Peroxidase-Generated Apoplastic ROS Impair Cuticle Integrity and Contribute to DAMP-Elicited Defenses

Cuticular defects trigger a battery of reactions including enhanced reactive oxygen species (ROS) production and resistance to necrotrophic pathogens. However, the source of ROS generated by such impaired cuticles has remained elusive. Here, we report the characterization of Arabidopsis thaliana ohyl mutant, a Peroxidase 57 (PER57) - overexpressing line that demonstrates enhanced defense responses that result both from increased accumulation of ROS and permeability of the leaf cuticle. The ohyl mutant was identified in a screen of A. thaliana seedlings for oligogalacturonides (OGs) insensitive/hypersensitive mutants that exhibit altered growth retardation in response to exogenous OGs. Mutants impaired in OG sensitivity were analyzed for disease resistance/susceptibility to the necrotrophic phytopathogens Botrytis cinerea and Pectobacterium carotovorum. In the ohyl line, the hypersensitivity to OGs was associated with resistance to the tested pathogens. This PER57 overexpressing line exhibited a significantly more permeable leaf cuticle than wild-type plants and this phenotype could be recapitulated by overexpressing other class III peroxidases. Such peroxidase overexpression was accompanied by the suppressed expression of cutin biosynthesis genes and the enhanced expression of genes associated with OG-signaling. Application of ABA completely removed ROS, restored the expression of genes associated with cuticle biosynthesis and led to decreased permeability of the leaf cuticle, and finally, abolished immunity to B. cinerea. Our work demonstrates that increased peroxidase activity increases permeability of the leaf cuticle. The loss of cuticle integrity primes plant defenses to necrotrophic pathogens via the activation of DAMP-responses.Peer reviewe

Comparative genome analysis of Lactobacillus casei strains isolated from Actimel and Yakult products reveals marked similarities and points to a common origin

The members of the Lactobacillus genus are widely used in the food and feed industry and show a remarkable ecological adaptability. Several Lactobacillus strains have been marketed as probiotics as they possess health-promoting properties for the host. In the present study, we used two complementary next-generation sequencing technologies to deduce the genome sequences of two Lactobacillus casei strains LcA and LcY, which were isolated from the products Actimel and Yakult, commercialized as probiotics. The LcA and LcY draft genomes have, respectively, an estimated size of 3067 and 3082 Mb and a G+ C content of 46.3%. Both strains are close to identical to each other and differ by no more than minor chromosomal re-arrangements, substitutions, insertions and deletions, as evident from the verified presence of one insertion-deletion (InDel) and only 29 single-nucleotide polymorphisms (SNPs). In terms of coding capacity, LcA and LcY are predicted to encode a comparable exoproteome, indicating that LcA and LcY are likely to establish similar interactions with human intestinal cells. Moreover, both L. casei LcA and LcY harboured a 59.6 kb plasmid that shared high similarities with plasmids found in other L. casei strains, such as W56 and BD-II. Further analysis revealed that the L. casei plasmids constitute a good evolution marker within the L. casei species. The plasmids of the LcA and LcY strains are almost identical, as testified by the presence of only three verified SNPs, and share a 3.5 kb region encoding a remnant of a lactose PTS system that is absent from the plasmids of W56 and BD-II but conserved in another smaller L. casei plasmid (pLC2W). Our observations imply that the results obtained in animal and human experiments performed with the Actimel and Yakult strains can be compared with each other as these strains share a very recent common ancestor

BopA does not have a major role in the adhesion of bifidobacterium bifidum to intestinal epithelial cells, extracellular matrix proteins, and mucus

he ability of bifidobacteria to adhere to the intestine of the human host is considered to be important for efficient colonization and achieving probiotic effects. Bifidobacterium bifidum strains DSM20456 and MIMBb75 adhere well to the human intestinal cell lines Caco-2 and HT-29. The surface lipoprotein BopA was previously described to be involved in mediating adherence of B. bifidum to epithelial cells, but thioacylated, purified BopA inhibited the adhesion of B. bifidum to epithelial cells in competitive adhesion assays only at very high concentrations, indicating an unspecific effect. In this study, the role of BopA in the adhesion of B. bifidum was readdressed. The gene encoding BopA was cloned and expressed without its lipobox and hydrophobic signal peptide in Escherichia coli, and an antiserum against the recombinant BopA was produced. The antiserum was used to demonstrate the abundant localization of BopA on the cell surface of B. bifidum. However, blocking of B. bifidum BopA with specific antiserum did not reduce adhesion of bacteria to epithelial cell lines, arguing that BopA is not an adhesin. Also, adhesion of B. bifidum to human colonic mucin and fibronectin was found to be BopA independent. The recombinant BopA bound only moderately to human epithelial cells and colonic mucus, and it failed to bind to fibronectin. Thus, our results contrast the earlier findings on the major role of BopA in adhesion, indicating that the strong adhesion of B. bifidum to epithelial cell lines is BopA independent

