Acid-induced Dissociation of VacA, the Helicobacter pylori Vacuolating Cytotoxin, Reveals Its Pattern of Assembly

In this study, we describe the ultrastructural changes associated with acid activation of Helicobacter pylori vacuolating cytotoxin (VacA). Purified VacA molecules imaged by deep-etch electron microscopy form ~30-nm hexagonal “flowers,” each composed of an ~15-nm central ring surrounded by six ~6-nm globular “petals.” Upon exposure to acidic pH, these oligomeric flowers dissociate into collections of up to 12 teardrop-shaped subunits, each measuring ~6 × 14 nm. Correspondingly, glycerol density gradient centrifugation shows that at neutral pH VacA sediments at ~22 S, whereas at acidic pH it dissociates and sediments at ~5 S. Immunoblot and EM analysis of the 5-S material demonstrates that it represents ~90-kD monomers with 6 × 14–nm “teardrop” morphology. These data indicate that the intact VacA oligomer consists of 12 ~90-kD subunits assembled into two interlocked six-membered arrays, overlap of which gives rise to the flower-like appearance. Support for this interpretation comes from EM identification of small numbers of relatively “flat” oligomers composed of six teardrop-shaped subunits, interpreted to be halves of the complete flower. These flat forms adsorb to mica in two different orientations, corresponding to hexameric surfaces that are either exposed or sandwiched inside the dodecamer, respectively. This view of VacA structure differs from a previous model in which the flowers were interpreted to be single layers of six monomers and the flat forms were thought to be proteolysed flowers. Since acidification has been shown to potentiate the cytotoxic effects of VacA, the present results suggest that physical disassembly of the VacA oligomer is an important feature of its activation.     

Slowness: An Objective for Spike-Timing–Dependent Plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this “temporal stability” or “slowness” approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing–dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the “trace rule.” The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.     

Brief Subthreshold Events Can Act as Hebbian Signals for Long-Term Plasticity

Background

Action potentials are thought to be determinant for the induction of long-term synaptic plasticity, the cellular basis of learning and memory. However, neuronal activity does not lead systematically to an action potential but also, in many cases, to synaptic depolarizing subthreshold events. This is particularly exemplified in corticostriatal information processing. Indeed, the striatum integrates information from the whole cerebral cortex and, due to the membrane properties of striatal medium spiny neurons, cortical inputs do not systematically trigger an action potential but a wide range of subthreshold postsynaptic depolarizations. Accordingly, we have addressed the following question: does a brief subthreshold event act as a Hebbian signal and induce long-term synaptic efficacy changes?

Methodology/Principal Findings

Here, using perforated patch-clamp recordings on rat brain corticostriatal slices, we demonstrate, that brief (30 ms) subthreshold depolarizing events in quasi-coincidence with presynaptic activity can act as Hebbian signals and are sufficient to induce long-term synaptic plasticity at corticostriatal synapses. This “subthreshold-depolarization dependent plasticity” (SDDP) induces strong, significant and bidirectional long-term synaptic efficacy changes at a very high occurrence (81%) for time intervals between pre- and postsynaptic stimulations (Δt) of −110<Δt<+110 ms. Such subthreshold depolarizations are able to induce robust long-term depression (cannabinoid type-1 receptor-activation dependent) as well as long-term potentiation (NMDA receptor-activation dependent).

Conclusion/Significance

Our data show the existence of a robust, reliable and timing-dependent bidirectional long-term plasticity induced by brief subthreshold events paired with presynaptic activity. The existence of a subthreshold-depolarization dependent plasticity extends considerably, beyond the action potential, the neuron''s capabilities to express long-term synaptic efficacy changes.     

A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement

Patterned spontaneous activity in the developing retina is necessary to drive synaptic refinement in the lateral geniculate nucleus (LGN). Using perforated patch recordings from neurons in LGN slices during the period of eye segregation, we examine how such burst-based activity can instruct this refinement. Retinogeniculate synapses have a novel learning rule that depends on the latencies between pre- and postsynaptic bursts on the order of one second: coincident bursts produce long-lasting synaptic enhancement, whereas non-overlapping bursts produce mild synaptic weakening. It is consistent with “Hebbian” development thought to exist at this synapse, and we demonstrate computationally that such a rule can robustly use retinal waves to drive eye segregation and retinotopic refinement. Thus, by measuring plasticity induced by natural activity patterns, synaptic learning rules can be linked directly to their larger role in instructing the patterning of neural connectivity.     

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia,Catastrophic Forgetting,and the Spacing Effect

Although already William James and, more explicitly, Donald Hebb''s theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect.     

Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients

The negative symptoms of schizophrenia (SZ) are associated with a pattern of reinforcement learning (RL) deficits likely related to degraded representations of reward values. However, the RL tasks used to date have required active responses to both reward and punishing stimuli. Pavlovian biases have been shown to affect performance on these tasks through invigoration of action to reward and inhibition of action to punishment, and may be partially responsible for the effects found in patients. Forty-five patients with schizophrenia and 30 demographically-matched controls completed a four-stimulus reinforcement learning task that crossed action (“Go” or “NoGo”) and the valence of the optimal outcome (reward or punishment-avoidance), such that all combinations of action and outcome valence were tested. Behaviour was modelled using a six-parameter RL model and EEG was simultaneously recorded. Patients demonstrated a reduction in Pavlovian performance bias that was evident in a reduced Go bias across the full group. In a subset of patients administered clozapine, the reduction in Pavlovian bias was enhanced. The reduction in Pavlovian bias in SZ patients was accompanied by feedback processing differences at the time of the P3a component. The reduced Pavlovian bias in patients is suggested to be due to reduced fidelity in the communication between striatal regions and frontal cortex. It may also partially account for previous findings of poorer “Go-learning” in schizophrenia where “Go” responses or Pavlovian consistent responses are required for optimal performance. An attenuated P3a component dynamic in patients is consistent with a view that deficits in operant learning are due to impairments in adaptively using feedback to update representations of stimulus value.     

Generalization of Auditory Sensory and Cognitive Learning in Typically Developing Children

Despite the well-established involvement of both sensory (“bottom-up”) and cognitive (“top-down”) processes in literacy, the extent to which auditory or cognitive (memory or attention) learning transfers to phonological and reading skills remains unclear. Most research has demonstrated learning of the trained task or even learning transfer to a closely related task. However, few studies have reported “far-transfer” to a different domain, such as the improvement of phonological and reading skills following auditory or cognitive training. This study assessed the effectiveness of auditory, memory or attention training on far-transfer measures involving phonological and reading skills in typically developing children. Mid-transfer was also assessed through untrained auditory, attention and memory tasks. Sixty 5- to 8-year-old children with normal hearing were quasi-randomly assigned to one of five training groups: attention group (AG), memory group (MG), auditory sensory group (SG), placebo group (PG; drawing, painting), and a control, untrained group (CG). Compliance, mid-transfer and far-transfer measures were evaluated before and after training. All trained groups received 12 x 45-min training sessions over 12 weeks. The CG did not receive any intervention. All trained groups, especially older children, exhibited significant learning of the trained task. On pre- to post-training measures (test-retest), most groups exhibited improvements on most tasks. There was significant mid-transfer for a visual digit span task, with highest span in the MG, relative to other groups. These results show that both sensory and cognitive (memory or attention) training can lead to learning in the trained task and to mid-transfer learning on a task (visual digit span) within the same domain as the trained tasks. However, learning did not transfer to measures of language (reading and phonological awareness), as the PG and CG improved as much as the other trained groups. Further research is required to investigate the effects of various stimuli and lengths of training on the generalization of sensory and cognitive learning to literacy skills.     

Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

What makes cognition ‘advanced’ is an open and not precisely defined question. In this study, a cognitive model of cleaner fish learning the ephemeral-reward task demonstrates how a critical step in cognitive evolution may be understood as the evolution of chunking and its tuning to fit ecological conditions.     

A mathematical model for neutrophil gradient sensing and polarization

Directed cell migration in response to chemical cues, also known as chemotaxis, is an important physiological process involved in wound healing, foraging, and the immune response. Cell migration requires the simultaneous formation of actin polymers at the leading edge and actomyosin complexes at the sides and back of the cell. An unresolved question in eukaryotic chemotaxis is how the same chemoattractant signal determines both the cell's front and back. Recent experimental studies have begun to reveal the biochemical mechanisms necessary for this polarized cellular response. We propose a mathematical model of neutrophil gradient sensing and polarization based on experimentally characterized biochemical mechanisms. The model demonstrates that the known dynamics for Rho GTPase and phosphatidylinositol-3-kinase (PI3K) activation are sufficient for both gradient sensing and polarization. In particular, the model demonstrates that these mechanisms can correctly localize the “front” and “rear” pathways in response to both uniform concentrations and gradients of chemical attractants, including in actin-inhibited cells. Furthermore, the model predictions are robust to the values of many parameters. A key result of the model is the proposed coincidence circuit involving PI3K and Ras that obviates the need for the “global inhibitors” proposed, though never experimentally verified, in many previous mathematical models of eukaryotic chemotaxis. Finally, experiments are proposed to (in)validate this model and further our understanding of neutrophil chemotaxis.     

Epithelial cell plasticity: breaking boundaries and changing landscapes

Epithelial tissues respond to a wide variety of environmental and genotoxic stresses. As an adaptive mechanism, cells can deviate from their natural paths to acquire new identities, both within and across lineages. Under extreme conditions, epithelial tissues can utilize “shape‐shifting” mechanisms whereby they alter their form and function at a tissue‐wide scale. Mounting evidence suggests that in order to acquire these alternate tissue identities, cells follow a core set of “tissue logic” principles based on developmental paradigms. Here, we review the terminology and the concepts that have been put forward to describe cell plasticity. We also provide insights into various cell intrinsic and extrinsic factors, including genetic mutations, inflammation, microbiota, and therapeutic agents that contribute to cell plasticity. Additionally, we discuss recent studies that have sought to decode the “syntax” of plasticity—i.e., the cellular and molecular principles through which cells acquire new identities in both homeostatic and malignant epithelial tissues—and how these processes can be manipulated for developing novel cancer therapeutics.     

The Convallis Rule for Unsupervised Learning in Cortical Networks

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.     

A Neural Mechanism for Background Information-Gated Learning Based on Axonal-Dendritic Overlaps

Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such “axonal-dendritic overlap” (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.     

Effects of Acute Cortisol Administration on Perceptual Priming of Trauma-Related Material

Intrusive memories are a hallmark symptom of posttraumatic stress disorder (PTSD). They reflect excessive and uncontrolled retrieval of the traumatic memory. Acute elevations of cortisol are known to impair the retrieval of already stored memory information. Thus, continuous cortisol administration might help in reducing intrusive memories in PTSD. Strong perceptual priming for neutral stimuli associated with a “traumatic” context has been shown to be one important learning mechanism that leads to intrusive memories. However, the memory modulating effects of cortisol have only been shown for explicit declarative memory processes. Thus, in our double blind, placebo controlled study we aimed to investigate whether cortisol influences perceptual priming of neutral stimuli that appeared in a “traumatic” context. Two groups of healthy volunteers (N = 160) watched either neutral or “traumatic” picture stories on a computer screen. Neutral objects were presented in between the pictures. Memory for these neutral objects was tested after 24 hours with a perceptual priming task and an explicit memory task. Prior to memory testing half of the participants in each group received 25 mg of cortisol, the other half received placebo. In the placebo group participants in the “traumatic” stories condition showed more perceptual priming for the neutral objects than participants in the neutral stories condition, indicating a strong perceptual priming effect for neutral stimuli presented in a “traumatic” context. In the cortisol group this effect was not present: Participants in the neutral stories and participants in the “traumatic” stories condition in the cortisol group showed comparable priming effects for the neutral objects. Our findings show that cortisol inhibits perceptual priming for neutral stimuli that appeared in a “traumatic” context. These findings indicate that cortisol influences PTSD-relevant memory processes and thus further support the idea that administration of cortisol might be an effective treatment strategy in reducing intrusive reexperiencing.     

Molecular and Genetic Determinants of the NMDA Receptor for Superior Learning and Memory Functions

The opening-duration of the NMDA receptors implements Hebb''s synaptic coincidence-detection and is long thought to be the rate-limiting factor underlying superior memory. Here, we investigate the molecular and genetic determinants of the NMDA receptors by testing the “synaptic coincidence-detection time-duration” hypothesis vs. “GluN2B intracellular signaling domain” hypothesis. Accordingly, we generated a series of GluN2A, GluN2B, and GluN2D chimeric subunit transgenic mice in which C-terminal intracellular domains were systematically swapped and overexpressed in the forebrain excitatory neurons. The data presented in the present study supports the second hypothesis, the “GluN2B intracellular signaling domain” hypothesis. Surprisingly, we found that the voltage-gated channel opening-durations through either GluN2A or GluN2B are sufficient and their temporal differences are marginal. In contrast, the C-terminal intracellular domain of the GluN2B subunit is necessary and sufficient for superior performances in long-term novel object recognition and cued fear memories and superior flexibility in fear extinction. Intriguingly, memory enhancement correlates with enhanced long-term potentiation in the 10–100 Hz range while requiring intact long-term depression capacity at the 1–5 Hz range.     

Forces of a single-beam gradient laser trap on a dielectric sphere in the ray optics regime

We calculate the forces of single-beam gradient radiation pressure laser traps, also called “optical tweezers,” on micron-sized dielectric spheres in the ray optics regime. This serves as a simple model system for describing laser trapping and manipulation of living cells and organelles within cells. The gradient and scattering forces are defined for beams of complex shape in the ray-optics limit. Forces are calculated over the entire cross-section of the sphere using TEM₀₀ and TEM₀₁^* mode input intensity profiles and spheres of varying index of refraction. Strong uniform traps are possible with force variations less than a factor of 2 over the sphere cross-section. For a laser power of 10 mW and a relative index of refraction of 1.2 we compute trapping forces as high as ~ 1.2 × 10^-6 dynes in the weakest (backward) direction of the gradient trap. It is shown that good trapping requires high convergence beams from a high numerical aperture objective. A comparison is given of traps made using bright field or differential interference contrast optics and phase contrast optics.     

Counterproductive Effect of Saccadic Suppression during Attention Shifts

During saccadic eye movements, the processing of visual information is transiently interrupted by a mechanism known as “saccadic suppression” [1] that is thought to ensure perceptual stability [2]. If, as proposed in the premotor theory of attention [3], covert shifts of attention rely on sub-threshold recruitment of oculomotor circuits, then saccadic suppression should also occur during covert shifts. In order to test this prediction, we designed two experiments in which participants had to orient towards a cued letter, with or without saccades. We analyzed the time course of letter identification score in an “attention” task performed without saccades, using the saccadic latencies measured in the “saccade” task as a marker of covert saccadic preparation. Visual conditions were identical in all tasks. In the “attention” task, we found a drop in perceptual performance around the predicted onset time of saccades that were never performed. Importantly, this decrease in letter identification score cannot be explained by any known mechanism aligned on cue onset such as inhibition of return, masking, or microsaccades. These results show that attentional allocation triggers the same suppression mechanisms as during saccades, which is relevant during eye movements but detrimental in the context of covert orienting.     

Reinforcement Learning of Targeted Movement in a Spiking Neuronal Model of Motor Cortex

Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (−1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.     

Binocular Glaucomatous Visual Field Loss and Its Impact on Visual Exploration - A Supermarket Study

Advanced glaucomatous visual field loss may critically interfere with quality of life. The purpose of this study was to (i) assess the impact of binocular glaucomatous visual field loss on a supermarket search task as an example of everyday living activities, (ii) to identify factors influencing the performance, and (iii) to investigate the related compensatory mechanisms. Ten patients with binocular glaucoma (GP), and ten healthy-sighted control subjects (GC) were asked to collect twenty different products chosen randomly in two supermarket racks as quickly as possible. The task performance was rated as “passed” or “failed” with regard to the time per correctly collected item. Based on the performance of control subjects, the threshold value for failing the task was defined as μ+3σ (in seconds per correctly collected item). Eye movements were recorded by means of a mobile eye tracker. Eight out of ten patients with glaucoma and all control subjects passed the task. Patients who failed the task needed significantly longer time (111.47 s ±12.12 s) to complete the task than patients who passed (64.45 s ±13.36 s, t-test, p<0.001). Furthermore, patients who passed the task showed a significantly higher number of glances towards the visual field defect (VFD) area than patients who failed (t-test, p<0.05). According to these results, glaucoma patients with defects in the binocular visual field display on average longer search times in a naturalistic supermarket task. However, a considerable number of patients, who compensate by frequent glancing towards the VFD, showed successful task performance. Therefore, systematic exploration of the VFD area seems to be a “time-effective” compensatory mechanism during the present supermarket task.     

Classification of Domain Movements in Proteins Using Dynamic Contact Graphs

A new method for the classification of domain movements in proteins is described and applied to 1822 pairs of structures from the Protein Data Bank that represent a domain movement in two-domain proteins. The method is based on changes in contacts between residues from the two domains in moving from one conformation to the other. We argue that there are five types of elemental contact changes and that these relate to five model domain movements called: “free”, “open-closed”, “anchored”, “sliding-twist”, and “see-saw.” A directed graph is introduced called the “Dynamic Contact Graph” which represents the contact changes in a domain movement. In many cases a graph, or part of a graph, provides a clear visual metaphor for the movement it represents and is a motif that can be easily recognised. The Dynamic Contact Graphs are often comprised of disconnected subgraphs indicating independent regions which may play different roles in the domain movement. The Dynamic Contact Graph for each domain movement is decomposed into elemental Dynamic Contact Graphs, those that represent elemental contact changes, allowing us to count the number of instances of each type of elemental contact change in the domain movement. This naturally leads to sixteen classes into which the 1822 domain movements are classified.