Nanomolar anti-SARS-CoV-2 Omicron activity of the host-directed TMPRSS2 inhibitor N-0385 and synergistic action with direct-acting antivirals.

SARS-CoV-2 Omicron subvariants with increased transmissibility and immune evasion are spreading globally with alarming persistence. Whether the mutations and evolution of spike (S) Omicron subvariants alter the viral hijacking of human TMPRSS2 for viral entry remains to be elucidated. This is particularly important to investigate because of the large number and diversity of mutations of S Omicron subvariants reported since the emergence of BA.1. Here we report that human TMPRSS2 is a molecular determinant of viral entry for all the Omicron clinical isolates tested in human lung cells, including ancestral Omicron subvariants (BA.1, BA.2, BA.5), contemporary Omicron subvariants (BQ.1.1, XBB.1.5, EG.5.1) and currently circulating Omicron BA.2.86. First, we used a co-transfection assay to demonstrate the endoproteolytic cleavage by TMPRSS2 of spike Omicron subvariants. Second, we found that N-0385, a highly potent TMPRSS2 inhibitor, is a robust entry inhibitor of virus-like particles harbouring the S protein of Omicron subvariants. Third, we show that N-0385 exhibits nanomolar broad-spectrum antiviral activity against live Omicron subvariants in human Calu-3 lung cells and primary patient-derived bronchial epithelial cells. Interestingly, we found that N-0385 is 10-20 times more potent than the repositioned TMPRSS2 inhibitor, camostat, against BA.5, EG.5.1, and BA.2.86. We further found that N-0385 shows broad synergistic activity with clinically approved direct-acting antivirals (DAAs), i.e., remdesivir and nirmatrelvir, against Omicron subvariants, demonstrating the potential therapeutic benefits of a multi-targeted treatment based on N-0385 and DAAs.

DataCurator.jl: efficient, portable and reproducible validation, curation and transformation of large heterogeneous datasets using human-readable recipes compiled into machine-verifiable templates.

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator, a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable, machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine-verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.

Discovery of lead natural products for developing pan-SARS-CoV-2 therapeutics.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global public health crisis. The reduced efficacy of therapeutic monoclonal antibodies against emerging SARS-CoV-2 variants of concern (VOCs), such as omicron BA.5 subvariants, has underlined the need to explore a novel spectrum of antivirals that are effective against existing and evolving SARS-CoV-2 VOCs. To address the need for novel therapeutic options, we applied cell-based high-content screening to a library of natural products (NPs) obtained from plants, fungi, bacteria, and marine sponges, which represent a considerable diversity of chemical scaffolds. The antiviral effect of 373 NPs was evaluated using the mNeonGreen (mNG) reporter SARS-CoV-2 virus in a lung epithelial cell line (Calu-3). The screening identified 26 NPs with half-maximal effective concentrations (EC50) below 50 µM against mNG-SARS-CoV-2; 16 of these had EC50 values below 10 µM and three NPs (holyrine A, alotaketal C, and bafilomycin D) had EC50 values in the nanomolar range. We demonstrated the pan-SARS-CoV-2 activity of these three lead antivirals against SARS-CoV-2 highly transmissible Omicron subvariants (BA.5, BA.2 and BA.1) and highly pathogenic Delta VOCs in human Calu-3 lung cells. Notably, holyrine A, alotaketal C, and bafilomycin D, are potent nanomolar inhibitors of SARS-CoV-2 Omicron subvariants BA.5 and BA.2. The pan-SARS-CoV-2 activity of alotaketal C [protein kinase C (PKC) activator] and bafilomycin D (V-ATPase inhibitor) suggest that these two NPs are acting as host-directed antivirals (HDAs). Future research should explore whether PKC regulation impacts human susceptibility to and the severity of SARS-CoV-2 infection, and it should confirm the important role of human V-ATPase in the VOC lifecycle. Interestingly, we observed a synergistic action of bafilomycin D and N-0385 (a highly potent inhibitor of human TMPRSS2 protease) against Omicron subvariant BA.2 in human Calu-3 lung cells, which suggests that these two highly potent HDAs are targeting two different mechanisms of SARS-CoV-2 entry. Overall, our study provides insight into the potential of NPs with highly diverse chemical structures as valuable inspirational starting points for developing pan-SARS-CoV-2 therapeutics and for unravelling potential host factors and pathways regulating SARS-CoV-2 VOC infection including emerging omicron BA.5 subvariants.

SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy.

Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-ß deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer's disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability.

A TMPRSS2 inhibitor acts as a pan-SARS-CoV-2 prophylactic and therapeutic.

The COVID-19 pandemic caused by the SARS-CoV-2 virus remains a global public health crisis. Although widespread vaccination campaigns are underway, their efficacy is reduced owing to emerging variants of concern1,2. Development of host-directed therapeutics and prophylactics could limit such resistance and offer urgently needed protection against variants of concern3,4. Attractive pharmacological targets to impede viral entry include type-II transmembrane serine proteases (TTSPs) such as TMPRSS2; these proteases cleave the viral spike protein to expose the fusion peptide for cell entry, and thus have an essential role in the virus lifecycle5,6. Here we identify and characterize a small-molecule compound, N-0385, which exhibits low nanomolar potency and a selectivity index of higher than 106 in inhibiting SARS-CoV-2 infection in human lung cells and in donor-derived colonoids7. In Calu-3 cells it inhibits the entry of the SARS-CoV-2 variants of concern B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma) and B.1.617.2 (Delta). Notably, in the K18-human ACE2 transgenic mouse model of severe COVID-19, we found that N-0385 affords a high level of prophylactic and therapeutic benefit after multiple administrations or even after a single administration. Together, our findings show that TTSP-mediated proteolytic maturation of the spike protein is critical for SARS-CoV-2 infection in vivo, and suggest that N-0385 provides an effective early treatment option against COVID-19 and emerging SARS-CoV-2 variants of concern.

A novel highly potent inhibitor of TMPRSS2-like proteases blocks SARS-CoV-2 variants of concern and is broadly protective against infection and mortality in mice.

The COVID-19 pandemic caused by the SARS-CoV-2 virus remains a global public health crisis. Although widespread vaccination campaigns are underway, their efficacy is reduced against emerging variants of concern (VOCs) 1,2 . Development of host-directed therapeutics and prophylactics could limit such resistance and offer urgently needed protection against VOCs 3,4 . Attractive pharmacological targets to impede viral entry include type-II transmembrane serine proteases (TTSPs), such as TMPRSS2, whose essential role in the virus lifecycle is responsible for the cleavage and priming of the viral spike protein 5-7 . Here, we identify and characterize a small-molecule compound, N-0385, as the most potent inhibitor of TMPRSS2 reported to date. N-0385 exhibited low nanomolar potency and a selectivity index of >10 6 at inhibiting SARS-CoV-2 infection in human lung cells and in donor-derived colonoids 8 . Importantly, N-0385 acted as a broad-spectrum coronavirus inhibitor of two SARS-CoV-2 VOCs, B.1.1.7 and B.1.351. Strikingly, single daily intranasal administration of N-0385 early in infection significantly improved weight loss and clinical outcomes, and yielded 100% survival in the severe K18-human ACE2 transgenic mouse model of SARS-CoV-2 disease. This demonstrates that TTSP-mediated proteolytic maturation of spike is critical for SARS-CoV-2 infection in vivo and suggests that N-0385 provides a novel effective early treatment option against COVID-19 and emerging SARS-CoV-2 VOCs.

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods.

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

Hypoxia Attenuates Trastuzumab Uptake and Trastuzumab-Emtansine (T-DM1) Cytotoxicity through Redistribution of Phosphorylated Caveolin-1.