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

BACKGROUND: Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz), alpha- (8-12 Hz), beta- (12-32 Hz), and gamma- (32-40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in functional connectivity and topological properties during retention period may result in the decline of behavioral performance in RVF task

Critical synchronization dynamics of the Kuramoto model on connectome and small world graphs

The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits universal critical exponents which marks the Kuramoto equation, a fundamental model for synchronization, as a prime candidate for an underlying universal model. Here, we determined the synchronization behavior of this model by solving it numerically on a large, weighted human connectome network, containing 804092 nodes, in an assumed homeostatic state. Since this graph has a topological dimension $d < 4$, a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states. At this crossover point we observe power-law--tailed synchronization durations, with $\tau_t \simeq 1.2(1)$, away from experimental values for the brain. For comparison, on a large two-dimensional lattice, having additional random, long-range links, we obtain a mean-field value: $\tau_t \simeq 1.6(1)$. However, below the transition of the connectome we found global coupling control-parameter dependent exponents $1 < \tau_t \le 2$, overlapping with the range of human brain experiments. We also studied the effects of random flipping of a small portion of link weights, mimicking a network with inhibitory interactions, and found similar results. The control-parameter dependent exponent suggests extended dynamical criticality below the transition point.Comment: 12 pages, 9 figures + Supplemenraty material pdf 2 pages 4 figs, 1 table, accepted version in Scientific Report

Covert Waking Brain Activity Reveals Instantaneous Sleep Depth

The neural correlates of the wake-sleep continuum remain incompletely understood, limiting the development of adaptive drug delivery systems for promoting sleep maintenance. The most useful measure for resolving early positions along this continuum is the alpha oscillation, an 8–13 Hz electroencephalographic rhythm prominent over posterior scalp locations. The brain activation signature of wakefulness, alpha expression discloses immediate levels of alertness and dissipates in concert with fading awareness as sleep begins. This brain activity pattern, however, is largely ignored once sleep begins. Here we show that the intensity of spectral power in the alpha band actually continues to disclose instantaneous responsiveness to noise—a measure of sleep depth—throughout a night of sleep. By systematically challenging sleep with realistic and varied acoustic disruption, we found that sleepers exhibited markedly greater sensitivity to sounds during moments of elevated alpha expression. This result demonstrates that alpha power is not a binary marker of the transition between sleep and wakefulness, but carries rich information about immediate sleep stability. Further, it shows that an empirical and ecologically relevant form of sleep depth is revealed in real-time by EEG spectral content in the alpha band, a measure that affords prediction on the order of minutes. This signal, which transcends the boundaries of classical sleep stages, could potentially be used for real-time feedback to novel, adaptive drug delivery systems for inducing sleep

Duration of Coherence Intervals in Electrical Brain Activity in Perceptual Organization

We investigated the relationship between visual experience and temporal intervals of synchronized brain activity. Using high-density scalp electroencephalography, we examined how synchronized activity depends on visual stimulus information and on individual observer sensitivity. In a perceptual grouping task, we varied the ambiguity of visual stimuli and estimated observer sensitivity to this variation. We found that durations of synchronized activity in the beta frequency band were associated with both stimulus ambiguity and sensitivity: the lower the stimulus ambiguity and the higher individual observer sensitivity the longer were the episodes of synchronized activity. Durations of synchronized activity intervals followed an extreme value distribution, indicating that they were limited by the slowest mechanism among the multiple neural mechanisms engaged in the perceptual task. Because the degree of stimulus ambiguity is (inversely) related to the amount of stimulus information, the durations of synchronous episodes reflect the amount of stimulus information processed in the task. We therefore interpreted our results as evidence that the alternating episodes of desynchronized and synchronized electrical brain activity reflect, respectively, the processing of information within local regions and the transfer of information across regions

Reorganization of Functional Networks in Mild Cognitive Impairment

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD