Human TDP-43 and FUS selectively affect motor neuron maturation and survival in a murine cell model of ALS by non-cell-autonomous mechanisms

<p>TAR DNA-binding protein 43 (TDP-43) and fused in sarcoma (FUS) were recently found to cause familial and sporadic amyotrophic lateral sclerosis (ALS). The mechanisms by which mutations within these genes cause ALS are not understood. We established murine embryonic stem cell (ESC)-based cell models that stably express the human wild-type (WT) and various ALS causing mutations of <i>TDP-43</i> (A315T) and <i>FUS</i> (R514S, R521C and P525L). We investigated their effect on pan-neuron as well as motor neuron degeneration. Finally, non-cell-autonomous mediated neurodegeneration by muscle cells was investigated. Expression of mutant hTDP-43, but not wild-type TDP-43, as well as wild-type and mutant hFUS proteins induced neuronal degeneration with partial selectivity for motor neurons. Motor neuron loss was accompanied by abnormal neurite morphology and length. In chimeric coculture experiments with control motor neurons and mutant muscle cells (as their major target cells), we detected that mutant hTDP-43 A315T as well as wild-type and hFUS P525L expression only in muscle cells is sufficient to exert degenerative effects on control motor neurons. In conclusion, our data indicate that a selective vulnerability of motor neurons expressing the pathogenic ALS-causing genes <i>TDP-43</i> and <i>FUS</i>, is, at least in part, mediated through non-cell-autonomous mechanisms.</p

The distribution of phase-locking intervals deviates from a power-law during epileptic seizures.

<p>Top: The electrocorticogram (ECoG) recording shows the onset of a focal epileptic seizure attack around 300 seconds time. Bottom: Cumulative distributions of phase-locking intervals (PLI) are obtained during three time intervals of 150 seconds: pre-ictal (left), ictal (middle) and post-ictal (right). Dashed lines indicate a power-law with exponent −3.1. While the distribution appears to follow a power-law during the pre-ictal period, intervals of increased phase-locking disturb this characteristic distribution with the onset of seizure activity. Data shown are from patient 1 at scale 3, corresponding to the frequency band 25–12.5 Hz.</p

Distribution of PLI in a model exhibiting self-organized criticality.

<p>A Through an adaptive interplay of network dynamics and topology, the Bornholdt model self-organizes toward a characteristic connectivity independent of initial conditions. The plot shows the evolution to a characteristic connectivity of approximately in a network of 1024 nodes for three different initial connectivities, , and . B At this self-organized connectivity the network exhibits a phase transition between order and disorder. The plot shows the frozen component defined as the fraction of nodes that do not change their state along the attractor as a function of networks' average connectivities for a network of 1024 nodes. The data were measured along the dynamical attractor reached by the system, averaged over 100 random topologies for each value of . A transition around a value can be observed. C After a period of self-organization based on the adaptive interplay between topology and dynamics (aSO on, full black line), links were added and deleted solely with a certain probability independent of node activity (aSO off, dashed line: links were added with and deleted with , point-dashed line: links added with , deleted with ). Each iteration marks a topological update of the network, between iterations network activity was limited to 1000 time steps where topology was not changed. Phase-lock intervals between 20 randomly chosen nodes were calculated for scale 1 from 100 consecutive iterations at three time points: at the self-organized connectivity (bottom left), at a connectivity lower (bottom middle) and higher (bottom right) than the evolved connectivity. The distribution of PLI follows a power-law only at the self-organized connectivity (bottom left). All depicted distributions are cumulative distributions. The dashed line marks a power-law with exponent −1.5 to guide the eye.</p

Development of the deviation from a power-law.

<p>ECoG recordings from 8 patients showing a focal seizure attack are shown along with values for consecutive time windows of 150 seconds duration overlapping by 100 seconds. The power-law fit of data in the first time window was taken as the reference to calculate . Although different in extent, an increase of quantifying the deviation from the initial pre-ictal distribution can be observed during seizures for all patients and different scales (scale 2 red, scale 3 blue, scale 4 green).</p

Change of olfactory function of more than 5.5 points in the TDI score (individual improvement).

<p>Change of olfactory function of more than 5.5 points in the TDI score (individual improvement).</p

Dimensionality of Rolled-up Nanomembranes Controls Neural Stem Cell Migration Mechanism

We employ glass microtube structures fabricated by rolled-up nanotechnology to infer the influence of scaffold dimensionality and cell confinement on neural stem cell (NSC) migration. Thereby, we observe a pronounced morphology change that marks a reversible mesenchymal to amoeboid migration mode transition. Space restrictions preset by the diameter of nanomembrane topography modify the cell shape toward characteristics found in living tissue. We demonstrate the importance of substrate dimensionality for the migration mode of NSCs and thereby define rolled-up nanomembranes as the ultimate tool for single-cell migration studies

Olfactory function as expressed by the TDI score (comprehensive score of threshold, discrimination, and identification abilities) at baseline and after 12 weeks in the training group and the control group without training.

<p>Higher scores express higher olfactory sensitivity.</p

Threshold testing of the training odors citronellal, eucalyptol and eugenol before and after olfactory training.

<p>Higher odor thresholds express higher olfactory sensitivity.</p

Correlation of Quantitative Motor State Assessment Using a Kinetograph and Patient Diaries in Advanced PD: Data from an Observational Study

<div><p>Introduction</p><p>Effective management and development of new treatment strategies for response fluctuations in advanced Parkinson’s disease (PD) largely depends on clinical rating instruments such as the PD home diary. The Parkinson’s kinetigraph (PKG) measures movement accelerations and analyzes the spectral power of the low frequencies of the accelerometer data. New algorithms convert each hour of continuous PKG data into one of the three motor categories used in the PD home diary, namely motor Off state and On state with and without dyskinesia.</p><p>Objective</p><p>To compare quantitative motor state assessment in fluctuating PD patients using the PKG with motor state ratings from PD home diaries.</p><p>Methods</p><p>Observational cohort study on 24 in-patients with documented motor fluctuations who completed diaries by rating motor Off, On without dyskinesia, On with dyskinesia, and asleep for every hour for 5 consecutive days. Simultaneously collected PKG data (recorded between 6 am and 10 pm) were analyzed and calibrated to the patient’s individual thresholds for Off and dyskinetic state by novel algorithms classifying the continuous accelerometer data into these motor states for every hour between 6 am and 10 pm.</p><p>Results</p><p>From a total of 2,040 hours, 1,752 hours (87.4%) were available for analyses from calibrated PKG data (7.5% sleeping time and 5.1% unclassified motor state time were excluded from analyses). Distributions of total motor state hours per day measured by PKG showed moderate-to-strong correlation to those assessed by diaries for the different motor states (Pearson’s correlations coefficients: 0.404–0.658), but inter-rating method agreements on the single-hour-level were only low-to-moderate (Cohen’s κ: 0.215–0.324).</p><p>Conclusion</p><p>The PKG has been shown to capture motor fluctuations in patients with advanced PD. The limited correlation of hour-to-hour diary and PKG recordings should be addressed in further studies.</p></div

Correlations on the total-hours-per-day-level of diary data with calibrated PKG data with respect to the 5 consecutive days of recording.

<p><b>A-D)</b> Displayed are Pearson’s correlation coefficients for correlations on the total-hours-per-day-level for each of the 5 consecutive days of recordings and for the days 1 to 4 for motor Off state <b>(A)</b>, motor On state without dyskinesia <b>(B)</b>, dyskinetic state <b>(C)</b> and for motor state switches <b>(D)</b>.</p