Autophagy signaling in hypertrophied muscles of diabetic and control rats.

Autophagy plays a vital role in cell homeostasis by eliminating nonfunctional components and promoting cell survival. Here, we examined the levels of autophagy signaling proteins after 7 days of overload hypertrophy in the extensor digitorum longus (EDL) and soleus muscles of control and diabetic rats. We compared control and 3-day streptozotocin-induced diabetic rats, an experimental model for type 1 diabetes mellitus (T1DM). EDL muscles showed increased levels of basal autophagy signaling proteins. The diabetic state did not affect the extent of overload-induced hypertrophy or the levels of autophagy signaling proteins (p-ULK1, Beclin-1, Atg5, Atg12-5, Atg7, Atg3, LC3-I and II, and p62) in either muscle. The p-ULK-1, Beclin-1, and p62 protein expression levels were higher in the EDL muscle than in the soleus before the hypertrophic stimulus. On the contrary, the soleus muscle exhibited increased autophagic signaling after overload-induced hypertrophy, with increases in Beclin-1, Atg5, Atg12-5, Atg7, Atg3, and LC3-I expression in the control and diabetic groups, in addition to p-ULK-1 in the control groups. After hypertrophy, Beclin-1 and Atg5 levels increased in the EDL muscle of both groups, while p-ULK1 and LC3-I increased in the control group. In conclusion, the baseline EDL muscle exhibited higher autophagy than the soleus muscle. Although TDM1 promotes skeletal muscle mass loss and strength reduction, it did not significantly alter the extent of overload-induced hypertrophy and autophagy signaling proteins in EDL and soleus muscles, with the two groups exhibiting different patterns of autophagy activation.

New insights into the function of melatonin and its role in metabolic disturbances.

INTRODUCTION: Melatonin is a pineal hormone that has acquired several unique modes of regulating the physiological effects in mammals due to its characteristic phylogenetic history. While melatonin exhibits immediate nocturnal effects, it also has next-day prospective effects that take place in the absence of this hormone. Besides that, the daily repetition and the annual variation in the duration of its synthesis determine its circadian and seasonal effects that characterize melatonin as a chronobiotic, a molecule that encodes time to the internal environment. Additionally, it presents transgenerational effects that are important for fetal programming, leading to a balanced energy metabolism in the adult life. AREAS COVERED: Physiology, pathophysiology and therapeutic value of melatonin in metabolism and metabolic disorders. EXPERT OPINION: The typical mechanisms of action of melatonin (immediate, prospective, chronobiotic and transgenerational) should be considered to adequately understand its physiological effects on the regulation of metabolism in humans and, as a result, to understand the metabolic pathophysiological consequences caused by its synthesis and/or signaling disturbances. That points to the importance of a broader understanding of melatonin actions, besides the classical endocrinological point of view, that would allow the clinician/research to proper interpret its role in health maintenance.

Palmitoleic acid (16:1n7) increases oxygen consumption, fatty acid oxidation and ATP content in white adipocytes.

BACKGROUND: We have recently demonstrated that palmitoleic acid (16:1n7) increases lipolysis, glucose uptake and glucose utilization for energy production in white adipose cells. In the present study, we tested the hypothesis that palmitoleic acid modulates bioenergetic activity in white adipocytes. METHODS: For this, 3 T3-L1 pre-adipocytes were differentiated into mature adipocytes in the presence (or absence) of palmitic (16:0) or palmitoleic (16:1n7) acid at 100 or 200 µM. The following parameters were evaluated: lipolysis, lipogenesis, fatty acid (FA) oxidation, ATP content, oxygen consumption, mitochondrial mass, citrate synthase activity and protein content of mitochondrial oxidative phosphorylation (OXPHOS) complexes. RESULTS: Treatment with 16:1n7 during 9 days raised basal and isoproterenol-stimulated lipolysis, FA incorporation into triacylglycerol (TAG), FA oxidation, oxygen consumption, protein expression of subunits representing OXPHOS complex II, III, and V and intracellular ATP content. These effects were not observed in adipocytes treated with 16:0. CONCLUSIONS: Palmitoleic acid, by concerted action on lipolysis, FA esterification, mitochondrial FA oxidation, oxygen consumption and ATP content, does enhance white adipocyte energy expenditure and may act as local hormone.

