Role of neuregulin-1/ErbB2 signaling in endothelium-cardiomyocyte cross-talk

Neuregulin-1 (NRG-1), a cardioactive growth factor released from endothelial cells, has been shown to be indispensable for the normal function of the adult heart by binding to ErbB4 receptors on cardiomyocytes. In the present study, we have investigated to what extent ErbB2, the favored co-factor of ErbB4 for heterodimerization, participates in the cardiac effects of endothelium-derived NRG-1. In addition, in view of our previously described anti-adrenergic effects of NRG-1, we have studied which neurohormonal stimuli affect endothelial NRG-1 expression and release and how this may fit into a broader frame of cardiovascular physiology. Immunohistochemical staining of rat heart and aorta showed that NRG-1 expression was restricted to the endocardial endothelium and the cardiac microvascular endothelium (CMVE); by contrast, NRG-1 expression was absent in larger coronary arteries and veins and in aortic endothelium. In rat CMVE in culture, NRG-1 mRNA and protein expression was down-regulated by angiotensin II and phenylephrine and up-regulated by endothelin-1 and mechanical strain. CMVE-derived NRG-1 was shown to phosphorylate cardiomyocyte ErbB2, an event prevented by a 24-h preincubation of myocytes with monoclonal ErbB2 antibodies. Pretreating cardiomyocytes with these inhibitory anti-ErbB2 antibodies significantly attenuated CMVE-induced cardiomyocyte hypertrophy and abolished the protective actions of CMVE against cardiomyocyte apoptosis. Accordingly, ErbB2 signaling participated in the paracrine survival and growth controlling effects of NRG-1 on cardiomyocytes in vitro, explaining the cardiotoxicity of ErbB2 antibodies in patients. Cardiac NRG-1 synthesis occurs in endothelial cells adjacent to cardiac myocytes and is sensitive to factors related to the regulation of blood pressure.     

The Anopheles gambiae Odorant Binding Protein 1 (AgamOBP1) Mediates Indole Recognition in the Antennae of Female Mosquitoes

Haematophagous insects are frequently carriers of parasitic diseases, including malaria. The mosquito Anopheles gambiae is the major vector of malaria in sub-Saharan Africa and is thus responsible for thousands of deaths daily. Although the role of olfaction in A. gambiae host detection has been demonstrated, little is known about the combinations of ligands and odorant binding proteins (OBPs) that can produce specific odor-related responses in vivo. We identified a ligand, indole, for an A. gambiae odorant binding protein, AgamOBP1, modeled the interaction in silico and confirmed the interaction using biochemical assays. RNAi-mediated gene silencing coupled with electrophysiological analyses confirmed that AgamOBP1 binds indole in A. gambiae and that the antennal receptor cells do not respond to indole in the absence of AgamOBP1. This case represents the first documented instance of a specific A. gambiae OBP–ligand pairing combination, demonstrates the significance of OBPs in odor recognition, and can be expanded to the identification of other ligands for OBPs of Anopheles and other medically important insects.     

Strategies towards high-quality binary protein interactome maps

Many processes in a cell depend on protein–protein interactions (PPIs) and perturbations of these interactions can lead to diseases. Comprehensive knowledge of PPI networks will not only give us information on how the cell is organized, but will also provide new drug targets. Current binary PPI networks are mainly generated by high-throughput yeast two-hybrid. Due to the small overlap of these maps, it has long been assumed that these maps are of low quality containing many false positives. However, by using an orthogonal two-hybrid method, MAPPIT (mammalian protein–protein interaction trap), these maps were shown to be of high quality suggesting that the limited overlap is likely due to low sensitivity and not to low specificity.     

Kinase Substrate Sensor (KISS), a Mammalian In Situ Protein Interaction Sensor

Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent β-arrestin recruitment to GPCRs illustrated the method''s potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges.A protein''s function is largely mediated through its interactions with other proteins, hence the critical importance of protein-protein interaction (PPI)¹ maps for understanding cellular mechanisms of action in health and disease. Whereas many proteins are organized in stable multi-protein complexes, the majority of cellular processes are governed by transient protein encounters, the dynamics of which are directed by a diversity of both intra- and extracellular signals. Our view of protein networks is still, however, mainly a static one (1). Current interactomes consist mainly of data generated by yeast 2-hybrid (Y2H) (2) and (tandem) affinity purification combined with mass spectrometry (3) and should be interpreted as scaffolds of potential PPIs that might occur at a certain time and place in the cell or as snapshots of PPIs taking place under a specific cellular condition. Although very robust and highly efficient, these approaches do not allow studying PPI modulation because they do not offer the proper context for mammalian PPI analysis, e.g. they operate in yeast cells (Y2H) or make use of cell lysates (affinity purification-based methods). Moreover, because these interactome mapping tools are biased against interactions that involve transmembrane proteins, the latter are underrepresented in current interactome network versions (4). Yet, membrane-associated proteins constitute around one third of the entire proteome and their significance is underscored by the fact that over half of currently marketed drugs target membrane proteins (5). These observations support the need for approaches that allow PPIs, including those involving transmembrane proteins, to be assayed in their native cellular environment.Apart from the high-throughput methods mentioned above, a diverse arsenal of other PPI technologies has been developed, a number of which actually operate in mammalian cells. FRET and BRET, which rely on fluorescence or bioluminescence energy transfer between interacting fusion proteins, make assays with high spatiotemporal resolution (6, 7). A variety of PCAs have been reported, including split fluorescent protein or reporter enzyme technologies, that are able to capture aspects of PPI dynamics in a mammalian background (8, 9). A recent addition is an infrared fluorescent PCA that, unlike previous fluorescent PCAs, exhibits reversible complementation, thus enabling spatiotemporal analysis of dynamic PPIs (10). Another binary interaction assay, luminescence-based mammalian interactome mapping (LUMIER), has been applied to map TGFβ induced modulation of PPIs with components of the TGFβ signaling pathway (11). MaMTH, a mammalian version of the split ubiquitin approach, was designed particularly for the analysis of PPIs involving integral membrane proteins, also allowing the detection of functional PPI modulation (12). Efforts to apply purification-based methods for detecting context-dependent PPI modulation recently resulted in the development of AP-SRM (13) and AP-SWATH (14).Our group previously conceived mammalian protein-protein interaction trap (MAPPIT) (supplemental Fig. S1A) (15, 16), a mammalian two-hybrid approach based on complementation of a cytokine receptor that was developed into a broad platform for PPI analysis (17, 18), screening for small molecule PPI disruptors (19, 20) and drug target profiling (21, 22). Although MAPPIT operates in intact human cells, thus providing the natural environment for human protein analysis, the interaction sensor is anchored to the plasma membrane, precluding the analysis of PPIs at their native subcellular localization. In addition, MAPPIT is incompatible with full size transmembrane proteins. Here we describe KInase Substrate Sensor (KISS), a novel binary PPI mapping approach that enables in situ analysis in living mammalian cells of protein interactions and their responses to physiological or pharmacological challenges.     

A novel stable isotope labelling assisted workflow for improved untargeted LC–HRMS based metabolomics research

Many untargeted LC–ESI–HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly ¹³C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-¹³C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC–HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87–135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-¹³C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.