Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Sources of Variability in Metabolite Measurements from Urinary Samples

Background

The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.

Methods

We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2–3 samples per person from 17 male subjects (age 38–70) over 2–10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies.

Results

Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of “usual” levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations.

Conclusions

The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.     

Detection of Protein–Protein Interactions Through Vesicle Targeting

The detection of protein–protein interactions through two-hybrid assays has revolutionized our understanding of biology. The remarkable impact of two-hybrid assay platforms derives from their speed, simplicity, and broad applicability. Yet for many organisms, the need to express test proteins in Saccharomyces cerevisiae or Escherichia coli presents a substantial barrier because variations in codon specificity or bias may result in aberrant protein expression. In particular, nonstandard genetic codes are characteristic of several eukaryotic pathogens, for which there are currently no genetically based systems for detection of protein–protein interactions. We have developed a protein–protein interaction assay that is carried out in native host cells by using GFP as the only foreign protein moiety, thus circumventing these problems. We show that interaction can be detected between two protein pairs in both the model yeast S. cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence.     

Advances in enzyme-mediated proximity labeling and its potential for plant research

Cellular processes rely on the intimate interplay of different molecules, including DNA, RNA, proteins, and metabolites. Obtaining and integrating data on their abundance and dynamics at high temporal and spatial resolution are essential for our understanding of plant growth and development. In the past decade, enzymatic proximity labeling (PL) has emerged as a powerful tool to study local protein and nucleotide ensembles, discover protein–protein and protein–nucleotide interactions, and resolve questions about protein localization and membrane topology. An ever-growing number and continuous improvement of enzymes and methods keep broadening the spectrum of possible applications for PL and make it more accessible to different organisms, including plants. While initial PL experiments in plants required high expression levels and long labeling times, recently developed faster enzymes now enable PL of proteins on a cell type-specific level, even with low-abundant baits, and in different plant species. Moreover, expanding the use of PL for additional purposes, such as identification of locus-specific gene regulators or high-resolution electron microscopy may now be in reach. In this review, we give an overview of currently available PL enzymes and their applications in mammalian cell culture and plants. We discuss the challenges and limitations of PL methods and highlight open questions and possible future directions for PL in plants.

Innovation of new enzymes and methods makes proximity labeling an attractive tool for discovery of protein interactions and subcellular proteomes and broadens the spectrum of applications in plants.     

Proteomic analysis of red blood cells and the potential for the clinic: what have we learned so far?

Introduction: Red blood cells (RBC) are the most abundant host cells in the human body. Mature erythrocytes are devoid of nuclei and organelles and have always been regarded as circulating ‘bags of hemoglobin’. The advent of proteomics has challenged this assumption, revealing unanticipated complexity and novel roles for RBCs not just in gas transport, but also in systemic metabolic homeostasis in health and disease.

Areas covered: In this review we will summarize the main advancements in the field of discovery mode and redox/quantitative proteomics with respect to RBC biology. We thus focus on translational/clinical applications, such as transfusion medicine, hematology (e.g. hemoglobinopathies) and personalized medicine. Synergy of omics technologies – especially proteomics and metabolomics – are highlighted as a hallmark of clinical metabolomics applications for the foreseeable future.

Expert commentary: The introduction of advanced proteomics technologies, especially quantitative and redox proteomics, and the integration of proteomics data with omics information gathered through orthogonal technologies (especially metabolomics) promise to revolutionize many biomedical areas, from hematology and transfusion medicine to personalized medicine and clinical biochemistry.     

A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys

RNA-binding proteins play many essential roles in the regulation of gene expression in the cell. Despite the significant increase in the number of structures for RNA–protein complexes in the last few years, the molecular basis of specificity remains unclear even for the best-studied protein families. We have developed a distance and orientation-dependent hydrogen-bonding potential based on the statistical analysis of hydrogen-bonding geometries that are observed in high-resolution crystal structures of protein–DNA and protein–RNA complexes. We observe very strong geometrical preferences that reflect significant energetic constraints on the relative placement of hydrogen-bonding atom pairs at protein–nucleic acid interfaces. A scoring function based on the hydrogen-bonding potential discriminates native protein–RNA structures from incorrectly docked decoys with remarkable predictive power. By incorporating the new hydrogen-bonding potential into a physical model of protein–RNA interfaces with full atom representation, we were able to recover native amino acids at protein–RNA interfaces.     

The rise of proteome‐wide biophysics

While informative, protein amounts and physical protein associations do not provide a full picture of protein function. This Commentary highlights the potential of structural and stability proteomic technologies to derive new insights in biology and medicine.

The ultimate goal of proteomics is to provide a holistic view of the biological processes that explain phenotypes. To this end, proteomics approaches have been frequently used to map protein expression in multiple experimental conditions and to identify protein–protein interactions. While informative to a certain extent, protein amounts and physical protein associations do not provide a full picture of protein function. Here, we highlight the novel insights that are made possible in biology and medicine by structural and stability proteomic technologies.     

Constitutive patterns of gene expression regulated by RNA-binding proteins

Background

RNA-binding proteins regulate a number of cellular processes, including synthesis, folding, translocation, assembly and clearance of RNAs. Recent studies have reported that an unexpectedly large number of proteins are able to interact with RNA, but the partners of many RNA-binding proteins are still uncharacterized.

