automated-detection-and-analysis-of-exocytosis

Automated Detection and Analysis of Exocytosis

Timelapse TIRF microscopy of pH-sensitive GFP (pHluorin) attached to vesicle SNARE proteins is an effective method to visualize single vesicle exocytic ...

University of North Carolina at Chapel Hill

Extracellular netrin-1 and its receptor deleted in colorectal cancer (DCC) promote axon branching in developing cortical neurons. Netrin-dependent morphogenesis is preceded by multimerization of DCC, activation of FAK and Src family kinases, and increases in exocytic vesicle fusion, yet how these occurrences are linked is unknown. Here we demonstrate that partite otif protein 9 (TRIM9)-dependent ubiquitination of DCC blocks the interaction with and phosphorylation of FAK. Upon netrin-1 stimulation TRIM9 promotes DCC multimerization, but TRIM9-dependent ubiquitination of DCC is reduced, which promotes an interaction with FAK and subsequent FAK activation. We found that inhibition of FAK activity blocks elevated frequencies of exocytosis in vitro and elevated axon branching in vitro and in vivo. Although FAK inhibition decreased oluble -ethylmaleimide ttachment protein ceptor (SNARE)-mediated exocytosis, assembled SNARE complexes and vesicles adjacent to the plasma membrane increased, suggesting a novel role for FAK in the progression from assembled SNARE complexes to vesicle fusion in developing murine neurons.

TRIM9-dependent ubiquitination of DCC constrains kinase signaling, exocytosis, and axon branching.

Neurite elongation and branching in developing neurons requires plasmalemma expansion, hypothesized to occur primarily via exocytosis. We posited that exocytosis in developing neurons and nonneuronal cells would exhibit distinct spatiotemporal organization. We exploited total internal reflection fluorescence microscopy to image vesicle-associated membrane protein (VAMP)-pHluorin-mediated exocytosis in mouse embryonic cortical neurons and interphase melanoma cells, and developed computer-vision software and statistical tools to uncover spatiotemporal aspects of exocytosis. Vesicle fusion behavior differed between vesicle types, cell types, developmental stages, and extracellular environments. Experiment-based mathematical calculations indicated that VAMP2-mediated vesicle fusion supplied excess material for the plasma membrane expansion that occurred early in neuronal morphogenesis, which was balanced by clathrin-mediated endocytosis. Spatial statistics uncovered distinct spatiotemporal regulation of exocytosis in the soma and neurites of developing neurons that was modulated by developmental stage, exposure to the guidance cue netrin-1, and the brain-enriched ubiquitin ligase tripartite motif 9. In melanoma cells, exocytosis occurred less frequently, with distinct spatial clustering patterns.

Spatiotemporal organization of exocytosis emerges during neuronal shape change.

Familial dysautonomia (FD) is an autonomic and sensory neuropathy caused by a mutation in the splice donor site of intron 20 of the ELP1 gene. Variable skipping of exon 20 leads to a tissue-specific reduction in the level of ELP1 protein. We have shown that the plant cytokinin kinetin is able to increase cellular ELP1 protein levels in vivo and in vitro through correction of ELP1 splicing. Studies in FD patients determined that kinetin is not a practical therapy due to low potency and rapid elimination. To identify molecules with improved potency and efficacy, we developed a cell-based luciferase splicing assay by inserting renilla (Rluc) and firefly (Fluc) luciferase reporters into our previously well-characterized ELP1 minigene construct. Evaluation of the Fluc/Rluc signal ratio enables a fast and accurate way to measure exon 20 inclusion. Further, we developed a secondary assay that measures ELP1 splicing in FD patient-derived fibroblasts. Here we demonstrate the quality and reproducibility of our screening method. Development and implementation of this screening platform has allowed us to efficiently screen for new compounds that robustly and specifically enhance ELP1 pre-mRNA splicing.

Development of a Screening Platform to Identify Small Molecules That Modify ELP1 Pre-mRNA Splicing in Familial Dysautonomia.

Vinculin (Vcn) is a ubiquitously expressed cytoskeletal protein that links transmembrane receptors to actin filaments, and plays a key role in regulating cell adhesion, motility, and force transmission. Metavinculin (MVcn) is a Vcn splice isoform that contains an additional exon encoding a 68-residue insert within the actin binding tail domain. MVcn is selectively expressed at sub-stoichiometic amounts relative to Vcn in smooth and cardiac muscle cells. Mutations in the MVcn insert are linked to various cardiomyopathies. In vitro analysis has previously shown that while both proteins can engage filamentous (F)-actin, only Vcn can promote F-actin bundling. Moreover, we and others have shown that MVcn can negatively regulate Vcn-mediated F-actin bundling in vitro. To investigate functional differences between MVcn and Vcn, we stably expressed either Vcn or MVcn in Vcn-null mouse embryonic fibroblasts. While both MVcn and Vcn were observed at FAs, MVcn-expressing cells had larger but fewer focal adhesions per cell compared to Vcn-expressing cells. MVcn-expressing cells migrated faster and exhibited greater persistence compared to Vcn-expressing cells, even though Vcn-containing FAs assembled and disassembled faster. Magnetic tweezer measurements on Vcn-expressing cells show a typical cell stiffening phenotype in response to externally applied force; however, this was absent in Vcn-null and MVcn-expressing cells. Our findings that MVcn expression leads to larger but fewer FAs per cell, in conjunction with the inability of MVcn to bundle F-actin in vitro and rescue the cell stiffening response, are consistent with our previous findings of actin bundling deficient Vcn variants, suggesting that deficient actin-bundling may account for some of the differences between Vcn and MVcn.

Vinculin and metavinculin exhibit distinct effects on focal adhesion properties, cell migration, and mechanotransduction.

The formation of the nervous system involves establishing complex networks of synaptic connections between proper partners. This developmental undertaking requires the rapid expansion of the plasma membrane surface area as neurons grow and polarize, extending axons through the extracellular environment. Critical to the expansion of the plasma membrane and addition of plasma membrane material is exocytic vesicle fusion, a regulated mechanism driven by soluble N-ethylmaleimide-sensitive factor attachment proteins receptors (SNAREs). Since their discovery, SNAREs have been implicated in several critical neuronal functions involving exocytic fusion in addition to synaptic transmission, including neurite initiation and outgrowth, axon specification, axon extension, and synaptogenesis. Decades of research have uncovered a rich variety of SNARE expression and function. The basis of SNARE-mediated fusion, the opening of a fusion pore, remains an enigmatic event, despite an incredible amount of research, as fusion is not only heterogeneous but also spatially small and temporally fast. Multiple modes of exocytosis have been proposed, with full-vesicle fusion (FFV) and kiss-and-run (KNR) being the best described. Whereas most  work has reconstituted fusion using VAMP-2, SNAP-25, and syntaxin-1; there is much to learn regarding the behaviors of distinct SNARE complexes. In the past few years, robust heterogeneity in the kinetics and fate of the fusion pore that varies by cell type have been uncovered, suggesting a paradigm shift in how the modes of exocytosis are viewed is warranted. Here, we explore both classic and recent work uncovering the variety of SNAREs and their importance in the development of neurons, as well as historical and newly proposed modes of exocytosis, their regulation, and proteins involved in the regulation of fusion kinetics.

SNARE-Mediated Exocytosis in Neuronal Development.

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and in comparison of our proprietary software Assay Central with random forest, k-nearest neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (three layers). Model performance was assessed using an array of fivefold cross-validation metrics including area-under-the-curve, F1 score, Cohen's kappa, and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets, all methods appeared comparable, while the distance from the top indicated that Assay Central and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case. If anything, Assay Central may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central performance, although support vector classification seems to be a strong competitor. We also applied Assay Central to perform prospective predictions for the toxicity targets PXR and hERG to further validate these models. This work appears to be the largest scale comparison of these machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors, and machine learning algorithms and further refine the methods for evaluating and comparing such models.

Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.

Neuronal morphogenesis involves dramatic plasma membrane expansion, fueled by soluble N-ethylmaleimide-sensitive factor attachment protein eceptors (SNARE)-mediated exocytosis. Distinct fusion modes described at synapses include full-vesicle fusion (FVF) and kiss-and-run fusion (KNR). During FVF, lumenal cargo is secreted and vesicle membrane incorporates into the plasma membrane. During KNR, a transient fusion pore secretes cargo but closes without membrane addition. In contrast, fusion modes are not described in developing neurons. Here, we resolve individual exocytic events in developing murine cortical neurons and use classification tools to identify four distinguishable fusion modes: two FVF-like modes that insert membrane material and two KNR-like modes that do not. Discrete fluorescence profiles suggest distinct behavior of the fusion pore. Simulations and experiments agree that FVF-like exocytosis provides sufficient membrane material for morphogenesis. We find the E3 ubiquitin ligase TRIM67 promotes FVF-like exocytosis in part by limiting incorporation of the Qb/Qc SNARE SNAP47 into SNARE complexes and, thus, SNAP47 involvement in exocytosis.

TRIM67 regulates exocytic mode and neuronal morphogenesis via SNAP47.

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without  or  data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.

Fabio Urbina

UNC Neuroscience Center,
Department of Cell Biology and Physiology,
UNC Neuroscience Center, Department of Cell Biology and Physiology

University of North Carolina at Chapel Hill

Automated Detection and Analysis of Exocytosis

Fabio Urbina

UNC Neuroscience Center,Department of Cell Biology and Physiology,UNC Neuroscience Center, Department of Cell Biology and Physiology

University of North Carolina at Chapel Hill

Automated Detection and Analysis of Exocytosis

UNC Neuroscience Center,
Department of Cell Biology and Physiology,
UNC Neuroscience Center, Department of Cell Biology and Physiology