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Ohno R, Mainka M, Kirchhoff R, Hartung NM, Schebb NH. Sterol Derivatives Specifically Increase Anti-Inflammatory Oxylipin Formation in M2-like Macrophages by LXR-Mediated Induction of 15-LOX. Molecules 2024; 29:1745. [PMID: 38675565 PMCID: PMC11052137 DOI: 10.3390/molecules29081745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/29/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
The understanding of the role of LXR in the regulation of macrophages during inflammation is emerging. Here, we show that LXR agonist T09 specifically increases 15-LOX abundance in primary human M2 macrophages. In time- and dose-dependent incubations with T09, an increase of 3-fold for ALOX15 and up to 15-fold for 15-LOX-derived oxylipins was observed. In addition, LXR activation has no or moderate effects on the abundance of macrophage marker proteins such as TLR2, TLR4, PPARγ, and IL-1RII, as well as surface markers (CD14, CD86, and CD163). Stimulation of M2-like macrophages with FXR and RXR agonists leads to moderate ALOX15 induction, probably due to side activity on LXR. Finally, desmosterol, 24(S),25-Ep cholesterol and 22(R)-OH cholesterol were identified as potent endogenous LXR ligands leading to an ALOX15 induction. LXR-mediated ALOX15 regulation is a new link between the two lipid mediator classes sterols, and oxylipins, possibly being an important tool in inflammatory regulation through anti-inflammatory oxylipins.
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Affiliation(s)
| | | | | | | | - Nils Helge Schebb
- Chair of Food Chemistry, Faculty of Mathematics and Natural Sciences, University of Wuppertal, Gaußstr. 20, 42119 Wuppertal, Germany
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2
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Loganathan L, Jeyaraman J, Muthusamy K. HTNpedia: A Knowledge Base for Hypertension Research. Comb Chem High Throughput Screen 2024; 27:745-753. [PMID: 37202885 DOI: 10.2174/1386207326666230518162439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/27/2023] [Accepted: 03/31/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and renal failure. It is reported to be the fourth leading disease that leads to death worldwide. OBJECTIVES Currently, there is no active operational knowledge base or database for hypertension or cardiovascular illness. METHODS The primary data source was retrieved from the research outputs obtained from our laboratory team working on hypertension research. We have presented a preliminary dataset and external links to the public repository for detailed analysis to readers. RESULTS As a result, HTNpedia was created to provide information regarding hypertension-related proteins and genes. CONCLUSION The complete webpage is accessible via www.mkarthikeyan.bioinfoau.org/HTNpedia.
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Affiliation(s)
- Lakshmanan Loganathan
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Jeyakanthan Jeyaraman
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Karthikeyan Muthusamy
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
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3
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Halder A, Biswas D, Chauhan A, Saha A, Auromahima S, Yadav D, Nissa MU, Iyer G, Parihari S, Sharma G, Epari S, Shetty P, Moiyadi A, Ball GR, Srivastava S. A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors. Clin Proteomics 2023; 20:41. [PMID: 37770851 PMCID: PMC10540342 DOI: 10.1186/s12014-023-09426-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.
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Affiliation(s)
- Ankit Halder
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Deeptarup Biswas
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Aparna Chauhan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Adrita Saha
- Motilal Nehru National Institute of Technology, Allahabad, 211004, UP, India
| | - Shreeman Auromahima
- Department of Bioscience & Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Deeksha Yadav
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, New Delhi, 110025, India
| | - Mehar Un Nissa
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Gayatri Iyer
- Koita Centre for Digital Health, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Shashwati Parihari
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Gautam Sharma
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Centre, Mumbai, India
| | - Prakash Shetty
- Department of Neurosurgery, Tata Memorial Centre, Mumbai, India
| | | | - Graham Roy Ball
- Medical Technology Research Centre, Anglia Ruskin University, Cambridge Campus, East Rd, Cambridge, CB1 1PT, UK
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 185 Berry St., Suite 290, San Francisco, CA, 94107, USA.
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4
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Foreman J, Perrett D, Mazaika E, Hunt SE, Ware JS, Firth HV. DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data. Annu Rev Genomics Hum Genet 2023; 24:151-176. [PMID: 37285546 PMCID: PMC7615097 DOI: 10.1146/annurev-genom-102822-100509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.
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Affiliation(s)
- Julia Foreman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom; ,
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Daniel Perrett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom; ,
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Erica Mazaika
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; ,
| | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom; ,
| | - James S Ware
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; ,
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, United Kingdom
- East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom;
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5
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Yildiz P, Ozcan S. A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis. Talanta 2023; 265:124878. [PMID: 37392709 DOI: 10.1016/j.talanta.2023.124878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/30/2023] [Accepted: 06/22/2023] [Indexed: 07/03/2023]
Abstract
Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.
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Affiliation(s)
- Pelin Yildiz
- Department of Chemistry, Middle East Technical University (METU), 06800, Ankara, Turkiye; Nanografi Nanotechnology Co, Middle East Technical University (METU) Technopolis, 06531, Ankara, Turkiye
| | - Sureyya Ozcan
- Department of Chemistry, Middle East Technical University (METU), 06800, Ankara, Turkiye; Cancer Systems Biology Laboratory (CanSyL), Middle East Technical University (METU), 06800, Ankara, Turkiye.
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6
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Zhang J, Kanoatov M, Jarvi K, Gauthier-Fisher A, Moskovtsev SI, Librach C, Drabovich AP. Germ cell-specific proteins AKAP4 and ASPX facilitate identification of rare spermatozoa in non-obstructive azoospermia. Mol Cell Proteomics 2023; 22:100556. [PMID: 37087050 DOI: 10.1016/j.mcpro.2023.100556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 04/06/2023] [Accepted: 04/16/2023] [Indexed: 04/24/2023] Open
Abstract
Non-obstructive azoospermia (NOA), the most severe form of male infertility, could be treated with intra-cytoplasmic sperm injection, providing spermatozoa were retrieved with the microdissection testicular sperm extraction (mTESE). We hypothesized that testis- and germ cell-specific proteins would facilitate flow cytometry-assisted identification of rare spermatozoa in semen cell pellets of NOA patients, thus enabling non-invasive diagnostics prior to mTESE. Data mining, targeted proteomics, and immunofluorescent microscopy identified and verified a panel of highly testis-specific proteins expressed at the continuum of germ cell differentiation. Late germ cell-specific proteins AKAP4_HUMAN and ASPX_HUMAN (ACRV1 gene) revealed exclusive localization in spermatozoa tails and acrosomes, respectively. A multiplex imaging flow cytometry assay facilitated fast and unambiguous identification of rare but morphologically intact AKAP4+/ASPX+/Hoechst+ spermatozoa within debris-laden semen pellets of NOA patients. While the previously suggested markers for spermatozoa retrieval suffered from low diagnostic specificity, the multi-step gating strategy and visualization of AKAP4+/ASPX+/Hoechst+ cells with elongated tails and acrosome-capped nuclei facilitated fast and unambiguous identification of the mature intact spermatozoa. AKAP4+/ASPX+/Hoechst+ assay may emerge as a non-invasive test to predict retrieval of morphologically intact spermatozoa by mTESE, thus improving diagnostics and treatment of severe forms of male infertility.