The antibody-drug conjugate trastuzumab-emtansine (T-DM1) offers an additional treatment option for patients with HER2-amplified tumors. However, primary and acquired resistance is a limiting factor in a significant subset of patients. Hypoxia, a hallmark of cancer, regulates the trafficking of several receptor proteins with potential implications for tumor targeting. Here, we have investigated how hypoxic conditions may regulate T-DM1 treatment efficacy in breast cancer. The therapeutic effect of T-DM1 and its metabolites was evaluated in conjunction with biochemical, flow cytometry, and high-resolution imaging studies to elucidate the functional and mechanistic aspects of hypoxic regulation. HER2 and caveolin-1 expression was investigated in a well-annotated breast cancer cohort. We find that hypoxia fosters relative resistance to T-DM1 in HER2+ cells (SKBR3 and BT474). This effect was not a result of deregulated HER2 expression or resistance to emtansine and its metabolites. Instead, we show that hypoxia-induced translocation of caveolin-1 from cytoplasmic vesicles to the plasma membrane contributes to deficient trastuzumab internalization and T-DM1 chemosensitivity. Caveolin-1 depletion mimicked the hypoxic situation, indicating that vesicular caveolin-1 is indispensable for trastuzumab uptake and T-DM1 cytotoxicity. In vitro studies suggested that HER2 and caveolin-1 are not coregulated, which was supported by IHC analysis in patient tumors. We find that phosphorylation-deficient caveolin-1 inhibits trastuzumab internalization and T-DM1 cytotoxicity, suggesting a specific role for caveolin-1 phosphorylation in HER2 trafficking. IMPLICATIONS: Together, our data for the first time identify hypoxic regulation of caveolin-1 as a resistance mechanism to T-DM1 with potential implications for individualized treatment of breast cancer.

ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy.

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning.

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.

Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae.

Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and larger hemispherical S2 scaffolds. Application here of network modularity analysis of SMLM data for endogenous Cav1 labeling in HeLa cells shows that small scaffolds combine to form larger scaffolds and caveolae. We find modules within Cav1 blobs by maximizing the intra-connectivity between Cav1 molecules within a module and minimizing the inter-connectivity between Cav1 molecules across modules, which is achieved via spectral decomposition of the localizations adjacency matrix. Features of modules are then matched with intact blobs to find the similarity between the module-blob pairs of group centers. Our results show that smaller S1A and S1B scaffolds are made up of small polygons, that S1B scaffolds correspond to S1A scaffold dimers and that caveolae and hemispherical S2 scaffolds are complex, modular structures formed from S1B and S1A scaffolds, respectively. Polyhedral interactions of Cav1 oligomers, therefore, leads progressively to the formation of larger and more complex scaffold domains and the biogenesis of caveolae.

Effect of caveolin-1 on Stat3-ptyr705 levels in breast and lung carcinoma cells.

We recently demonstrated that Cav1 (caveolin-1) is a negative regulator of Stat3 (signal transducer and activator of transcription-3) activity in mouse fibroblasts and human lung carcinoma SHP77 cells. We now examined whether the cellular context may affect their levels as well as the relationship between them, by assessing Cav1 and Stat3-ptyr705 amounts in different cell lines. In MDA-MB-231, A549, and HaCat cells, Cav1 levels were high and Stat3-ptyr705 levels were low, consistent with the notion of a negative effect of endogenous Cav1 on Stat3-ptyr705 levels in these lines. In addition, manipulation of Cav1 levels revealed a negative effect in MCF7 and mouse fibroblast cells, while Cav1 upregulation induced apoptosis in MCF7 cells. In contrast, however, line MRC9 had high Cav1 and high Stat3-ptyr705 levels, indicating that high Cav1 is insufficient to reduce Stat3-ptyr705 levels in this line. MCF7 and LuCi6 cells had very low Cav1 and Stat3-ptyr705 levels, indicating that the low Stat3-ptyr705 can be independent from Cav1 levels altogether. Our results reveal a further level of complexity in the relationship between Cav1 and Stat3-ptyr705 than previously thought. In addition, we demonstrate that in a feedback loop, Stat3 inhibition upregulates Cav1 in HeLa cells but not in other lines tested.

Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

MOTIVATION: Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs. RESULTS: Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50-100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds.

Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.

Galectin-3 Overrides PTRF/Cavin-1 Reduction of PC3 Prostate Cancer Cell Migration.

Expression of Caveolin-1 (Cav1), a key component of cell surface caveolae, is elevated in prostate cancer (PCa) and associated with PCa metastasis and a poor prognosis for PCa patients. Polymerase I and Transcript Release Factor (PTRF)/cavin-1 is a cytoplasmic protein required for Cav1-dependent formation of caveolae. Expression of PTRF reduces the motility of PC3 cells, a metastatic prostate cancer cell line that endogenously expresses abundant Cav1 but no PTRF and no caveolae, suggesting a role for non-caveolar Cav1 domains, or Cav1 scaffolds, in PCa cell migration. Tyrosine phosphorylated Cav1 (pCav1) functions in concert with Galectin-3 (Gal3) and the galectin lattice to stabilize focal adhesion kinase (FAK) within focal adhesions (FAs) and promote cancer cell motility. However, whether PTRF regulation of Cav1 function in PCa cell migration is related to Gal3 expression and functionality has yet to be determined. Here we show that PTRF expression in PC3 cells reduces FAK stabilization in focal adhesions and reduces cell motility without affecting pCav1 levels. Exogenous Gal3 stabilized FAK in focal adhesions of PTRF-expressing cells and restored cell motility of PTRF-expressing PC3 cells to levels of PC3 cells in a dose-dependent manner, with an optimal concentration of 2 µg/ml. Exogenous Gal3 stabilized FAK in focal adhesions of Gal3 knockdown PC3 cells but not in Cav1 knockdown PC3 cells. Cav1 knockdown also prevented Gal3 rescue of FA-associated FAK stabilization in PTRF-expressing PC3 cells. Our data support a role for PTRF/cavin-1, through caveolae formation, as an attenuator of the non-caveolar functionality of Cav1 in Gal3-Cav1 signalling and regulation of focal adhesion dynamics and cancer cell migration.

Caveolin-1: role in cell signaling.

Caveolins (Cavs) are integrated plasma membrane proteins that are complex signaling regulators with numerous partners and whose activity is highly dependent on cellular context. Cavs are both positive and negative regulators of cell signaling in and/or out of caveolae, invaginated lipid raft domains whose formation is caveolin expression dependent. Caveolins and rafts have been implicated in membrane compartmentalization; proteins and lipids accumulate in these membrane microdomains where they transmit fast, amplified and specific signaling cascades. The concept of plasma membrane organization within functional rafts is still in exploration and sometimes questioned. In this chapter, we discuss the opposing functions of caveolin in cell signaling regulation focusing on the role of caveolin both as a promoter and inhibitor of different signaling pathways and on the impact of membrane domain localization on caveolin functionality in cell proliferation, survival, apoptosis and migration.

Fluorescence-quenching and resonance energy transfer studies of lipid microdomains in model and biological membranes.

Measurements of contact-dependent fluorescence quenching and of fluorescence resonance energy transfer (FRET) within bilayers provide information concerning the spatial relationships between molecules on distance scales of a few nm or up a few tens of nm, respectively, and are therefore well suited to detect the presence and composition of membrane microdomains. As described in this review, techniques based on fluorescence quenching and FRET have been used to demonstrate the formation of nanoscale liquid-ordered domains in cholesterol-containing model membranes under physiological conditions, and to investigate the structural features of lipids and proteins that influence their partitioning between liquid-ordered and liquid-disordered domains. FRET-based methods have also been used to test for the presence of 'raft' microdomains in the plasma membranes of mammalian cells. We discuss the sometimes divergent findings of these studies, possible modifications to the 'raft hypothesis' suggested by studies using FRET and other techniques, and the further potential of FRET-based methods to test and to refine current models of the nature and organization of membrane microdomains.