1
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Feng T, Jie M, Deng K, Yang J, Jiang H. Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer. Clin Chim Acta 2024; 558:119675. [PMID: 38631604 DOI: 10.1016/j.cca.2024.119675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/30/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis. METHODS We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models. RESULTS In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively. CONCLUSIONS This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.
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Affiliation(s)
- Tong Feng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Minwen Jie
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Kai Deng
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jinlin Yang
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Hao Jiang
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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2
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Zemaitis KJ, Fulcher JM, Kumar R, Degnan DJ, Lewis LA, Liao YC, Veličković M, Williams SM, Moore RJ, Bramer LM, Veličković D, Zhu Y, Zhou M, Paša-Tolić L. Spatial top-down proteomics for the functional characterization of human kidney. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580062. [PMID: 38405958 PMCID: PMC10888776 DOI: 10.1101/2024.02.13.580062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
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Affiliation(s)
- Kevin J Zemaitis
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - James M Fulcher
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rashmi Kumar
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Logan A Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Yen-Chen Liao
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Marija Veličković
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Sarah M Williams
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Dušan Veličković
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, 1 DNA Way, San Francisco, CA 94080, United States
| | - Mowei Zhou
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
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3
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Fricker LD. Quantitative Peptidomics: General Considerations. Methods Mol Biol 2024; 2758:89-108. [PMID: 38549010 DOI: 10.1007/978-1-0716-3646-6_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Peptidomics is the detection and identification of the peptides present in a sample, and quantitative peptidomics provides additional information about the amounts of these peptides. It is possible to perform absolute quantitation of peptide levels in which the biological sample is compared to synthetic standards of each peptide. More commonly, relative quantitation is performed to compare peptide levels between two or more samples. Relative quantitation can measure differences between all peptides that are detectable, which can exceed 1000 peptides in a complex sample. In this chapter, various techniques used for quantitative peptidomics are described along with discussion of the advantages and disadvantages of each approach. A guide to selecting the optimal quantitative approach is provided, based on the goals of the experiment and the resources that are available.
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Affiliation(s)
- Lloyd D Fricker
- Departments of Molecular Pharmacology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.
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4
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Choi SB, Vatan T, Alexander TA, Zhang C, Mitchell SM, Speer CM, Nemes P. Microanalytical Mass Spectrometry with Super-Resolution Microscopy Reveals a Proteome Transition During Development of the Brain's Circadian Pacemaker. Anal Chem 2023; 95:15208-15216. [PMID: 37792996 PMCID: PMC10728713 DOI: 10.1021/acs.analchem.3c01987] [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] [Indexed: 10/06/2023]
Abstract
During brain development, neuronal proteomes are regulated in part by changes in spontaneous and sensory-driven activity in immature neural circuits. A longstanding model for studying activity-dependent circuit refinement is the developing mouse visual system where the formation of axonal projections from the eyes to the brain is influenced by spontaneous retinal activity prior to the onset of vision and by visual experience after eye-opening. The precise proteomic changes in retinorecipient targets that occur during this developmental transition are unknown. Here, we developed a microanalytical proteomics pipeline using capillary electrophoresis (CE) electrospray ionization (ESI) mass spectrometry (MS) in the discovery setting to quantify developmental changes in the chief circadian pacemaker, the suprachiasmatic nucleus (SCN), before and after the onset of photoreceptor-dependent visual function. Nesting CE-ESI with trapped ion mobility spectrometry time-of-flight (TOF) mass spectrometry (TimsTOF PRO) doubled the number of identified and quantified proteins compared to the TOF-only control on the same analytical platform. From 10 ng of peptide input, corresponding to <∼0.5% of the total local tissue proteome, technical triplicate analyses identified 1894 proteins and quantified 1066 proteins, including many with important canonical functions in axon guidance, synapse function, glial cell maturation, and extracellular matrix refinement. Label-free quantification revealed differential regulation for 166 proteins over development, with enrichment of axon guidance-associated proteins prior to eye-opening and synapse-associated protein enrichment after eye-opening. Super-resolution imaging of select proteins using STochastic Optical Reconstruction Microscopy (STORM) corroborated the MS results and showed that increased presynaptic protein abundance pre/post eye-opening in the SCN reflects a developmental increase in synapse number, but not presynaptic size or extrasynaptic protein expression. This work marks the first development and systematic application of TimsTOF PRO for CE-ESI-based microproteomics and the first integration of microanalytical CE-ESI TimsTOF PRO with volumetric super-resolution STORM imaging to expand the repertoire of technologies supporting analytical neuroscience.
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Affiliation(s)
- Sam B. Choi
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - Tarlan Vatan
- Department of Biology, University of Maryland, College Park, MD, 20742, USA
| | | | - Chenghang Zhang
- Department of Biology, University of Maryland, College Park, MD, 20742, USA
| | | | - Colenso M. Speer
- Department of Biology, University of Maryland, College Park, MD, 20742, USA
| | - Peter Nemes
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
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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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Scott AM, Karlsson C, Mohanty T, Hartman E, Vaara ST, Linder A, Malmström J, Malmström L. Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics. Commun Biol 2023; 6:628. [PMID: 37301900 PMCID: PMC10257694 DOI: 10.1038/s42003-023-04977-x] [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: 12/19/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.
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Affiliation(s)
- Aaron M Scott
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Erik Hartman
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Suvi T Vaara
- Division of Anaesthesia and Intensive Care Medicine Department of Surgery, Intensive Care Units, Helsinki University Central Hospital, Box 340, 00029 HUS, Helsinki, Finland
| | - Adam Linder
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
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7
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Ridder MD, van den Brandeler W, Altiner M, Daran-Lapujade P, Pabst M. Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast. Mol Cell Proteomics 2023; 22:100552. [PMID: 37076048 DOI: 10.1016/j.mcpro.2023.100552] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/23/2023] [Accepted: 04/13/2023] [Indexed: 04/21/2023] Open
Abstract
The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.
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Affiliation(s)
- Maxime den Ridder
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Wiebeke van den Brandeler
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Meryem Altiner
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Pascale Daran-Lapujade
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands.
| | - Martin Pabst
- Delft University of Technology, Department of Biotechnology, van der Maasweg 9, 2629 HZ Delft, The Netherlands.
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8
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Huffman RG, Leduc A, Wichmann C, Di Gioia M, Borriello F, Specht H, Derks J, Khan S, Khoury L, Emmott E, Petelski AA, Perlman DH, Cox J, Zanoni I, Slavov N. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat Methods 2023; 20:714-722. [PMID: 37012480 PMCID: PMC10172113 DOI: 10.1038/s41592-023-01830-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 02/27/2023] [Indexed: 04/05/2023]
Abstract
Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .
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Affiliation(s)
- R Gray Huffman
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Christoph Wichmann
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marco Di Gioia
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - David H Perlman
- Merck Exploratory Sciences Center, Merck Sharp and Dohme Corp., Cambridge, MA, USA
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ivan Zanoni
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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9
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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10
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Mohammed Y, Goodlett D, Borchers CH. Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics. Methods Mol Biol 2023; 2628:557-577. [PMID: 36781806 DOI: 10.1007/978-1-0716-2978-9_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.
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Affiliation(s)
- Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, ZA, Netherlands. .,University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada. .,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.
| | - David Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada.,Department of Pathology, McGill University, Montreal, QC, Canada
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11
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Casavant EP, Liang J, Sankhe S, Mathews WR, Anania VG. Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods. Methods Mol Biol 2023; 2603:245-257. [PMID: 36370285 DOI: 10.1007/978-1-0716-2863-8_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically, discovery approaches have used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.
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12
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Saccharomyces cerevisiae as a Model System for Eukaryotic Cell Biology, from Cell Cycle Control to DNA Damage Response. Int J Mol Sci 2022; 23:ijms231911665. [PMID: 36232965 PMCID: PMC9570374 DOI: 10.3390/ijms231911665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/08/2022] Open
Abstract
The yeast Saccharomyces cerevisiae has been used for bread making and beer brewing for thousands of years. In addition, its ease of manipulation, well-annotated genome, expansive molecular toolbox, and its strong conservation of basic eukaryotic biology also make it a prime model for eukaryotic cell biology and genetics. In this review, we discuss the characteristics that made yeast such an extensively used model organism and specifically focus on the DNA damage response pathway as a prime example of how research in S. cerevisiae helped elucidate a highly conserved biological process. In addition, we also highlight differences in the DNA damage response of S. cerevisiae and humans and discuss the challenges of using S. cerevisiae as a model system.
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13
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Ba Q, Hei Y, Dighe A, Li W, Maziarz J, Pak I, Wang S, Wagner GP, Liu Y. Proteotype coevolution and quantitative diversity across 11 mammalian species. SCIENCE ADVANCES 2022; 8:eabn0756. [PMID: 36083897 PMCID: PMC9462687 DOI: 10.1126/sciadv.abn0756] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.
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Affiliation(s)
- Qian Ba
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Yuanyuan Hei
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Anasuya Dighe
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Jamie Maziarz
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Irene Pak
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Günter P. Wagner
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06510, USA
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14
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Kubota N, Suyama M. Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits. PLoS Comput Biol 2022; 18:e1010436. [PMID: 36037215 PMCID: PMC9462676 DOI: 10.1371/journal.pcbi.1010436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 09/09/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022] Open
Abstract
Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant–gene associations. Furthermore, we identified genomic loci associated with the activity of “hidden” promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits. Many variations exist in the human genome, creating phenotypic diversity among individuals. It is well known that they are associated with the risk of disease development by affecting the expression levels of genes. Genes are transcribed from regulatory elements called promoters. Although some genes are transcribed from multiple promoters and translated into proteins with different functions, the relationship between genomic variations and promoter activities has not been investigated in depth compared to the relationship between genomic variations and gene expression levels. In this study, we proposed a new method to detect the association between genomic variations and promoter activities. Our method identified the associations between many variations and promoters using genomic and promoter activity data from blood cells of 438 individuals. This study allowed us to identify new functional associations between genomic variations and genes. Furthermore, we identified previously undiscovered variation-gene-disease associations. Our results will help to elucidate the molecular mechanisms of diseases in which genetic factors are involved.
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Affiliation(s)
- Naoto Kubota
- Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Mikita Suyama
- Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
- * E-mail:
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15
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Manousaki A, Bagnall J, Spiller D, Balarezo-Cisneros LN, White M, Delneri D. Quantitative Characterisation of Low Abundant Yeast Mitochondrial Proteins Reveals Compensation for Haplo-Insufficiency in Different Environments. Int J Mol Sci 2022; 23:8532. [PMID: 35955668 PMCID: PMC9369417 DOI: 10.3390/ijms23158532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/15/2022] [Accepted: 07/25/2022] [Indexed: 02/05/2023] Open
Abstract
The quantification of low abundant membrane-binding proteins such as transcriptional factors and chaperones has proven difficult, even with the most sophisticated analytical technologies. Here, we exploit and optimise the non-invasive Fluorescence Correlation Spectroscopy (FCS) for the quantitation of low abundance proteins, and as proof of principle, we choose two interacting proteins involved in the fission of mitochondria in yeast, Fis1p and Mdv1p. In Saccharomyces cerevisiae, the recruitment of Fis1p and Mdv1p to mitochondria is essential for the scission of the organelles and the retention of functional mitochondrial structures in the cell. We use FCS in single GFP-labelled live yeast cells to quantify the protein abundance in homozygote and heterozygote cells and to investigate the impact of the environments on protein copy number, bound/unbound protein state and mobility kinetics. Both proteins were observed to localise predominantly at mitochondrial structures, with the Mdv1p bound state increasing significantly in a strictly respiratory environment. Moreover, a compensatory mechanism that controls Fis1p abundance upon deletion of one allele was observed in Fis1p but not in Mdv1p, suggesting differential regulation of Fis1p and Mdv1p protein expression.
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Affiliation(s)
- Alkisti Manousaki
- Manchester Institute of Biotechnology, Faculty of Biology, Medicine and Health, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK; (A.M.); (L.N.B.-C.)
- Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - James Bagnall
- Division of Diabetes, Endocrinology and Gastroenterology Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;
| | - David Spiller
- Platform Sciences, Enabling Technologies & Infrastructure, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;
| | - Laura Natalia Balarezo-Cisneros
- Manchester Institute of Biotechnology, Faculty of Biology, Medicine and Health, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK; (A.M.); (L.N.B.-C.)
- Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Michael White
- Division of Molecular and Cellular Function, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;
| | - Daniela Delneri
- Manchester Institute of Biotechnology, Faculty of Biology, Medicine and Health, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK; (A.M.); (L.N.B.-C.)
- Division of Evolution and Genomic Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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16
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Grossbach J, Gillet L, Clément‐Ziza M, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R, Beyer A. The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol 2022; 18:e10712. [PMID: 35574625 PMCID: PMC9109056 DOI: 10.15252/msb.202110712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.
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Affiliation(s)
- Jan Grossbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Ludovic Gillet
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
| | - Mathieu Clément‐Ziza
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Lesaffre International Marcq‐en‐Barœul France
| | - Corinna L Schmalohr
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Olga T Schubert
- Department of Human Genetics University of California, Los Angeles Los Angeles CA USA
| | | | | | | | - Isabell Bludau
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany
| | - Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Peter Tessarz
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Martin Graef
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Ruedi Aebersold
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Faculty of Science University of Zurich Zurich Switzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Institute for Genetics Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany
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17
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CHD4 is recruited by GATA4 and NKX2-5 to repress noncardiac gene programs in the developing heart. Genes Dev 2022; 36:468-482. [PMID: 35450884 PMCID: PMC9067406 DOI: 10.1101/gad.349154.121] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 03/31/2022] [Indexed: 12/23/2022]
Abstract
In this study, Robbe et al. investigated how CHD4/NuRD is localized to specific cardiac target genes, as neither CHD4 nor NuRD can directly bind DNA. They coupled a bioinformatics-based approach with mass spectrometry analyses to demonstrate that CHD4 interacts with the core cardiac transcription factors GATA4, NKX2-5, and TBX5 during embryonic heart development, thus providing new insights into how mutations in the CHD4 protein lead to cardiac-specific disease states. The nucleosome remodeling and deacetylase (NuRD) complex is one of the central chromatin remodeling complexes that mediates gene repression. NuRD is essential for numerous developmental events, including heart development. Clinical and genetic studies have provided direct evidence for the role of chromodomain helicase DNA-binding protein 4 (CHD4), the catalytic component of NuRD, in congenital heart disease (CHD), including atrial and ventricular septal defects. Furthermore, it has been demonstrated that CHD4 is essential for mammalian cardiomyocyte formation and function. A key unresolved question is how CHD4/NuRD is localized to specific cardiac target genes, as neither CHD4 nor NuRD can directly bind DNA. Here, we coupled a bioinformatics-based approach with mass spectrometry analyses to demonstrate that CHD4 interacts with the core cardiac transcription factors GATA4, NKX2-5, and TBX5 during embryonic heart development. Using transcriptomics and genome-wide occupancy data, we characterized the genomic landscape of GATA4, NKX2-5, and TBX5 repression and defined the direct cardiac gene targets of the GATA4–CHD4, NKX2-5–CHD4, and TBX5-CHD4 complexes. These data were used to identify putative cis-regulatory elements controlled by these complexes. We genetically interrogated two of these silencers in vivo: Acta1 and Myh11. We show that deletion of these silencers leads to inappropriate skeletal and smooth muscle gene misexpression, respectively, in the embryonic heart. These results delineate how CHD4/NuRD is localized to specific cardiac loci and explicates how mutations in the broadly expressed CHD4 protein lead to cardiac-specific disease states.
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18
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Lin CL, Petersen MA, Mauch A, Gottlieb A. Towards lager beer aroma improvement via selective amino acid release by proteases during mashing. JOURNAL OF THE INSTITUTE OF BREWING 2022. [DOI: 10.1002/jib.682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Claire L. Lin
- Brewing AR 345 Novozymes A/S Biologiens Vej 2 Kongens Lyngby 2800 Denmark
- Department of Food Science University of Copenhagen Rolighedsvej 26 Frederiksberg 1958 Denmark
| | - Mikael A. Petersen
- Department of Food Science University of Copenhagen Rolighedsvej 26 Frederiksberg 1958 Denmark
| | - Alexander Mauch
- Brewing AR 345 Novozymes A/S Biologiens Vej 2 Kongens Lyngby 2800 Denmark
| | - Andrea Gottlieb
- Brewing AR 345 Novozymes A/S Biologiens Vej 2 Kongens Lyngby 2800 Denmark
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19
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Melani RD, Gerbasi VR, Anderson LC, Sikora JW, Toby TK, Hutton JE, Butcher DS, Negrão F, Seckler HS, Srzentić K, Fornelli L, Camarillo JM, LeDuc RD, Cesnik AJ, Lundberg E, Greer JB, Fellers RT, Robey MT, DeHart CJ, Forte E, Hendrickson CL, Abbatiello SE, Thomas PM, Kokaji AI, Levitsky J, Kelleher NL. The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells. Science 2022; 375:411-418. [PMID: 35084980 PMCID: PMC9097315 DOI: 10.1126/science.aaz5284] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Human biology is tightly linked to proteins, yet most measurements do not precisely determine alternatively spliced sequences or posttranslational modifications. Here, we present the primary structures of ~30,000 unique proteoforms, nearly 10 times more than in previous studies, expressed from 1690 human genes across 21 cell types and plasma from human blood and bone marrow. The results, compiled in the Blood Proteoform Atlas (BPA), indicate that proteoforms better describe protein-level biology and are more specific indicators of differentiation than their corresponding proteins, which are more broadly expressed across cell types. We demonstrate the potential for clinical application, by interrogating the BPA in the context of liver transplantation and identifying cell and proteoform signatures that distinguish normal graft function from acute rejection and other causes of graft dysfunction.
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Affiliation(s)
- Rafael D. Melani
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Vincent R. Gerbasi
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Lissa C. Anderson
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | - Jacek W. Sikora
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Timothy K. Toby
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Josiah E. Hutton
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - David S. Butcher
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | - Fernanda Negrão
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Henrique S. Seckler
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Kristina Srzentić
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Luca Fornelli
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Jeannie M. Camarillo
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Richard D. LeDuc
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Anthony J. Cesnik
- Department of Genetics, Stanford University, Stanford, CA, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Emma Lundberg
- Department of Genetics, Stanford University, Stanford, CA, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Joseph B. Greer
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Ryan T. Fellers
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Matthew T. Robey
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Caroline J. DeHart
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Eleonora Forte
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | | | - Paul M. Thomas
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | | | - Josh Levitsky
- Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Neil L. Kelleher
- Department of Molecular Biosciences, Department of Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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20
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Molendijk J, Blazev R, Mills RJ, Ng YK, Watt KI, Chau D, Gregorevic P, Crouch PJ, Hilton JBW, Lisowski L, Zhang P, Reue K, Lusis AJ, Hudson JE, James DE, Seldin MM, Parker BL. Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function. eLife 2022; 11:82951. [PMID: 36472367 PMCID: PMC9833826 DOI: 10.7554/elife.82951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Improving muscle function has great potential to improve the quality of life. To identify novel regulators of skeletal muscle metabolism and function, we performed a proteomic analysis of gastrocnemius muscle from 73 genetically distinct inbred mouse strains, and integrated the data with previously acquired genomics and >300 molecular/phenotypic traits via quantitative trait loci mapping and correlation network analysis. These data identified thousands of associations between protein abundance and phenotypes and can be accessed online (https://muscle.coffeeprot.com/) to identify regulators of muscle function. We used this resource to prioritize targets for a functional genomic screen in human bioengineered skeletal muscle. This identified several negative regulators of muscle function including UFC1, an E2 ligase for protein UFMylation. We show UFMylation is up-regulated in a mouse model of amyotrophic lateral sclerosis, a disease that involves muscle atrophy. Furthermore, in vivo knockdown of UFMylation increased contraction force, implicating its role as a negative regulator of skeletal muscle function.