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Affiliation(s)
| | - Mirzo Kanoatov
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Keith Jarvi
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Department of Surgery, Division of Urology, Mount Sinai Hospital, Toronto, ON, Canada
| | | | - Sergey I Moskovtsev
- CReATe Fertility Centre, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Clifford Librach
- CReATe Fertility Centre, Toronto, ON, Canada; Department of Obstetrics and Gynecology, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Sunnybrook Research Institute, Toronto, ON, Canada
| | - Andrei P Drabovich
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
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7
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Neely BA, Dorfer V, Martens L, Bludau I, Bouwmeester R, Degroeve S, Deutsch EW, Gessulat S, Käll L, Palczynski P, Payne SH, Rehfeldt TG, Schmidt T, Schwämmle V, Uszkoreit J, Vizcaíno JA, Wilhelm M, Palmblad M. Toward an Integrated Machine Learning Model of a Proteomics Experiment. J Proteome Res 2023; 22:681-696. [PMID: 36744821 PMCID: PMC9990124 DOI: 10.1021/acs.jproteome.2c00711] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
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Affiliation(s)
- Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina 29412, United States
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, 9000 Ghent, Belgium
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | - Lukas Käll
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Solna, Sweden
| | - Pawel Palczynski
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah 84602, United States
| | - Tobias Greisager Rehfeldt
- Institute for Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | | | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Julian Uszkoreit
- Medical Proteome Analysis, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801 Bochum, Germany.,Medizinisches Proteom-Center, Medical Faculty, Ruhr University Bochum, 44801 Bochum, Germany
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), 85354 Freising, Germany
| | - Magnus Palmblad
- Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, The Netherlands
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8
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Frantzi M, Culig Z, Heidegger I, Mokou M, Latosinska A, Roesch MC, Merseburger AS, Makridakis M, Vlahou A, Blanca-Pedregosa A, Carrasco-Valiente J, Mischak H, Gomez-Gomez E. Mass Spectrometry-Based Biomarkers to Detect Prostate Cancer: A Multicentric Study Based on Non-Invasive Urine Collection without Prior Digital Rectal Examination. Cancers (Basel) 2023; 15:cancers15041166. [PMID: 36831508 PMCID: PMC9954607 DOI: 10.3390/cancers15041166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.
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Affiliation(s)
- Maria Frantzi
- Department of Biomarker Research, Mosaiques Diagnostics GmbH, 30659 Hannover, Germany
- Correspondence: ; Tel.: +49-511-5547-4429
| | - Zoran Culig
- Experimental Urology Department of Urology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Isabel Heidegger
- Experimental Urology Department of Urology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marika Mokou
- Department of Biomarker Research, Mosaiques Diagnostics GmbH, 30659 Hannover, Germany
| | - Agnieszka Latosinska
- Department of Biomarker Research, Mosaiques Diagnostics GmbH, 30659 Hannover, Germany
| | - Marie C. Roesch
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Axel S. Merseburger
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany
| | - Manousos Makridakis
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Antonia Vlahou
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Ana Blanca-Pedregosa
- Maimonides Biomedical Research Institute of Córdoba, Department of Urology, University of Cordoba, 14004 Cordoba, Spain
| | - Julia Carrasco-Valiente
- Maimonides Biomedical Research Institute of Córdoba, Department of Urology, University of Cordoba, 14004 Cordoba, Spain
| | - Harald Mischak
- Department of Biomarker Research, Mosaiques Diagnostics GmbH, 30659 Hannover, Germany
- Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow G12 8TA, UK
| | - Enrique Gomez-Gomez
- Maimonides Biomedical Research Institute of Córdoba, Department of Urology, University of Cordoba, 14004 Cordoba, Spain
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9
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Abstract
Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.
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Affiliation(s)
- Rachel M Miller
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
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10
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Zimmermann MT. Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research. Compr Physiol 2022; 12:3141-3166. [PMID: 35578963 DOI: 10.1002/cphy.c190047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.
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Affiliation(s)
- Michael T Zimmermann
- Bioinformatics Research and Development Laboratory, Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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11
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Pasche E, Mottaz A, Caucheteur D, Gobeill J, Michel PA, Ruch P. Variomes: a high recall search engine to support the curation of genomic variants. Bioinformatics 2022; 38:2595-2601. [PMID: 35274687 PMCID: PMC9048643 DOI: 10.1093/bioinformatics/btac146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/07/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022] Open
Abstract
Motivation Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central. Results We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than LitVar for 90% of the queries when tested on a set of 803 queries; thus, establishing a new baseline for searching the literature about variants. Availability and implementation Variomes is publicly available at https://candy.hesge.ch/Variomes. Source code is freely available at https://github.com/variomes/sibtm-variomes. SynVar is publicly available at https://goldorak.hesge.ch/synvar. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Emilie Pasche
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Anaïs Mottaz
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Déborah Caucheteur
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Julien Gobeill
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Pierre-André Michel
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Patrick Ruch
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
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12
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The methyltransferase METTL3 negatively regulates nonalcoholic steatohepatitis (NASH) progression. Nat Commun 2021; 12:7213. [PMID: 34893641 PMCID: PMC8664922 DOI: 10.1038/s41467-021-27539-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/26/2021] [Indexed: 01/18/2023] Open
Abstract
Nonalcoholic steatohepatitis (NASH) is a key step in the progression of nonalcoholic fatty liver (NAFL) to cirrhosis. However, the molecular mechanisms of the NAFL-to-NASH transition are largely unknown. Here, we identify methyltransferase like 3 (METTL3) as a key negative regulator of NASH pathogenesis. Hepatocyte-specific deletion of Mettl3 drives NAFL-to-NASH progression by increasing CD36-mediated hepatic free fatty acid uptake and CCL2-induced inflammation, which is due to increased chromatin accessibility in the promoter region of Cd36 and Ccl2. Antibody blockade of CD36 and CCL2 ameliorates NASH progression in hepatic Mettl3 knockout mice. Hepatic overexpression of Mettl3 protects against NASH progression by inhibiting the expression of CD36 and CCL2. Mechanistically, METTL3 directly binds to the promoters of the Cd36 and Ccl2 genes and recruits HDAC1/2 to induce deacetylation of H3K9 and H3K27 in their promoters, thus suppressing Cd36 and Ccl2 transcription. Furthermore, METTL3 is translocated from the nucleus to the cytosol in NASH, which is associated with CDK9-mediated phosphorylation of METTL3. Our data reveal a mechanism by which METTL3 negatively regulates hepatic Cd36 and Ccl2 gene transcription via a histone modification pathway for protection against NASH progression.
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13
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Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module. Int J Mol Sci 2021; 22:ijms222112080. [PMID: 34769509 PMCID: PMC8584443 DOI: 10.3390/ijms222112080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/30/2021] [Accepted: 11/02/2021] [Indexed: 11/17/2022] Open
Abstract
According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.
