Structural Analysis of a Peptide Fragment of Transmembrane Transporter Protein Bilitranslocase

Using a combination of genomic and post-genomic approaches is rapidly altering the number of identified human influx carriers. A transmembrane protein bilitranslocase (TCDB 2.A.65) has long attracted attention because of its function as an organic anion carrier. It has also been identified as a potential membrane transporter for cellular uptake of several drugs and due to its implication in drug uptake, it is extremely important to advance the knowledge about its structure. However, at present, only the primary structure of bilitranslocase is known. In our work, transmembrane subunits of bilitranslocase were predicted by a previously developed chemometrics model and the stability of these polypeptide chains were studied by molecular dynamics (MD) simulation. Furthermore, sodium dodecyl sulfate (SDS) micelles were used as a model of cell membrane and herein we present a high-resolution 3D structure of an 18 amino acid residues long peptide corresponding to the third transmembrane part of bilitranslocase obtained by use of multidimensional NMR spectroscopy. It has been experimentally confirmed that one of the transmembrane segments of bilitranslocase has alpha helical structure with hydrophilic amino acid residues oriented towards one side, thus capable of forming a channel in the membrane

Modeling the Violation of Reward Maximization and Invariance in Reinforcement Schedules

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as “schedule length effect”). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: “framing,” wherein equivalent options are treated differently depending on the context in which they are presented, and the “sunk cost” effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys

Transcriptional Activation of OsDERF1 in OsERF3 and OsAP2-39 Negatively Modulates Ethylene Synthesis and Drought Tolerance in Rice

The phytohormone ethylene is a key signaling molecule that regulates a variety of developmental processes and stress responses in plants. Transcriptional modulation is a pivotal process controlling ethylene synthesis, which further triggers the expression of stress-related genes and plant adaptation to stresses; however, it is unclear how this process is transcriptionally modulated in rice. In the present research, we report the transcriptional regulation of a novel rice ethylene response factor (ERF) in ethylene synthesis and drought tolerance. Through analysis of transcriptional data, one of the drought-responsive ERF genes, OsDERF1, was identified for its activation in response to drought, ethylene and abscisic acid. Transgenic plants overexpressing OsDERF1 (OE) led to reduced tolerance to drought stress in rice at seedling stage, while knockdown of OsDERF1 (RI) expression conferred enhanced tolerance at seedling and tillering stages. This regulation was supported by negative modulation in osmotic adjustment response. To elucidate the molecular basis of drought tolerance, we identified the target genes of OsDERF1 using the Affymetrix GeneChip, including the activation of cluster stress-related negative regulators such as ERF repressors. Biochemical and molecular approaches showed that OsDERF1 at least directly interacted with the GCC box in the promoters of ERF repressors OsERF3 and OsAP2-39. Further investigations showed that OE seedlings had reduced expression (while RI lines showed enhanced expression) of ethylene synthesis genes, thereby resulting in changes in ethylene production. Moreover, overexpression of OsERF3/OsAP2-39 suppressed ethylene synthesis. In addition, application of ACC recovered the drought-sensitive phenotype in the lines overexpressing OsERF3, showing that ethylene production contributed to drought response in rice. Thus our data reveal that a novel ERF transcriptional cascade modulates drought response through controlling the ethylene synthesis, deepening our understanding of the regulation of ERF proteins in ethylene related drought response

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Recurrent, Robust and Scalable Patterns Underlie Human Approach and Avoidance

BACKGROUND. Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory. METHODOLOGY/PRINCIPAL FINDINGS. Three sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both. CONCLUSIONS/SIGNIFICANCE. These law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).National Institute on Drug Abuse (14118, 026002, 026104, DABK39-03-0098, DABK39-03-C-0098); The MGH Phenotype Genotype Project in Addiction and Mood Disorder from the Office of National Drug Control Policy - Counterdrug Technology Assessment Center; MGH Department of Radiology; the National Center for Research Resources (P41RR14075); National Institute of Neurological Disorders and Stroke (34189, 05236