Characterization of Eag1 Channel Lateral Mobility in Rat Hippocampal Cultures by Single-Particle-Tracking with Quantum Dots

Voltage-gated ion channels are main players involved in fast synaptic events. However, only slow intracellular mechanisms have so far been described for controlling their localization as real-time visualization of endogenous voltage-gated channels at high temporal and spatial resolution has not been achieved yet. Using a specific extracellular antibody and quantum dots we reveal and characterize lateral mobility as a faster mechanism to dynamically control the number of endogenous ether-a-go-go (Eag)1 ion channels inside synapses. We visualize Eag1 entering and leaving synapses by lateral diffusion in the plasma membrane of rat hippocampal neurons. Mathematical analysis of their trajectories revealed how the motion of Eag1 gets restricted when the channels diffuse into the synapse, suggesting molecular interactions between Eag1 and synaptic components. In contrast, Eag1 channels switch to Brownian movement when they exit synapses and diffuse into extrasynaptic membranes. Furthermore, we demonstrate that the mobility of Eag1 channels is specifically regulated inside synapses by actin filaments, microtubules and electrical activity. In summary, using single-particle-tracking techniques with quantum dots nanocrystals, our study shows for the first time the lateral diffusion of an endogenous voltage-gated ion channel in neurons. The location-dependent constraints imposed by cytoskeletal elements together with the regulatory role of electrical activity strongly suggest a pivotal role for the mobility of voltage-gated ion channels in synaptic activity.     

Evidence for STAT4 as a Common Autoimmune Gene: rs7574865 Is Associated with Colonic Crohn's Disease and Early Disease Onset

Background

Recent studies demonstrated an association of STAT4 variants with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), indicating that multiple autoimmune diseases share common susceptibility genes. We therefore investigated the influence of STAT4 variants on the susceptibility and phenotype of inflammatory bowel diseases (IBD) in a large patient and control cohort.

Methodology/Principal Findings

Genomic DNA from 2704 individuals of Caucasian origin including 857 patients with Crohn''s disease (CD), 464 patients with ulcerative colitis (UC), and 1383 healthy, unrelated controls was analyzed for seven SNPs in the STAT4 gene (rs11889341, rs7574865, rs7568275, rs8179673, rs10181656, rs7582694, rs10174238). In addition, a detailed genotype-phenotype analysis was performed. Our analysis revealed an association of the STAT4 SNP rs7574865 with overall decreased susceptibility to CD (p = 0.047, OR 0.86 [95% CI 0.74–0.99]). However, compared to CD patients carrying the wild type genotype, the STAT4 SNP rs7574865 was significantly associated with early CD onset (p = 0.021) and colonic CD (p = 0.008; OR = 4.60, 95% CI 1.63–12.96). For two other STAT4 variants, there was a trend towards protection against CD susceptibility (rs7568275, p = 0.058, OR 0.86 [95% CI 0.74–1.00]; rs10174238, p = 0.057, OR 0.86 [95% CI 0.75–1.00]). In contrast, we did not observe any association with UC susceptibility. Evidence for weak gene-gene interaction of STAT4 with the IL23R SNP rs11209026 was lost after Bonferroni correction.

Conclusions/Significance

Our results identified the STAT4 SNP rs7574865 as a disease-modifying gene variant in colonic CD. However, in contrast to SLE and RA, the effect of rs7574865 on CD susceptibility is only weak.     

Developing pulmonary vasculopathy in systemic sclerosis, detected with non-invasive cardiopulmonary exercise testing

Background

Patients with systemic sclerosis (SSc) may develop exercise intolerance due to musculoskeletal involvement, restrictive lung disease, left ventricular dysfunction, or pulmonary vasculopathy (PV). The latter is particularly important since it may lead to lethal pulmonary arterial hypertension (PAH). We hypothesized that abnormalities during cardiopulmonary exercise testing (CPET) in patients with SSc can identify PV leading to overt PAH.

Methods

Thirty SSc patients from the Harbor-UCLA Rheumatology clinic, not clinically suspected of having significant pulmonary vascular disease, were referred for this prospective study. Resting pulmonary function and exercise gas exchange were assessed, including peakVO₂, anaerobic threshold (AT), heart rate- VO₂ relationship (O₂-pulse), exercise breathing reserve and parameters of ventilation-perfusion mismatching, as evidenced by elevated ventilatory equivalent for CO₂ (VE/VCO₂) and reduced end-tidal pCO₂ (P_ETCO₂) at the AT.

Results

Gas exchange patterns were abnormal in 16 pts with specific cardiopulmonary disease physiology: Eleven patients had findings consistent with PV, while five had findings consistent with left-ventricular dysfunction (LVD). Although both groups had low peak VO₂ and AT, a higher VE/VCO₂ at AT and decreasing P_ETCO₂ during early exercise distinguished PV from LVD.

Conclusions

Previously undiagnosed exercise impairments due to LVD or PV were common in our SSc patients. Cardiopulmonary exercise testing may help to differentiate and detect these disorders early in patients with SSc.     

Aptamer-based multiplexed proteomic technology for biomarker discovery

Background

The interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.

Methodology/Principal Findings

We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.

Conclusions/Significance

We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.     

Observing the observer (II): deciding when to decide

In a companion paper [1], we have presented a generic approach for inferring how subjects make optimal decisions under uncertainty. From a Bayesian decision theoretic perspective, uncertain representations correspond to "posterior" beliefs, which result from integrating (sensory) information with subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden or unknown) state of the world. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task. Here, inter-trial variability in reaction times is modelled as reflecting the dynamics of the subjects' internal recognition process, i.e. the updating of representations (posterior densities) of hidden states over trials while subjects learn probabilistic audio-visual associations. We use this paradigm to demonstrate that our meta-Bayesian framework allows for (i) probabilistic inference on the dynamics of the subject's representation of environmental states, and for (ii) model selection to disambiguate between alternative preferences (loss functions) human subjects could employ when dealing with trade-offs, such as between speed and accuracy. Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour.     

Observing the observer (I): meta-bayesian models of learning and decision-making

In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context- or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task.     

Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.     

Structure Learning in a Sensorimotor Association Task

Learning is often understood as an organism''s gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.