rio de Janeiro, Av Athos da Silveria Ramos, 149, 21941-909, Rio de Janeiro, RJ, Brazil
| | - Yves Vandenbrouck
- University of Grenoble Alpes, Inserm, CEA, IRIG-BGE, U1038, 38000, Grenoble, France
| | - Maggie P Y Lam
- Departments of Medicine-Cardiology and Biochemistry and Molecular Genetics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Consortium for Fibrosis Research and Translation, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Sara Wennersten
- Division of Cardiology, Department of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Juan Antonio Vizcaino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Marc Wilkins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Nuno Bandeira
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA, 92093-0404, USA
| | | | - Susan T Weintraub
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center San Antonio, UT Health, 7703 Floyd Curl Drive, San Antonio, TX, 78229-3900, USA
| | - Charles Pineau
- University of Rennes, Inserm, EHESP, IREST, UMR_S 1085, F-35042, Rennes, France
| | - Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Seong Beom Ahn
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
| | - Magnus Palmblad
- Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Ruedi Aebersold
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
- Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Mark S Baker
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia.
- Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA.
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23
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Nice EC. The status of proteomics as we enter the 2020s: Towards personalised/precision medicine. Anal Biochem 2020; 644:113840. [PMID: 32745541 DOI: 10.1016/j.ab.2020.113840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 06/06/2020] [Accepted: 06/18/2020] [Indexed: 12/18/2022]
Abstract
The last decade has seen many major advances in proteomics, with over 70,000 publications in the field since 2010. A comprehensive omics toolbox has been developed facilitating rapid in depth analysis of the human proteome. Such studies are advancing our understanding of the biology of both health and disease. The combination of proteomics with other omics platforms (the omics pipeline), in particular proteogenomics, is giving important insights to the molecular changes leading to disease, covering the spectrum from genotype to phenotype and identifying potential biomarkers for disease detection, surveillance and monitoring, and revealing potential new drug targets. Discovery-based finding are now being translated to clinical application, supporting the rollout of precision/personalised medicine. This perspective has focused on twelve areas of importance that have fuelled the field. Recent exemplars are given to illustrate this and show how, together with some emerging technologies, they are anticipated to lead to further advances in the field. However, hurdles still remain to be overcome, especially in the area of Big Data analysis.
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Affiliation(s)
- Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, 3800, Australia.
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24
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Understanding the proteome encoded by "non-coding RNAs": new insights into human genome. SCIENCE CHINA. LIFE SCIENCES 2020; 63:986-995. [PMID: 32318910 DOI: 10.1007/s11427-019-1677-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/12/2020] [Indexed: 01/19/2023]
Abstract
A great number of non-coding RNAs (ncRNAs) account for the majority of the genome. The translation of these ncRNAs has been noted but seriously underestimated due to both technological and theoretical limitations. Based on the development of ribosome profiling (Ribo-seq), full length translating RNA analysis (RNC-seq) and mass spectrometry technology, more and more ncRNAs are being found to be translated in different organism, and some of them can produce functional peptides. While recently, not only individual new functional proteins, but also a new proteome have been experimentally discovered to be encoded by endogenous lncRNAs and circRNAs. These new proteins are of biological significance, suggesting the connection of the translation of ncRNAs to human physiology and diseases. Therefore, an in-depth and systematic understanding of the coding capabilities of ncRNAs is necessary for basic biology and medicine. In this review, we summarize the advances in the field of discovering this new proteome, i.e. "ncRNA-coded" proteins.
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25
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Taunk K, Kalita B, Kale V, Chanukuppa V, Naiya T, Zingde SM, Rapole S. The development and clinical applications of proteomics: an Indian perspective. Expert Rev Proteomics 2020; 17:433-451. [PMID: 32576061 DOI: 10.1080/14789450.2020.1787157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic. AREAS COVERED This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed. EXPERT OPINION The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.
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Affiliation(s)
- Khushman Taunk
- Proteomics Lab, National Centre for Cell Science , Pune, Maharashtra, India.,Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal , Haringhata, West Bengal, India
| | - Bhargab Kalita
- Proteomics Lab, National Centre for Cell Science , Pune, Maharashtra, India
| | - Vaikhari Kale
- Proteomics Lab, National Centre for Cell Science , Pune, Maharashtra, India
| | | | - Tufan Naiya
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal , Haringhata, West Bengal, India
| | - Surekha M Zingde
- CH3-53, Kendriya Vihar, Sector 11, Kharghar , Navi Mumbai, Maharashtra, India
| | - Srikanth Rapole
- Proteomics Lab, National Centre for Cell Science , Pune, Maharashtra, India
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26
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The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics. Nat Commun 2020; 11:3145. [PMID: 32561711 PMCID: PMC7305310 DOI: 10.1038/s41467-020-16784-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 05/18/2020] [Indexed: 11/08/2022] Open
Abstract
While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii, we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N-glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.
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27
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From personalised nutrition to precision medicine: the rise of consumer genomics and digital health. Proc Nutr Soc 2020; 79:300-310. [PMID: 32468984 DOI: 10.1017/s0029665120006977] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of 'big data' and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.
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28
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Affiliation(s)
- Monique Zahn-Zabal
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, Geneva, Switzerland
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, Geneva, Switzerland
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29
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Nice EC. The omics revolution: beyond genomics. A meeting report. Clin Proteomics 2020; 17:1. [PMID: 31997976 PMCID: PMC6979067 DOI: 10.1186/s12014-020-9266-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/14/2020] [Indexed: 11/30/2022] Open
Abstract
The "omics revolution: beyond genomics" satellite meeting, run under the auspices of the Australian Peptide Association, The Human Proteome Organisation (HUPO) and the HUPO Australia/New Zealand Chromosome 7 initiative, was held at the Oaks Resort, Port Douglas, Queensland, Australia, on 8th September 2019, immediately prior to the 13th Australian Peptide Conference. The meeting, which had over 100 participants representing Australasia, Europe and America, focused on recent advances in omics-related technologies, including mass spectrometry, biosensors and CryoEM, which will assist in the clinical translation of proteomics towards precision/personalized medicine. An overview of the conference and a summary of the oral presentations are presented.
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Affiliation(s)
- E. C. Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800 Australia
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30
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Archakov AI, Aseev AL, Bykov VA, Grigoriev AI, Govorun VM, Ilgisonis EV, Ivanov YD, Ivanov VT, Kiseleva OI, Kopylov AT, Lisitsa AV, Mazurenko SN, Makarov AA, Naryzhny SN, Pleshakova TO, Ponomarenko EA, Poverennaya EV, Pyatnitskii MA, Sagdeev RZ, Skryabin KG, Zgoda VG. Challenges of the Human Proteome Project: 10-Year Experience of the Russian Consortium. J Proteome Res 2019; 18:4206-4214. [PMID: 31599598 DOI: 10.1021/acs.jproteome.9b00358] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.
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Affiliation(s)
| | | | | | | | - Vadim M Govorun
- Federal Research and Clinical Center of Physical-Chemical Medicine , Moscow 119435 , Russia
| | | | - Yuri D Ivanov
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
| | - Vadim T Ivanov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow 117997 , Russia
| | | | | | | | - Sergey N Mazurenko
- Joint Institute for Nuclear Research , Dubna, Moscow region 141980 , Russia
| | | | | | | | | | | | | | - Renad Z Sagdeev
- International Tomography Center , Novosibirsk 630090 , Russia
| | - Konstantin G Skryabin
- The Federal Research Centre "Fundamentals of Biotechnology" , Moscow 119071 , Russia
| | - Victor G Zgoda
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
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31
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Deutsch EW, Lane L, Overall CM, Bandeira N, Baker MS, Pineau C, Moritz RL, Corrales F, Orchard S, Van Eyk JE, Paik YK, Weintraub ST, Vandenbrouck Y, Omenn GS. Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0. J Proteome Res 2019; 18:4108-4116. [PMID: 31599596 DOI: 10.1021/acs.jproteome.9b00542] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology , Seattle , Washington 98109 , United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine , University of Geneva , CMU, Michel Servet 1 , 1211 Geneva 4 , Switzerland
| | - Christopher M Overall
- Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry , The University of British Columbia , Vancouver , BC V6T 1Z4 , Canada
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Science , Macquarie University , Macquarie Park , NSW 2109 , Australia
| | - Charles Pineau
- Univ. Rennes , Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) - UMR_S 1085 , F-35042 Rennes cedex , France
| | - Robert L Moritz
- Institute for Systems Biology , Seattle , Washington 98109 , United States
| | - Fernando Corrales
- Functional Proteomics Laboratory, Centro Nacional de Biotecnología , Spanish Research Council , ProteoRed-.ISCIII , Madrid 117 , Spain
| | - Sandra Orchard
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus , Hinxton , Cambridge CB10 1SD , U.K
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California 90048 , United States
| | - Young-Ki Paik
- Yonsei Proteome Research Center , Yonsei University , 50 Yonsei-ro , Sudaemoon-ku , Seoul 03720 , Korea
| | - Susan T Weintraub
- The University of Texas Health Science Center at San Antonio , San Antonio , Texas 78229 , United States
| | - Yves Vandenbrouck
- Univ. Grenoble Alpes , CEA, INSERM, IRIG-BGE, U1038 , F-38000 Grenoble , France
| | - Gilbert S Omenn
- Institute for Systems Biology , Seattle , Washington 98109 , United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health , University of Michigan , Ann Arbor , Michigan 48109-2218 , United States
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Ignjatovic V, Geyer PE, Palaniappan KK, Chaaban JE, Omenn GS, Baker MS, Deutsch EW, Schwenk JM. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J Proteome Res 2019; 18:4085-4097. [PMID: 31573204 DOI: 10.1021/acs.jproteome.9b00503] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.
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Affiliation(s)
- Vera Ignjatovic
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia.,Department of Paediatrics , The University of Melbourne , Parkville , VIC 3052 , Australia
| | - Philipp E Geyer
- NNF Center for Protein Research, Faculty of Health Sciences , University of Copenhagen , 2200 Copenhagen , Denmark.,Department of Proteomics and Signal Transduction , Max Planck Institute of Biochemistry , 82152 Martinsried , Germany
| | - Krishnan K Palaniappan
- Freenome , 259 East Grand Avenue , South San Francisco , California 94080 , United States
| | - Jessica E Chaaban
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Human Genetics, and Internal Medicine and School of Public Health , University of Michigan , 100 Washtenaw Avenue , Ann Arbor , Michigan 48109-2218 , United States
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences , Macquarie University , 75 Talavera Road , North Ryde , NSW 2109 , Australia
| | - Eric W Deutsch
- Institute for Systems Biology , 401 Terry Avenue North , Seattle , Washington 98109 , United States
| | - Jochen M Schwenk
- Affinity Proteomics, SciLifeLab , KTH Royal Institute of Technology , 171 65 Stockholm , Sweden
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Bell PA, Solis N, Kizhakkedathu JN, Matthew I, Overall CM. Proteomic and N-Terminomic TAILS Analyses of Human Alveolar Bone Proteins: Improved Protein Extraction Methodology and LysargiNase Digestion Strategies Increase Proteome Coverage and Missing Protein Identification. J Proteome Res 2019; 18:4167-4179. [DOI: 10.1021/acs.jproteome.9b00445] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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