Antisense RNA foci in the motor neurons of C9ORF72-ALS patients are associated with TDP-43 proteinopathy

GGGGCC repeat expansions of C9ORF72 represent the most common genetic variant of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. We and others have proposed that RNA transcribed from the repeat sequence is toxic via sequestration of RNA-binding factors. Both GGGGCC-repeat (sense) and CCCCGG-repeat (antisense) molecules are detectable by fluorescence in situ hybridisation as RNA foci, but their relative expression pattern within the CNS and contribution to disease has not been determined. Blinded examination of CNS biosamples from ALS patients with a repeat expansion of C9ORF72 showed that antisense foci are present at a significantly higher frequency in cerebellar Purkinje neurons and motor neurons, whereas sense foci are present at a significantly higher frequency in cerebellar granule neurons. Consistent with this, inclusions containing sense or antisense derived dipeptide repeat proteins were present at significantly higher frequency in cerebellar granule neurons or motor neurons, respectively. Immunohistochemistry and UV-crosslinking studies showed that sense and antisense RNA molecules share similar interactions with SRSF2, hnRNP K, hnRNP A1, ALYREF, and hnRNP H/F. Together these data suggest that, although sense and antisense RNA molecules might be expected to be equally toxic via their shared protein binding partners, distinct patterns of expression in various CNS neuronal populations could lead to relative differences in their contribution to the pathogenesis of neuronal injury. Moreover in motor neurons, which are the primary target of pathology in ALS, the presence of antisense foci (χ2, p 2, p = 0.75) correlated with mislocalisation of TDP-43, which is the hallmark of ALS neurodegeneration. This has implications for translational approaches to C9ORF72 disease, and furthermore interacting RNA-processing factors and transcriptional activators responsible for antisense versus sense transcription might represent novel therapeutic targets

A Computational Model of the Ionic Currents, Ca2+ Dynamics and Action Potentials Underlying Contraction of Isolated Uterine Smooth Muscle

Uterine contractions during labor are discretely regulated by rhythmic action potentials (AP) of varying duration and form that serve to determine calcium-dependent force production. We have employed a computational biology approach to develop a fuller understanding of the complexity of excitation-contraction (E-C) coupling of uterine smooth muscle cells (USMC). Our overall aim is to establish a mathematical platform of sufficient biophysical detail to quantitatively describe known uterine E-C coupling parameters and thereby inform future empirical investigations of physiological and pathophysiological mechanisms governing normal and dysfunctional labors. From published and unpublished data we construct mathematical models for fourteen ionic currents of USMCs: currents (L- and T-type), current, an hyperpolarization-activated current, three voltage-gated currents, two -activated current, -activated current, non-specific cation current, - exchanger, - pump and background current. The magnitudes and kinetics of each current system in a spindle shaped single cell with a specified surface area∶volume ratio is described by differential equations, in terms of maximal conductances, electrochemical gradient, voltage-dependent activation/inactivation gating variables and temporal changes in intracellular computed from known fluxes. These quantifications are validated by the reconstruction of the individual experimental ionic currents obtained under voltage-clamp. Phasic contraction is modeled in relation to the time constant of changing . This integrated model is validated by its reconstruction of the different USMC AP configurations (spikes, plateau and bursts of spikes), the change from bursting to plateau type AP produced by estradiol and of simultaneous experimental recordings of spontaneous AP, and phasic force. In summary, our advanced mathematical model provides a powerful tool to investigate the physiological ionic mechanisms underlying the genesis of uterine electrical E-C coupling of labor and parturition. This will furnish the evolution of descriptive and predictive quantitative models of myometrial electrogenesis at the whole cell and tissue levels

Connecting mean field models of neural activity to EEG and fMRI data.

Contains fulltext : 89286reid.pdf (publisher's version ) (Closed access)Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.1 juni 201

NURR1 in Parkinson disease-from pathogenesis to therapeutic potential.

In Parkinson disease (PD), affected midbrain dopamine (DA) neurons lose specific dopaminergic properties before the neurons die. How the phenotype of DA neurons is normally established and the ways in which pathology affects the maintenance of cell identity are, therefore, important considerations. Orphan nuclear receptor NURR1 (NURR1, also known as NR4A2) is involved in the differentiation of midbrain DA neurons, but also has an important role in the adult brain. Emerging evidence indicates that impaired NURR1 function might contribute to the pathogenesis of PD: NURR1 and its transcriptional targets are downregulated in midbrain DA neurons that express high levels of the disease-causing protein α-synuclein. Clinical and experimental data indicate that disrupted NURR1 function contributes to induction of DA neuron dysfunction, which is seen in early stages of PD. The likely involvement of NURR1 in the development and progression of PD makes this protein a potentially interesting target for therapeutic intervention