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14
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Zaccaria A, Antinori P, Licker V, Kövari E, Lobrinus JA, Burkhard PR. Multiomic Analyses of Dopaminergic Neurons Isolated from Human Substantia Nigra in Parkinson's Disease: A Descriptive and Exploratory Study. Cell Mol Neurobiol 2021; 42:2805-2818. [PMID: 34528139 PMCID: PMC9561004 DOI: 10.1007/s10571-021-01146-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/02/2021] [Indexed: 11/21/2022]
Abstract
Dopaminergic neurons (DA) of the substantia nigra pars compacta (SNpc) selectively and progressively degenerate in Parkinson’s disease (PD). Until now, molecular analyses of DA in PD have been limited to genomic or transcriptomic approaches, whereas, to the best of our knowledge, no proteomic or combined multiomic study examining the protein profile of these neurons is currently available. In this exploratory study, we used laser capture microdissection to extract regions from DA in 10 human SNpc obtained at autopsy in PD patients and control subjects. Extracted RNA and proteins were identified by RNA sequencing and nanoliquid chromatography–mass spectrometry, respectively, and the differential expression between PD and control group was assessed. Qualitative analyses confirmed that the microdissection protocol preserves the integrity of our samples and offers access to specific molecular pathways. This multiomic analysis highlighted differential expression of 52 genes and 33 proteins, including molecules of interest already known to be dysregulated in PD, such as LRP2, PNMT, CXCR4, MAOA and CBLN1 genes, or the Aldehyde dehydrogenase 1 protein. On the other hand, despite the same samples were used for both analyses, correlation between RNA and protein expression was low, as exemplified by the CST3 gene encoding for the cystatin C protein. This is the first exploratory study analyzing both gene and protein expression of laser-dissected neuronal parts from SNpc in PD. Data are available via ProteomeXchange with identifier PXD024748 and via GEO with identifier GSE 169755.
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Affiliation(s)
- Affif Zaccaria
- Neuroproteomics Group, University Medical Center, Faculty of Medicine, Geneva University, Geneva, Switzerland.
| | - Paola Antinori
- Neuroproteomics Group, University Medical Center, Faculty of Medicine, Geneva University, Geneva, Switzerland
| | - Virginie Licker
- Neuroproteomics Group, University Medical Center, Faculty of Medicine, Geneva University, Geneva, Switzerland
| | - Enikö Kövari
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | | | - Pierre R Burkhard
- Neuroproteomics Group, University Medical Center, Faculty of Medicine, Geneva University, Geneva, Switzerland.,Department of Neurology, Geneva University Hospitals, Geneva, Switzerland
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15
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Li Y, Zhou H, Chen X, Zheng Y, Kang Q, Hao D, Zhang L, Song T, Luo H, Hao Y, Chen R, Zhang P, He S. SmProt: A Reliable Repository with Comprehensive Annotation of Small Proteins Identified from Ribosome Profiling. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:602-610. [PMID: 34536568 PMCID: PMC9039559 DOI: 10.1016/j.gpb.2021.09.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 12/30/2022]
Abstract
Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORF translation events or sequences, and remarkably increased data volume. More components such as non-ATG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets or collected from literature and other sources from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were also collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.
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Affiliation(s)
- Yanyan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Honghong Zhou
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaomin Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Zheng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Quan Kang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lili Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yajing Hao
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Geneway Decoding Bio-Tech Co. Ltd, Foshan 528316, China.
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Shunmin He
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
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16
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Combination of Antibody Arrays to Functionally Characterize Dark Proteins in Human Olfactory Neuroepithelial Cells. Methods Mol Biol 2021. [PMID: 34115363 DOI: 10.1007/978-1-0716-1562-1_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
The completion and annotation of the human proteome require the availability of information related to protein function. Currently, more than 1800 human genes constitute the "dark proteome," which include missing proteins, uncharacterized human genes validated at protein level, smORFs, proteins from lncRNAs, or any uncharacterized transcripts. During the last years, different experimental workflows based on multi-omics analyses, bioinformatics, and in vitro and in vivo studies have been promoted by the Human Proteome Project Consortium to enhance the annotation of dark proteins. In this chapter, we describe a method that utilizes recombinant proteins and antibody arrays to establish a straightforward methodology in order to rapidly characterize potential functional features of dark proteins associated to intracellular signaling dynamics and extracellular immune response in human cell cultures. Further validating the method, this workflow was applied to probe changes in the activation patterns of kinases and transcription factors as well as in cytokine production modulated by the dark C1orf128 (PITHD1) protein in human olfactory neuroepithelial cells.
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17
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Zhang P, Bu Y, Jiang P, Shi X, Lun B, Chen C, Syafiandini AF, Ding Y, Song M. Toward a Coronavirus Knowledge Graph. Genes (Basel) 2021; 12:genes12070998. [PMID: 34209818 PMCID: PMC8307964 DOI: 10.3390/genes12070998] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/31/2022] Open
Abstract
This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG’s usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.
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Affiliation(s)
- Peng Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
| | - Yi Bu
- Department of Information Management, Peking University, Beijing 100871, China;
| | - Peng Jiang
- Beijing Knowledge Atlas Technology Co., Ltd., Beijing 100043, China; (P.J.); (X.S.); (B.L.)
| | - Xiaowen Shi
- Beijing Knowledge Atlas Technology Co., Ltd., Beijing 100043, China; (P.J.); (X.S.); (B.L.)
| | - Bing Lun
- Beijing Knowledge Atlas Technology Co., Ltd., Beijing 100043, China; (P.J.); (X.S.); (B.L.)
| | - Chongyan Chen
- School of Information, University of Texas at Austin, Austin, TX 78701, USA; (C.C.); (Y.D.)
| | | | - Ying Ding
- School of Information, University of Texas at Austin, Austin, TX 78701, USA; (C.C.); (Y.D.)
- Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Min Song
- Department of Library and Information Science, Yonsei University, Seoul 03722, Korea;
- Correspondence: ; Tel.: +82-2-2123-2416
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18
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Abstract
INTRODUCTION The continuous technical improvement in sensitivity and specificity placed mass spectrometry as an alternative method for analyzing clinical samples. In parallel to the rapid development of discovery proteomics, targeted acquisition has been implemented as a complementary option for measuring a small set of proteins with high sensitivity and robustness in a large sample cohort. The combination of trapped ion mobility with a rapid time-of-flight (TOF) mass spectrometer improves the sensitivity even further and triggers the development of prm-PASEF. AREAS COVERED This article discusses the development of prm-PASEF and its advantages over the existing targeted and discovery methods for analyzing clinical samples. We are also highlighting the different requirements for the use of prm-PASEF on clinical samples. EXPERT OPINION prm-PASEF takes advantage of a dual ion-mobility trap enabling highly multiplexed targeted acquisition. It allows the implementation of a short chromatographic separation setup without sacrificing the number of targeted peptides. Analyzing clinical samples by prm-PASEF holds the promise to significantly improve throughput while maintaining sensitivity to detect the selected target proteins.
