Membrane protein oxidation determines neuronal degeneration

Oxidative stress is an early hallmark in neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, the critical biochemical effector mechanisms of oxidative neurotoxicity have remained surprisingly elusive. In screening various peroxides and potential substrates of oxidation for their effect on neuronal survival, we observed that intramembrane compounds were significantly more active than aqueous or amphiphilic compounds. To better understand this result, we synthesized a series of competitive and site‐specific membrane protein oxidation inhibitors termed aminoacyllipids, whose structures were designed on the basis of amino acids frequently found at the protein‐lipid interface of synaptic membrane proteins. Investigating the aminoacyllipids in primary neuronal culture, we found that the targeted protection of transmembrane tyrosine and tryptophan residues was sufficient to prevent neurotoxicity evoked by hydroperoxides, kainic acid, glutathione‐depleting drugs, and certain amyloidogenic peptides, but ineffective against non‐oxidative inducers of apoptosis such as sphingosine or Akt kinase inhibitors. Thus, the oxidative component of different neurotoxins appears to converge on neuronal membrane proteins, irrespective of the primary mechanism of cellular oxidant generation. Our results indicate the existence of a one‐electron redox cycle based on membrane protein aromatic surface amino acids, whose disturbance or overload leads to excessive membrane protein oxidation and neuronal death.

     

Influence of degree correlations on network structure and stability in protein-protein interaction networks

Background  

The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.     

Structural correlations in bacterial metabolic networks

Background  

Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution.     

How the global structure of protein interaction networks evolves

Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time-scales. For instance, more than 100 interactions may be added to the yeast network every million years, a fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain (without natural selection on global network structure) the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d) of proteins with d neighbours, which is broad-tailed and consistent with a power law, that is: P(d) proportional, variant d (-gamma).     

How the molecular structure determines the flow of excitation energy in plant light-harvesting complex II

Excitation energy transfer in the light-harvesting complex II of higher plants is modeled using excitonic couplings and local transition energies determined from structure-based calculations recently (Müh et al., 2010). A theory is introduced that implicitly takes into account protein induced dynamic localization effects of the exciton wavefunction between weakly coupled optical and vibronic transitions of different pigments. Linear and non-linear optical spectra are calculated and compared with experimental data reaching qualitative agreement. High-frequency intramolecular vibrational degrees of freedom are found important for ultrafast subpicosecond excitation energy transfer between chlorophyll (Chl) b and Chla, since they allow for fast dissipation of the excess energy. The slower ps component of this transfer is due to the monomeric excited state of Chlb 605. The majority of exciton relaxation in the Chla spectral region is characterized by slow ps exciton equilibration between the Chla domains within one layer and between the lumenal and stromal layers in the 10-20 ps time range. Subpicosecond exciton relaxation in the Chla region is only found within the terminal emitter domain (Chls a 610/611/612) and within the Chla 613/614 dimer. Deviations between measured and calculated exciton state life times are obtained for the intermediate spectral region between the main absorbance bands of Chla and Chlb that indicate that besides Chlb 608 another pigment should absorb there. Possible candidates, so far not identified by structure-based calculations, but by fitting of optical spectra and mutagenesis studies, are discussed. Additional mutagenesis studies are suggested to resolve this issue.     

Bridging neuronal correlations and dimensionality reduction

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Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.     

Self-stabilization of neuronal networks

This study is concerned with synaptic reorganization in local neuronal networks. Within networks of 30 neurons, an initial disequilibrium in connectivity has to be compensated by reorganization of synapses. Such plasticity is not a genetically determined process, but depends on results of neuronal interaction. Neurobiological experiments have lead to a model of the behavior of individual neurons during neuroplastic reorganization, formalized as a synaptogenetic rule that governs changes in the amount of synaptic elements on each neuron. — When this synaptogenetic rule is applied to a system of neurons, there is some freedom left to the choice of further conditions. In this study it is examined, which assumptions additional to the synaptogenetic rule are essential in order to obtain morphogenetic stability. By explicating these assumptions, their plausibility can be tested. It is analysed, in which respect these conditions are important, in which part of the model they exert their influence, and what kind of instability and degeneration happens if the assumptions are violated. —Our essentials for reaching morphogenetic stability are: (1) A network structure that guarantees the possibility of oscillations, (2) a compensation algorithm that guarantees a smooth morphogenesis, (3) kinetic parameters that guarantee convergence in the synaptic elements' change, and (4) a synaptic modification rule that prohibits Hebb-like as well as anti-Hebb-like synaptic changes. — It is concluded that many structural features of the mammalian cerebral cortex are in accordance with the requirements of the model.     

Self-stabilization of neuronal networks

Between the extreme views concerning ontogenesis (genetic vs. environmental determination), we use a moderate approach: a somehow pre-established neuronal model network reacts to activity deviations (reflecting input to be compensated), and stabilizes itself during a complex feed-back process. Morphogenesis is based on an algorithm formalizing the compensation theory of synaptogenesis (Wolff and Wagner 1983). This algorithm is applied to randomly connected McCulloch-Pitts networks that are able to maintain oscillations of their activity patterns over time. The algorithm can lead to networks which are morphogenetically stable but preserve self-maintained oscillations in activity. This is in contrast to most of the current models of synaptogenesis and synaptic modification based on Hebbian rules of plasticity. Hebbian networks are morphogenetically unstable without additional assumptions. The effects of compensation on structural and functional properties of the networks are described. It is concluded that the compensation theory of synaptogenesis can account for the development of morphogenetically stable neuronal networks out of randomly connected networks via selective stabilization and elimination of synapses.The logic of the compensation algorithm is based on experimental results. The present paper shows that the compensation theory can not only predict the behavior of synaptic populations (Wagner and Wolff, in preparation), but it can also describe the behavior of neurons interconnected in a network, with the resulting additional system properties. The neuronal interactions-leading to equilibrium in certain cases-are a self-organizing process in the sense that all decisions are performed on the individual cell level without knowing the overall network situation or goal.     

Observing and interpreting correlations in metabolomic networks

MOTIVATION: Metabolite profiling aims at an unbiased identification and quantification of all the metabolites present in a biological sample. Based on their pair-wise correlations, the data obtained from metabolomic experiments are organized into metabolic correlation networks and the key challenge is to deduce unknown pathways based on the observed correlations. However, the data generated is fundamentally different from traditional biological measurements and thus the analysis is often restricted to rather pragmatic approaches, such as data mining tools, to discriminate between different metabolic phenotypes. METHODS AND RESULTS: We investigate to what extent the data generated networks reflect the structure of the underlying biochemical pathways. The purpose of this work is 2-fold: Based on the theory of stochastic systems, we first introduce a framework which shows that the emergent correlations can be interpreted as a 'fingerprint' of the underlying biophysical system. This result leads to a systematic relationship between observed correlation networks and the underlying biochemical pathways. In a second step, we investigate to what extent our result is applicable to the problem of reverse engineering, i.e. to recover the underlying enzymatic reaction network from data. The implications of our findings for other bioinformatics approaches are discussed.     

Self-organized criticality in developing neuronal networks

Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.     

Imaging neuronal networks in behaving animals

Neuronal activity has recently been imaged with single-cell resolution in behaving vertebrates. This was accomplished by using fluorescent calcium indicators in conjunction with confocal or two-photon microscopy. These optical techniques, along with other new approaches for imaging synaptic activity, second messengers, and neurotransmitters and their receptors offer great promise for the study of neuronal networks at high resolution in vivo.     

Host centrality in food web networks determines parasite diversity

Background

Parasites significantly alter topological metrics describing food web structure, yet few studies have explored the relationship between food web topology and parasite diversity.

Methods/Principal Findings

This study uses quantitative metrics describing network structure to investigate the relationship between the topology of the host food web and parasite diversity. Food webs were constructed for four restored brackish marshes that vary in species diversity, time post restoration and levels of parasitism. Our results show that the topology of the food web in each brackish marsh is highly nested, with clusters of generalists forming a distinct modular structure. The most consistent predictors of parasite diversity within a host were: trophic generality, and eigenvector centrality. These metrics indicate that parasites preferentially colonise host species that are highly connected, and within modules of tightly interacting species in the food web network.

Conclusions/Significance

These results suggest that highly connected free-living species within the food web may represent stable trophic relationships that allow for the persistence of complex parasite life cycles. Our data demonstrate that the structure of host food webs can have a significant effect on the establishment of parasites, and on the potential for evolution of complex parasite life cycles.     

Phase-resetting curve determines how BK currents affect neuronal firing

BK channels are large conductance potassium channels gated by calcium and voltage. Paradoxically, blocking these channels has been shown experimentally to increase or decrease the firing rate of neurons, depending on the neural subtype and brain region. The mechanism for how this current can alter the firing rates of different neurons remains poorly understood. Using phase-resetting curve (PRC) theory, we determine when BK channels increase or decrease the firing rates in neural models. The addition of BK currents always decreases the firing rate when the PRC has only a positive region. When the PRC has a negative region (type II), BK currents can increase the firing rate. The influence of BK channels on firing rate in the presence of other conductances, such as I _m and I _h , as well as with different amplitudes of depolarizing input, were also investigated. These results provide a formal explanation for the apparently contradictory effects of BK channel antagonists on firing rates.     

Secondary structure determines protein topology

Using a test set of 13 small, compact proteins, we demonstrate that a remarkably simple protocol can capture native topology from secondary structure information alone, in the absence of long-range interactions. It has been a long-standing open question whether such information is sufficient to determine a protein's fold. Indeed, even the far simpler problem of reconstructing the three-dimensional structure of a protein from its exact backbone torsion angles has remained a difficult challenge owing to the small, but cumulative, deviations from ideality in backbone planarity, which, if ignored, cause large errors in structure. As a familiar example, a small change in an elbow angle causes a large displacement at the end of your arm; the longer the arm, the larger the displacement. Here, correct secondary structure assignments (alpha-helix, beta-strand, beta-turn, polyproline II, coil) were used to constrain polypeptide backbone chains devoid of side chains, and the most stable folded conformations were determined, using Monte Carlo simulation. Just three terms were used to assess stability: molecular compaction, steric exclusion, and hydrogen bonding. For nine of the 13 proteins, this protocol restricts the main chain to a surprisingly small number of energetically favorable topologies, with the native one prominent among them.     

How sequence determines elasticity of disordered proteins

How nature tunes sequences of disordered protein to yield the desired coiling properties is not yet well understood. To shed light on the relationship between protein sequence and elasticity, we here investigate four different natural disordered proteins with elastomeric function, namely: FG repeats in the nucleoporins; resilin in the wing tendon of dragonfly; PPAK in the muscle protein titin; and spider silk. We obtain force-extension curves for these proteins from extensive explicit solvent molecular dynamics simulations, which we compare to purely entropic coiling by modeling the four proteins as entropic chains. Although proline and glycine content are in general indicators for the entropic elasticity as expected, divergence from simple additivity is observed. Namely, coiling propensities correlate with polyproline II content more strongly than with proline content, and given a preponderance of glycines for sufficient backbone flexibility, nonlocal interactions such as electrostatic forces can result in strongly enhanced coiling, which results for the case of resilin in a distinct hump in the force-extension curve. Our results, which are directly testable by force spectroscopy experiments, shed light on how evolution has designed unfolded elastomeric proteins for different functions.     

Organization of neocortical neuronal networks

Multiple unit activity in deep layers of the frontal and motor cortices was recorded by chronically implanted semimicroelectrodes in waking cats with different levels of food motivation. From four to seven neuronal spike trains were selected from the recorded multiunit activity. Interactions between neighbouring neurons in the motor and frontal areas of the neocortex (within the local neuronal networks) and between the neurons of these areas (distributed neuronal networks) were estimated by means of statistical crosscorrelation analysis of spike trains within the range of delays from 0 to 100 ms. Neurons in the local networks were divided in two subgroups: the neurons with higher spike amplitudes with the dominance of divergent connections and neurons with lower spike amplitudes with the dominance of convergent connections. Strong monosynaptic connections (discharges with a delay of less than 2 ms) between the neurons with high- and low-amplitude spikes formed the background of the local networks. Connections between low-amplitude neurons in the frontal cortex and high-amplitude neurons in the motor cortex dominated in the distributed networks. A 24-hour food deprivation predominantly altered the late interneuronal crosscorrelations with time delays within the range of 2-100 ms in both local and distributed networks.     

Coping with variability in small neuronal networks

Experimental and corresponding modeling studies indicate that there is a 2- to 5-fold variation of intrinsic and synaptic parameters across animals while functional output is maintained. Here, we review experiments, using the heartbeat central pattern generator (CPG) in medicinal leeches, which explore the consequences of animal-to-animal variation in synaptic strength for coordinated motor output. We focus on a set of segmental heart motor neurons that all receive inhibitory synaptic input from the same four premotor interneurons. These four premotor inputs fire in a phase progression and the motor neurons also fire in a phase progression because of differences in synaptic strength profiles of the four inputs among segments. Our work tested the hypothesis that functional output is maintained in the face of animal-to-animal variation in the absolute strength of connections because relative strengths of the four inputs onto particular motor neurons is maintained across animals. Our experiments showed that relative strength is not strictly maintained across animals even as functional output is maintained, and animal-to-animal variations in strength of particular inputs do not correlate strongly with output phase. Further experiments measured the precise temporal pattern of the premotor inputs, the segmental synaptic strength profiles of their connections onto motor neurons, and the temporal pattern (phase progression) of those motor neurons all in the same animal for a series of 12 animals. The analysis of input and output in this sample of 12 individuals suggests that the number (four) of inputs to each motor neuron and the variability of the temporal pattern of input from the CPG across individuals weaken the influence of the strength of individual inputs. Moreover, the temporal pattern of the output varies as much across individuals as that of the input. Essentially, each animal arrives at a unique solution for how the network produces functional output.     

Manifestation of function-follow-form in cultured neuronal networks

We expose hidden function-follow-form schemata in the recorded activity of cultured neuronal networks by comparing the activity with simulation results of a new modeling approach. Cultured networks grown from an arbitrary mixture of neuron and glia cells in the absence of external stimulations and chemical cues spontaneously form networks of different sizes (from 50 to several millions of neurons) that exhibit non-arbitrary complex spatio-temporal patterns of activity. The latter is marked by formation of a sequence of synchronized bursting events (SBEs)--short time windows (approximately 200 ms) of rapid neuron firing, separated by longer time intervals (seconds) of sporadic neuron firing. The new dynamical synapse and soma (DSS) model, used here, has been successful in generating sequences of SBEs with the same statistical scaling properties (over six time decades) as those of the small networks. Large networks generate statistically distinct sub-groups of SBEs, each with its own characteristic pattern of neuronal firing ('fingerprint'). This special function (activity) motif has been proposed to emanate from a structural (form) motif--self-organization of the large networks into a fabric of overlapping sub-networks of about 1 mm in size. Here we test this function-follow-form idea by investigating the influence of the connectivity architecture of a model network (form) on the structure of its spontaneous activity (function). We show that a repertoire of possible activity states similar to the observed ones can be generated by networks with proper underlying architecture. For example, networks composed of two overlapping sub-networks exhibit distinct types of SBEs, each with its own characteristic pattern of neuron activity that starts at a specific sub-network. We further show that it is possible to regulate the temporal appearance of the different sub-groups of SBEs by an additional non-synaptic current fed into the soma of the modeled neurons. The ability to regulate the relative temporal ordering of different SBEs might endow the networks with higher plasticity and complexity. These findings call for additional mechanisms yet to be discovered. Recent experimental observations indicate that glia cells coupled to neuronal soma might generate such non-synaptic regulating currents.     

Redundancy and synergy arising from pairwise correlations in neuronal ensembles

Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by measures of the information conveyed by neuronal activity; a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et al., 1999). Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise components of neuronal firing are small in absolute value and entirely random in origin. Even such small random correlations are shown to lead to large possible synergy or redundancy, whenever the time window for extracting information from neuronal firing extends to the order of the mean interspike interval. In addition, a sample of recordings from rat barrel cortex illustrates the mean time window at which such corrections dominate when correlations are, as often in the real brain, neither random nor small. The presence of this kind of correlations for a large ensemble of cells restricts further the time of validity of the expansion.