Arnoud J. Groen

Sub‐cellular localization of membrane proteins

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.

α-Klotho Expression in Human Tissues

Context: 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. Objective: This is the first study to characterize systemwide tissue expression of transmembrane α-Klotho in humans. We have employed next-generation targeted proteomic analysis using parallel reaction monitoring in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health. Results: 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, whereas 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 parallel reaction monitoring mass spectrometry (data deposited at ProteomeXchange, PXD002775) identifying peptides specific for the full-length, transmembrane α-Klotho isoform. Conclusions: The data presented confirm α-Klotho expression in the kidney tubule and in the artery and provide evidence of α-Klotho expression across organ systems and cell types that has not previously been described in humans.

Identification of Trans-Golgi Network Proteins in Arabidopsis thaliana Root Tissue

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully 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 localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to 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 immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent 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 favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

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

UNLABELLED 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.

A Foundation for Reliable Spatial Proteomics Data Analysis

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 for establishing high-quality proteome-wide datasets. 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 the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Comprehensive assessment of estrogen receptor beta antibodies in cancer cell line models and tissue reveals critical limitations in reagent specificity.

Estrogen Receptor-β (ERβ) has been implicated in many cancers. In prostate and breast cancer its function is controversial, but genetic studies implicate a role in cancer progression. Much of the confusion around ERβ stems from antibodies that are inadequately validated, yet have become standard tools for deciphering its role. Using an ERβ-inducible cell system we assessed commonly utilized ERβ antibodies and show that one of the most commonly used antibodies, NCL-ER-BETA, is non-specific for ERβ. Other antibodies have limited ERβ specificity or are only specific in one experimental modality. ERβ is commonly studied in MCF-7 (breast) and LNCaP (prostate) cancer cell lines, but we found no ERβ expression in either, using validated antibodies and independent mass spectrometry-based approaches. Our findings question conclusions made about ERβ using the NCL-ER-BETA antibody, or LNCaP and MCF-7 cell lines. We describe robust reagents, which detect ERβ across multiple experimental approaches and in clinical samples.

A Quantitative Phosphoproteome Analysis of cGMP-Dependent Cellular Responses in Arabidopsis thaliana

The second messenger cyclic nucleotide 3′,5′-cyclic guanosine monophosphate (cGMP) is increasingly recognized as a key signaling molecule that mediates many physiological and developmental processes in plants (Supplemental Figure 1A). While cGMP-dependent phosphorylation of Arabidopsis proteins is a known phenomenon (Isner et al., 2012), a quantification of the cGMP-dependent protein phosphorylation at the system level has not been reported previously. Here, we applied a Ti4+-IMAC (immobilized metal-ion affinity chromatography) phosphopeptide enrichment technique combined with tandem mass spectrometry to analyze the cGMP-dependent phosphoproteome of Arabidopsis thaliana cell suspension culture cells that are metabolically labeled with 15N (Supplemental Figure 1B) and show highly specific response signatures.

Proteomics of total membranes and subcellular membranes

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.

A Proteomics Approach to Membrane Trafficking

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

The brassinosteroid receptor BRI1 can generate cGMP enabling cGMP-dependent downstream signaling

The brassinosteroid receptor brassinosteroid insensitive 1 (BRI1) is a member of the leucine-rich repeat receptor-like kinase family. The intracellular kinase domain of BRI1 is an active kinase and also encapsulates a guanylate cyclase catalytic centre. Using liquid chromatography tandem mass spectrometry, we confirmed that the recombinant cytoplasmic domain of BRI1 generates pmol amounts of cGMP per μg protein with a preference for magnesium over manganese as a co-factor. Importantly, a functional BRI1 kinase is essential for optimal cGMP generation. Therefore, the guanylate cyclase activity of BRI1 is modulated by the kinase while cGMP, the product of the guanylate cyclase, in turn inhibits BRI1 kinase activity. Furthermore, we show using Arabidopsis root cell cultures that cGMP rapidly potentiates phosphorylation of the downstream substrate brassinosteroid signaling kinase 1 (BSK1). Taken together, our results suggest that cGMP acts as a modulator that enhances downstream signaling while dampening signal generation from the receptor.