A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP), and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards). We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD). We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments.

BACKGROUND AND AIMS: Hypothermia provides neuroprotection after cardiac arrest, hypoxic-ischemic encephalopathy, and in animal models of ischemic stroke. However, as drug development for stroke has been beset by translational failure, we sought additional evidence that hypothermia protects human neurons against ischemic injury. METHODS: Human embryonic stem cells were cultured and differentiated to provide a source of neurons expressing β III tubulin, microtubule-associated protein 2, and the Neuronal Nuclei antigen. Oxygen deprivation, oxygen-glucose deprivation, and H2 O2 -induced oxidative stress were used to induce relevant injury. RESULTS: Hypothermia to 33°C protected these human neurons against H2 O2 -induced oxidative stress reducing lactate dehydrogenase release and Terminal deoxynucleotidyl transferase dUTP nick end labeling-staining by 53% (P ≤ 0·0001; 95% confidence interval 34·8-71·04) and 42% (P ≤ 0·0001; 95% confidence interval 27·5-56·6), respectively, after 24 h in culture. Hypothermia provided similar protection against oxygen-glucose deprivation (42%, P ≤ 0·001, 95% confidence interval 18·3-71·3 and 26%, P ≤ 0·001; 95% confidence interval 12·4-52·2, respectively) but provided no protection against oxygen deprivation alone. Protection (21%) persisted against H2 O2 -induced oxidative stress even when hypothermia was initiated six-hours after onset of injury (P ≤ 0·05; 95% confidence interval 0·57-43·1). CONCLUSION: We conclude that hypothermia protects stem cell-derived human neurons against insults relevant to stroke over a clinically relevant time frame. Protection against H2 O2 -induced injury and combined oxygen and glucose deprivation but not against oxygen deprivation alone suggests an interaction in which protection benefits from reduction in available glucose under some but not all circumstances.

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

BACKGROUND: The Ca(2+)-activated K(+) channel K(Ca)3.1 is expressed in several structural and inflammatory airway cell types and is proposed to play an important role in the pathophysiology of asthma. The aim of the current study was to determine whether inhibition of K(Ca)3.1 modifies experimental asthma in sheep. METHODOLOGY AND PRINCIPAL FINDINGS: Atopic sheep were administered either 30 mg/kg Senicapoc (ICA-17073), a selective inhibitor of the K(Ca)3.1-channel, or vehicle alone (0.5% methylcellulose) twice daily (orally). Both groups received fortnightly aerosol challenges with house dust mite allergen for fourteen weeks. A separate sheep group received no allergen challenges or drug treatment. In the vehicle-control group, twelve weeks of allergen challenges resulted in a 60±19% increase in resting airway resistance, and this was completely attenuated by treatment with Senicapoc (0.25±12%; n = 10, P = 0.0147). The vehicle-control group had a peak-early phase increase in lung resistance of 82±21%, and this was reduced by 58% with Senicapoc treatment (24±14%; n = 10, P = 0.0288). Senicapoc-treated sheep also demonstrated reduced airway hyperresponsiveness, requiring a significantly higher dose of carbachol to increase resistance by 100% compared to allergen-challenged vehicle-control sheep (20±5 vs. 52±18 breath-units of carbachol; n = 10, P = 0.0340). Senicapoc also significantly reduced eosinophil numbers in bronchoalveolar lavage taken 48 hours post-allergen challenge, and reduced vascular remodelling. CONCLUSIONS: These findings suggest that K(Ca)3.1-activity contributes to allergen-induced airway responses, inflammation and vascular remodelling in a sheep model of asthma, and that inhibition of K(Ca)3.1 may be an effective strategy for blocking allergen-induced airway inflammation and hyperresponsiveness in humans.

Huntington's disease (HD) is an incurable neurodegenerative disorder caused by a CAG repeat expansion in exon 1 of the Huntingtin (HTT) gene. Recently, induced pluripotent stem cell (iPSC) lines carrying atypical and aggressive (CAG60+) HD variants have been generated and exhibit disparate molecular pathologies. Here we investigate two human embryonic stem cell (hESC) lines carrying CAG37 and CAG51 typical late-onset repeat expansions in comparison to wildtype control lines during undifferentiated states and throughout forebrain neuronal differentiation. Pluripotent HD lines demonstrate growth, viability, pluripotent gene expression, mitochondrial activity and forebrain specification that is indistinguishable from control lines. Expression profiles of crucial genes known to be dysregulated in HD remain unperturbed in the presence of mutant protein and throughout differentiation; however, elevated glutamate-evoked responses were observed in HD CAG51 neurons. These findings suggest typical late-onset HD mutations do not alter pluripotent parameters or the capacity to generate forebrain neurons, but that such progeny may recapitulate hallmarks observed in established HD model systems. Such HD models will help further our understanding of the cascade of pathological events leading to disease onset and progression, while simultaneously facilitating the identification of candidate HD therapeutics.