The use of infrared thermography and accelerometers for remote monitoring of dairy cow health and welfare.

Increasing reliance on automated systems on-farm has led to a need for remote monitoring of health and welfare. We aimed to validate 2 methods that could be integrated into automated systems currently in use: infrared thermography (IRT) to measure respiration rate (RR), and accelerometers to measure the flinch, step, kick (FSK) response and assessing stress and discomfort. We monitored 22 multiparous, nonlactating, Friesian and Friesian × Jersey cows (average 5.1 yr of age) during a baseline period (2 min), a restraint in a crush (2 min), and then a recovery period after exposure to a startle (2 min). We measured RR with continuous IRT imaging of airflow through the nostrils and by counting flank movements from video and live recordings. We recorded heart rate (HR) and HR variability using HR monitors, and we recorded FSK from continuous video analysis of leg movements and indirectly using accelerometers attached to both hind legs. The FSK response was scored between 1 and 4 based on the height and direction of each leg movement. We observed no change in RR, HR variability, or FSK in response to the startle; however, HR increased briefly by 10 bpm. Bland-Altman plots indicated good agreement between the different methods of measuring RR, with average differences of -0.01 ± 0.87, 0.83 ± 0.57, and 0.37 ± 1.02 breaths/min for video versus live, IRT versus live and IRT versus video, respectively. Acceleration was also highly correlated with FSK scores of ≤3 (R2 = 0.96) and ≤2 (R2 = 0.89) and moderately correlated with FSK scores of 1 (R2 = 0.66) over the 4-min sampling period. The results show that accelerometers can provide an indirect measure of the FSK response, and IRT can be used reliably to measure RR. With further development, both technologies could be integrated into existing systems for remote monitoring of dairy cows' health and welfare on-farm.

Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model.

The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.

Electrical impedance of mouse brain cortex in vitro from 4.7 kHz to 2.0 MHz.

The electrical impedance of samples of mouse brain cortex has been measured between 4.7 kHz and 2.0 MHz. Brain slices of thickness 400 µm were prepared from two mice. Each slice was placed in either normal artificial cerebrospinal fluid or magnesium-free artificial cerebrospinal fluid; the latter induces seizure-like electrical behaviour. A total of 74 samples of cortex of approximate size 2 mm × 2 mm were then cut from these slices. Each sample in turn was placed between two flat Ag/AgCl electrodes and electrical impedance measured with an Agilent E4980A four-point impedance monitor. The measurements showed two regions of significant dispersion. Circuits based on the Cole-Cole and Fricke models, consisting of inductive, nonlinear capacitive and resistive elements were used to model the behaviour. Distributions of values for each circuit element have been determined for the samples prepared in seizing and non-seizing conditions. Few differences were found between the values of circuit elements between the seizing and non-seizing groups.

Numerical modelling of plasticity induced by transcranial magnetic stimulation.

We use neural field theory and spike-timing dependent plasticity to make a simple but biophysically reasonable model of long-term plasticity changes in the cortex due to transcranial magnetic stimulation (TMS). We show how common TMS protocols can be captured and studied within existing neural field theory. Specifically, we look at repetitive TMS protocols such as theta burst stimulation and paired-pulse protocols. Continuous repetitive protocols result mostly in depression, but intermittent repetitive protocols in potentiation. A paired pulse protocol results in depression at short ( < ∼ 10 ms) and long ( > ∼ 100 ms) interstimulus intervals, but potentiation for mid-range intervals. The model is sensitive to the choice of neural populations that are driven by the TMS pulses, and to the parameters that describe plasticity, which may aid interpretation of the high variability in existing experimental results. Driving excitatory populations results in greater plasticity changes than driving inhibitory populations. Modelling also shows the merit in optimizing a TMS protocol based on an individual's electroencephalogram. Moreover, the model can be used to make predictions about protocols that may lead to improvements in repetitive TMS outcomes.

