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Salazar OR, Chen K, Melino VJ, Reddy MP, Hřibová E, Čížková J, Beránková D, Arciniegas Vega JP, Cáceres Leal LM, Aranda M, Jaremko L, Jaremko M, Fedoroff NV, Tester M, Schmöckel SM. SOS1 tonoplast neo-localization and the RGG protein SALTY are important in the extreme salinity tolerance of Salicornia bigelovii. Nat Commun 2024; 15:4279. [PMID: 38769297 PMCID: PMC11106269 DOI: 10.1038/s41467-024-48595-5] [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: 05/01/2023] [Accepted: 05/07/2024] [Indexed: 05/22/2024] Open
Abstract
The identification of genes involved in salinity tolerance has primarily focused on model plants and crops. However, plants naturally adapted to highly saline environments offer valuable insights into tolerance to extreme salinity. Salicornia plants grow in coastal salt marshes, stimulated by NaCl. To understand this tolerance, we generated genome sequences of two Salicornia species and analyzed the transcriptomic and proteomic responses of Salicornia bigelovii to NaCl. Subcellular membrane proteomes reveal that SbiSOS1, a homolog of the well-known SALT-OVERLY-SENSITIVE 1 (SOS1) protein, appears to localize to the tonoplast, consistent with subcellular localization assays in tobacco. This neo-localized protein can pump Na+ into the vacuole, preventing toxicity in the cytosol. We further identify 11 proteins of interest, of which SbiSALTY, substantially improves yeast growth on saline media. Structural characterization using NMR identified it as an intrinsically disordered protein, localizing to the endoplasmic reticulum in planta, where it can interact with ribosomes and RNA, stabilizing or protecting them during salt stress.
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Affiliation(s)
- Octavio R Salazar
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Ke Chen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Vanessa J Melino
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Muppala P Reddy
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Eva Hřibová
- Institute of Experimental Botany of the Czech Academy of Sciences, Centre of Plant Structural and Functional Genomics, Šlechtitelů 31, 77900, Olomouc, Czech Republic
| | - Jana Čížková
- Institute of Experimental Botany of the Czech Academy of Sciences, Centre of Plant Structural and Functional Genomics, Šlechtitelů 31, 77900, Olomouc, Czech Republic
| | - Denisa Beránková
- Institute of Experimental Botany of the Czech Academy of Sciences, Centre of Plant Structural and Functional Genomics, Šlechtitelů 31, 77900, Olomouc, Czech Republic
| | - Juan Pablo Arciniegas Vega
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Lina María Cáceres Leal
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Manuel Aranda
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Lukasz Jaremko
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Mariusz Jaremko
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Nina V Fedoroff
- Department of Biology, Penn State University, University Park, PA, 16801, US
| | - Mark Tester
- Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Center for Desert Agriculture, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
| | - Sandra M Schmöckel
- Department Physiology of Yield Stability, Institute of Crop Science, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
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2
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Currie J, Manda V, Robinson SK, Lai C, Agnihotri V, Hidalgo V, Ludwig RW, Zhang K, Pavelka J, Wang ZV, Rhee JW, Lam MPY, Lau E. Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions. Nat Commun 2024; 15:2207. [PMID: 38467653 PMCID: PMC10928085 DOI: 10.1038/s41467-024-46600-5] [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: 05/01/2023] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
The spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi. In parallel, UPR triggers broad differential localization of proteins including RNA-binding proteins and amino acid transporters. Moreover, we observe newly synthesized proteins including EGFR that show a differential localization under stress than the existing protein pools, reminiscent of protein trafficking disruptions. We next applied SPLAT to an induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) model of cancer drug cardiotoxicity upon treatment with the proteasome inhibitor carfilzomib. Paradoxically, carfilzomib has little effect on global average protein half-life, but may instead selectively disrupt sarcomere protein homeostasis. This study provides a view into the interactions of protein spatial and temporal dynamics and demonstrates a method to examine protein homeostasis regulations in stress and drug response.
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Affiliation(s)
- Jordan Currie
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Vyshnavi Manda
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sean K Robinson
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Celine Lai
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Vertica Agnihotri
- Department of Medicine, Division of Cardiology, City of Hope Comprehensive Cancer Center, CA, 91010, Duarte, USA
| | - Veronica Hidalgo
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - R W Ludwig
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Kai Zhang
- Department of Diabetes and Cancer Metabolism, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Jay Pavelka
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Zhao V Wang
- Department of Diabetes and Cancer Metabolism, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - June-Wha Rhee
- Department of Medicine, Division of Cardiology, City of Hope Comprehensive Cancer Center, CA, 91010, Duarte, USA
| | - Maggie P Y Lam
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Edward Lau
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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3
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024. [PMID: 38451675 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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4
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Currie J, Manda V, Robinson SK, Lai C, Agnihotri V, Hidalgo V, Ludwig RW, Zhang K, Pavelka J, Wang ZV, Rhee JW, Lam MPY, Lau E. Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.04.521821. [PMID: 36711879 PMCID: PMC9881985 DOI: 10.1101/2023.01.04.521821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The functions of proteins depend on their spatial and temporal distributions, which are not directly measured by static protein abundance. Under endoplasmic reticulum (ER) stress, the unfolded protein response (UPR) pathway remediates proteostasis in part by altering the turnover kinetics and spatial distribution of proteins. A global view of these spatiotemporal changes has yet to emerge and it is unknown how they affect different cellular compartments and pathways. Here we describe a mass spectrometry-based proteomics strategy and data analysis pipeline, termed Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently the changes in protein turnover and subcellular distribution in the same experiment. Investigating two common UPR models of thapsigargin and tunicamycin challenge in human AC16 cells, we find that the changes in protein turnover kinetics during UPR varies across subcellular localizations, with overall slowdown but an acceleration in endoplasmic reticulum and Golgi proteins involved in stress response. In parallel, the spatial proteomics component of the experiment revealed an externalization of amino acid transporters and ion channels under UPR, as well as the migration of RNA-binding proteins toward an endosome co-sedimenting compartment. The SPLAT experimental design classifies heavy and light SILAC labeled proteins separately, allowing the observation of differential localization of new and old protein pools and capturing a partition of newly synthesized EGFR and ITGAV to the ER under stress that suggests protein trafficking disruptions. Finally, application of SPLAT toward human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) exposed to the cancer drug carfilzomib, identified a selective disruption of proteostasis in sarcomeric proteins as a potential mechanism of carfilzomib-mediated cardiotoxicity. Taken together, this study provides a global view into the spatiotemporal dynamics of human cardiac cells and demonstrates a method for inferring the coordinations between spatial and temporal proteome regulations in stress and drug response.
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Affiliation(s)
- Jordan Currie
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Vyshnavi Manda
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Sean K. Robinson
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Celine Lai
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA
| | - Vertica Agnihotri
- Department of Medicine, Division of Cardiology, City of Hope Comprehensive Cancer Center, Durante, CA 91010, USA
| | - Veronica Hidalgo
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - R. W. Ludwig
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Kai Zhang
- Department of Diabetes and Cancer Metabolism, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Jay Pavelka
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Zhao V. Wang
- Department of Diabetes and Cancer Metabolism, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - June-Wha Rhee
- Department of Medicine, Division of Cardiology, City of Hope Comprehensive Cancer Center, Durante, CA 91010, USA
| | - Maggie P. Y. Lam
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Edward Lau
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA
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5
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Di Fraia D, Marino A, Lee JH, Kelmer Sacramento E, Baumgart M, Bagnoli S, Tomaz da Silva P, Kumar Sahu A, Siano G, Tiessen M, Terzibasi-Tozzini E, Gagneur J, Frydman J, Cellerino A, Ori A. Impaired biogenesis of basic proteins impacts multiple hallmarks of the aging brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.20.549210. [PMID: 38260253 PMCID: PMC10802395 DOI: 10.1101/2023.07.20.549210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Aging and neurodegeneration entail diverse cellular and molecular hallmarks. Here, we studied the effects of aging on the transcriptome, translatome, and multiple layers of the proteome in the brain of a short-lived killifish. We reveal that aging causes widespread reduction of proteins enriched in basic amino acids that is independent of mRNA regulation, and it is not due to impaired proteasome activity. Instead, we identify a cascade of events where aberrant translation pausing leads to reduced ribosome availability resulting in proteome remodeling independently of transcriptional regulation. Our research uncovers a vulnerable point in the aging brain's biology - the biogenesis of basic DNA/RNA binding proteins. This vulnerability may represent a unifying principle that connects various aging hallmarks, encompassing genome integrity and the biosynthesis of macromolecules.
