Myxococcus xanthus gliding motors are elastically coupled to the substrate as predicted by the focal adhesion model of gliding motility

Myxococcus xanthus is a model organism for studying bacterial social behaviors due to its ability to form complex multi-cellular structures. Knowledge of M. xanthus surface gliding motility and the mechanisms that coordinate it are critically important to our understanding of collective cell behaviors. Although the mechanism of gliding motility is still under investigation, recent experiments suggest that there are two possible mechanisms underlying force production for cell motility: the focal adhesion mechanism and the helical rotor mechanism which differ in the biophysics of the cell-substrate interactions. Whereas the focal adhesion model predicts an elastic coupling, the helical rotor model predicts a viscous coupling. Using a combination of computational modeling, imaging, and force microscopy, we find evidence for elastic coupling in support of the focal adhesion model. Using a biophysical model of the M. xanthus cell, we investigated how the mechanical interactions between cells are affected by interactions with the substrate. Comparison of modeling results with experimental data for cell-cell collision events pointed to a strong, elastic attachment between the cell and substrate. These results are robust to variations in the mechanical and geometrical parameters of the model. We then directly measured the motor-substrate coupling by monitoring the motion of optically trapped beads and find that motor velocity decreases exponentially with opposing load. At high loads, motor velocity approaches zero velocity asymptotically and motors remain bound to beads indicating a strong, elastic attachment

The Mechanistic Basis of Myxococcus xanthus Rippling Behavior and Its Physiological Role during Predation

Myxococcus xanthus cells self-organize into periodic bands of traveling waves, termed ripples, during multicellular fruiting body development and predation on other bacteria. To investigate the mechanistic basis of rippling behavior and its physiological role during predation by this Gram-negative soil bacterium, we have used an approach that combines mathematical modeling with experimental observations. Specifically, we developed an agent-based model (ABM) to simulate rippling behavior that employs a new signaling mechanism to trigger cellular reversals. The ABM has demonstrated that three ingredients are sufficient to generate rippling behavior: (i) side-to-side signaling between two cells that causes one of the cells to reverse, (ii) a minimal refractory time period after each reversal during which cells cannot reverse again, and (iii) physical interactions that cause the cells to locally align. To explain why rippling behavior appears as a consequence of the presence of prey, we postulate that prey-associated macromolecules indirectly induce ripples by stimulating side-toside contact-mediated signaling. In parallel to the simulations, M. xanthus predatory rippling behavior was experimentally observed and analyzed using time-lapse microscopy. A formalized relationship between the wavelength, reversal time, and cell velocity has been predicted by the simulations and confirmed by the experimental data. Furthermore, the results suggest that the physiological role of rippling behavior during M. xanthus predation is to increase the rate of spreading over prey cells due to increased side-to-side contact-mediated signaling and to allow predatory cells to remain on the prey longer as a result of more periodic cell motility

Hypertension in children with chronic kidney disease: pathophysiology and management

Arterial hypertension is very common in children with all stages of chronic kidney disease (CKD). While fluid overload and activation of the renin–angiotensin system have long been recognized as crucial pathophysiological pathways, sympathetic hyperactivation, endothelial dysfunction and chronic hyperparathyroidism have more recently been identified as important factors contributing to CKD-associated hypertension. Moreover, several drugs commonly administered in CKD, such as erythropoietin, glucocorticoids and cyclosporine A, independently raise blood pressure in a dose-dependent fashion. Because of the deleterious consequences of hypertension on the progression of renal disease and cardiovascular outcomes, an active screening approach should be adapted in patients with all stages of CKD. Before one starts antihypertensive treatment, non-pharmacological options should be explored. In hemodialysis patients a low salt diet, low dialysate sodium and stricter dialysis towards dry weight can often achieve adequate blood pressure control. Angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers are first-line therapy for patients with proteinuria, due to their additional anti-proteinuric properties. Diuretics are a useful alternative for non-proteinuric patients or as an add-on to renin–angiotensin system blockade. Multiple drug therapy is often needed to maintain blood pressure below the 90th percentile target, but adequate blood pressure control is essential for better renal and cardiovascular long-term outcomes

Large-Scale simulations of plastic neural networks on neuromorphic hardware

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Comparison of ripple initiation in the ABM simulations (top panels) and experiments (bottom panels).

<p>The timing of the snapshot is indicated for each column. The initial time (0 hrs) corresponds to the initiation of the simulation with a uniform cell distribution or the time <i>M. xanthus</i> cells fully cover the prey in the field of view. The fields of view of both the ABM simulation images and experimental images have the same dimensions; the scale bar is 100 µm.</p

Ripples cause faster expansion of cells into the prey region.

<p>(A) Initial configuration of the ABM simulation with <i>M. xanthus</i> agents placed in the center area and thereafter expanded in both directions. On the right, a grey region represents the prey area where the probability of agents signaling to one another is increased (from <i>p₀</i> = 0.03 to <i>p₀</i> = 0.10) and therefore ripples are formed. (B) Using cell flux to measure the expansion <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002715#pcbi.1002715-Wu1" target="_blank">[32]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002715#pcbi.1002715-Wu2" target="_blank">[33]</a>, we observed higher cell flux on prey (high signaling probability area) corresponding to a higher expansion rate on prey as demonstrated by the increased slope of regression line (grey). (C) Using ImageJ software to track the edge of a <i>M. xanthus</i> colony, the rate of the edge movement was computed. The solid line represents the edge of <i>M. xanthus</i> colony in this image and the dash line indicates its location 30 min later. (D) The experimentally observed expansion is plotted over time to show that the expansion rate over prey is about 1.6-fold larger than off the prey, as demonstrated by the increased slope of regression line (grey).</p

Mechanical interactions between two cells during head-to-side collisions in the biophysical models and experiments.

<p>(A) Viscous coupling model – both cells change directions. (B) Elastic coupling model – only the secondary cell changes direction. (C) Experimental time-lapse images (rotated to match with simulation configuration) showing collision between two isolated cells where only the secondary cell changes its direction. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003619#pcbi.1003619.s009" target="_blank">videos S1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003619#pcbi.1003619.s010" target="_blank">S2</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003619#pcbi.1003619.s011" target="_blank">S3</a> for corresponding movies.</p

Bead/molecular motor motility behavior under optical trap loading.

<p>(A) A gliding motor moves a bead along the cell axis. Past a preset threshold movement, the shutter in front of the optical trap is opened, pulling the bead in the direction opposite to the motor by the preset force, resulting in a slowing of bead movement. (B–D) For opposing forces of 12 pN or greater (18 pN) bead movement has stopped and for lower forces (9 pN) bead movement is slowed down but not completely stopped. Here an experiment is associated with the activity of a single motor only if the bead moves before and after trapping with the same direction and speed (green lines). A linear fit to the position versus time during force application provides the velocity (blue lines). (E) Bead velocity decreases exponentially with force but never becomes negative consistent with an elastic coupling and inconsistent with a viscous coupling between the bead and motor. The dashed lines are an exponential fit to the data. Error bars represent the standard error of the mean across trials (>6 trials per data point). (F) Force-velocity curves normalized by unloaded velocity corresponding to different nigericin concentrations (blue circles – 0 µM, brown circles – 10 µM, red circles – 20 µM; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003619#pcbi.1003619.s006" target="_blank">Fig. S6</a> for individual curves) collapse on to a single line on a semi-log plot.</p

Schematics of alternative mechanisms of gliding motility and their representation in biophysical models of the <i>M. xanthus</i> cell.

<p>(A) Focal adhesion mechanism (FAM) - Multi-protein complexes (green bars) spanning from the cytoplasm to the outside of the cell attach to the underlying substrate at specific points. Cells move forward as a result of the force generated by the components of these complexes against cytoskeleton (B) Helical rotor mechanism (HRM) - Motor proteins (green dots) tracking on a helical cytoskeleton create distortions in cell wall. These distortions generate drag forces between the substrate and the cell surface and result in cell movement. (C) Distinctions in cell-substrate interactions for the two alternative models of gliding motility. In the elastic coupling model during a cell-cell collision, a restoration force acts on the cell at the cell-substrate interaction points (green dots) in the direction perpendicular to cell axis. No such force exists in the viscous coupling model.</p