Biology subjects

Cui, Y.

Publications and source records attributed to Cui, Y..

26 records · Page 2Linked to original sources

bioRxiv2017

Mechanochemical Coupling and Bi-Phasic Force-Velocity Dependence in the Ultra-Fast Ring ATPase SpoIIIE

Multi-subunit ring-shaped ATPases are molecular motors that harness chemical free energy to perform vital mechanical tasks such as polypeptide translocation, DNA unwinding, and chromosome segregation. Previously we reported the intersubunit coordination and stepping behavior of the hexameric ring-shaped ATPase SpoIIIE (Liu et al., 2015). Here we use optical tweezers to characterize the motors mechanochemistry. Analysis of the motor response to external force at various nucleotide concentrations identifies phosphate release as the likely force-generating step. Analysis of SpoIIIE pausing indicates that pauses are off-pathway events. Characterization of SpoIIIE slipping behavior reveals that individual motor subunits engage DNA upon ATP binding. Furthermore, we find that SpolIIEs velocity exhibits an intriguing bi-phasic dependence on force. We hypothesize that this behavior is an adaptation of ultra-fast motors tasked with translocating DNA from which they must also remove DNA-bound protein roadblocks. Based on these results, we formulate a comprehensive mechanochemical model for SpoIIIE.

biochemistry↗

bioRxiv2017

Ennet: exert enhaner-only somatic mutations to discover potential cancer-driving biological networks

Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in the SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including a large SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and neural development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work suggests that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.

bioinformatics↗

bioRxiv2017

Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen, Rieger et al. 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop. #ddct #qsp

pharmacology and toxicology↗

bioRxiv2017

Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

neuroscience↗

bioRxiv2017

Revealing The Cell-Material Interface With Nanometer Resolution By FIB-SEM

The interface between biological cells and non-biological surfaces profoundly influences cellular activities, chronic tissue responses, and ultimately the success of medical implants. Materials in contact with cells can be plastics, metal, ceramics or other synthetic materials, and their surfaces vary widely in chemical compositions, stiffness, topography and levels of roughness. To understand the molecular mechanism of how cells and tissues respond to different materials, it is of critical importance to directly visualize the cell-material interface at the relevant length scale of nanometers. Conventional ultrastructural analysis by transmission electron microscopy (TEM) often requires substrate removal before microtome sectioning, which is not only challenging for most substrates but also can cause structural distortions of the interface. Here, we present a new method for in situ examination of the cell-to-material interface at any desired cellular location, based on focused-ion beam milling and scanning electron microscopy imaging (FIB-SEM). This method involves a thin-layer plastification procedure that preserves adherent cells as well as enhances the contrast of biological specimen. We demonstrate that this unique procedure allows the visualization of cell-to-material interface and intracellular structures with 10nm resolution, compatible with a variety of materials and surface topographies, and capable of volume and multi-directional imaging. We expect that this method will be very useful for studies of cell-to-material interactions and also suitable for in vivo studies such as examining osteoblast adhesion and new bone formation in response to titanium implants.

biophysics↗

bioRxiv2017

Nanoscale Manipulation Of Membrane Curvature For Probing Endocytosis In Live Cells

Clathrin-mediated endocytosis (CME) involves nanoscale bending and inward budding of the plasma membrane, by which cells regulate both the distribution of membrane proteins and the entry of extracellular species1,2. Extensive studies have shown that CME proteins actively modulate the plasma membrane curvature1,3,4. However, the reciprocal regulation of how plasma membrane curvature affects the activities of endocytic proteins is much less explored, despite studies suggesting that membrane curvature itself can trigger biochemical reactions5-8. This gap in our understanding is largely due to technical challenges in precisely controlling the membrane curvature in live cells. In this work, we use patterned nanostructures to generate well-defined membrane curvatures ranging from +50 nm to -500 nm radius of curvature. We find that the positively curved membranes are CME hotspots, and that key CME proteins, clathrin and dynamin, show a strong preference toward positive membrane curvatures with a radius < 200 nm. Of ten CME related proteins we examined, all show preferences to positively curved membrane. By contrast, other membrane-associated proteins and non-CME endocytic protein, caveolin1, show no such curvature preference. Therefore, nanostructured substrates constitute a novel tool for investigating curvature-dependent processes in live cells.

cell biology↗

bioRxiv2017

Sinusoidal Voltage Protocols For Rapid Characterization Of Ion Channel Kinetics

Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics -- the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fit a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells over-expressing hERG1a. The model is then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprised of a collection of physiologically-relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches.\n\nTable of Contents CategoryTechniques for Physiology1\n\nKey PointsO_LIIon current kinetics are commonly represented by current-voltage relationships, time-constant voltage relationships, and subsequently mathematical models fitted to these. These experiments take substantial time which means they are rarely performed in the same cell.\nC_LIO_LIRather than traditional square-wave voltage clamps, we fit a model to the current evoked by a novel sum-of-sinusoids voltage clamp that is only 8 seconds long.\nC_LIO_LIShort protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions.\nC_LIO_LIThe new model predicts the current under traditional square-wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically-relevant series of action potential clamps is predicted extremely well.\nC_LIO_LIThe short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell-cell variability in current kinetics for the first time.\nC_LI

biophysics↗

bioRxiv2016

The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding

1.Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

neuroscience↗