The TPLATE adaptor complex drives clathrin-mediated endocytosis in plants.

Clathrin-mediated endocytosis is the major mechanism for eukaryotic plasma membrane-based proteome turn-over. In plants, clathrin-mediated endocytosis is essential for physiology and development, but the identification and organization of the machinery operating this process remains largely obscure. Here, we identified an eight-core-component protein complex, the TPLATE complex, essential for plant growth via its role as major adaptor module for clathrin-mediated endocytosis. This complex consists of evolutionarily unique proteins that associate closely with core endocytic elements. The TPLATE complex is recruited as dynamic foci at the plasma membrane preceding recruitment of adaptor protein complex 2, clathrin, and dynamin-related proteins. Reduced function of different complex components severely impaired internalization of assorted endocytic cargoes, demonstrating its pivotal role in clathrin-mediated endocytosis. Taken together, the TPLATE complex is an early endocytic module representing a unique evolutionary plant adaptation of the canonical eukaryotic pathway for clathrin-mediated endocytosis.

NTRC Plays a Crucial Role in Starch Metabolism, Redox Balance, and Tomato Fruit Growth.

NADPH-thioredoxin reductase C (NTRC) forms a separate thiol-reduction cascade in plastids, combining both NADPH-thioredoxin reductase and thioredoxin activities on a single polypeptide. While NTRC is an important regulator of photosynthetic processes in leaves, its function in heterotrophic tissues remains unclear. Here, we focus on the role of NTRC in developing tomato (Solanum lycopersicum) fruits representing heterotrophic storage organs important for agriculture and human diet. We used a fruit-specific promoter to decrease NTRC expression by RNA interference in developing tomato fruits by 60% to 80% compared to the wild type. This led to a decrease in fruit growth, resulting in smaller and lighter fully ripe fruits containing less dry matter and more water. In immature fruits, NTRC downregulation decreased transient starch accumulation, which led to a subsequent decrease in soluble sugars in ripe fruits. The inhibition of starch synthesis was associated with a decrease in the redox-activation state of ADP-Glc pyrophosphorylase and soluble starch synthase, which catalyze the first committed and final polymerizing steps, respectively, of starch biosynthesis. This was accompanied by a decrease in the level of ADP-Glc. NTRC downregulation also led to a strong increase in the reductive states of NAD(H) and NADP(H) redox systems. Metabolite profiling of NTRC-RNA interference lines revealed increased organic and amino acid levels, but reduced sugar levels, implying that NTRC regulates the osmotic balance of developing fruits. These results indicate that NTRC acts as a central hub in regulating carbon metabolism and redox balance in heterotrophic tomato fruits, affecting fruit development as well as final fruit size and quality.

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.

In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms.

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.

Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures.

One of the major outcomes of neuroscientific research are models of Neural Network Structures (NNSs). Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of NNSs is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing NNSs in general, a set of current visualizations of models of NNSs is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale NNSs.