Mechanisms of Synapse Assembly and Disassembly

AbstractThe mechanisms that govern synapse formation and elimination are fundamental to our understanding of neural development and plasticity. The wiring of neural circuitry requires that vast numbers of synapses be formed in a relatively short time. The subsequent refinement of neural circuitry involves the formation of additional synapses coincident with the disassembly of previously functional synapses. There is increasing evidence that activity-dependent plasticity also involves the formation and disassembly of synapses. While we are gaining insight into the mechanisms of both synapse assembly and disassembly, we understand very little about how these phenomena are related to each other and how they might be coordinately controlled to achieve the precise patterns of synaptic connectivity in the nervous system. Here, we review our current understanding of both synapse assembly and disassembly in an effort to unravel the relationship between these fundamental developmental processes

Hexagonal packing of Drosophila wing epithelial cells by the Planar Cell Polarity pathway

The mechanisms that order cellular packing geometry are critical for the functioning of many tissues, but are poorly understood. Here we investigate this problem in the developing wing of Drosophila. The surface of the wing is decorated by hexagonally packed hairs that are uniformly oriented towards the distal wing tip. They are constructed by a hexagonal array of wing epithelial cells. We find that wing epithelial cells are irregularly arranged throughout most of development but become hexagonally packed shortly before hair formation. During the process, individual cell junctions grow and shrink, resulting in local neighbor exchanges. These dynamic changes mediate hexagonal packing and require the efficient delivery of E-cadherin to remodeling junctions; a process that depends on both the large GTPase Dynamin and the function of Rab11 recycling endosomes. We suggest that E-cadherin is actively internalized and recycled as wing epithelial cells pack into a regular hexagonal array. Hexagonal packing furthermore depends on the activity of the Planar Cell Polarity proteins. The Planar Cell Polarity group of proteins coordinates complex and polarized cell behavior in many contexts. No common cell biological mechanism has yet been identified to explain their functions in different tissues. A genetic interaction between Dynamin and the Planar Cell Polarity mutants suggests that the planar cell polarity proteins may modulate Dynamin-dependent trafficking of E-cadherin to enable the dynamic remodeling of junctions. We furthermore show that the Planar Cell Polarity protein Flamingo can recruit the exocyst component Sec5. Sec5 vesicles also co-localizes with E-cadherin and Flamingo. Based on these observations we propose that during the hexagonal repacking of the wing epithelium these proteins polarize the trafficking of E-cadherin-containing exocyst vesicles to remodeling junctions. The work presented in this thesis shows that one of the basic cellular functions of planar cell polarity signaling may be the regulation of dynamic cell adhesion. In doing so, the planar cell polarity pathway mediates the acquisition of a regular packing geometry of Drosophila wing epithelial cells. We identify polarized exocyst-dependent membrane traffic as the first basic cellular mechanism that can explain the role of PCP proteins in different developmental systems

Regulation of junction configuration by cell tension

The maintenance of cell-cell contacts is essential for tissue cohesion and a variety of different physiological processes in morphogenesis and homeostasis. Adherens junctions are protein complexes that mediate cell-cell contacts in epithelial cells and E-cadherin receptors are their main component. During junction formation, thin bundles of actin localise towards cell-cell contacts in the characteristic cytoskeletal organization of epithelia. Tension at the underlying cortex and thin bundle compaction help form tight, straight junctions and maintain cadherin receptors in place. However, how these epithelia-specific structures are formed and remodelled lacks in-depth understanding. In this study, I have addressed how contractile forces modulate junction configuration and molecular composition (adhesion receptors and actin cytoskeleton). Micropatterning was used to precisely confine the geometry of cells, control cortical forces and provide a permissive, simplistic way in which cells are allowed to interact. Three different shapes, namely squares, triangles and circles were patterned to study biophysical and junction properties. Although the average cell heights and volumes are similar between different geometries, cortical stiffness (i.e. Young’s modulus) is two-fold higher in cells grown in geometries that impose higher contractility: squares and triangles. Doublets seeded on these shapes also position their nuclei further apart and exhibit preferences in junction orientation. A majority of cells cultured on triangular and square geometries have shorter and straighter junctions with a clear presence of thin bundles parallel to the cell-cell interface. Localisation of phosphorylated myosin light chain to thin bundles reinforce the notion that these are the main contractile pool instead of the junctional actin at contacts. Counter-intuitively, E-cadherin and F-actin density are also reduced with increased contractility and tension. Taken together, higher levels of contractility and cortical tension imposed by the square and triangle geometric shapes, are necessary to properly generate the epithelial cellular architecture, configuration of junctions and their molecular makeup. This suggests that tensional constraints play an important role in regulating the stability of junctions and the organization of underlying actin filaments that support the characteristic epithelial cell shape.Open Acces