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Affiliation(s)
- Domenico Di Fraia
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
| | - Antonio Marino
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
| | - Jae Ho Lee
- Department of Biology, Stanford University, Stanford, CA, USA
| | | | - Mario Baumgart
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
| | | | - Pedro Tomaz da Silva
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Amit Kumar Sahu
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
| | | | - Max Tiessen
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
| | | | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Alessandro Cellerino
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
- BIO@SNS, Scuola Normale Superiore, Pisa, Italy
| | - Alessandro Ori
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
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6
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Moloney NM, Barylyuk K, Tromer E, Crook OM, Breckels LM, Lilley KS, Waller RF, MacGregor P. Mapping diversity in African trypanosomes using high resolution spatial proteomics. Nat Commun 2023; 14:4401. [PMID: 37479728 PMCID: PMC10361982 DOI: 10.1038/s41467-023-40125-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/06/2023] [Indexed: 07/23/2023] Open
Abstract
African trypanosomes are dixenous eukaryotic parasites that impose a significant human and veterinary disease burden on sub-Saharan Africa. Diversity between species and life-cycle stages is concomitant with distinct host and tissue tropisms within this group. Here, the spatial proteomes of two African trypanosome species, Trypanosoma brucei and Trypanosoma congolense, are mapped across two life-stages. The four resulting datasets provide evidence of expression of approximately 5500 proteins per cell-type. Over 2500 proteins per cell-type are classified to specific subcellular compartments, providing four comprehensive spatial proteomes. Comparative analysis reveals key routes of parasitic adaptation to different biological niches and provides insight into the molecular basis for diversity within and between these pathogen species.
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Affiliation(s)
- Nicola M Moloney
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | | | - Eelco Tromer
- Cell Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG, Groningen, Netherlands
| | - Oliver M Crook
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Lisa M Breckels
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | - Kathryn S Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | - Ross F Waller
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | - Paula MacGregor
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK.
- School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK.
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7
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Reduced mitochondria provide an essential function for the cytosolic methionine cycle. Curr Biol 2022; 32:5057-5068.e5. [PMID: 36347252 PMCID: PMC9746703 DOI: 10.1016/j.cub.2022.10.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/15/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022]
Abstract
The loss of mitochondria in oxymonad protists has been associated with the redirection of the essential Fe-S cluster assembly to the cytosol. Yet as our knowledge of diverse free-living protists broadens, the list of functions of their mitochondrial-related organelles (MROs) expands. We revealed another such function in the closest oxymonad relative, Paratrimastix pyriformis, after we solved the proteome of its MRO with high accuracy, using localization of organelle proteins by isotope tagging (LOPIT). The newly assigned enzymes connect to the glycine cleavage system (GCS) and produce folate derivatives with one-carbon units and formate. These are likely to be used by the cytosolic methionine cycle involved in S-adenosyl methionine recycling. The data provide consistency with the presence of the GCS in MROs of free-living species and its absence in most endobionts, which typically lose the methionine cycle and, in the case of oxymonads, the mitochondria.
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8
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Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE. Nat Commun 2022; 13:5948. [PMID: 36216816 PMCID: PMC9550814 DOI: 10.1038/s41467-022-33570-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 09/20/2022] [Indexed: 11/08/2022] Open
Abstract
The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different subcellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well-studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.
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9
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Crook OM, Chung CW, Deane CM. Challenges and Opportunities for Bayesian Statistics in Proteomics. J Proteome Res 2022; 21:849-864. [PMID: 35258980 PMCID: PMC8982455 DOI: 10.1021/acs.jproteome.1c00859] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Indexed: 12/27/2022]
Abstract
Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.
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Affiliation(s)
- Oliver M. Crook
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - Chun-wa Chung
- Structural
and Biophysical Sciences, GlaxoSmithKline
R&D, Stevenage SG1 2NY, United Kingdom
| | - Charlotte M. Deane
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
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10
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Oom AL, Stoneham CA, Lewinski MK, Richards A, Wozniak JM, Shams-Ud-Doha K, Gonzalez DJ, Krogan NJ, Guatelli J. Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression. Mol Cell Proteomics 2022; 21:100194. [PMID: 35017099 PMCID: PMC8956815 DOI: 10.1016/j.mcpro.2022.100194] [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: 03/17/2021] [Revised: 11/29/2021] [Accepted: 01/05/2022] [Indexed: 11/29/2022] Open
Abstract
As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.
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Affiliation(s)
- Aaron L Oom
- Biomedical Sciences Doctoral Program, University of California San Diego, La Jolla, California, USA; School of Medicine, University of California San Diego, La Jolla, California, USA; Veterans Medical Research Foundation, La Jolla, California, USA; VA San Diego Healthcare System, La Jolla, California, USA.
| | - Charlotte A Stoneham
- School of Medicine, University of California San Diego, La Jolla, California, USA; Veterans Medical Research Foundation, La Jolla, California, USA; VA San Diego Healthcare System, La Jolla, California, USA
| | - Mary K Lewinski
- School of Medicine, University of California San Diego, La Jolla, California, USA; VA San Diego Healthcare System, La Jolla, California, USA
| | - Alicia Richards
- Proteomics Facility, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA; Institute for Virology and Immunology, J. David Gladstone Institutes, San Francisco, California, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - Jacob M Wozniak
- Biomedical Sciences Doctoral Program, University of California San Diego, La Jolla, California, USA; Department of Pharmacology, University of California San Diego, La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Km Shams-Ud-Doha
- Proteomics Facility, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA
| | - David J Gonzalez
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA; Institute for Virology and Immunology, J. David Gladstone Institutes, San Francisco, California, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - John Guatelli
- Biomedical Sciences Doctoral Program, University of California San Diego, La Jolla, California, USA; School of Medicine, University of California San Diego, La Jolla, California, USA; Veterans Medical Research Foundation, La Jolla, California, USA; VA San Diego Healthcare System, La Jolla, California, USA
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11
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Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras AC, Warscheid B, Lehtiö J, Cristea IM, Foster LJ, Emili A, Lilley KS. Subcellular proteomics. NATURE REVIEWS. METHODS PRIMERS 2021; 1:32. [PMID: 34549195 PMCID: PMC8451152 DOI: 10.1038/s43586-021-00029-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.
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Affiliation(s)
- Josie A. Christopher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| | - Charlotte Stadler
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Claire E. Martin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Cora N. Betsinger
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - David G. Rattray
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diana Mahdessian
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Ileana M. Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Kathryn S. Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
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12
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Courtland JL, Bradshaw TWA, Waitt G, Soderblom EJ, Ho T, Rajab A, Vancini R, Kim IH, Soderling SH. Genetic disruption of WASHC4 drives endo-lysosomal dysfunction and cognitive-movement impairments in mice and humans. eLife 2021; 10:e61590. [PMID: 33749590 PMCID: PMC7984842 DOI: 10.7554/elife.61590] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 02/09/2021] [Indexed: 12/12/2022] Open
Abstract
Mutation of the Wiskott-Aldrich syndrome protein and SCAR homology (WASH) complex subunit, SWIP, is implicated in human intellectual disability, but the cellular etiology of this association is unknown. We identify the neuronal WASH complex proteome, revealing a network of endosomal proteins. To uncover how dysfunction of endosomal SWIP leads to disease, we generate a mouse model of the human WASHC4c.3056C>G mutation. Quantitative spatial proteomics analysis of SWIPP1019R mouse brain reveals that this mutation destabilizes the WASH complex and uncovers significant perturbations in both endosomal and lysosomal pathways. Cellular and histological analyses confirm that SWIPP1019R results in endo-lysosomal disruption and uncover indicators of neurodegeneration. We find that SWIPP1019R not only impacts cognition, but also causes significant progressive motor deficits in mice. A retrospective analysis of SWIPP1019R patients reveals similar movement deficits in humans. Combined, these findings support the model that WASH complex destabilization, resulting from SWIPP1019R, drives cognitive and motor impairments via endo-lysosomal dysfunction in the brain.
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Affiliation(s)
- Jamie L Courtland
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
| | - Tyler WA Bradshaw
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
| | - Greg Waitt
- Proteomics and Metabolomics Shared Resource, Duke University School of MedicineDurhamUnited States
| | - Erik J Soderblom
- Proteomics and Metabolomics Shared Resource, Duke University School of MedicineDurhamUnited States
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
| | - Tricia Ho
- Proteomics and Metabolomics Shared Resource, Duke University School of MedicineDurhamUnited States
| | - Anna Rajab
- Burjeel Hospital, VPS HealthcareMuscatOman
| | - Ricardo Vancini
- Department of Pathology, Duke University School of MedicineDurhamUnited States
| | - Il Hwan Kim
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
- Department of Anatomy and Neurobiology, University of Tennessee Heath Science CenterMemphisUnited States
| | - Scott H Soderling
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
- Department of Cell Biology, Duke University School of MedicineDurhamUnited States
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13
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Shin JJH, Crook OM, Borgeaud AC, Cattin-Ortolá J, Peak-Chew SY, Breckels LM, Gillingham AK, Chadwick J, Lilley KS, Munro S. Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers. Nat Commun 2020; 11:5987. [PMID: 33239640 PMCID: PMC7689464 DOI: 10.1038/s41467-020-19840-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 10/27/2020] [Indexed: 02/07/2023] Open
Abstract
Intracellular traffic between compartments of the secretory and endocytic pathways is mediated by vesicle-based carriers. The proteomes of carriers destined for many organelles are ill-defined because the vesicular intermediates are transient, low-abundance and difficult to purify. Here, we combine vesicle relocalisation with organelle proteomics and Bayesian analysis to define the content of different endosome-derived vesicles destined for the trans-Golgi network (TGN). The golgin coiled-coil proteins golgin-97 and GCC88, shown previously to capture endosome-derived vesicles at the TGN, were individually relocalised to mitochondria and the content of the subsequently re-routed vesicles was determined by organelle proteomics. Our findings reveal 45 integral and 51 peripheral membrane proteins re-routed by golgin-97, evidence for a distinct class of vesicles shared by golgin-97 and GCC88, and various cargoes specific to individual golgins. These results illustrate a general strategy for analysing intracellular sub-proteomes by combining acute cellular re-wiring with high-resolution spatial proteomics.
