The effect of organelle discovery upon sub-cellular protein localisation.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line.

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

Proteome Mapping of a Cyanobacterium Reveals Distinct Compartment Organization and Cell-Dispersed Metabolism.

Cyanobacteria are complex prokaryotes, incorporating a Gram-negative cell wall and internal thylakoid membranes (TMs). However, localization of proteins within cyanobacterial cells is poorly understood. Using subcellular fractionation and quantitative proteomics, we produced an extensive subcellular proteome map of an entire cyanobacterial cell, identifying ∼67% of proteins in Synechocystis sp. PCC 6803, ∼1000 more than previous studies. Assigned to six specific subcellular regions were 1,712 proteins. Proteins involved in energy conversion localized to TMs. The majority of transporters, with the exception of a TM-localized copper importer, resided in the plasma membrane (PM). Most metabolic enzymes were soluble, although numerous pathways terminated in the TM (notably those involved in peptidoglycan monomer, NADP+, heme, lipid, and carotenoid biosynthesis) or PM (specifically, those catalyzing lipopolysaccharide, molybdopterin, FAD, and phylloquinol biosynthesis). We also identified the proteins involved in the TM and PM electron transport chains. The majority of ribosomal proteins and enzymes synthesizing the storage compound polyhydroxybuyrate formed distinct clusters within the data, suggesting similar subcellular distributions to one another, as expected for proteins operating within multicomponent structures. Moreover, heterogeneity within membrane regions was observed, indicating further cellular complexity. Cyanobacterial TM protein localization was conserved in Arabidopsis (Arabidopsis thaliana) chloroplasts, suggesting similar proteome organization in more developed photosynthetic organisms. Successful application of this technique in Synechocystis suggests it could be applied to mapping the proteomes of other cyanobacteria and single-celled organisms. The organization of the cyanobacterial cell revealed here substantially aids our understanding of these environmentally and biotechnologically important organisms

The sperm factor: paternal impact beyond genes

The fact that sperm carry more than the paternal DNA has only been discovered just over a decade ago. With this discovery, the idea that the paternal condition may have direct implications for the fitness of the offspring had to be revisited. While this idea is still highly debated, empirical evidence for paternal effects is accumulating. Male condition not only affects male fertility but also offspring early development and performance later in life. Several factors have been identified as possible carriers of non-genetic information, but we still know little about their origin and function and even less about their causation. I consider four possible non-mutually exclusive adaptive and non-adaptive explanations for the existence of paternal effects in an evolutionary context. In addition, I provide a brief overview of the main non-genetic components found in sperm including DNA methylation, chromatin modifications, RNAs and proteins. I discuss their putative functions and present currently available examples for their role in transferring non-genetic information from the father to the offspring. Finally, I identify some of the most important open questions and present possible future research avenues

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.Wellcome Trust BBSR

Proteome Mapping of a Cyanobacterium Reveals Distinct Compartment Organization and Cell-Dispersed Metabolism.

Cyanobacteria are complex prokaryotes, incorporating a Gram-negative cell wall and internal thylakoid membranes (TMs). However, localization of proteins within cyanobacterial cells is poorly understood. Using subcellular fractionation and quantitative proteomics, we produced an extensive subcellular proteome map of an entire cyanobacterial cell, identifying ∼67% of proteins in Synechocystis sp. PCC 6803, ∼1000 more than previous studies. Assigned to six specific subcellular regions were 1,712 proteins. Proteins involved in energy conversion localized to TMs. The majority of transporters, with the exception of a TM-localized copper importer, resided in the plasma membrane (PM). Most metabolic enzymes were soluble, although numerous pathways terminated in the TM (notably those involved in peptidoglycan monomer, NADP+, heme, lipid, and carotenoid biosynthesis) or PM (specifically, those catalyzing lipopolysaccharide, molybdopterin, FAD, and phylloquinol biosynthesis). We also identified the proteins involved in the TM and PM electron transport chains. The majority of ribosomal proteins and enzymes synthesizing the storage compound polyhydroxybuyrate formed distinct clusters within the data, suggesting similar subcellular distributions to one another, as expected for proteins operating within multicomponent structures. Moreover, heterogeneity within membrane regions was observed, indicating further cellular complexity. Cyanobacterial TM protein localization was conserved in Arabidopsis (Arabidopsis thaliana) chloroplasts, suggesting similar proteome organization in more developed photosynthetic organisms. Successful application of this technique in Synechocystis suggests it could be applied to mapping the proteomes of other cyanobacteria and single-celled organisms. The organization of the cyanobacterial cell revealed here substantially aids our understanding of these environmentally and biotechnologically important organisms

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics

A Bioconductor workflow for the Bayesian analysis of spatial proteomics

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