Results

We combined prediction of ribonucleoprotein interactions, based on catRAPID calculations, with analysis of protein and RNA expression profiles from human tissues. We found strong interaction propensities for both positively and negatively correlated expression patterns. Our integration of in silico and ex vivo data unraveled two major types of protein–RNA interactions, with positively correlated patterns related to cell cycle control and negatively correlated patterns related to survival, growth and differentiation. To facilitate the investigation of protein–RNA interactions and expression networks, we developed the catRAPID express web server.

Conclusions

Our analysis sheds light on the role of RNA-binding proteins in regulating proliferation and differentiation processes, and we provide a data exploration tool to aid future experimental studies.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Metabolomics in the fields of oncology: a review of recent research

The study of all endogenously produced metabolites, known as metabolomics, is the youngest of the "omics" sciences. It is becoming increasingly clear that, of all of the "omics" techniques, metabolomic approaches will become increasingly useful in disease diagnosis and have potential power to improve our understanding of the underlying mechanisms of cancer. The primary aim of the review is to discuss the relationship between metabolomics and tumors are elucidated in detail. Then the review is also to introduce the technologies of metabolomics, especially emphasizing the application of metabolomics in the fields of oncology.     

Protein–DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.     

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure,correlation of metabolites to cell number,and impact of the cell harvesting method

Introduction

Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.

Objectives

We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.

Methods

We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.

Results

We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.

Conclusion

We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

     

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry(MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes.The metabolomes of cells,tissues,and organisms comprise a variety of molecules including lipids,amino acids,sugars,organic acids,and so on.Metabolomics mainly focus on the hydrophilic classes,while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes.The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases,but system-level understanding is largely lacking,which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies.While scientists are continuously striving to develop high-coverage omics approaches,integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation.Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape,enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology,facilitating the study of interconnection between lipids and other metabolites in disease progression.In this review,we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health.We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.     

Interaction of Saccharomyces Cdc13p with Pol1p, Imp4p, Sir4p and Zds2p is involved in telomere replication, telomere maintenance and cell growth control

Telomeres are the physical ends of eukaryotic chromosomes. They are important for maintaining the integrity of chromosomes and this function is mediated through a number of protein factors. In Saccharomyces cerevisiae, Cdc13p binds to telomeres and affects telomere maintenance, telomere position effects and cell cycle progression through G₂/M phase. We identified four genes encoding Pol1p, Sir4p, Zds2p and Imp4p that interact with amino acids 1–252 of Cdc13p using a yeast two-hybrid screening system. Interactions of these four proteins with Cdc13p were through direct protein–protein interactions as judged by in vitro pull-down assays. Direct protein–protein interactions were also observed between Pol1p–Imp4p, Pol1p–Sir4p and Sir4p–Zds2p, whereas no interaction was detected between Imp4p–Sir4p and Zds2p–Imp4p, suggesting that protein interactions were specific in the complex. Pol1p was shown to interact with Cdc13p. Here we show that Zds2p and Imp4p also form a stable complex with Cdc13p in yeast cells, because Zds2p and Imp4p co-immunoprecipitate with Cdc13p, whereas Sir4p does not. The function of the N-terminal 1–252 region of Cdc13p was also analyzed. Expressing Cdc13(252–924)p, which lacks amino acids 1–252 of Cdc13p, causes defects in progressive cell growth and eventually arrested in the G₂/M phase of the cell cycle. These growth defects were not caused by progressive shortening of telomeres because telomeres in these cells were long. Point mutants in the amino acids 1–252 region of Cdc13p that reduced the interaction between Cdc13p and its binding proteins resulted in varying level of defects in cell growth and telomeres. These results indicate that the interactions between Cdc13(1–252)p and its binding proteins are important for the function of Cdc13p in telomere regulation and cell growth. Together, our results provide evidence for the formation of a Cdc13p-mediated telosome complex through its N-terminal region that is involved in telomere maintenance, telomere length regulation and cell growth control.     

I’m coming to GEF you: Regulation of RhoGEFs during cell migration

Cell migration is a highly regulated multistep process that requires the coordinated regulation of cell adhesion, protrusion, and contraction. These processes require numerous protein–protein interactions and the activation of specific signaling pathways. The Rho family of GTPases plays a key role in virtually every aspect of the cell migration cycle. The activation of Rho GTPases is mediated by a large and diverse family of proteins; the guanine nucleotide exchange factors (RhoGEFs). GEFs work immediately upstream of Rho proteins to provide a direct link between Rho activation and cell–surface receptors for various cytokines, growth factors, adhesion molecules, and G protein-coupled receptors. The regulated targeting and activation of RhoGEFs is essential to coordinate the migratory process. In this review, we summarize the recent advances in our understanding of the role of RhoGEFs in the regulation of cell migration.     

An atlas of posttranslational modifications on RNA binding proteins

RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins (RBPs; a PTM RBP Atlas). We curate published datasets and primary literature to understand the landscape of PTMs and use protein–protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA–protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology.