APPsα Rescues Tau-Induced Synaptic Pathology.

Alzheimer's disease (AD) is histopathologically characterized by Aß plaques and the accumulation of hyperphosphorylated Tau species, the latter also constituting key hallmarks of primary tauopathies. Whereas Aß is produced by amyloidogenic APP processing, APP processing along the competing nonamyloidogenic pathway results in the secretion of neurotrophic and synaptotrophic APPsα. Recently, we demonstrated that APPsα has therapeutic effects in transgenic AD model mice and rescues Aß-dependent impairments. Here, we examined the potential of APPsα to mitigate Tau-induced synaptic deficits in P301S mice (both sexes), a widely used mouse model of tauopathy. Analysis of synaptic plasticity revealed an aberrantly increased LTP in P301S mice that could be normalized by acute application of nanomolar amounts of APPsα to hippocampal slices, indicating a homeostatic function of APPsα on a rapid time scale. Further, AAV-mediated in vivo expression of APPsα restored normal spine density of CA1 neurons even at stages of advanced Tau pathology not only in P301S mice, but also in independent THY-Tau22 mice. Strikingly, when searching for the mechanism underlying aberrantly increased LTP in P301S mice, we identified an early and progressive loss of major GABAergic interneuron subtypes in the hippocampus of P301S mice, which may lead to reduced GABAergic inhibition of principal cells. Interneuron loss was paralleled by deficits in nest building, an innate behavior highly sensitive to hippocampal impairments. Together, our findings indicate that APPsα has therapeutic potential for Tau-mediated synaptic dysfunction and suggest that loss of interneurons leads to disturbed neuronal circuits that compromise synaptic plasticity as well as behavior.SIGNIFICANCE STATEMENT Our findings indicate, for the first time, that APPsα has the potential to rescue Tau-induced spine loss and abnormal synaptic plasticity. Thus, APPsα might have therapeutic potential not only because of its synaptotrophic functions, but also its homeostatic capacity for neuronal network activity. Hence, APPsα is one of the few molecules which has proven therapeutic effects in mice, both for Aß- and Tau-dependent synaptic impairments and might therefore have therapeutic potential for patients suffering from AD or primary tauopathies. Furthermore, we found in P301S mice a pronounced reduction of inhibitory interneurons as the earliest pathologic event preceding the accumulation of hyperphosphorylated Tau species. This loss of interneurons most likely disturbs neuronal circuits that are important for synaptic plasticity and behavior.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

Deep probabilistic tracking of particles in fluorescence microscopy images.

Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods.

A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.

Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.