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Affiliation(s)
- Antoine Lesur
- Head of the Proteomics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Gunnar Dittmar
- Co-Head of the Quantitative Biology Unit, Proteomics of Cellular Signaling Research Group Luxembourg Institute of Health, Strassen, Luxembourg
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19
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Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform 2021; 22:1656-1678. [PMID: 32055842 PMCID: PMC7986597 DOI: 10.1093/bib/bbaa003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/09/2019] [Indexed: 02/07/2023] Open
Abstract
Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Umair Seemab
- Haartman Institute, University of Helsinki, Finland
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Denmark
| | - Jing Tang
- Faculty of medicine, University of Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
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20
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Affiliation(s)
- 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, British Columbia V6T 1Z3, Canada
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21
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Overall CM. The HUPO High-Stringency Inventory of Humanity's Shared Human Proteome Revealed. J Proteome Res 2020; 19:4211-4214. [PMID: 33074000 DOI: 10.1021/acs.jproteome.0c00794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- 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, British Columbia V6T 1Z3, Canada
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22
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Pooyan P, Karamzadeh R, Mirzaei M, Meyfour A, Amirkhan A, Wu Y, Gupta V, Baharvand H, Javan M, Salekdeh GH. The Dynamic Proteome of Oligodendrocyte Lineage Differentiation Features Planar Cell Polarity and Macroautophagy Pathways. Gigascience 2020; 9:5945159. [PMID: 33128372 PMCID: PMC7601170 DOI: 10.1093/gigascience/giaa116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 07/22/2020] [Accepted: 09/28/2020] [Indexed: 12/15/2022] Open
Abstract
Background Generation of oligodendrocytes is a sophisticated multistep process, the mechanistic underpinnings of which are not fully understood and demand further investigation. To systematically profile proteome dynamics during human embryonic stem cell differentiation into oligodendrocytes, we applied in-depth quantitative proteomics at different developmental stages and monitored changes in protein abundance using a multiplexed tandem mass tag-based proteomics approach. Findings Our proteome data provided a comprehensive protein expression profile that highlighted specific expression clusters based on the protein abundances over the course of human oligodendrocyte lineage differentiation. We identified the eminence of the planar cell polarity signalling and autophagy (particularly macroautophagy) in the progression of oligodendrocyte lineage differentiation—the cooperation of which is assisted by 106 and 77 proteins, respectively, that showed significant expression changes in this differentiation process. Furthermore, differentially expressed protein analysis of the proteome profile of oligodendrocyte lineage cells revealed 378 proteins that were specifically upregulated only in 1 differentiation stage. In addition, comparative pairwise analysis of differentiation stages demonstrated that abundances of 352 proteins differentially changed between consecutive differentiation time points. Conclusions Our study provides a comprehensive systematic proteomics profile of oligodendrocyte lineage cells that can serve as a resource for identifying novel biomarkers from these cells and for indicating numerous proteins that may contribute to regulating the development of myelinating oligodendrocytes and other cells of oligodendrocyte lineage. We showed the importance of planar cell polarity signalling in oligodendrocyte lineage differentiation and revealed the autophagy-related proteins that participate in oligodendrocyte lineage differentiation.
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Affiliation(s)
- Paria Pooyan
- Department of Molecular Systems Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Brain and Cognitive Science, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran
| | - Razieh Karamzadeh
- Department of Molecular Systems Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Brain and Cognitive Science, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran
| | - Mehdi Mirzaei
- Department of Molecular Sciences, Macquarie University, North Ryde, Sydney, NSW 2109, Australia.,Australian Proteome Analysis Facility, Macquarie University, North Ryde, NSW 2109, Australia
| | - Anna Meyfour
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Daneshjoo Blv., Velenjak, Tehran 19839-63113, Iran
| | - Ardeshir Amirkhan
- Australian Proteome Analysis Facility, Macquarie University, North Ryde, NSW 2109, Australia
| | - Yunqi Wu
- Australian Proteome Analysis Facility, Macquarie University, North Ryde, NSW 2109, Australia
| | - Vivek Gupta
- Department of Clinical Medicine, Macquarie University, North Ryde, Sydney, NSW 2109, Australia
| | - Hossein Baharvand
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Brain and Cognitive Science, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Developmental Biology, University of Science and Culture, Ashrafi Esfahani, Tehran 1461968151, Iran
| | - Mohammad Javan
- Department of Brain and Cognitive Science, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Jalal AleAhmad, Tehran 14115-111, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Banihashem St., ACECR, Tehran 16635-148, Iran.,Department of Molecular Sciences, Macquarie University, North Ryde, Sydney, NSW 2109, Australia
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23
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The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits. Genes (Basel) 2020; 11:genes11111273. [PMID: 33137877 PMCID: PMC7693691 DOI: 10.3390/genes11111273] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/19/2020] [Accepted: 10/26/2020] [Indexed: 12/29/2022] Open
Abstract
The Human Y chromosome (ChrY) has been demonstrated to be a powerful tool for phylogenetics, population genetics, genetic genealogy and forensics. However, the importance of ChrY genetic variation in relation to human complex traits is less clear. In this review, we summarise existing evidence about the inherent complexities of ChrY variation and their use in association studies of human complex traits. We present and discuss the specific particularities of ChrY genetic variation, including Y chromosomal haplogroups, that need to be considered in the design and interpretation of genetic epidemiological studies involving ChrY.
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24
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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: 131] [Impact Index Per Article: 32.8] [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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25
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Gobeill J, Caucheteur D, Michel PA, Mottin L, Pasche E, Ruch P. SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts. Nucleic Acids Res 2020; 48:W12-W16. [PMID: 32379317 PMCID: PMC7319474 DOI: 10.1093/nar/gkaa328] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 01/05/2023] Open
Abstract
Thanks to recent efforts by the text mining community, biocurators have now access to plenty of good tools and Web interfaces for identifying and visualizing biomedical entities in literature. Yet, many of these systems start with a PubMed query, which is limited by strong Boolean constraints. Some semantic search engines exploit entities for Information Retrieval, and/or deliver relevance-based ranked results. Yet, they are not designed for supporting a specific curation workflow, and allow very limited control on the search process. The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature. Indeed, SIBiLS allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies. The services have been used and favourably evaluated to assist the curation of genes and gene products, by delivering customized literature triage engines to different curation teams. SIBiLS (https://candy.hesge.ch/SIBiLS) are freely accessible via REST APIs and are ready to empower any curation workflow, built on modern technologies scalable with big data: MongoDB and Elasticsearch. They cover MEDLINE and PubMed Central Open Access enriched by nearly 2 billion of mapped biomedical entities, and are daily updated.
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Affiliation(s)
- Julien Gobeill
- To whom correspondence should be addressed. Tel: +41 22 388 17 86; Fax: +41 22 546 97 38;
| | - Déborah Caucheteur
- BiTeM group, Information Sciences, HES-SO / HEG Geneva, 1227 Carouge, Switzerland
| | - Pierre-André Michel
- SIB Text Mining group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland
| | - Luc Mottin
- BiTeM group, Information Sciences, HES-SO / HEG Geneva, 1227 Carouge, Switzerland
| | - Emilie Pasche
- SIB Text Mining group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland
- BiTeM group, Information Sciences, HES-SO / HEG Geneva, 1227 Carouge, Switzerland
| | - Patrick Ruch
- Correspondence may also be addressed to Patrick Ruch. Tel: +41 22 388 17 81; Fax: +41 22 546 97 38;
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26
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Abstract
Our understanding of the human genome has continuously expanded since its draft publication in 2001. Over the years, novel assays have allowed us to progressively overlay layers of knowledge above the raw sequence of A's, T's, G's, and C's. The reference human genome sequence is now a complex knowledge base maintained under the shared stewardship of multiple specialist communities. Its complexity stems from the fact that it is simultaneously a template for transcription, a record of evolution, a vehicle for genetics, and a functional molecule. In short, the human genome serves as a frame of reference at the intersection of a diversity of scientific fields. In recent years, the progressive fall in sequencing costs has given increasing importance to the quality of the human reference genome, as hundreds of thousands of individuals are being sequenced yearly, often for clinical applications. Also, novel sequencing-based assays shed light on novel functions of the genome, especially with respect to gene expression regulation. Keeping the human genome annotation up to date and accurate is therefore an ongoing partnership between reference annotation projects and the greater community worldwide.
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Affiliation(s)
- Daniel R Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SD, United Kingdom; , ,
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SD, United Kingdom; , ,
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SD, United Kingdom; , ,
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27
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Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:91-103. [PMID: 32652120 PMCID: PMC7646089 DOI: 10.1016/j.gpb.2018.11.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 10/24/2018] [Accepted: 12/14/2018] [Indexed: 11/27/2022]
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28
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Poverennaya E, Kiseleva O, Romanova A, Pyatnitskiy M. Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms. Genes (Basel) 2020; 11:E677. [PMID: 32575886 PMCID: PMC7350264 DOI: 10.3390/genes11060677] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/19/2020] [Indexed: 01/22/2023] Open
Abstract
Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein-protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes.
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Affiliation(s)
- Ekaterina Poverennaya
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Institute of Environmental and Agricultural Biology (X-BIO),Tyumen State University, 625003 Tyumen, Russia
| | - Olga Kiseleva
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
| | - Anastasia Romanova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Faculty of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, 141701 Moscow, Russia
| | - Mikhail Pyatnitskiy
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
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29
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Mohanty V, Pinto SM, Subbannayya Y, Najar MA, Murthy KB, Prasad TSK, Murthy KR. Digging Deeper for the Eye Proteome in Vitreous Substructures: A High-Resolution Proteome Map of the Normal Human Vitreous Base. ACTA ACUST UNITED AC 2020; 24:379-389. [DOI: 10.1089/omi.2020.0020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Varshasnata Mohanty
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Sneha M. Pinto
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Yashwanth Subbannayya
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Mohd. Altaf Najar
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Kalpana Babu Murthy
- Department of vitreo retina, Vittala International Institute of Ophthalmology, Bangalore, India
- Department of vitreo retina, Prabha Eye Clinic and Research Centre, Bangalore, India
| | | | - Krishna R. Murthy
- Department of vitreo retina, Vittala International Institute of Ophthalmology, Bangalore, India
- Department of vitreo retina, Prabha Eye Clinic and Research Centre, Bangalore, India
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Manipal Academy of Higher Education, Manipal, India
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30
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Zahn-Zabal M, Michel PA, Gateau A, Nikitin F, Schaeffer M, Audot E, Gaudet P, Duek PD, Teixeira D, Rech de Laval V, Samarasinghe K, Bairoch A, Lane L. The neXtProt knowledgebase in 2020: data, tools and usability improvements. Nucleic Acids Res 2020; 48:D328-D334. [PMID: 31724716 PMCID: PMC7145669 DOI: 10.1093/nar/gkz995] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/10/2019] [Accepted: 10/18/2019] [Indexed: 11/23/2022] Open
Abstract
The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.
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Affiliation(s)
- Monique Zahn-Zabal
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | - Alain Gateau
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Frédéric Nikitin
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Mathieu Schaeffer
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Department of microbiology and molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Estelle Audot
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Pascale Gaudet
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Paula D Duek
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Daniel Teixeira
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Valentine Rech de Laval
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Department of microbiology and molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Haute école spécialisée de Suisse occidentale, Haute Ecole de Gestion de Genève, Carouge, Switzerland
| | - Kasun Samarasinghe
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Department of microbiology and molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Amos Bairoch
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Department of microbiology and molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lydie Lane
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Department of microbiology and molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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31
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Giglio M, Tauber R, Nadendla S, Munro J, Olley D, Ball S, Mitraka E, Schriml LM, Gaudet P, Hobbs ET, Erill I, Siegele DA, Hu JC, Mungall C, Chibucos MC. ECO, the Evidence & Conclusion Ontology: community standard for evidence information. Nucleic Acids Res 2020; 47:D1186-D1194. [PMID: 30407590 PMCID: PMC6323956 DOI: 10.1093/nar/gky1036] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 10/16/2018] [Indexed: 12/03/2022] Open
Abstract
The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product X has function Y as supported by evidence Z). Capture of this information allows tracking of annotation provenance, establishment of quality control measures and query of evidence. ECO contains over 1500 terms and is in use by many leading biological resources including the Gene Ontology, UniProt and several model organism databases. ECO is continually being expanded and revised based on the needs of the biocuration community. The ontology is freely available for download from GitHub (https://github.com/evidenceontology/) or the project’s website (http://evidenceontology.org/). Users can request new terms or changes to existing terms through the project’s GitHub site. ECO is released into the public domain under CC0 1.0 Universal.
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Affiliation(s)
- Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Rebecca Tauber
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - James Munro
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Dustin Olley
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Shoshannah Ball
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Elvira Mitraka
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Pascale Gaudet
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Elizabeth T Hobbs
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Ivan Erill
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Deborah A Siegele
- Department of Biology, Texas A&M University, College Station, TX 77840, USA
| | - James C Hu
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77840, USA
| | - Chris Mungall
- Molecular Ecosystems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Marcus C Chibucos
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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32
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Singh G, Inoue A, Gutkind JS, Russell RB, Raimondi F. PRECOG: PREdicting COupling probabilities of G-protein coupled receptors. Nucleic Acids Res 2020; 47:W395-W401. [PMID: 31143927 PMCID: PMC6602504 DOI: 10.1093/nar/gkz392] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 04/13/2019] [Accepted: 05/01/2019] [Indexed: 01/08/2023] Open
Abstract
G-protein coupled receptors (GPCRs) control multiple physiological states by transducing a multitude of extracellular stimuli into the cell via coupling to intra-cellular heterotrimeric G-proteins. Deciphering which G-proteins couple to each of the hundreds of GPCRs present in a typical eukaryotic organism is therefore critical to understand signalling. Here, we present PRECOG (precog.russelllab.org): a web-server for predicting GPCR coupling, which allows users to: (i) predict coupling probabilities for GPCRs to individual G-proteins instead of subfamilies; (ii) visually inspect the protein sequence and structural features that are responsible for a particular coupling; (iii) suggest mutations to rationally design artificial GPCRs with new coupling properties based on predetermined coupling features.
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Affiliation(s)
- Gurdeep Singh
- CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.,Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany
| | - Asuka Inoue
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan
| | - J Silvio Gutkind
- Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert B Russell
- CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.,Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany
| | - Francesco Raimondi
- CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.,Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany
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33
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Blanco-Míguez A, Fdez-Riverola F, Sánchez B, Lourenço A. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Brief Bioinform 2020; 20:1032-1056. [PMID: 29186315 DOI: 10.1093/bib/bbx156] [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: 07/26/2017] [Revised: 10/23/2017] [Indexed: 12/18/2022] Open
Abstract
The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.