Recent advances in branching mechanisms underlying neuronal morphogenesis [version 1; referees: 2 approved]

Proper neuronal wiring is central to all bodily functions, sensory perception, cognition, memory, and learning. Establishment of a functional neuronal circuit is a highly regulated and dynamic process involving axonal and dendritic branching and navigation toward appropriate targets and connection partners. This intricate circuitry includes axo-dendritic synapse formation, synaptic connections formed with effector cells, and extensive dendritic arborization that function to receive and transmit mechanical and chemical sensory inputs. Such complexity is primarily achieved by extensive axonal and dendritic branch formation and pruning. Fundamental to neuronal branching are cytoskeletal dynamics and plasma membrane expansion, both of which are regulated via numerous extracellular and intracellular signaling mechanisms and molecules. This review focuses on recent advances in understanding the biology of neuronal branching

A Multiple-Mechanism Developmental Model for Defining Self-Organizing Geometric Structures

This thesis introduces a model of multicellular development. The model combines elements of the chemical, cell lineage, and mechanical models of morphogenesis pioneered by Turing, Lindenmayer, and Odell, respectively. The internal state of each cell in the model is represented by a time-varying state vector that is updated by a differential equation. The differential equation is formulated as a sum of contributions from different sources, describing gene transcription, kinetics, and cell metabolism. Each term in the differential equation is multiplied by a conditional expression that models regulatory processes specific to the process described by that term. The resulting model has a broader range of fundamental mechanisms than other developmental models. Since gene transcription is included, the model can represent the genetic orchestration of a developmental process involving multiple mechanisms. We show that a computational implementation of the model represents a wide range of biologically relevant phenomena in two and three dimensions. This is illustrated by a diverse collection of simulation experiments exhibiting phenomena such as lateral inhibition, differentiation, segment formation, size regulation, and regeneration of damaged structures. We have explored several application areas with the model: Synthetic biology. We advocate the use of mathematical modeling and simulation for generating intuitions about complex biological systems, in addition to the usual application of mathematical biology to perform analysis on a simplified model. The breadth of our model makes it useful as a tool for exploring biological questions about pattern formation and morphogenesis. We show that simulated experiments to address a particular question can be done quickly and can generate useful biological intuitions. As an example, we document a simulation experiment exploring inhibition via surface chemicals. This experiment suggests that the final pattern depends strongly on the temporal sequence of events. This intuition was obtained quickly using the simulator as an aid to understanding the general behavior of the developmental system. Artificial evolution of neural networks. Neural networks can be represented using a developmental model. We investigate the use of artificial evolution to select equations and parameters that cause the model to create desired structures. We compare our approach to other work in evolutionary neural networks, and discuss the difficulties involved. Computer graphics modeling. We extend the model to allow cells to sense the presence of a 3D surface model, and then use the multicellular simulator to grow cells on the surface. This database amplification technique enables the creation of cellular textures to represent detailed geometry on a surface (e.g., scales, feathers, thorns). In the process of writing many developmental programs, we have gained some experience in the construction of self-organizing cellular structures. We identify some critical issues (size regulation and scalability), and suggest biologically-plausible strategies for addressing them

Geometric Learning on Graph Structured Data

Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs. This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution. In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks