Archive of Medical Research

ATLAS-A High-Throughput Affinity-Based Screening Technology for Soluble Proteins: Technology Application Using p38 MAP Kinase.

Abstract: A general affinity-based screening assay for discovery of lead compounds binding to potential protein drug targets that is based upon protein thermal unfolding and aggregation is described. ATLAS() (Any Target Ligand Affinity Screen) (Anadys Pharmaceuticals, Inc., San Diego, CA) is a simple, homogeneous, and high-throughput affinity-based screening technology that can identify compounds that bind and protect the target protein from thermal unfolding, denaturation, and subsequent aggregation. ATLAS detection of thermally unfolded and aggregated hexahistidine [(His)(6)]-tagged proteins uses time-resolved fluorescence resonance energy transfer between two anti-(His)(6) antibodies, labeled with either a donor or acceptor fluorophore, that are simultaneously bound to the aggregated protein. The ATLAS assay is simple to perform and easily automated for screening large compound libraries. The technology is applicable to lead discovery for soluble proteins of known and unknown functions, and particularly for proteins that are difficult to assay functionally. The ATLAS technology has been evaluated using p38 mitogen-activated protein (MAP) kinase as the target protein. Known inhibitors of p38 MAP kinase were examined by ATLAS and a functional assay; the results showed good correlation between the two methods.

Patel R, Lebrun LA, Wang S, Howett LJ, Thompson PA, Appleman JR, Li B.

Department of Biology, Anadys Pharmaceuticals, Inc., San Diego, California.

Optimization and Utilization of the SureFire Phospho-STAT5 Assay for a Cell-Based Screening Campaign.

Abstract: The family of signal transducers and activators of transcription (STATs) consists of seven transcription factors that respond to a variety of cytokines, hormones, and growth factors. STATs are activated by tyrosine phosphorylation, which results in their dimerization and translocation into the nucleus where they exert their effect on transcription of regulated target genes. The phosphorylation of STATs is mediated mainly by Janus kinases (JAKs). The JAK/STAT pathway plays a critical role in hematopoietic and immune cell function. Here we focus on one member of the STAT family, STAT5. STAT5 is phosphorylated by several JAKs, including Jak3, Jak2, and Tyk2, in response to interleukin-2, erythropoietin (EPO), and interleukin-22, respectively. Activation of STAT5 is essential to T cell development and has been associated with hematologic malignancies. Therefore, the ability to assess STAT5 phosphorylation is important for discovery efforts targeting these indications. The assay formats available to detect phosphorylated STAT5 (pSTAT5) are relatively low throughput and involve lengthy protocols. These formats include western blot analysis, enzyme-linked immunosorbent assay (ELISA), and flow cytometry. The SureFire() (Perkin Elmer, Waltham, MA) pSTAT5 assay is a homogeneous assay that utilizes AlphaScreen((R)) (Perkin Elmer) technology to detect pSTAT5 in cell lysates. We have used this assay format to evaluate EPO-induced STAT5 phosphorylation in HEL cells and successfully complete a small-scale screening campaign to identify inhibitors of this event. The results obtained in these studies demonstrate that the SureFire pSTAT5 assay is a robust, reliable assay format that is amenable to high-throughput screening (HTS) applications.

Binder C, Lafayette A, Archibeque I, Sun Y, Plewa C, Sinclair A, Emkey R.

Amgen Inc., Cambridge, Massachusetts.

An interview with david newman, d.phil. Chief, natural products branch, national cancer institute frederick, MD.

Recoding Patterns of Sensory Input: Higher-Order Features and the Function of Nonlinear Dendritic Trees.

Here analytical and simulation results are presented characterizing the recoding arising when overlapping patterns of sensor input impinge on an array of model neurons with branched thresholded dendritic trees. Thus, the neural units employed are intended to capture the integrative behavior of pyramidal cells that sustain isolated Na(+) or NMDA spikes in their branches. Given a defined set of sensor vectors, equations were derived for the probability of firing of both branches and neurons and for the expected overlap between the neural firing patterns triggered by two afferent patterns of given overlap. Thus, both the sparseness of the neural representation and the orthogonalization of overlapping vectors were computed. Simulations were then performed with an array of 1000 neurons comprising 30,000 branches to verify the analytical results and confirm their applicability to systems (which include any practicable artificial system) in which the combinatorically possible branches and neurons are severely subsampled. A means of readout and a measure of discrimination performance were provided so that the accuracy of discrimination among overlapping sensor vectors could be optimized as a function of neuron structure parameters. Good performance required both orthogonalization of the afferent patterns, so that discrimination was accurate and free of interference, and maintenance of a minimum level of neural activity, so that some neurons fired in response to each sensor pattern. It is shown that the discrimination performance achieved by arrays of neurons with branched dendritic trees could not be reached with single-compartment units, regardless of how many of the latter are used. The analytical results furnish a benchmark against which to measure further enhancements in the performance of subsequent simulated systems incorporating local neural mechanisms which, while often less amenable to closed-form analysis, are ubiquitous in biological neural circuitry.

Rhodes PA.

Evolved Machines, Palo Alto, CA 94301, U.S.A. prhodes@evolvedmachines.com.

Oscillations and Spiking Pairs: Behavior of a Neuronal Model with STDP Learning.

In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this article, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making.

Shen X, Lin X, De Wilde P.

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, U.K. victorshenshx@gmail.com.

Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits.

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network\’s time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

van Rossum MC, van der Meer MA, Xiao D, Oram MW.

Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, EH1 2QL, U.K. mvanross@inf.ed.ac.uk.

Temporal Coding: Assembly Formation Through Constructive Interference.

Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration generates the desynchronization that is needed for self-organized segmentation of two overlapping patterns. In this letter, we continue the discussion of this remarkable feature, giving also an example with several overlapping patterns. Due to acceleration, Hebbian memory implies a frequency spectrum for pure pattern states, defined as coherent patterns with decoherent overlapping patterns. With reference to this frequency spectrum and related frequency bands, the process of pattern retrieval, corresponding to the formation of temporal coding assemblies, is described as resulting from constructive interference (with frequency differences due to acceleration) and phase locking (due to synchronization).

Burwick T.

Institut für Neuroinformatik, Ruhr-Universität Bochum, 44306 Bochum, Germany. Thomas.Burwick@neuroinformatik.rub.de.

Temporal Dynamics of Rate-Based Synaptic Plasticity Rules in a Stochastic Model of Spike-Timing-Dependent Plasticity.

In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, Delta S(n), induced by a typical sequence of precisely n spikes. We found that the rules Delta S(n), n >/= 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules Delta S(n), however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strength are conducted over definite periods of time during which a potentially unknown number of spikes may occur. Here, therefore, we derive an expression, Delta S(t), for the expected change in synaptic strength of a synapse experiencing an average sequence of spikes of typical length occurring during a fixed period of time, t. We find that the resulting synaptic plasticity rule Delta S(t) exhibits a number of remarkable properties. It is an entirely self-stabilizing learning rule in all regions of parameter space. Further, its parameter space is carved up into three distinct, contiguous regions in which the exhibited synaptic interactions undergo different transitions as the time t is increased. In one region, the synaptic dynamics change from noncompetitive to competitive to entirely depressing. In a second region, the dynamics change from noncompetitive to competitive without the second transition to entirely depressing dynamics. In a third region, the dynamics are always noncompetitive. The locations of these regions are not fixed in parameter space but may be modified by changing the mean presynaptic firing rates. Thus, neurons may be moved among these three different regions and so exhibit different sets of synaptic dynamics depending on their mean firing rates.

Elliott T.

Department of Electronics and Computer Science, University of Southampton, Highfield, Southampton, SO17 1BJ, U.K. te@ecs.soton.ac.uk.

Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons.

Mathematical models of neurons are widely used to improve understanding of neuronal spiking behavior. These models can produce artificial spike trains that resemble actual spike train data in important ways, but they are not very easy to apply to the analysis of spike train data. Instead, statistical methods based on point process models of spike trains provide a wide range of data-analytical techniques. Two simplified point process models have been introduced in the literature: the time-rescaled renewal process (TRRP) and the multiplicative inhomogeneous Markov interval (m-IMI) model. In this article we investigate the extent to which the TRRP and m-IMI models are able to fit spike trains produced by stimulus-driven leaky integrate-and-fire (LIF) neurons. With a constant stimulus, the LIF spike train is a renewal process, and the m-IMI and TRRP models will describe accurately the LIF spike train variability. With a time-varying stimulus, the probability of spiking under all three of these models depends on both the experimental clock time relative to the stimulus and the time since the previous spike, but it does so differently for the LIF, m-IMI, and TRRP models. We assessed the distance between the LIF model and each of the two empirical models in the presence of a time-varying stimulus. We found that while lack of fit of a Poisson model to LIF spike train data can be evident even in small samples, the m-IMI and TRRP models tend to fit well, and much larger samples are required before there is statistical evidence of lack of fit of the m-IMI or TRRP models. We also found that when the mean of the stimulus varies across time, the m-IMI model provides a better fit to the LIF data than the TRRP, and when the variance of the stimulus varies across time, the TRRP provides the better fit.

Koyama S, Kass RE.

Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. koyama@stat.cmu.edu.

Can Spike Coordination Be Differentiated from Rate Covariation?

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.

Staude B, Rotter S, Grün S.

Computational Neuroscience Group, RIKEN Brain Science Institute, Wako-Shi 351-0198, Japan. staude@brain.riken.jp.