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Affiliation(s)
- John J H Shin
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
| | - Oliver M Crook
- The Milner Therapeutics Institute, University of Cambridge, Cambridge, CB2 0AW, UK
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Alicia C Borgeaud
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Jérôme Cattin-Ortolá
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Sew Y Peak-Chew
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Lisa M Breckels
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Alison K Gillingham
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Jessica Chadwick
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Kathryn S Lilley
- The Milner Therapeutics Institute, University of Cambridge, Cambridge, CB2 0AW, UK
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Sean Munro
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
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14
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Barylyuk K, Koreny L, Ke H, Butterworth S, Crook OM, Lassadi I, Gupta V, Tromer E, Mourier T, Stevens TJ, Breckels LM, Pain A, Lilley KS, Waller RF. A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions. Cell Host Microbe 2020; 28:752-766.e9. [PMID: 33053376 PMCID: PMC7670262 DOI: 10.1016/j.chom.2020.09.011] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/30/2020] [Accepted: 09/15/2020] [Indexed: 12/15/2022]
Abstract
Apicomplexan parasites cause major human disease and food insecurity. They owe their considerable success to highly specialized cell compartments and structures. These adaptations drive their recognition, nondestructive penetration, and elaborate reengineering of the host's cells to promote their growth, dissemination, and the countering of host defenses. The evolution of unique apicomplexan cellular compartments is concomitant with vast proteomic novelty. Consequently, half of apicomplexan proteins are unique and uncharacterized. Here, we determine the steady-state subcellular location of thousands of proteins simultaneously within the globally prevalent apicomplexan parasite Toxoplasma gondii. This provides unprecedented comprehensive molecular definition of these unicellular eukaryotes and their specialized compartments, and these data reveal the spatial organizations of protein expression and function, adaptation to hosts, and the underlying evolutionary trajectories of these pathogens.
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Affiliation(s)
| | - Ludek Koreny
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Huiling Ke
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Simon Butterworth
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Oliver M Crook
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB20 0AW, UK; MRC Biostatistics Unit, Cambridge Institute for Public Health, Cambridge CB2 0SR, UK
| | - Imen Lassadi
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Vipul Gupta
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Eelco Tromer
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Tobias Mourier
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Tim J Stevens
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Lisa M Breckels
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB20 0AW, UK
| | - Arnab Pain
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia; Global Station for Zoonosis Control, Gi-CoRE, Hokkaido University, Sapporo 060-0808, Japan; Nuffield Division of Clinical Laboratory Sciences (NDCLS), University of Oxford, Oxford OX3 9DU, UK
| | - Kathryn S Lilley
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB20 0AW, UK
| | - Ross F Waller
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK.
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15
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Crook OM, Geladaki A, Nightingale DJH, Vennard OL, Lilley KS, Gatto L, Kirk PDW. A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection. PLoS Comput Biol 2020; 16:e1008288. [PMID: 33166281 PMCID: PMC7707549 DOI: 10.1371/journal.pcbi.1008288] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 12/01/2020] [Accepted: 08/25/2020] [Indexed: 01/13/2023] Open
Abstract
The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.
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Affiliation(s)
- Oliver M. Crook
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, Cambridge, UK
| | - Aikaterini Geladaki
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
- Department of Genetics, Universtiy of Cambridge, Cambridge, UK
| | - Daniel J. H. Nightingale
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Owen L. Vennard
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, Cambridge, UK
| | - Kathryn S. Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, Cambridge, UK
| | - Laurent Gatto
- de Duve Institute, UCLouvain, Avenue Hippocrate 75, 1200 Brussels, Belgium
| | - Paul D. W. Kirk
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, UK
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16
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Borner GHH. Organellar Maps Through Proteomic Profiling - A Conceptual Guide. Mol Cell Proteomics 2020; 19:1076-1087. [PMID: 32345598 PMCID: PMC7338086 DOI: 10.1074/mcp.r120.001971] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/27/2020] [Indexed: 11/29/2022] Open
Abstract
Protein subcellular localization is an essential and highly regulated determinant of protein function. Major advances in mass spectrometry and imaging have allowed the development of powerful spatial proteomics approaches for determining protein localization at the whole cell scale. Here, a brief overview of current methods is presented, followed by a detailed discussion of organellar mapping through proteomic profiling. This relatively simple yet flexible approach is rapidly gaining popularity, because of its ability to capture the localizations of thousands of proteins in a single experiment. It can be used to generate high-resolution cell maps, and as a tool for monitoring protein localization dynamics. This review highlights the strengths and limitations of the approach and provides guidance to designing and interpreting profiling experiments.
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Affiliation(s)
- Georg H H Borner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.
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17
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Crook OM, Smith T, Elzek M, Lilley KS. Moving Profiling Spatial Proteomics Beyond Discrete Classification. Proteomics 2020; 20:e1900392. [PMID: 32558233 DOI: 10.1002/pmic.201900392] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/18/2020] [Indexed: 12/12/2022]
Abstract
The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post-translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi-localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi-localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.
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Affiliation(s)
- Oliver M Crook
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Tom Smith
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Mohamed Elzek
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
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18
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Manfredi M, Brandi J, Di Carlo C, Vita Vanella V, Barberis E, Marengo E, Patrone M, Cecconi D. Mining cancer biology through bioinformatic analysis of proteomic data. Expert Rev Proteomics 2019; 16:733-747. [PMID: 31398064 DOI: 10.1080/14789450.2019.1654862] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.
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Affiliation(s)
- Marcello Manfredi
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale , Novara , Italy.,Department of Translation Medicine, University of Piemonte Orientale , Novara , Italy
| | - Jessica Brandi
- Department of Biotechnology, University of Verona , Verona , Italy
| | - Claudia Di Carlo
- Department of Biotechnology, University of Verona , Verona , Italy
| | - Virginia Vita Vanella
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale , Novara , Italy.,Department of Sciences and Technological Innovation, University of Piemonte Orientale , Alessandria , Italy
| | - Elettra Barberis
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale , Novara , Italy.,Department of Sciences and Technological Innovation, University of Piemonte Orientale , Alessandria , Italy.,ISALIT , Novara , Italy
| | - Emilio Marengo
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale , Novara , Italy.,Department of Sciences and Technological Innovation, University of Piemonte Orientale , Alessandria , Italy.,ISALIT , Novara , Italy
| | - Mauro Patrone
- Department of Sciences and Technological Innovation, University of Piemonte Orientale , Alessandria , Italy
| | - Daniela Cecconi
- Department of Biotechnology, University of Verona , Verona , Italy
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19
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Crook OM, Breckels LM, Lilley KS, Kirk PD, Gatto L. A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Res 2019; 8:446. [PMID: 31119032 PMCID: PMC6509962 DOI: 10.12688/f1000research.18636.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/25/2019] [Indexed: 02/02/2023] Open
Abstract
Knowledge of the subcellular location of a protein gives valuable insight into its function. The field of spatial proteomics has become increasingly popular due to improved multiplexing capabilities in high-throughput mass spectrometry, which have made it possible to systematically localise thousands of proteins per experiment. In parallel with these experimental advances, improved methods for analysing spatial proteomics data have also been developed. In this workflow, we demonstrate using `pRoloc` for the Bayesian analysis of spatial proteomics data. We detail the software infrastructure and then provide step-by-step guidance of the analysis, including setting up a pipeline, assessing convergence, and interpreting downstream results. In several places we provide additional details on Bayesian analysis to provide users with a holistic view of Bayesian analysis for spatial proteomics data.
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Affiliation(s)
- Oliver M. Crook
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- MRC Biostatistics Unit, Cambridge Institute for Public Health, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Lisa M. Breckels
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Kathryn S. Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Paul D.W. Kirk
- MRC Biostatistics Unit, Cambridge Institute for Public Health, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Laurent Gatto
- Université catholique de Louvain, Brussels, 1200, Belgium
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