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Affiliation(s)
- Jeffrey Molendijk
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Ronnie Blazev
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | | | - Yaan-Kit Ng
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Kevin I Watt
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Daryn Chau
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, IrvineIrvineUnited States
| | - Paul Gregorevic
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Peter J Crouch
- Department of Biochemistry and Pharmacology, University of MelbourneMelbourneAustralia
| | - James BW Hilton
- Department of Biochemistry and Pharmacology, University of MelbourneMelbourneAustralia
| | - Leszek Lisowski
- Children's Medical Research Institute, University of SydneySydneyAustralia,Military Institute of MedicineWarszawaPoland
| | - Peixiang Zhang
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Karen Reue
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Aldons J Lusis
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States,Department of Microbiology, Immunology and Molecular Genetics, University of California, Los AngelesLos AngelesUnited States
| | - James E Hudson
- QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Science, School of Medical Science, University of SydneySydneyAustralia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, IrvineIrvineUnited States
| | - Benjamin L Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
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21
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Choi SB, Muñoz-LLancao P, Manzini MC, Nemes P. Data-Dependent Acquisition Ladder for Capillary Electrophoresis Mass Spectrometry-Based Ultrasensitive (Neuro)Proteomics. Anal Chem 2021; 93:15964-15972. [PMID: 34812615 DOI: 10.1021/acs.analchem.1c03327] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in sensitivity in high-resolution mass spectrometry (HRMS). Microanalytical capillary electrophoresis electrospray ionization (CE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. Here, we systematically evaluate performance limitations in this technology to develop a data acquisition strategy with deeper coverage of the neuroproteome from trace amounts of starting materials than traditional dynamic exclusion. During standard data-dependent acquisition (DDA), compact migration challenged the duty cycle of second-stage transitions and redundant targeting of abundant peptide signals lowered their identification success rate. DDA was programmed to progressively exclude a static set of high-intensity peptide signals throughout replicate measurements, essentially forming rungs of a "DDA ladder." The method was tested for ∼500 pg portions of a protein digest from cultured hippocampal (primary) neurons (mouse), which estimated the total amount of protein from a single neuron. The analysis of ∼5 ng of protein digest over all replicates, approximating ∼10 neurons, identified 428 nonredundant proteins (415 quantified), an ∼35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder enhances CE-HRMS sensitivity to single-neuron equivalent amounts of proteins, thus expanding the analytical toolbox of neuroscience.
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Affiliation(s)
- Sam B Choi
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
| | - Pablo Muñoz-LLancao
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States.,Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut 06510, United States
| | - M Chiara Manzini
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
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22
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Sarkar A, Nazir A. Carrying Excess Baggage Can Slowdown Life: Protein Clearance Machineries That Go Awry During Aging and the Relevance of Maintaining Them. Mol Neurobiol 2021; 59:821-840. [PMID: 34792731 DOI: 10.1007/s12035-021-02640-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/05/2021] [Indexed: 01/07/2023]
Abstract
Cellular homeostasis is maintained by rapid and systematic cleansing of aberrant and aggregated proteins within cells. Neurodegenerative diseases (NDs) especially Parkinson's and Alzheimer's disease are known to be associated with multiple factors, most important being impaired clearance of aggregates, resulting in the accumulation of specific aggregated protein in the brain. Protein quality control (PQC) of proteostasis network comprises proteolytic machineries and chaperones along with their regulators to ensure precise operation and maintenance of proteostasis. Such regulatory factors coordinate among each other multiple functional aspects related to proteins, including their synthesis, folding, transport, and degradation. During aging due to inevitable endogenous and external stresses, sustaining a proteome balance is a challenging task. Such stresses decline the capacity of the proteostasis network compromising the proteome integrity, affecting the fundamental physiological processes including reproductive fitness of the organism. This review focuses on highlighting proteome-wide changes during aging and the strategies for proteostasis improvements. The possibility of augmenting the proteostasis network either via genetic or pharmacological interventions may be a promising strategy towards delaying age-associated pathological consequences due to proteome disbalance, thus promoting healthy aging and prolonged longevity.
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Affiliation(s)
- Arunabh Sarkar
- Division of Neuroscience and Ageing Biology, CSIR-Central Drug Research Institute, Lucknow, UP, 226031, India
| | - Aamir Nazir
- Division of Neuroscience and Ageing Biology, CSIR-Central Drug Research Institute, Lucknow, UP, 226031, India.
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23
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Matsuzaki Y, Aoki W, Miyazaki T, Aburaya S, Ohtani Y, Kajiwara K, Koike N, Minakuchi H, Miura N, Kadonosono T, Ueda M. Peptide barcoding for one-pot evaluation of sequence-function relationships of nanobodies. Sci Rep 2021; 11:21516. [PMID: 34728738 PMCID: PMC8563947 DOI: 10.1038/s41598-021-01019-6] [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: 08/29/2021] [Accepted: 10/21/2021] [Indexed: 11/17/2022] Open
Abstract
Optimisation of protein binders relies on laborious screening processes. Investigation of sequence–function relationships of protein binders is particularly slow, since mutants are purified and evaluated individually. Here we developed peptide barcoding, a high-throughput approach for accurate investigation of sequence–function relationships of hundreds of protein binders at once. Our approach is based on combining the generation of a mutagenised nanobody library fused with unique peptide barcodes, the formation of nanobody–antigen complexes at different ratios, their fine fractionation by size-exclusion chromatography and quantification of peptide barcodes by targeted proteomics. Applying peptide barcoding to an anti-GFP nanobody as a model, we successfully identified residues important for the binding affinity of anti-GFP nanobody at once. Peptide barcoding discriminated subtle changes in KD at the order of nM to sub-nM. Therefore, peptide barcoding is a powerful tool for engineering protein binders, enabling reliable one-pot evaluation of sequence–function relationships.
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Affiliation(s)
- Yusei Matsuzaki
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan
| | - Wataru Aoki
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan. .,Kyoto Integrated Science and Technology Bio-Analysis Center, Simogyo-ku, Kyoto, 600-8813, Japan. .,JST, CREST, Chiyoda-ku, Tokyo, 102-0076, Japan. .,JST, COI-NEXT, Chiyoda-ku, Tokyo, 102-0076, Japan. .,JST, FOREST, Chiyoda-ku, Tokyo, 102-0076, Japan.
| | - Takumi Miyazaki
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan
| | - Shunsuke Aburaya
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan
| | - Yuta Ohtani
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan
| | - Kaho Kajiwara
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan
| | - Naoki Koike
- TechnoPro, Inc. TechnoPro R&D, Company, Tokyo, 106-6135, Japan
| | | | - Natsuko Miura
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Naka-ku, Sakai, 599-8531, Japan
| | - Tetsuya Kadonosono
- School of Life Science and Technology, Tokyo Institute of Technology, Midori-ku, Yokohama, 226-8501, Japan
| | - Mitsuyoshi Ueda
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Sakyo-ku, Kyoto, 606-8502, Japan.,Kyoto Integrated Science and Technology Bio-Analysis Center, Simogyo-ku, Kyoto, 600-8813, Japan.,JST, CREST, Chiyoda-ku, Tokyo, 102-0076, Japan.,JST, COI-NEXT, Chiyoda-ku, Tokyo, 102-0076, Japan
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24
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Keele GR, Zhang T, Pham DT, Vincent M, Bell TA, Hock P, Shaw GD, Paulo JA, Munger SC, Pardo-Manuel de Villena F, Ferris MT, Gygi SP, Churchill GA. Regulation of protein abundance in genetically diverse mouse populations. CELL GENOMICS 2021; 1:100003. [PMID: 36212994 PMCID: PMC9536773 DOI: 10.1016/j.xgen.2021.100003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 04/01/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022]
Abstract
Genetically diverse mouse populations are powerful tools for characterizing the regulation of the proteome and its relationship to whole-organism phenotypes. We used mass spectrometry to profile and quantify the abundance of 6,798 proteins in liver tissue from mice of both sexes across 58 Collaborative Cross (CC) inbred strains. We previously collected liver proteomics data from the related Diversity Outbred (DO) mice and their founder strains. We show concordance across the proteomics datasets despite being generated from separate experiments, allowing comparative analysis. We map protein abundance quantitative trait loci (pQTLs), identifying 1,087 local and 285 distal in the CC mice and 1,706 local and 414 distal in the DO mice. We find that regulatory effects on individual proteins are conserved across the mouse populations, in particular for local genetic variation and sex differences. In comparison, proteins that form complexes are often co-regulated, displaying varying genetic architectures, and overall show lower heritability and map fewer pQTLs. We have made this resource publicly available to enable quantitative analyses of the regulation of the proteome.
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Affiliation(s)
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Timothy A. Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ginger D. Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | | | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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25
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Toyokawa Y, Koonthongkaew J, Takagi H. An overview of branched-chain amino acid aminotransferases: functional differences between mitochondrial and cytosolic isozymes in yeast and human. Appl Microbiol Biotechnol 2021; 105:8059-8072. [PMID: 34622336 DOI: 10.1007/s00253-021-11612-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/20/2021] [Accepted: 09/20/2021] [Indexed: 01/07/2023]
Abstract
Branched-chain amino acid aminotransferase (BCAT) catalyzes bidirectional transamination in the cell between branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) and branched-chain α-keto acids (BCKAs; α-ketoisovalerate, α-ketoisocaproate, and α-keto-β-methylvalerate). Eukaryotic cells contain two types of paralogous BCATs: mitochondrial BCAT (BCATm) and cytosolic BCAT (BCATc). Both isozymes have identical enzymatic functions, so they have long been considered to perform similar physiological functions in the cells. However, many studies have gradually revealed the differences in physiological functions and regulatory mechanisms between them. In this article, we present overviews of BCATm and BCATc in both yeast and human. We also introduce BCAT variants found natively or constructed artificially, which could have significant implications for research into the relationship between the primary structures and protein functions of BCATs. KEY POINTS: • BCAT catalyzes bidirectional transamination in the cell between BCAAs and BCKAs. • BCATm and BCATc are different in the metabolic roles and regulatory mechanisms. • BCAT variants offer insight into a relationship between the structure and function.
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Affiliation(s)
- Yoichi Toyokawa
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan
| | - Jirasin Koonthongkaew
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan
| | - Hiroshi Takagi
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.
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26
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Qualitative Characterization of the Rat Liver Mitochondrial Lipidome Using All Ion Fragmentation on an Exactive Benchtop Orbitrap MS. Methods Mol Biol 2021. [PMID: 34118051 DOI: 10.1007/978-1-0716-1262-0_24] [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
Untargeted lipidomics profiling by liquid chromatography -mass spectrometry (LC-MS) allows researchers to observe the occurrences of lipids in a biological sample without showing intentional bias to any specific class of lipids and allows retrospective reanalysis of data collected. Typically, and in the specific method described, a general extraction method followed by LC separation is used to achieve nonspecific class coverage of the lipidome prior to high resolution accurate mass (HRAM) MS detection . Here we describe a workflow including the isolation of mitochondria from liver tissue, followed by mitochondrial lipid extraction and the LC-MS conditions used for data acquisition. We also highlight how, in this method, all ion fragmentation can be used to identify species of lower abundances, often missed by data dependent fragmentation techniques. Here we describe the isolation of mitochondria from liver tissue, followed by mitochondrial lipid extraction and the LC-MS conditions used for data acquisition.
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27
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Generation of a mouse SWATH-MS spectral library to quantify 10148 proteins involved in cell reprogramming. Sci Data 2021; 8:118. [PMID: 33903600 PMCID: PMC8076245 DOI: 10.1038/s41597-021-00896-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 03/23/2021] [Indexed: 11/09/2022] Open
Abstract
Murine models are amongst the most widely used systems to study biology and pathology. Targeted quantitative proteomic analysis is a relatively new tool to interrogate such systems. Recently the need for relative quantification on hundreds to thousands of samples has driven the development of Data Independent Acquisition methods. One such technique is SWATH-MS, which in the main requires prior acquisition of mass spectra to generate an assay reference library. In stem cell research, it has been shown pluripotency can be induced starting with a fibroblast population. In so doing major changes in expressed proteins is inevitable. Here we have created a reference library to underpin such studies. This is inclusive of an extensively documented script to enable replication of library generation from the raw data. The documented script facilitates reuse of data and adaptation of the library to novel applications. The resulting library provides deep coverage of the mouse proteome. The library covers 29519 proteins (53% of the proteome) of which 7435 (13%) are supported by a proteotypic peptide.
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28
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Bhowmick P, Roome S, Borchers CH, Goodlett DR, Mohammed Y. An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community. J Proteome Res 2021; 20:2105-2115. [PMID: 33683131 PMCID: PMC8041396 DOI: 10.1021/acs.jproteome.0c00961] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Precise multiplexed
quantification of proteins in biological samples
can be achieved by targeted proteomics using multiple or parallel
reaction monitoring (MRM/PRM). Combined with internal standards, the
method achieves very good repeatability and reproducibility enabling
excellent protein quantification and allowing longitudinal and cohort
studies. A laborious part of performing such experiments lies in the
preparation steps dedicated to the development and validation of individual
protein assays. Several public repositories host information on targeted
proteomics assays, including NCI’s Clinical Proteomic Tumor
Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library,
SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying
levels of details. We introduced MRMAssayDB in 2018 as an integrated
resource for targeted proteomics assays. The Web-based application
maps and links the assays from the repositories, includes comprehensive
up-to-date protein and sequence annotations, and provides multiple
visualization options on the peptide and protein level. We have extended
MRMAssayDB with more assays and extensive annotations. Currently it
contains >828 000 assays covering >51 000 proteins
from
94 organisms, of which >17 000 proteins are present in >2400
biological pathways, and >48 000 mapping to >21 000
Gene Ontology terms. This is an increase of about four times the number
of assays since introduction. We have expanded annotations of interaction,
biological pathways, and disease associations. A newly added visualization
module for coupled molecular structural annotation browsing allows
the user to interactively examine peptide sequence and any known PTMs
and disease mutations, and map all to available protein 3D structures.
Because of its integrative approach, MRMAssayDB enables a holistic
view of suitable proteotypic peptides and commonly used transitions
in empirical data. Availability: http://mrmassaydb.proteincentre.com.
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Affiliation(s)
- Pallab Bhowmick
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Simon Roome
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, Quebec H3T 1E2, Canada.,Department of Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel Street, Moscow 121205, Russia
| | - David R Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, 80-309 Gdansk, Poland
| | - Yassene Mohammed
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, Netherlands
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29
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Liu S, Ma D, Li Z, Sun H, Mao J, Shi Y, Han X, Zhou Z, Mao J. Assimilable nitrogen reduces the higher alcohols content of huangjiu. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107660] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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30
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Liu S, Ma D, Li Z, Sun H, Mao J, Shi Y, Han X, Zhou Z, Mao J. Assimilable nitrogen reduces the higher alcohols content of huangjiu. Food Control 2021. [DOI: 10.766010.1016/j.foodcont.2020.107660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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31
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Gao Y, Ping L, Duong D, Zhang C, Dammer EB, Li Y, Chen P, Chang L, Gao H, Wu J, Xu P. Mass-Spectrometry-Based Near-Complete Draft of the Saccharomyces cerevisiae Proteome. J Proteome Res 2021; 20:1328-1340. [PMID: 33443437 DOI: 10.1021/acs.jproteome.0c00721] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Proteomics approaches designed to catalogue all open reading frames (ORFs) under a defined set of growth conditions of an organism have flourished in recent years. However, no proteome has been sequenced completely so far. Here, we generate the largest yeast proteome data set, including 5610 identified proteins, using a strategy based on optimized sample preparation and high-resolution mass spectrometry. Among the 5610 identified proteins, 94.1% are core proteins, which achieves near-complete coverage of the yeast ORFs. Comprehensive analysis of missing proteins showed that proteins are missed mainly due to physical properties. A review of protein abundance shows that our proteome encompasses a uniquely broad dynamic range. Additionally, these values highly correlate with mRNA abundance, implying a high level of accuracy, sensitivity, and precision. We present examples of how the data could be used, including reannotating gene localization, providing expression evidence of pseudogenes. Our near-complete yeast proteome data set will be a useful and important resource for further systematic studies.
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Affiliation(s)
- Yuan Gao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Lingyan Ping
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Duc Duong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.,Center for Neurodegenerative Diseases, Emory Proteomics Service Center, and Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia 30322, United States
| | - Chengpu Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Eric B Dammer
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.,Center for Neurodegenerative Diseases, Emory Proteomics Service Center, and Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia 30322, United States
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Peiru Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Huiying Gao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China
| | - Junzhu Wu
- School of Basic Medical Science, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery of Ministry of Education, School of Pharmaceutical Sciences, School of Medicine, Wuhan University, Wuhan 430072, P. R. China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.,School of Basic Medical Science, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery of Ministry of Education, School of Pharmaceutical Sciences, School of Medicine, Wuhan University, Wuhan 430072, P. R. China.,Anhui Medical University, Hefei 230032, P. R. China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
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32
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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33
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Gao E, Li W, Wu C, Shao W, Di Y, Liu Y. Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes. Mol Omics 2021; 17:413-425. [PMID: 33728422 DOI: 10.1039/d0mo00188k] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.
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Affiliation(s)
- Erli Gao
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.
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34
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Brion C, Lutz SM, Albert FW. Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation. eLife 2020; 9:60645. [PMID: 33191917 PMCID: PMC7707838 DOI: 10.7554/elife.60645] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/14/2020] [Indexed: 01/27/2023] Open
Abstract
Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.
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Affiliation(s)
- Christian Brion
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Sheila M Lutz
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Frank Wolfgang Albert
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
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35
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Krasny L, Huang PH. Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 2020; 17:29-42. [PMID: 33034323 DOI: 10.1039/d0mo00072h] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.
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Affiliation(s)
- Lukas Krasny
- Division of Molecular Pathology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
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36
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Fernández-Costa C, Martínez-Bartolomé S, McClatchy DB, Saviola AJ, Yu NK, Yates JR. Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results. J Proteome Res 2020; 19:3153-3161. [PMID: 32510229 PMCID: PMC7898222 DOI: 10.1021/acs.jproteome.0c00153] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.
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Affiliation(s)
- Carolina Fernández-Costa
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Daniel B. McClatchy
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Anthony J. Saviola
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nam-Kyung Yu
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - John R. Yates
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
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37
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Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes. Nat Protoc 2020; 15:2341-2386. [PMID: 32690956 DOI: 10.1038/s41596-020-0332-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 04/17/2020] [Indexed: 01/03/2023]
Abstract
Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.
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38
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Krasny L, Bland P, Burns J, Lima NC, Harrison PT, Pacini L, Elms ML, Ning J, Martinez VG, Yu YR, Acton SE, Ho PC, Calvo F, Swain A, Howard BA, Natrajan RC, Huang PH. A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts. Dis Model Mech 2020; 13:dmm044586. [PMID: 32493768 PMCID: PMC7375474 DOI: 10.1242/dmm.044586] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/20/2020] [Indexed: 12/11/2022] Open
Abstract
SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.
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MESH Headings
- Animals
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinoma, Intraductal, Noninfiltrating/metabolism
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Cell Communication
- Cell Line, Tumor
- Chromatography, Liquid
- Databases, Protein
- Female
- Heterografts
- Humans
- Male
- Mice
- Mice, Inbred C57BL
- Mice, Nude
- Mice, SCID
- NIH 3T3 Cells
- Neoplasm Proteins/metabolism
- Neoplasm Transplantation
- Proteome
- Proteomics
- Species Specificity
- Stromal Cells/metabolism
- Stromal Cells/pathology
- Tandem Mass Spectrometry
- Tumor Microenvironment
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Affiliation(s)
- Lukas Krasny
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Philip Bland
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Jessica Burns
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Nadia Carvalho Lima
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Peter T Harrison
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Laura Pacini
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Mark L Elms
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Jian Ning
- Tumour Profiling Unit, The Institute of Cancer Research, London SW3 6JB, UK
| | - Victor Garcia Martinez
- Stromal Immunology Group, MRC Laboratory for Molecular Cell Biology, University College London WC1E 6BT, London, UK
| | - Yi-Ru Yu
- Department of Oncology, University of Lausanne, Lausanne CH-1066, Switzerland
- Ludwig Institute for Cancer Research, Lausanne CH-1066, Switzerland
| | - Sophie E Acton
- Stromal Immunology Group, MRC Laboratory for Molecular Cell Biology, University College London WC1E 6BT, London, UK
| | - Ping-Chih Ho
- Department of Oncology, University of Lausanne, Lausanne CH-1066, Switzerland
- Ludwig Institute for Cancer Research, Lausanne CH-1066, Switzerland
| | - Fernando Calvo
- The Tumour Microenvironment Team, Institute of Biomedicine and Biotechnology of Cantabria, Santander 39011, Spain
| | - Amanda Swain
- Tumour Profiling Unit, The Institute of Cancer Research, London SW3 6JB, UK
| | - Beatrice A Howard
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Rachael C Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Paul H Huang
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
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39
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Bludau I, Aebersold R. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat Rev Mol Cell Biol 2020; 21:327-340. [PMID: 32235894 DOI: 10.1038/s41580-020-0231-2] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
The ability of living systems to adapt to changing conditions originates from their capacity to change their molecular constitution. This is achieved by multiple mechanisms that modulate the quantitative composition and the diversity of the molecular inventory. Molecular diversification is particularly pronounced on the proteome level, at which multiple proteoforms derived from the same gene can in turn combinatorially form different protein complexes, thus expanding the repertoire of functional modules in the cell. The study of molecular and modular diversity and their involvement in responses to changing conditions has only recently become possible through the development of new 'omics'-based screening technologies. This Review explores our current knowledge of the mechanisms regulating functional diversification along the axis of gene expression, with a focus on the proteome and interactome. We explore the interdependence between different molecular levels and how this contributes to functional diversity. Finally, we highlight several recent techniques for studying molecular diversity, with specific focus on mass spectrometry-based analysis of the proteome and its organization into functional modules, and examine future directions for this rapidly growing field.
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Affiliation(s)
- Isabell Bludau
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. .,Faculty of Science, University of Zurich, Zurich, Switzerland.
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40
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Abstract
Ageing is a major risk factor for the development of many diseases, prominently including neurodegenerative disorders such as Alzheimer disease and Parkinson disease. A hallmark of many age-related diseases is the dysfunction in protein homeostasis (proteostasis), leading to the accumulation of protein aggregates. In healthy cells, a complex proteostasis network, comprising molecular chaperones and proteolytic machineries and their regulators, operates to ensure the maintenance of proteostasis. These factors coordinate protein synthesis with polypeptide folding, the conservation of protein conformation and protein degradation. However, sustaining proteome balance is a challenging task in the face of various external and endogenous stresses that accumulate during ageing. These stresses lead to the decline of proteostasis network capacity and proteome integrity. The resulting accumulation of misfolded and aggregated proteins affects, in particular, postmitotic cell types such as neurons, manifesting in disease. Recent analyses of proteome-wide changes that occur during ageing inform strategies to improve proteostasis. The possibilities of pharmacological augmentation of the capacity of proteostasis networks hold great promise for delaying the onset of age-related pathologies associated with proteome deterioration and for extending healthspan.
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41
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Mustafa S, Mobashir M. LC–MS and docking profiling reveals potential difference between the pure and crude fucoidan metabolites. Int J Biol Macromol 2020; 143:11-29. [DOI: 10.1016/j.ijbiomac.2019.11.232] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 11/24/2019] [Accepted: 11/29/2019] [Indexed: 12/19/2022]
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42
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Gauthier L, Stynen B, Serohijos AWR, Michnick SW. Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics. Bioessays 2019; 42:e1900169. [PMID: 31854021 DOI: 10.1002/bies.201900169] [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: 09/16/2019] [Revised: 11/15/2019] [Indexed: 11/07/2022]
Abstract
How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.
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Affiliation(s)
- Louis Gauthier
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Bram Stynen
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Adrian W R Serohijos
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Stephen W Michnick
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
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43
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Zhang J, Jia S, Lu W, Li W, Jiang R, Liu Y, Yang X, Zou S, Zou X, Zhong H. Real-time laser induced chemical derivatizations of peptide N-Terminus for in-situ mass spectrometric sequencing at sub-picomole and nanosecond scale. Anal Chim Acta 2019; 1100:1-11. [PMID: 31987129 DOI: 10.1016/j.aca.2019.12.029] [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: 10/10/2019] [Revised: 12/10/2019] [Accepted: 12/12/2019] [Indexed: 11/30/2022]
Abstract
Distinguishing b- and y-ions is essential to compute amino acid sequences from either N- or C-terminus in mass spectrometry. We described herein a solvent free and real time on-plate derivatization approach that can tag N-terminus of peptides at microliter level with p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde for matrix assisted laser desorption ionization mass spectrometry (MALDI MS). Less than 1 μL of sample solutions can be directly mixed with equal volumes of p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde and α-cyano-4-hydroxycinnamic acid (CHCA), a matrix compound to co-crystalize with analytes for efficient absorption of laser energy and peptide ionization. When the mixture spotted on the sample plate is irradiated with the 3rd harmonic (355 nm) of Nd3+:YAG laser pulses (3 ns width), N-terminal amine groups of peptides instantly react with carbonyl groups of chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde. Resultant peptides carrying with on-plate formed azomethine group (-CN-) are simultaneously protonated and isolated as precursor ions for subsequent collision-activated dissociation. The mass shift with unique Cl isotopic signature unambiguously distinguishes b ions from y ions and other ions. This method does not need extensive sample preparation and is useful for those samples with limited quantities down to sub-picomole level in sub-microliter volumes. The efficiency was demonstrated with synthetic peptides and tryptic peptides of model proteins. It was found that 2-hydroxy-5-methylisophthalaldehyde provides improved yield for peptides containing lysine residues. Unknown proteins of human saliva and bovine milk as well as phosphopeptides have been identified.
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Affiliation(s)
- Juan Zhang
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Shanshan Jia
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Wenting Lu
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Weidan Li
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Ruowei Jiang
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Yanping Liu
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Xiaojie Yang
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Si Zou
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Xuekun Zou
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China
| | - Hongying Zhong
- Laboratory of Mass Spectrometry, College of Chemistry, Central China Normal University, Wuhan, Hubei, 430079, PR China; Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, PR China.
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44
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Zhang H, Liu P, Guo T, Zhao H, Bensaddek D, Aebersold R, Xiong L. Arabidopsis proteome and the mass spectral assay library. Sci Data 2019; 6:278. [PMID: 31757973 DOI: 10.6084/m9.figshare.c.4647293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/27/2019] [Indexed: 05/26/2023] Open
Abstract
Arabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.
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Affiliation(s)
- Huoming Zhang
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia.
| | - Pei Liu
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Tiannan Guo
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Huayan Zhao
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Dalila Bensaddek
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Faculry of Science, University of Zurich, Zurich, Switzerland
| | - Liming Xiong
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
- Department of Biology, Hong Kong Baptist University, Kowlong Tong, Hong Kong, SAR, China
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45
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Zhang H, Liu P, Guo T, Zhao H, Bensaddek D, Aebersold R, Xiong L. Arabidopsis proteome and the mass spectral assay library. Sci Data 2019. [PMID: 31757973 DOI: 10.1038/s41597-019-0294-0)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Arabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.
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Affiliation(s)
- Huoming Zhang
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia.
| | - Pei Liu
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Tiannan Guo
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Huayan Zhao
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Dalila Bensaddek
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Faculry of Science, University of Zurich, Zurich, Switzerland
| | - Liming Xiong
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
- Department of Biology, Hong Kong Baptist University, Kowlong Tong, Hong Kong, SAR, China
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46
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Zhang H, Liu P, Guo T, Zhao H, Bensaddek D, Aebersold R, Xiong L. Arabidopsis proteome and the mass spectral assay library. Sci Data 2019; 6:278. [PMID: 31757973 PMCID: PMC6874543 DOI: 10.1038/s41597-019-0294-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/27/2019] [Indexed: 12/19/2022] Open
Abstract
Arabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses. Measurement(s) | Proteome | Technology Type(s) | mass spectrometry assay • computational modeling technique | Sample Characteristic - Organism | Arabidopsis thaliana |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9959036
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Affiliation(s)
- Huoming Zhang
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia.
| | - Pei Liu
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Tiannan Guo
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Huayan Zhao
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Dalila Bensaddek
- King Abdallah University of Science and Technology, Core Labs, Thuwal, Kingdom of Saudi Arabia
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Faculry of Science, University of Zurich, Zurich, Switzerland
| | - Liming Xiong
- Division of Biological and Environmental Science and Engineering, King Abdallah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.,Department of Biology, Hong Kong Baptist University, Kowlong Tong, Hong Kong, SAR, China
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47
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Zhong CQ, Wu R, Chen X, Wu S, Shuai J, Han J. Systematic Assessment of the Effect of Internal Library in Targeted Analysis of SWATH-MS. J Proteome Res 2019; 19:477-492. [DOI: 10.1021/acs.jproteome.9b00669] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Rui Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Xi Chen
- Medical Research Institute, Wuhan University, Wuhan 430072, China
- SpecAlly Life Technology Co., Ltd., Wuhan 430072, China
| | - Suqin Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Jianwei Shuai
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
- Department of Physics, Xiamen University, Xiamen 361005, China
| | - Jiahuai Han
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
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48
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Kompella VPS, Stansfield I, Romano MC, Mancera RL. Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm. Front Mol Biosci 2019; 6:97. [PMID: 31632983 PMCID: PMC6783697 DOI: 10.3389/fmolb.2019.00097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 09/11/2019] [Indexed: 11/13/2022] Open
Abstract
The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.
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Affiliation(s)
- Vijay Phanindra Srikanth Kompella
- School of Pharmacy and Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, WA, Australia.,Physics Department, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Ian Stansfield
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Maria Carmen Romano
- Physics Department, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom.,Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Ricardo L Mancera
- School of Pharmacy and Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, WA, Australia
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Talbert LE, Julian RR. Methionine and Selenomethionine as Energy Transfer Acceptors for Biomolecular Structure Elucidation in the Gas Phase. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:1601-1608. [PMID: 31222676 PMCID: PMC6697561 DOI: 10.1007/s13361-019-02262-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 05/31/2019] [Accepted: 06/01/2019] [Indexed: 06/09/2023]
Abstract
Mass spectrometry affords rapid and sensitive analysis of peptides and proteins. Coupling spectroscopy with mass spectrometry allows for the development of new methods to enhance biomolecular structure determination. Herein, we demonstrate two new energy acceptors that can be utilized for action-excitation energy transfer experiments. In the first system, C-S bonds in methionine act as energy acceptors from native chromophores, including tyrosine, tryptophan, and phenylalanine. Comparison among chromophores reveals that tyrosine transfers energy most efficiently at 266 nm, but phenylalanine and tryptophan also transfer energy with comparable efficiencies. Overall, the C-S bond dissociation yields following energy transfer are low for methionine, which led to an investigation of selenomethionine, a common analog that is found in many naturally occurring proteins. Sulfur and selenium are chemically similar, but C-Se bonds are weaker than C-S bonds and have lower lying σ* anti-bonding orbitals. Excitation of peptides containing tyrosine and tryptophan results in efficient energy transfer to selenomethionine and abundant C-Se bond dissociation. A series of helical peptides were examined where the positions of the donor or acceptor were systematically scanned to explore the influence of distance and helix orientation on energy transfer. The distance was found to be the primary factor affecting energy transfer efficiency, suggesting that selenomethionine may be a useful acceptor for probing protein structure in the gas phase.
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Affiliation(s)
- Lance E Talbert
- Department of Chemistry, University of California, Riverside, 501 Big Springs Road, Riverside, CA, 92521, USA
| | - Ryan R Julian
- Department of Chemistry, University of California, Riverside, 501 Big Springs Road, Riverside, CA, 92521, USA.
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50
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Vitrinel B, Koh HWL, Mujgan Kar F, Maity S, Rendleman J, Choi H, Vogel C. Exploiting Interdata Relationships in Next-generation Proteomics Analysis. Mol Cell Proteomics 2019; 18:S5-S14. [PMID: 31126983 PMCID: PMC6692783 DOI: 10.1074/mcp.mr118.001246] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 05/01/2019] [Indexed: 12/11/2022] Open
Abstract
Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.
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Affiliation(s)
- Burcu Vitrinel
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY
| | - Hiromi W L Koh
- Department of Medicine, Yong Loo Lin School of Medicine, National University Singapore, Singapore; Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore
| | - Funda Mujgan Kar
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY
| | - Shuvadeep Maity
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY
| | - Justin Rendleman
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY
| | - Hyungwon Choi
- Department of Medicine, Yong Loo Lin School of Medicine, National University Singapore, Singapore; Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore
| | - Christine Vogel
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY.
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