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Affiliation(s)
| | | | | | - Anália Lourenço
- Dpto. de Informática - Universidade de Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
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34
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Singh S, Qin F, Kumar S, Elfman J, Lin E, Pham LP, Yang A, Li H. The landscape of chimeric RNAs in non-diseased tissues and cells. Nucleic Acids Res 2020; 48:1764-1778. [PMID: 31965184 PMCID: PMC7038929 DOI: 10.1093/nar/gkz1223] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 12/13/2019] [Accepted: 01/20/2020] [Indexed: 12/17/2022] Open
Abstract
Chimeric RNAs and their encoded proteins have been traditionally viewed as unique features of neoplasia, and have been used as biomarkers and therapeutic targets for multiple cancers. Recent studies have demonstrated that chimeric RNAs also exist in non-cancerous cells and tissues, although large-scale, genome-wide studies of chimeric RNAs in non-diseased tissues have been scarce. Here, we explored the landscape of chimeric RNAs in 9495 non-diseased human tissue samples of 53 different tissues from the GTEx project. Further, we established means for classifying chimeric RNAs, and observed enrichment for particular classifications as more stringent filters are applied. We experimentally validated a subset of chimeric RNAs from each classification and demonstrated functional relevance of two chimeric RNAs in non-cancerous cells. Importantly, our list of chimeric RNAs in non-diseased tissues overlaps with some entries in several cancer fusion databases, raising concerns for some annotations. The data from this study provides a large repository of chimeric RNAs present in non-diseased tissues, which can be used as a control dataset to facilitate the identification of true cancer-specific chimeras.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Fujun Qin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Shailesh Kumar
- National Institute of Plant Genome Research (NIPGR), New Delhi 110067, India
| | - Justin Elfman
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Lin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Lam-Phong Pham
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Amy Yang
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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35
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Marini F, Carregari VC, Greco V, Ronci M, Iavarone F, Persichilli S, Castagnola M, Urbani A, Pieroni L. Exploring the HeLa Dark Mitochondrial Proteome. Front Cell Dev Biol 2020; 8:137. [PMID: 32195257 PMCID: PMC7066081 DOI: 10.3389/fcell.2020.00137] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 02/18/2020] [Indexed: 12/04/2022] Open
Abstract
In the framework of the Human Proteome Project initiative, we aim to improve mapping and characterization of mitochondrial proteome. In this work we implemented an experimental workflow, combining classical biochemical enrichments and mass spectrometry, to pursue a much deeper definition of mitochondrial proteome and possibly mine mitochondrial uncharacterized dark proteins. We fractionated in two compartments mitochondria enriched from HeLa cells in order to annotate 4230 proteins in both fraction by means of a multiple-enzyme digestion (trypsin, chymotrypsin and Glu-C) followed by mass spectrometry analysis using a combination of Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). We detected 22 mitochondrial dark proteins not annotated for their function and we provide their relative abundance inside the mitochondrial organelle. Considering this work as a pilot study we expect that the same approach, in different biological system, could represent an advancement in the characterization of the human mitochondrial proteome providing uncharted ground to explore the mitonuclear phenotypic relationships. All spectra have been deposited to ProteomeXchange with PXD014201 and PXD014200 identifier.
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Affiliation(s)
- Federica Marini
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Victor Corasolla Carregari
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Viviana Greco
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maurizio Ronci
- Department of Pharmacy, University G. D'Annunzio Chieti, Chieti, Italy
| | - Federica Iavarone
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Silvia Persichilli
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Massimo Castagnola
- Proteomics and Metabonomics Unit, IRCCS-Fondazione Santa Lucia, Rome, Italy
| | - Andrea Urbani
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, Rome, Italy.,Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luisa Pieroni
- Proteomics and Metabonomics Unit, IRCCS-Fondazione Santa Lucia, Rome, Italy
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Schaschl H, Wallner B. Population-specific, recent positive directional selection suggests adaptation of human male reproductive genes to different environmental conditions. BMC Evol Biol 2020; 20:27. [PMID: 32054438 PMCID: PMC7020506 DOI: 10.1186/s12862-019-1575-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 12/30/2019] [Indexed: 01/18/2023] Open
Abstract
Background Recent human transcriptomic analyses revealed a very large number of testis-enriched genes, many of which are involved in spermatogenesis. This comprehensive transcriptomic data lead us to the question whether positive selection was a decisive force influencing the evolution and variability of testis-enriched genes in humans. We used two methodological approaches to detect different levels of positive selection, namely episodic positive diversifying selection (i.e., past selection) in the human lineage within primate phylogeny, potentially driven by sperm competition, and recent positive directional selection in contemporary human populations, which would indicate adaptation to different environments. Results In the human lineage (after correction for multiple testing) we found that only the gene TULP2, for which no functional data are yet available, is subject to episodic positive diversifying selection. Using less stringent statistical criteria (uncorrected p-values), also the gene SPATA16, which has a pivotal role in male fertility and for which episodes of adaptive evolution have been suggested, also displays a putative signal of diversifying selection in the human branch. At the same time, we found evidence for recent positive directional selection acting on several human testis-enriched genes (MORC1, SLC9B1, ROPN1L, DMRT1, PLCZ1, RNF17, FAM71D and WBP2NL) that play important roles in human spermatogenesis and fertilization. Most of these genes are population-specifically under positive selection. Conclusion Episodic diversifying selection, possibly driven by sperm competition, was not an important force driving the evolution of testis-enriched genes in the human lineage. Population-specific, recent positive directional selection suggests an adaptation of male reproductive genes to different environmental conditions. Positive selection acts on eQTLS and sQTLs, indicating selective effects on important gene regulatory functions. In particular, the transcriptional diversity regulated by sQTLs in testis-enriched genes may be important for spermatocytes to respond to environmental and physiological stress.
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Affiliation(s)
- Helmut Schaschl
- Department of Evolutionary Anthropology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
| | - Bernard Wallner
- Department of Behavioural Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
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Finding the Keys to the CAR: Identifying Novel Target Antigens for T Cell Redirection Immunotherapies. Int J Mol Sci 2020; 21:ijms21020515. [PMID: 31947597 PMCID: PMC7014258 DOI: 10.3390/ijms21020515] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 02/06/2023] Open
Abstract
Oncology immunotherapy has been a significant advancement in cancer treatment and involves harnessing and redirecting a patient’s immune response towards their own tumour. Specific recognition and elimination of tumour cells was first proposed over a century ago with Paul Erlich’s ‘magic bullet’ theory of therapy. In the past decades, targeting cancer antigens by redirecting T cells with antibodies using either bispecific T cell engagers (BiTEs) or chimeric antigen receptor (CAR) T cell therapy has achieved impressive clinical responses. Despite recent successes in haematological cancers, linked to a high and uniformly expressed CD19 antigen, the efficacy of T cell therapies in solid cancers has been disappointing, in part due to antigen escape. Targeting heterogeneous solid tumours with T cell therapies will require the identification of novel tumour specific targets. These targets can be found among a range of cell-surface expressed antigens, including proteins, glycolipids or carbohydrates. In this review, we will introduce the current tumour target antigen classification, outline existing approaches to discover novel tumour target antigens and discuss considerations for future design of antibodies with a focus on their use in CAR T cells.
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Cascarina SM, Ross ED. Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes. BMC Genomics 2020; 21:23. [PMID: 31914925 PMCID: PMC6947906 DOI: 10.1186/s12864-019-6425-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 12/23/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among "reference" proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined. RESULTS Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity. CONCLUSIONS Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.
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Affiliation(s)
- Sean M Cascarina
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Eric D Ross
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, 80523, USA.
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Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, García-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA. The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics. Nucleic Acids Res 2020; 48:D1145-D1152. [PMID: 31686107 PMCID: PMC7145525 DOI: 10.1093/nar/gkz984] [Citation(s) in RCA: 331] [Impact Index Per Article: 82.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 10/11/2019] [Accepted: 10/14/2019] [Indexed: 11/24/2022] Open
Abstract
The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including 'big data' approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.
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Affiliation(s)
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | | | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Jeremy J Carver
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - David García-Seisdedos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Andrew F Jarnuczak
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Suresh Hewapathirana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Benjamin S Pullman
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Julie Wertz
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Zhi Sun
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Shin Kawano
- Faculty of Contemporary Society, Toyama University of International Studies, Toyama 930–1292, Japan
- Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba 277–0871, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951–8510, Japan
| | - Yu Watanabe
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951–8510, Japan
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | | | | | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606–8501, Japan
| | - Juan A Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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40
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Pillonel T, Tagini F, Bertelli C, Greub G. ChlamDB: a comparative genomics database of the phylum Chlamydiae and other members of the Planctomycetes-Verrucomicrobiae-Chlamydiae superphylum. Nucleic Acids Res 2020; 48:D526-D534. [PMID: 31665454 PMCID: PMC7145651 DOI: 10.1093/nar/gkz924] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/04/2019] [Accepted: 10/08/2019] [Indexed: 01/12/2023] Open
Abstract
ChlamDB is a comparative genomics database containing 277 genomes covering the entire Chlamydiae phylum as well as their closest relatives belonging to the Planctomycetes-Verrucomicrobiae-Chlamydiae (PVC) superphylum. Genomes can be compared, analyzed and retrieved using accessions numbers of the most widely used databases including COG, KEGG ortholog, KEGG pathway, KEGG module, Pfam and InterPro. Gene annotations from multiple databases including UniProt (curated and automated protein annotations), KEGG (annotation of pathways), COG (orthology), TCDB (transporters), STRING (protein-protein interactions) and InterPro (domains and signatures) can be accessed in a comprehensive overview page. Candidate effectors of the Type III secretion system (T3SS) were identified using four in silico methods. The identification of orthologs among all PVC genomes allows users to perform large-scale comparative analyses and to identify orthologs of any protein in all genomes integrated in the database. Phylogenetic relationships of PVC proteins and their closest homologs in RefSeq, comparison of transmembrane domains and Pfam domains, conservation of gene neighborhood and taxonomic profiles can be visualized using dynamically generated graphs, available for download. As a central resource for researchers working on chlamydia, chlamydia-related bacteria, verrucomicrobia and planctomyces, ChlamDB facilitates the access to comprehensive annotations, integrates multiple tools for comparative genomic analyses and is freely available at https://chlamdb.ch/. Database URL: https://chlamdb.ch/.
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Affiliation(s)
- Trestan Pillonel
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Bugnon 48, 1011 Lausanne, Switzerland
| | - Florian Tagini
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Bugnon 48, 1011 Lausanne, Switzerland
| | - Claire Bertelli
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Bugnon 48, 1011 Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Bugnon 48, 1011 Lausanne, Switzerland
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41
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Timp W, Timp G. Beyond mass spectrometry, the next step in proteomics. SCIENCE ADVANCES 2020; 6:eaax8978. [PMID: 31950079 PMCID: PMC6954058 DOI: 10.1126/sciadv.aax8978] [Citation(s) in RCA: 167] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/19/2019] [Indexed: 05/08/2023]
Abstract
Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.
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Affiliation(s)
- Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Gregory Timp
- Departments of Electrical Engineering and Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
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42
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Gavali S, Cowart J, Chen C, Ross KE, Arighi C, Wu CH. RESTful API for iPTMnet: a resource for protein post-translational modification network discovery. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5829784. [PMID: 32395768 PMCID: PMC7216315 DOI: 10.1093/database/baz157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/09/2019] [Accepted: 12/23/2019] [Indexed: 11/12/2022]
Abstract
iPTMnet is a bioinformatics resource that integrates protein post-translational modification (PTM) data from text mining and curated databases and ontologies to aid in knowledge discovery and scientific study. The current iPTMnet website can be used for querying and browsing rich PTM information but does not support automated iPTMnet data integration with other tools. Hence, we have developed a RESTful API utilizing the latest developments in cloud technologies to facilitate the integration of iPTMnet into existing tools and pipelines. We have packaged iPTMnet API software in Docker containers and published it on DockerHub for easy redistribution. We have also developed Python and R packages that allow users to integrate iPTMnet for scientific discovery, as demonstrated in a use case that connects PTM sites to kinase signaling pathways.
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Affiliation(s)
- Sachin Gavali
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA
| | - Julie Cowart
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA
| | - Chuming Chen
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular & Cellular Biology, 337 Basic Science Building, 3900 Reservoir Road, N.W, Washington D.C. 20057, USA
| | - Cecilia Arighi
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Cathy H Wu
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Biochemistry and Molecular & Cellular Biology, 337 Basic Science Building, 3900 Reservoir Road, N.W, Washington D.C. 20057, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
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43
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Tarnovskaya SI, Korkosh VS, Zhorov BS, Frishman D. Predicting novel disease mutations in the cardiac sodium channel. Biochem Biophys Res Commun 2020; 521:603-611. [DOI: 10.1016/j.bbrc.2019.10.142] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 10/20/2019] [Indexed: 12/22/2022]
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Abstract
Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
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45
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Klein JA, Zaia J. A Perspective on the Confident Comparison of Glycoprotein Site-Specific Glycosylation in Sample Cohorts. Biochemistry 2019; 59:3089-3097. [PMID: 31833756 DOI: 10.1021/acs.biochem.9b00730] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein glycosylation, resulting from glycosyl transferase reactions under complex control in the secretory pathway, consists of a distribution of related glycoforms at each glycosylation site. Because the biosynthetic substrate concentration and transport rates depend on architecture and other aspects of cellular phenotypes, site-specific glycosylation cannot be predicted accurately from genomic, transcriptomic, or proteomic information. Rather, it is necessary to quantify glycosylation at each protein site and how this changes among a sample cohort to provide information about disease mechanisms. At present, mature mass spectrometry-based methods allow for qualitative assignment of the glycan composition and glycosylation site of singly glycosylated proteolytic peptides. To make such quantitative comparisons, it is necessary to sample the glycosylation distribution with sufficient coverage and accuracy for confident assessment of the glycosylation changes that occur in the biological cohort. In this Perspective, we discuss the unmet needs for mass spectrometry acquisition methods and bioinformatics for the confident comparison of protein site-specific glycosylation among sample cohorts.
