1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Crook OM, Lilley KS, Gatto L, Kirk PD. Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics. Ann Appl Stat 2022; 16:22-aoas1603. [PMID: 36507469 PMCID: PMC7613899 DOI: 10.1214/22-aoas1603] [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: 02/02/2023]
Abstract
Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of marker proteins (i.e. proteins with a priori known labelled locations) motivates a semi-supervised learning approach to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. Furthermore, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied to reduce the computational complexity of inversion and hence accelerate computation. We provide detailed case-studies on Drosophila embryos and mouse pluripotent embryonic stem cells to illustrate the benefit of semi-supervised functional Bayesian modelling of the data.
Collapse
|
3
|
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.
Collapse
|
4
|
Watson J, Smith M, Francavilla C, Schwartz JM. SubcellulaRVis: a web-based tool to simplify and visualise subcellular compartment enrichment. Nucleic Acids Res 2022; 50:W718-W725. [PMID: 35536291 PMCID: PMC9252817 DOI: 10.1093/nar/gkac336] [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: 02/07/2022] [Revised: 04/10/2022] [Accepted: 05/09/2022] [Indexed: 12/16/2022] Open
Abstract
Cells contain intracellular compartments, including membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins are localised to one or more of these cellular compartments; the correct localisation of proteins is crucial for their correct processing and function. Moreover, proteins and the cellular processes they partake in are regulated by relocalisation in response to various cellular stimuli. High-throughput ‘omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through the knowledge of their subcellular localisation, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatics tool, named SubcellulaRVis, that compellingly visualises the results of GOCC enrichment for quick interpretation of the localisation of a group of proteins (rather than single proteins). We demonstrate that SubcellulaRVis precisely describes the subcellular localisation of gene lists whose locations have been previously ascertained. SubcellulaRVis can be accessed via the web (http://phenome.manchester.ac.uk/subcellular/) or as a stand-alone app (https://github.com/JoWatson2011/subcellularvis). SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise who want to analyse data related to protein (re)localisation and location-specific modules within the intracellular protein network.
Collapse
Affiliation(s)
- Joanne Watson
- Division of Evolution, Infection & Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK.,Division of Molecular & Cell Biology, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK
| | - Michael Smith
- Division of Molecular & Cell Biology, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK
| | - Chiara Francavilla
- Division of Molecular & Cell Biology, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK.,Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, M139PT, Manchester, UK
| | - Jean-Marc Schwartz
- Division of Evolution, Infection & Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, UK
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Elzek MAW, Christopher JA, Breckels LM, Lilley KS. Localization of Organelle Proteins by Isotope Tagging: Current status and potential applications in drug discovery research. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 39:57-67. [PMID: 34906326 DOI: 10.1016/j.ddtec.2021.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/26/2021] [Accepted: 06/11/2021] [Indexed: 12/18/2022]
Abstract
Spatial proteomics has provided important insights into the relationship between protein function and subcellular location. Localization of Organelle Proteins by Isotope Tagging (LOPIT) and its variants are proteome-wide techniques, not matched in scale by microscopy-based or proximity tagging-based techniques, allowing holistic mapping of protein subcellular location and re-localization events downstream of cellular perturbations. LOPIT can be a powerful and versatile tool in drug discovery for unlocking important information on disease pathophysiology, drug mechanism of action, and off-target toxicity screenings. Here, we discuss technical concepts of LOPIT with its potential applications in drug discovery and development research.
Collapse
Affiliation(s)
- Mohamed A W Elzek
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, United Kingdom; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, United Kingdom
| | - Josie A Christopher
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, United Kingdom; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, United Kingdom
| | - Lisa M Breckels
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, United Kingdom; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, United Kingdom
| | - Kathryn S Lilley
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, United Kingdom; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, United Kingdom.
| |
Collapse
|
7
|
Christopher JA, Geladaki A, Dawson CS, Vennard OL, Lilley KS. SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW. Mol Cell Proteomics 2021; 21:100186. [PMID: 34922010 PMCID: PMC8864473 DOI: 10.1016/j.mcpro.2021.100186] [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: 07/14/2021] [Revised: 11/16/2021] [Accepted: 12/13/2021] [Indexed: 12/23/2022] Open
Abstract
The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics. Subcellular information of protein and RNA give insights into molecular function. This review discusses strategies available to measure subcellular information. Hybridization of methods shows promise for exploring the composition of organelles. Advances are aiding understanding of the organisation and dynamics of cells.
Collapse
Affiliation(s)
- Josie A Christopher
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Aikaterini Geladaki
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Department of Genetics, University of Cambridge, 20 Downing Place, Cambridge, CB2 3EJ, UK
| | - Charlotte S Dawson
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Owen L Vennard
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.
| |
Collapse
|
8
|
Mulvey CM, Breckels LM, Crook OM, Sanders DJ, Ribeiro ALR, Geladaki A, Christoforou A, Britovšek NK, Hurrell T, Deery MJ, Gatto L, Smith AM, Lilley KS. Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line. Nat Commun 2021; 12:5773. [PMID: 34599159 PMCID: PMC8486773 DOI: 10.1038/s41467-021-26000-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023] Open
Abstract
Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.
Collapse
Affiliation(s)
- Claire M Mulvey
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
| | - Lisa M Breckels
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Oliver M Crook
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- MRC Biostatistics Unit, Cambridge Institute for Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - David J Sanders
- Department of Microbial Diseases, Eastman Dental Institute, University College London, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK
| | - Andre L R Ribeiro
- Department of Microbial Diseases, Eastman Dental Institute, University College London, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK
| | - Aikaterini Geladaki
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | | | - Nina Kočevar Britovšek
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- Lek d.d., Kolodvorska 27, Mengeš, 1234, Slovenia
| | - Tracey Hurrell
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Michael J Deery
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Laurent Gatto
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK
- de Duve Institute, UCLouvain, Avenue Hippocrate 75, Brussels, 1200, Belgium
| | - Andrew M Smith
- Department of Microbial Diseases, Eastman Dental Institute, University College London, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK.
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK.
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Valli M, Grillitsch K, Grünwald-Gruber C, Tatto NE, Hrobath B, Klug L, Ivashov V, Hauzmayer S, Koller M, Tir N, Leisch F, Gasser B, Graf AB, Altmann F, Daum G, Mattanovich D. A subcellular proteome atlas of the yeast Komagataella phaffii. FEMS Yeast Res 2021; 20:5700286. [PMID: 31922548 PMCID: PMC6981350 DOI: 10.1093/femsyr/foaa001] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
The compartmentalization of metabolic and regulatory pathways is a common pattern of living organisms. Eukaryotic cells are subdivided into several organelles enclosed by lipid membranes. Organelle proteomes define their functions. Yeasts, as simple eukaryotic single cell organisms, are valuable models for higher eukaryotes and frequently used for biotechnological applications. While the subcellular distribution of proteins is well studied in Saccharomyces cerevisiae, this is not the case for other yeasts like Komagataella phaffii (syn. Pichia pastoris). Different to most well-studied yeasts, K. phaffii can grow on methanol, which provides specific features for production of heterologous proteins and as a model for peroxisome biology. We isolated microsomes, very early Golgi, early Golgi, plasma membrane, vacuole, cytosol, peroxisomes and mitochondria of K. phaffii from glucose- and methanol-grown cultures, quantified their proteomes by liquid chromatography-electrospray ionization-mass spectrometry of either unlabeled or tandem mass tag-labeled samples. Classification of the proteins by their relative enrichment, allowed the separation of enriched proteins from potential contaminants in all cellular compartments except the peroxisomes. We discuss differences to S. cerevisiae, outline organelle specific findings and the major metabolic pathways and provide an interactive map of the subcellular localization of proteins in K. phaffii.
Collapse
Affiliation(s)
- Minoska Valli
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Karlheinz Grillitsch
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria
| | - Clemens Grünwald-Gruber
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Nadine E Tatto
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Bernhard Hrobath
- Institute of Statistics, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Lisa Klug
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Vasyl Ivashov
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Sandra Hauzmayer
- School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Martina Koller
- School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Nora Tir
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Friedrich Leisch
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Statistics, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Brigitte Gasser
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Friedrich Altmann
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Günther Daum
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Diethard Mattanovich
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Proteomic Analysis Identifies Markers of Exposure to Cadmium Sulphide Quantum Dots (CdS QDs). NANOMATERIALS 2020; 10:nano10061214. [PMID: 32580447 PMCID: PMC7353101 DOI: 10.3390/nano10061214] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/10/2020] [Accepted: 06/17/2020] [Indexed: 12/11/2022]
Abstract
The use of cadmium sulphide quantum dot (CdS QD)-enabled products has become increasingly widespread. The prospect of their release in the environment is raising concerns. Here we have used the yeast model Saccharomyces cerevisiae to determine the potential impact of CdS QD nanoparticles on living organisms. Proteomic analyses and cell viability assays performed after 9 h exposure revealed expression of proteins involved in oxidative stress and reduced lethality, respectively, whereas oxidative stress declined, and lethality increased after 24 h incubation in the presence of CdS QDs. Quantitative proteomics using the iTRAQ approach (isobaric tags for relative and absolute quantitation) revealed that key proteins involved in essential biological pathways were differentially regulated over the time course of the experiment. At 9 h, most of the glycolytic functions increased, and the abundance of the number of heat shock proteins increased. This contrasts with the situation at 24 h where glycolytic functions, some heat shock proteins as well as oxidative phosphorylation and ATP synthesis were down-regulated. It can be concluded from our data that cell exposure to CdS QDs provokes a metabolic shift from respiration to fermentation, comparable to the situation reported in some cancer cell lines.
