Stochastic theory of large-scale enzyme-reaction networks: Finite copy number corrections to rate equation models

Chemical reactions inside cells occur in compartment volumes in the range of atto- to femtolitres. Physiological concentrations realized in such small volumes imply low copy numbers of interacting molecules with the consequence of considerable fluctuations in the concentrations. In contrast, rate equation models are based on the implicit assumption of infinitely large numbers of interacting molecules, or equivalently, that reactions occur in infinite volumes at constant macroscopic concentrations. In this article we compute the finite-volume corrections (or equivalently the finite copy number corrections) to the solutions of the rate equations for chemical reaction networks composed of arbitrarily large numbers of enzyme-catalyzed reactions which are confined inside a small sub-cellular compartment. This is achieved by applying a mesoscopic version of the quasi-steady state assumption to the exact Fokker-Planck equation associated with the Poisson Representation of the chemical master equation. The procedure yields impressively simple and compact expressions for the finite-volume corrections. We prove that the predictions of the rate equations will always underestimate the actual steady-state substrate concentrations for an enzyme-reaction network confined in a small volume. In particular we show that the finite-volume corrections increase with decreasing sub-cellular volume, decreasing Michaelis-Menten constants and increasing enzyme saturation. The magnitude of the corrections depends sensitively on the topology of the network. The predictions of the theory are shown to be in excellent agreement with stochastic simulations for two types of networks typically associated with protein methylation and metabolism.Comment: 13 pages, 4 figures; published in The Journal of Chemical Physic

Crosstalk between membrane trafficking and cell adhesion:The role of the SNARE protein TI-VAMP in neuronal morphogenesis

The membrane trafficking pathway mediated by the SNARE protein Tetanus neurotoxin-Insensitive Vesicle Associated Membrane Protein (TI-VAMP) in neurons is still unknown. In this work, I show that TI-VAMP expression is necessary for neurite outgrowth in PC12 cells in culture. TI-VAMP interacts with plasma membrane and endosomal target SNAREs suggesting that TI-VAMP mediates a recycling pathway. This view is supported by the direct demonstration that TI-VAMP recycles from and to the plasma membrane. L1, a cell-cell adhesion molecule involved in axonal outgrowth, colocalizes with TI-VAMP in the developing brain, neurons in culture, and PC12 cells. Plasma membrane L1 is internalized into the TI-VAMP-containing compartment. Silencing of TI-VAMP results in reduced expression of L1 at the plasma membrane and impaired L1- but not N-Cadherin-mediated adhesion. Futhermore, the TI-VAMP-compartment is specifically recruited to L1 bead-cell junctions in an actin-dependent manner suggesting that axon guidance cues like L1-ligation act by controlling cytoskeletal and membrane dynamics in a coordinated manner. In conclusion, TI-VAMP mediates the intracellular transport of L1 and L1-mediated adhesion controls this membrane trafficking, thereby suggesting that cross-talk between membrane trafficking and cell-cell adhesion plays a central role in coordinating axonal outgrowth and pathfinding

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

Comparison of substrate specificity of the ubiquitin ligases Nedd4 and Nedd4-2 using proteome arrays

Target recognition by the ubiquitin system is mediated by E3 ubiquitin ligases. Nedd4 family members are E3 ligases comprised of a C2 domain, 2–4 WW domains that bind PY motifs (L/PPxY) and a ubiquitin ligase HECT domain. The nine Nedd4 family proteins in mammals include two close relatives: Nedd4 (Nedd4-1) and Nedd4L (Nedd4-2), but their global substrate recognition or differences in substrate specificity are unknown. We performed in vitro ubiquitylation and binding assays of human Nedd4-1 and Nedd4-2, and rat-Nedd4-1, using protein microarrays spotted with ∼8200 human proteins. Top hits (substrates) for the ubiquitylation and binding assays mostly contain PY motifs. Although several substrates were recognized by both Nedd4-1 and Nedd4-2, others were specific to only one, with several Tyr kinases preferred by Nedd4-1 and some ion channels by Nedd4-2; this was subsequently validated in vivo. Accordingly, Nedd4-1 knockdown or knockout in cells led to sustained signalling via some of its substrate Tyr kinases (e.g. FGFR), suggesting Nedd4-1 suppresses their signalling. These results demonstrate the feasibility of identifying substrates and deciphering substrate specificity of mammalian E3 ligases

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

International audienceWe introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation

Regulation of lipid droplet turnover by ubiquitin ligases

Abstract Mutation of the protein spartin is a cause of one form of spastic paraplegia. Spartin interacts with ubiquitin ligases of the Nedd4 family, and a recent report in BMC Biology now shows that it acts as an adaptor to recruit and activate the ubiquitin ligase AIP4 onto lipid droplets, leading to the ubiquitination and degradation of droplet-associated proteins. A deficiency of spartin apparently causes lipid droplets to accumulate. See research article: http://www.biomedcentral.com/1741-7007/8/72/</p

Regulation of lipid droplet turnover by ubiquitin ligases

Abstract Mutation of the protein spartin is a cause of one form of spastic paraplegia. Spartin interacts with ubiquitin ligases of the Nedd4 family, and a recent report in BMC Biology now shows that it acts as an adaptor to recruit and activate the ubiquitin ligase AIP4 onto lipid droplets, leading to the ubiquitination and degradation of droplet-associated proteins. A deficiency of spartin apparently causes lipid droplets to accumulate. See research article: http://www.biomedcentral.com/1741-7007/8/72