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46
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Uhlen M, Karlsson MJ, Zhong W, Tebani A, Pou C, Mikes J, Lakshmikanth T, Forsström B, Edfors F, Odeberg J, Mardinoglu A, Zhang C, von Feilitzen K, Mulder J, Sjöstedt E, Hober A, Oksvold P, Zwahlen M, Ponten F, Lindskog C, Sivertsson Å, Fagerberg L, Brodin P. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science 2019; 366:366/6472/eaax9198. [DOI: 10.1126/science.aax9198] [Citation(s) in RCA: 183] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 11/06/2019] [Indexed: 12/14/2022]
Abstract
Blood is the predominant source for molecular analyses in humans, both in clinical and research settings. It is the target for many therapeutic strategies, emphasizing the need for comprehensive molecular maps of the cells constituting human blood. In this study, we performed a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations to characterize the expression levels of each individual gene across the blood cell types. All data are presented in an interactive, open-access Blood Atlas as part of the Human Protein Atlas and are integrated with expression profiles across all major tissues to provide spatial classification of all protein-coding genes. This allows for a genome-wide exploration of the expression profiles across human immune cell populations and all major human tissues and organs.
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Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Max J. Karlsson
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Wen Zhong
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Christian Pou
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Jaromir Mikes
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Tadepally Lakshmikanth
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Björn Forsström
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Jacob Odeberg
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Coagulation Unit, Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, UK
| | - Cheng Zhang
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Evelina Sjöstedt
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Andreas Hober
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Per Oksvold
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Martin Zwahlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Ponten
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Åsa Sivertsson
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Petter Brodin
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
- Unit of Pediatric Rheumatology, Karolinska University Hospital, Stockholm, Sweden
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Lee SY, Hwang H, Kang YM, Kim H, Kim DG, Jeong JE, Kim JY, Yoo JS. SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation. J Proteome Res 2019; 18:4133-4142. [PMID: 31612721 DOI: 10.1021/acs.jproteome.9b00366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).
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Affiliation(s)
- Soo Youn Lee
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea
| | - Heeyoun Hwang
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea
| | - Young-Mook Kang
- Drug Information Platform Center , Korea Research Institute of Chemical Technology , 141 Gajeong-ro , Daejeon 34114 , Korea
| | - Hyejin Kim
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea.,Graduate School of Analytical Science and Technology , Chungnam National University , 99 Daehak-ro , Daejeon 34134 , Korea
| | - Dong Geun Kim
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea.,Graduate School of Analytical Science and Technology , Chungnam National University , 99 Daehak-ro , Daejeon 34134 , Korea
| | - Ji Eun Jeong
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea.,Graduate School of Analytical Science and Technology , Chungnam National University , 99 Daehak-ro , Daejeon 34134 , Korea
| | - Jin Young Kim
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis , Korea Basic Science Institute , 162 Yeongudaji-ro , Cheongju 28119 , Korea.,Graduate School of Analytical Science and Technology , Chungnam National University , 99 Daehak-ro , Daejeon 34134 , Korea
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48
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Zhang C, Lane L, Omenn GS, Zhang Y. Blinded Testing of Function Annotation for uPE1 Proteins by I-TASSER/COFACTOR Pipeline Using the 2018-2019 Additions to neXtProt and the CAFA3 Challenge. J Proteome Res 2019; 18:4154-4166. [PMID: 31581775 PMCID: PMC6900986 DOI: 10.1021/acs.jproteome.9b00537] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In 2018, we reported a hybrid pipeline that predicts protein structures with I-TASSER and function with COFACTOR. I-TASSER/COFACTOR achieved Gene Ontology (GO) high prediction accuracies of Fmax = 0.69 and 0.57 for molecular function (MF) and biological process (BP), respectively, on 100 comprehensively annotated proteins. Now we report blinded analyses of newly annotated proteins in the critical assessment of function annotation (CAFA) three function prediction challenge and in neXtProt. For CAFA3 results released in May 2019, our predictions on 267 and 912 human proteins with newly annotated MF and BP terms achieved Fmax = 0.50 and 0.42, respectively, on "No Knowledge" proteins, and 0.51 and 0.74, respectively, on "Limited Knowledge" proteins. While COFACTOR consistently outperforms simple homology-based analysis, its accuracy still depends on template availability. Meanwhile, in neXtProt 2019-01, 25 proteins acquired new function annotation through literature curation at UniProt/Swiss-Prot. Before the release of these curated results, we submitted to neXtProt blinded predictions of free-text function annotation based on predicted GO terms. For 10 of the 25, a good match of free-text or GO term annotation was obtained. These blind tests represent rigorous assessments of I-TASSER/COFACTOR. neXtProt now provides links to precomputed I-TASSER/COFACTOR predictions for proteins without function annotation to facilitate experimental planning on "dark proteins".
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
- Departments of Internal Medicine and Human Genetics and School of Public Health, and University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
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49
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Omenn GS, Lane L, Overall CM, Corrales FJ, Schwenk JM, Paik YK, Van Eyk JE, Liu S, Pennington S, Snyder MP, Baker MS, Deutsch EW. Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project. J Proteome Res 2019; 18:4098-4107. [PMID: 31430157 PMCID: PMC6898754 DOI: 10.1021/acs.jproteome.9b00434] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.
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Affiliation(s)
- Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
| | - Lydie Lane
- CALIPHO Group, 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
- Life Sciences Institute, Faculty of Dentistry, University of British Columbia, 2350 Health Sciences Mall, Room 4.401, Vancouver, British Columbia V6T 1Z3, Canada
| | | | - Jochen M. Schwenk
- Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Young-Ki Paik
- Yonsei Proteome Research Center, Yonsei University, Room 425, Building #114, 50 Yonsei-ro, Seodaemoon-ku, Seoul 120-749, South Korea
| | - Jennifer E. Van Eyk
- Advanced Clinical BioSystems Research Institute, Cedars Sinai Precision Biomarker Laboratories, Barbra Streisand Women’s Heart Center, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Siqi Liu
- BGI Group-Shenzhen, Yantian District, Shenzhen 518083, China
| | - Stephen Pennington
- School of Medicine, University College Dublin, Conway Institute Belfield, Dublin 4, Ireland
| | - Michael P. Snyder
- Department of Genetics, Stanford University, Alway Building, 300 Pasteur Drive and 3165 Porter Drive, Palo Alto, California 94304, 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 98109-5263, United States
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50
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Zhang Y, Lin Z, Tan Y, Bu F, Hao P, Zhang K, Yang H, Liu S, Ren Y. Exploration of Missing Proteins by a Combination Approach to Enrich the Low-Abundance Hydrophobic Proteins from Four Cancer Cell Lines. J Proteome Res 2019; 19:401-408. [DOI: 10.1021/acs.jproteome.9b00590] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Yuanliang Zhang
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
| | - Zhilong Lin
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
| | - Yifan Tan
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
| | - Fanyu Bu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, Guangdong, China
| | - Piliang Hao
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Keren Zhang
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
| | - Huanming Yang
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Siqi Liu
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
| | - Yan Ren
- BGI-Shenzhen, Beishan Industrial Zone, 11th Building, Yantian District, Shenzhen 518083, Guangdong, China
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
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