Collapse
|
13
|
Justinić I, Katić A, Uršičić D, Ćurko-Cofek B, Blagović B, Čanadi Jurešić G. Combining proteomics and lipid analysis to unravel Confidor stress response in Saccharomyces cerevisiae. ENVIRONMENTAL TOXICOLOGY 2020; 35:346-358. [PMID: 31696623 DOI: 10.1002/tox.22870] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 06/10/2023]
Abstract
The yeast Saccharomyces cerevisiae is a useful model for studying the influence of different stress factors on eukaryotic cells. In this work we used the pesticide imidacloprid, in the Confidor formulation, as the stress factor and analyzed its influence on the metabolic activity, proteome and lipid content and composition of Saccharomyces cerevisiae yeast. During the cultivation of yeast, the lowest recommended application dose of Confidor (0.025%, v/v) was added to the growth media and its influence on the mitochondria, cytosol with microsomes, and the whole yeast cells was monitored. The results show that under the stress provoked by the toxic effects of Confidor, yeast cells density significantly decreased and the percentage of metabolically disturbed cells significantly increased comparing with untreated control. Also, there was a downregulation of majority of glycolytic, gluconeogenesis, and TCA cycle enzymes (Fba1, Adh1, Hxk2, Tal1, Tdh1,Tdh3, Eno1) thus providing enough acetyl-CoA for the lipid restructuring and accumulation mechanism since we have found the changes in the cell and mitochondrial lipid content and FA composition. This data suggest that lipids could be the molecules that orchestrate the answer of the cells in the stress response to the Confidor treatment.
Collapse
Affiliation(s)
- Iva Justinić
- Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Ana Katić
- Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Deni Uršičić
- Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Božena Ćurko-Cofek
- Department of Physiology, Immunology and Patophysiology, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Branka Blagović
- Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Gordana Čanadi Jurešić
- Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| |
Collapse
|
14
|
Joshi RN, Stadler C, Lehmann R, Lehtiö J, Tegnér J, Schmidt A, Vesterlund M. TcellSubC: An Atlas of the Subcellular Proteome of Human T Cells. Front Immunol 2019; 10:2708. [PMID: 31849937 PMCID: PMC6902019 DOI: 10.3389/fimmu.2019.02708] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 11/04/2019] [Indexed: 01/07/2023] Open
Abstract
We have curated an in-depth subcellular proteomic map of primary human CD4+ T cells, divided into cytosolic, nuclear and membrane fractions generated by an optimized fractionation and HiRIEF-LC-MS/MS workflow for limited amounts of primary cells. The subcellular proteome of T cells was mapped under steady state conditions, as well as upon 15 min and 1 h of T cell receptor (TCR) stimulation, respectively. We quantified the subcellular distribution of 6,572 proteins and identified a subset of 237 potentially translocating proteins, including both well-known examples and novel ones. Microscopic validation confirmed the localization of selected proteins with previously known and unknown localization, respectively. We further provide the data in an easy-to-use web platform to facilitate re-use, as the data can be relevant for basic research as well as for clinical exploitation of T cells as therapeutic targets.
Collapse
Affiliation(s)
- Rubin Narayan Joshi
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital and Science for Life Laboratory, Stockholm, Sweden
| | - Charlotte Stadler
- Department of Protein Sciences, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Robert Lehmann
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital and Science for Life Laboratory, Stockholm, Sweden.,Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Angelika Schmidt
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital and Science for Life Laboratory, Stockholm, Sweden
| | - Mattias Vesterlund
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| |
Collapse
|
15
|
Nightingale DJH, Lilley KS, Oliver SG. A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae. Bio Protoc 2019; 9:e3303. [PMID: 33654815 PMCID: PMC7854154 DOI: 10.21769/bioprotoc.3303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/12/2019] [Accepted: 06/25/2019] [Indexed: 11/02/2022] Open
Abstract
The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.
Collapse
Affiliation(s)
- Daniel J. H. Nightingale
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, United Kingdom
- Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, United Kingdom
| | - Kathryn S. Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, United Kingdom
- Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, United Kingdom
| | - Stephen G. Oliver
- Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, United Kingdom
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|