Octopus cells, located in the mammalian auditory brainstem, receive their excitatory synaptic input exclusively from auditory nerve fibers (ANFs). They respond with accurately timed spikes but are broadly tuned for sound frequency. Since the representation of information in the auditory nerve is well understood, it is possible to pose a number of questions about the relationship between the intrinsic electrophysiology, dendritic morphology, synaptic connectivity, and the ultimate functional role of octopus cells in the brainstem. This study employed a multi-compartmental Hodgkin-Huxley model to determine whether dendritic delay in octopus cells improves synaptic input coincidence detection in octopus cells by compensating for the cochlear traveling wave delay. The propagation time of post-synaptic potentials from synapse to soma was investigated. We found that the total dendritic delay was approximately 0.275 ms. It was observed that low-threshold potassium channels in the dendrites reduce the amplitude dependence of the dendritic delay of post-synaptic potentials. As our hypothesis predicted, the model was most sensitive to acoustic onset events, such as the glottal pulses in speech when the synaptic inputs were arranged such that the model's dendritic delay compensated for the cochlear traveling wave delay across the ANFs. The range of sound frequency input from ANFs was also investigated. The results suggested that input to octopus cells is dominated by high frequency ANFs.

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Gamma oscillations are thought to be critical for a number of behavioral functions, they occur in many regions of the brain and through a variety of mechanisms. Fast repetitive bursting (FRB) neurons in layer 2 of the cortex are able to drive gamma oscillations over long periods of time. Even though the oscillation is driven by FRB neurons, strong feedback within the rest of the cortex must modulate properties of the oscillation such as frequency and power. We used a highly detailed model of the cortex to determine how a cohort of 33 parameters controlling synaptic drive might modulate gamma oscillation properties. We were interested in determining not just the effects of parameters individually, but we also wanted to reveal interactions between parameters beyond additive effects. To prevent a combinatorial explosion in parameter combinations that might need to be simulated, we used a fractional factorial design (FFD) that estimated the effects of individual parameters and two parameter interactions. This experiment required only 4096 model runs. We found that the largest effects on both gamma power and frequency came from a complex interaction between efficacy of synaptic connections from layer 2 inhibitory neurons to layer 2 excitatory neurons and the parameter for the reciprocal connection. As well as the effect of the individual parameters determining synaptic efficacy, there was an interaction between these parameters beyond the additive effects of the parameters alone. The magnitude of this effect was similar to that of the individual parameters, predicting that it is physiologically important in setting gamma oscillation properties.

BACKGROUND: Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. METHODOLOGY/PRINCIPAL FINDINGS: Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. CONCLUSIONS/SIGNIFICANCE: This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.

BACKGROUND: Increased mast cell (MC) density and changes in their distribution in airway tissues is thought to contribute significantly to the pathophysiology of asthma. However, the time sequence for these changes and how they impact small airway function in asthma is not fully understood. The aim of the current study was to characterise temporal changes in airway MC density and correlate these changes with functional airway responses in sheep chronically challenged with house dust mite (HDM) allergen. METHODOLOGY/PRINCIPAL FINDINGS: MC density was examined on lung tissue from four spatially separate lung segments of allergic sheep which received weekly challenges with HDM allergen for 0, 8, 16 or 24 weeks. Lung tissue was collected from each segment 7 days following the final challenge. The density of tryptase-positive and chymase-positive MCs (MC(T) and MC(TC) respectively) was assessed by morphometric analysis of airway sections immunohistochemically stained with antibodies against MC tryptase and chymase. MC(T) and MC(TC) density was increased in small bronchi following 24 weeks of HDM challenges compared with controls (P<0.05). The MC(TC)/MC(T) ratio was significantly increased in HDM challenged sheep compared to controls (P<0.05). MC(T) and MC(TC) density was inversely correlated with allergen-induced increases in peripheral airway resistance after 24 weeks of allergen exposure (P<0.05). MC(T) density was also negatively correlated with airway responsiveness after 24 challenges (P<0.01). CONCLUSIONS: MC(T) and MC(TC) density in the small airways correlates with better lung function in this sheep model of chronic asthma. Whether this finding indicates that under some conditions mast cells have protective activities in asthma, or that other explanations are to be considered requires further investigation.