In vitro electrical conductivity of seizing and non-seizing mouse brain slices at 10 kHz.

The electrical conductivity of small samples of mouse cortex (in vitro) has been measured at 10 kHz through the four-electrode method of van der Pauw. Brain slices from three mice were prepared under seizing and non-seizing conditions by changing the concentration of magnesium in the artificial cerebrospinal fluid used to maintain the tissue. These slices provided 121 square samples of cortical tissue; the conductivity of these samples was measured with an Agilent E4980A four-point impedance monitor. Of these, 73 samples were considered acceptable on the grounds of having good electrical contact between electrodes and tissue excluding outlier measurements. Results show that there is a significant difference (p = 0.03) in the conductivities of the samples under the two conditions. The seizing and non-seizing samples have mean conductivities of 0.33 and 0.36 S m(-1), respectively; however, these quantitative values should be used with caution as they are both subject to similar systematic uncertainties due to non-ideal temperature conditions and non-ideal placement of electrodes. We hypothesize that the difference between them, which is more robust to uncertainty, is due to the changing gap junction connectivity during seizures.

Baseline corticosterone in wintering marine birds: methodological considerations and ecological patterns.

Previous studies have related levels of plasma corticosterone (CORT) of seabirds to variation in foraging conditions during the breeding period, but it is unclear whether similar relationships between foraging conditions and baseline CORT exist during other life stages. We validated methods for identifying baseline CORT of lethally sampled birds and assessed variation in baseline CORT relative to winter habitat conditions. We collected free-living white-winged scoters (Melanitta fusca) at four wintering sites during December and February. We found increasing CORT values beyond 3 min after time since flush (the duration between initial flush and death), presumably reflecting acute stress responses. Our results demonstrate that it is possible to obtain baseline CORT from lethally sampled birds if the time from initial flush until death is measured. Our study sites varied appreciably in exposure to wind and waves, predation danger, diving depths, and the fraction of preferred foods in scoter diets. Despite these habitat differences, baseline CORT did not vary across sites or winter periods. We interpret this lack of variation as evidence that birds select wintering areas where they can successfully manage site-specific costs and maintain physiological homeostasis.

Nitric oxide binding to the cardiolipin complex of ferric cytochrome C.

Cardiolipin, a phospholipid specific to the mitochondrion, interacts with the small electron transfer heme protein cytochrome c through both electrostatic and hydrophobic interactions. Once in a complex with cardiolipin, cytochrome c has been shown to undergo a conformational change that leads to the rupture of the bond between the heme iron and the intrinsic sulfur ligand of a methionine residue and to enhance the peroxidatic properties of the protein considered important to its apoptotic activity. Here we report that the ferric cytochrome c/cardiolipin complex binds nitric oxide tightly through a multistep process in which the first step is the relatively slow displacement (5 s(-1)) from heme coordination of an intrinsic ligand that replaces methionine in the complex. Nanosecond photolysis of the nitrosyl adduct demonstrated that a fraction of the nitric oxide escapes from the heme pocket and subsequently recombines to the heme in second-order processes (k = 1.8 × 10(6) and 5.5 × 10(5) M(-1) s(-1)) that, under these conditions, were much faster than recombination of the intrinsic ligand with which they compete. Ultrafast (femtosecond) laser photolysis showed that the geminate recombination of nitric oxide to the heme occurred with time constants (τ = 22 and 72 ps) and that ~23% of the photolyzed nitric oxide escaped into the bulk phase. This high value for the escape fraction relative to other heme proteins indicates the open nature of the heme pocket in this complex. These results are summarized in a scheme and are discussed in terms of the possible modulation of the apoptotic activity of cytochrome c by nitric oxide.

Complementarity of spike- and rate-based dynamics of neural systems.

Relationships between spiking-neuron and rate-based approaches to the dynamics of neural assemblies are explored by analyzing a model system that can be treated by both methods, with the rate-based method further averaged over multiple neurons to give a neural-field approach. The system consists of a chain of neurons, each with simple spiking dynamics that has a known rate-based equivalent. The neurons are linked by propagating activity that is described in terms of a spatial interaction strength with temporal delays that reflect distances between neurons; feedback via a separate delay loop is also included because such loops also exist in real brains. These interactions are described using a spatiotemporal coupling function that can carry either spikes or rates to provide coupling between neurons. Numerical simulation of corresponding spike- and rate-based methods with these compatible couplings then allows direct comparison between the dynamics arising from these approaches. The rate-based dynamics can reproduce two different forms of oscillation that are present in the spike-based model: spiking rates of individual neurons and network-induced modulations of spiking rate that occur if network interactions are sufficiently strong. Depending on conditions either mode of oscillation can dominate the spike-based dynamics and in some situations, particularly when the ratio of the frequencies of these two modes is integer or half-integer, the two can both be present and interact with each other.

A mechanism for ultra-slow oscillations in the cortical default network.

When the brain is in its noncognitive "idling" state, functional MRI measurements reveal the activation of default cortical networks whose activity is suppressed during cognitive processing. This default or background mode is characterized by ultra-slow BOLD oscillations (∼0.05 Hz), signaling extremely slow cycling in cortical metabolic demand across distinct cortical regions. Here we describe a model of the cortex which predicts that slow cycling of cortical activity can arise naturally as a result of nonlinear interactions between temporal (Hopf) and spatial (Turing) instabilities. The Hopf instability is triggered by delays in the inhibitory postsynaptic response, while the Turing instability is precipitated by increases in the strength of the gap-junction coupling between interneurons. We comment on possible implications for slow dendritic computation and information processing.

Modeling brain activation patterns for the default and cognitive states.

We argue that spatial patterns of cortical activation observed with EEG, MEG and fMRI might arise from spontaneous self-organisation of interacting populations of excitatory and inhibitory neurons. We examine the dynamical behavior of a mean-field cortical model that includes chemical and electrical (gap-junction) synapses, focusing on two limiting cases: the "slow-soma" limit with slow voltage feedback from soma to dendrite, and the "fast-soma" limit in which the feedback action of soma voltage onto dendrite reversal potentials is instantaneous. For slow soma-dendrite feedback, we find a low-frequency (approximately 1 Hz) dynamic Hopf instability, and a stationary Turing instability that catalyzes formation of patterned distributions of cortical firing-rate activity with pattern wavelength approximately 2 cm. Turing instability can only be triggered when gap-junction diffusion between inhibitory neurons is strong, but patterning is destroyed if the tonic level of subcortical excitation is raised sufficiently. Interaction between the Hopf and Turing instabilities may describe the non-cognitive background or "default" state of the brain, as observed by BOLD imaging. In the fast-soma limit, the model predicts a high-frequency Hopf (approximately 35 Hz) instability, and a traveling-wave gamma-band instability that manifests as a 2-D standing-wave pattern oscillating in place at approximately 30 Hz. Small levels of inhibitory diffusion enhance and broaden the definition of the gamma antinodal regions by suppressing higher-frequency spatial modes, but gamma emergence is not contingent on the presence of inhibitory gap junctions; higher levels of diffusion suppress gamma activity. Fast-soma instabilities are enhanced by increased subcortical stimulation. Prompt soma-dendrite feedback may be an essential component of the genesis and large-scale cortical synchrony of gamma activity observed at the point of cognition.

Characteristics of temporal fluctuations in the hyperpolarized state of the cortical slow oscillation.

We present evidence for the hypothesis that transitions between the low- and high-firing states of the cortical slow oscillation correspond to neuronal phase transitions. By analyzing intracellular recordings of the membrane potential during the cortical slow oscillation in rats, we quantify the temporal fluctuations in power and the frequency centroid of the power spectrum in the period of time before "down" to "up" transitions. By taking appropriate averages over such events, we present these statistics as a function of time before transition. The results demonstrate an increase in fluctuation power and time scale broadly consistent with the slowing of systems close to phase transitions. The analysis is complicated and limited by the difficulty in identifying when transitions begin, and removing dc trends in membrane potential.

Subthreshold dynamics of a single neuron from a Hamiltonian perspective.

We use Hamilton's equations of classical mechanics to investigate the behavior of a cortical neuron on the approach to an action potential. We use a two-component dynamic model of a single neuron, due to Wilson, with added noise inputs. We derive a Lagrangian for the system, from which we construct Hamilton's equations. The conjugate momenta are found to be linear combinations of the noise input to the system. We use this approach to consider theoretically and computationally the most likely manner in which such a modeled neuron approaches a firing event. We find that the firing of a neuron is a result of a drop in inhibition, due to a temporary increase in negative bias of the mean noise input to the inhibitory control equation. Moreover, we demonstrate through theory and simulation that, on average, the bias in the noise increases in an exponential manner on the approach to an action potential. In the Hamiltonian description, an action potential can therefore be considered a result of the exponential growth of the conjugate momenta variables pulling the system away from its equilibrium state, into a nonlinear regime.

Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise.

One of the grand puzzles in neuroscience is establishing the link between cognition and the disparate patterns of spontaneous and task-induced brain activity that can be measured clinically using a wide range of detection modalities such as scalp electrodes and imaging tomography. High-level brain function is not a single-neuron property, yet emerges as a cooperative phenomenon of multiply-interacting populations of neurons. Therefore a fruitful modeling approach is to picture the cerebral cortex as a continuum characterized by parameters that have been averaged over a small volume of cortical tissue. Such mean-field cortical models have been used to investigate gross patterns of brain behavior such as anesthesia, the cycles of natural sleep, memory and erasure in slow-wave sleep, and epilepsy. There is persuasive and accumulating evidence that direct gap-junction connections between inhibitory neurons promote synchronous oscillatory behavior both locally and across distances of some centimeters, but, to date, continuum models have ignored gap-junction connectivity. In this paper we employ simple mean-field arguments to derive an expression for D2, the diffusive coupling strength arising from gap-junction connections between inhibitory neurons. Using recent neurophysiological measurements reported by Fukuda [J. Neurosci. 26, 3434 (2006)], we estimate an upper limit of D2 approximately 0.6cm2. We apply a linear stability analysis to a standard mean-field cortical model, augmented with gap-junction diffusion, and find this value for the diffusive coupling strength to be close to the critical value required to destabilize the homogeneous steady state. Computer simulations demonstrate that larger values of D2 cause the noise-driven model cortex to spontaneously crystalize into random mazelike Turing structures: centimeter-scale spatial patterns in which regions of high-firing activity are intermixed with regions of low-firing activity. These structures are consistent with the spatial variations in brain activity patterns detected with the BOLD (blood oxygen-level-dependent) signal detected with magnetic resonance imaging, and may provide a natural substrate for synchronous gamma-band rhythms observed across separated EEG (electroencephalogram) electrodes.

Antenatal risk factors, cytokines and the development of atopic disease in early childhood.

Atopic diseases are complex entities influenced by an array of risk factors, including genetic predisposition, environmental allergens, antenatal exposures, infections and psychosocial factors. One proposed mechanism by which these risk factors contribute to the development of atopic disease is through changes in the production of T helper cell type 1 (Th1) and T helper cell type 2 (Th2) cytokines. The objectives of this review are to discuss antenatal exposures that are associated with paediatric atopic diseases, to discuss the influence of the intrauterine environment on neonatal immune responses, to provide an overview of the Th1 and Th2 pathways and how they relate to atopic disease, and to summarise our current understanding of the association between cytokine responses in cord blood and the development of atopic disease in early childhood.

Going beyond a mean-field model for the learning cortex: second-order statistics.

Mean-field models of the cortex have been used successfully to interpret the origin of features on the electroencephalogram under situations such as sleep, anesthesia, and seizures. In a mean-field scheme, dynamic changes in synaptic weights can be considered through fluctuation-based Hebbian learning rules. However, because such implementations deal with population-averaged properties, they are not well suited to memory and learning applications where individual synaptic weights can be important. We demonstrate that, through an extended system of equations, the mean-field models can be developed further to look at higher-order statistics, in particular, the distribution of synaptic weights within a cortical column. This allows us to make some general conclusions on memory through a mean-field scheme. Specifically, we expect large changes in the standard deviation of the distribution of synaptic weights when fluctuation in the mean soma potentials are large, such as during the transitions between the "up" and "down" states of slow-wave sleep. Moreover, a cortex that has low structure in its neuronal connections is most likely to decrease its standard deviation in the weights of excitatory to excitatory synapses, relative to the square of the mean, whereas a cortex with strongly patterned connections is most likely to increase this measure. This suggests that fluctuations are used to condense the coding of strong (presumably useful) memories into fewer, but dynamic, neuron connections, while at the same time removing weaker (less useful) memories.

The K-complex and slow oscillation in terms of a mean-field cortical model.

We use a mean-field macrocolumn model of the cerebral cortex to offer an interpretation of the K-complex of the electroencephalogram to complement those of more detailed neuron-by-neuron models. We interpret the K-complex as a momentary excursion of the cortex from a stable low-firing state to an unstable high-firing state, and hypothesize that the related slow oscillation can be considered as the periodic oscillation between two meta-stable solutions of the mean-field model. By incorporating a Hebbian-style learning rule that links the growth in synapse strength to fluctuations in soma potential, we demonstrate a self-organization behaviour that draws the modelled cortex close to the edge of stability of the low-firing state. Furthermore, a very slow oscillation can occur in the excitability of the cortex that has similarities with the infra-slow oscillation of sleep.

White-noise susceptibility and critical slowing in neurons near spiking threshold.

We present mathematical and simulation analyses of the below-threshold noisy response of two biophysically motivated models for excitable membrane due to H. R. Wilson: a squid axon ("resonator") and a human cortical neuron ("integrator"). When stimulated with a low-intensity white noise superimposed on a dc control current, both membrane types generate voltage fluctuations that exhibit critical slowing down--that is, the voltage responsiveness to noisy input currents grows in amplitude while slowing in frequency--as the membrane approaches spiking threshold from below. We define threshold unambiguously as that dc current that renders a zero real eigenvalue for the Jacobian matrix for the integrator neuron, and, for the resonator neuron, as the dc current that gives a complex eigenvalue pair whose real part is zero. Using a linear Ornstein-Uhlenbeck analysis, we give exact small-noise expressions for the variance, power spectrum, and correlation function of the voltage fluctuations, and we derive the scaling laws for the divergence of susceptibility and correlation times for approach to threshold. We compare these predictions with numerical simulations of the nonlinear stochastic equations, and demonstrate that, provided the white-noise perturbations are kept sufficiently small, the linearized theory works well. These predictions should be testable in the laboratory using a current-clamped cell configuration. If confirmed, then the proximity of a neuron to its spike-transition point can be judged by measuring its subthreshold susceptibility to white-noise stimulation. We postulate that such temporally correlated fluctuations could provide a means of subthreshold signaling via gap-junction connections with neighboring neurons.