[Show abstract][Hide abstract] ABSTRACT: α-Klotho has emerged as a powerful regulator of the aging process. To-date, the expression profile of α-Klotho in human tissues is unknown and its existence in some human tissue types is subject to much controversy.
This is the first study to characterize system-wide tissue expression of transmembrane α-Klotho in humans. We have employed next generation targeted proteomic analysis using Parallel Reaction Monitoring (PRM) in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health.
The distribution of α-Klotho in human tissues from various organ systems, including arterial, epithelial, endocrine, reproductive and neuronal tissues was first identified by immunohistochemistry. Kidney tissues showed strong α-Klotho expression, while liver did not reveal a detectable signal. These results were next confirmed by western blotting of both whole tissues and primary cells. To validate our antibody-based results, α-Klotho expressing tissues were subjected to PRM mass spectrometry identifying peptides specific for the full length, transmembrane α-Klotho isoform.
The data presented confirms α-Klotho expression in the kidney tubule and in artery, and provides evidence of α-Klotho expression across organ systems and cell-types that have not previously been described in humans.
The Journal of Clinical Endocrinology and Metabolism 08/2015; DOI:10.1210/jc.2015-1800 · 6.31 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The study of proteomes provides new insights into stimulus-specific responses of protein synthesis and turnover, and the role of post-translational modifications at the systems level. Due to the diverse chemical nature of proteins and shortcomings in the analytical techniques used in their study, only a partial display of the proteome is achieved in any study, and this holds particularly true for plant proteomes. Here we show that different solubilization and separation methods have profound effects on the resulting proteome. In particular, we observed that the type of detergents employed in the solubilization buffer preferentially enriches proteins in different functional categories. These include proteins with a role in signaling, transport, response to temperature stimuli and metabolism. This data may offer a functional bias on comparative analysis studies. In order to obtain a broader coverage, we propose a two-step solubilization protocol with first a detergent-free buffer and then a second step utilizing a combination of two detergents to solubilize proteins.
International Journal of Molecular Sciences 01/2015; 16(1):857-70. DOI:10.3390/ijms16010857 · 2.86 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.
PLoS ONE 05/2014; 9(5):1-17. DOI:10.1371/journal.pone.0096480 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Quantitative mass spectrometry based spatial proteomics involves elaborate, expensive and time consuming experimental procedures and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches to establish high quality proteome-wide data sets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate these tools using several case studies, from multiple organisms, experimental designs, mass spectrometry platforms and quantitation techniques. We also propose sound analysis strategies to identify dynamic changes in sub-cellular localisation by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.
[Show abstract][Hide abstract] ABSTRACT: Knowledge of protein subcellular localisation assists in the elucidation of protein function and understanding of different biological mechanisms which occur at discrete subcellular niches. Organelle-centric proteomics enables localisation of thousands of proteins simultaneously. Although such techniques have succesfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localisation based on co-migration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled with sophisticated computational tools. Here, we apply and compare multiple approaches to establish a high confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immuno isolations of the TGN, coupled with probability based organelle proteomics techniques. Specifically the technique known as LOPIT (Localisation of Organelle Protein by Isotope Tagging), couples density centrifugation with quantitative mass spectometry based proteomics using isobaric labelling and targeted methods with semi-supervised machine learning methods. We demonstrate that whilst the immuno isolation method gives rise to a significant dataset, the approach is unable to distinguish cargo proteins and persistant contaminants from full time residents of the TGN. The LOPIT approach however, returns information about many subcellular niches simultaneously and the steady state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady state location favours the TGN. Using this approach we present a robust list of Arabidopsis TGN proteins.
Journal of Proteome Research 12/2013; 13(2). DOI:10.1021/pr4008464 · 5.00 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have lead 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 classification of protein-organelle membership from quantitative MS experiments. This article is part of a Special Issue entitled: EUPA 2012: NEW HORIZONS. 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 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.
Journal of proteomics 03/2013; 88. DOI:10.1016/j.jprot.2013.02.019 · 3.93 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phosphoproteomics is a fast-growing field that aims at characterizing phosphorylated proteins in a cell or a tissue at a given time. Phosphorylation of proteins is an important regulatory mechanism in many cellular processes. Gel-free phosphoproteome technique involving enrichment of phosphopeptide coupled with mass spectrometry has proven to be invaluable to detect and characterize phosphorylated proteins. In this chapter, a gel-free quantitative approach involving (15)N metabolic labelling in combination with phosphopeptide enrichment by titanium dioxide (TiO2) and their identification by MS is described. This workflow can be used to gain insights into the role of signalling molecules such as cyclic nucleotides on regulatory networks through the identification and quantification of responsive phospho(proteins).
[Show abstract][Hide abstract] ABSTRACT: Blood serum is one of the easiest accessible sources of biomarkers and its proteome presents a significant parcel of immune system proteins. These proteins can provide not only biological explanation but also diagnostic and drug response answers independently of the type of disease or condition in question. Shotgun mass spectrometry has profoundly contributed to proteome analysis and is presently considered as an indispensible tool in the field of biomarker discovery. In addition, the multiplexing potential of isotopic labeling techniques such as iTRAQ can increase statistical relevance and accuracy of proteomic data through the simultaneous analysis of different biological samples. Here, we describe a complete protocol using iTRAQ in a shotgun proteomics workflow along with data analysis steps, customized for the challenges associated with the serum proteome.
[Show abstract][Hide abstract] ABSTRACT: Membrane proteins are key molecules in the cell and are important targets for drug development. Much effort has, therefore, been directed towards research of this group of proteins, but their hydrophobic nature can make working with them challenging. Here we discuss methodologies used in the study of the membrane proteome, specifically discussing approaches that circumvent technical issues specific to the membrane. In addition, we review several techniques used for visualization, qualification, quantitation and localization of membrane proteins. The combination of the techniques we describe holds great promise to allow full characterization of the membrane proteome and to map the dynamic changes within it essential for cellular function.
[Show abstract][Hide abstract] ABSTRACT: Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.
[Show abstract][Hide abstract] ABSTRACT: In eukaryotes, numerous complex sub-cellular structures exist. The majority of these are delineated by membranes. Many proteins are trafficked to these in order to be able to carry out their correct physiological function. Assigning the sub-cellular location of a protein is of paramount importance to biologists in the elucidation of its role and in the refinement of knowledge of cellular processes by tracing certain activities to specific organelles. Membrane proteins are a key set of proteins as these form part of the boundary of the organelles and represent many important functions such as transporters, receptors, and trafficking. They are, however, some of the most challenging proteins to work with due to poor solubility, a wide concentration range within the cell and inaccessibility to many of the tools employed in proteomics studies. This review focuses on membrane proteins with particular emphasis on sub-cellular localization in terms of methodologies that can be used to determine the accurate location of membrane proteins to organelles. We also discuss what is known about the membrane protein cohorts of major organelles.
[Show abstract][Hide abstract] ABSTRACT: Membrane trafficking, including that of integral membrane proteins as well as peripherally associated proteins, appears to be a vital process common to all eukaryotes. An important element of membrane trafficking is to determine the protein composition of the various endomembrane compartments. A major issue with such a compositional analysis is the difficulty of having to distinguish between resident components involved in specific tasks and the proteins that are in transit through the endomembrane system. Examples of resident proteins include components of the SNARE complex used to target membrane vesicles to different locations in the cell. In the case of functionally important residents, one would expect such proteins to have a fairly precise subcellular localization. In the case of proteins "passing through" an endosomal compartment en route to a final destination, one would expect to find the proteins colocalizing with many membrane compartments. As is evident from several Update articles in this issue, ambiguity exists when employing cytological techniques to identify specific endomembrane compartments, while markers identified based on homology may behave differently in plant cells. Therefore, a proteomics approach based on proteins that would traffic through various parts of the endomembrane system, such as plasma membrane (PM) receptors, would be a welcome addition to membrane-trafficking studies. PM receptors are highly dependent on correct trafficking for their eventual localization, their biological function, and finally their degradation, while recent evidence suggests that endocytosis of PM receptors is an integral part of their biological function. In this review, first, a short update on endocytosis and endosomal trafficking in Arabidopsis (Arabidopsis thaliana) is provided. In this section, we emphasize trafficking of PM receptors as a proteomics tool by looking at how the PM receptors traffic in a time-dependent fashion in order to determine the relationship between different endosomal compartments. Second, we describe the recent progress in advanced proteomics techniques such as localization of organelle proteins by isotope tagging (LOPIT), by which proteins are assigned to different endosomal compartments.