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Krach et al. dissect the molecular mechanism of the alternative splicing modulator Branaplam in Huntington’s disease. They show that the drug lowers mutant HTT protein levels and ameliorates alternative splicing pathology in an iPSC disease model.

Cell fusion regulates several physiological events, for example, fusion of myoblasts in skeletal muscle formation, but it is unclear if this process occurs in the heart. Here, the authors use transgenic reporters in zebrafish to show transient cardiomyocyte fusion, modulating cardiac development and function.

Abstract Despite our increasing understanding of zebrafish heart development and regeneration, there is limited information about the distribution of endothelial cells (ECs) in the adult zebrafish heart. Here, we investigate and compare the distribution of cardiac ECs (cECs) in adult mouse and zebrafish ventricles. Surprisingly, we find that (i) active coronary vessel growth is present in adult zebrafish, (ii) ~37 and ~39% of cells in the zebrafish heart are ECs and cardiomyocytes, respectively, a composition similar to that seen in mouse. However, we find that in zebrafish, ~36% of the ventricular tissue is covered with ECs, i.e., a substantially larger proportion than in mouse. Capitalising on the high abundance of cECs in zebrafish, we established a protocol to isolate them with high purity using fluorescent transgenic lines. Our approach eliminates side-effects due to antibody utilisation. Moreover, the isolated cECs maintained a high proliferation index even after three passages and were amenable to pharmacological treatments to study cEC migration in vitro. Such primary cultures will be a useful tool for supplementary in vitro studies on the accumulating zebrafish mutant lines as well as the screening of small molecule libraries on cardiac specific endothelial cells.

Transcriptional adaptation is a recently described phenomenon by which a mutation in one gene leads to the transcriptional modulation of related genes, termed adapting genes. At the molecular level, it has been proposed that the mutant mRNA, rather than the loss of protein function, activates this response. While several examples of transcriptional adaptation have been reported in zebrafish embryos and in mouse cell lines, it is not known whether this phenomenon is observed across metazoans. Here we report transcriptional adaptation in C. elegans, and find that this process requires factors involved in mutant mRNA decay, as in zebrafish and mouse. We further uncover a requirement for Argonaute proteins and Dicer, factors involved in small RNA maturation and transport into the nucleus. Altogether, these results provide evidence for transcriptional adaptation in C. elegans, a powerful model to further investigate underlying molecular mechanisms.

Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) data is presented, towards the estimation of water stress in vineyards at canopy scale for entire parcels. In particular, aerial broadband spectral data were collected from an integrated SWIR and multispectral instrumentation, onboard an unmanned aerial vehicle (UAV). Concurrently, in-situ leaf stomatal conductance measurements and supplementary data for radiometric and geometric corrections were acquired. A processing pipeline has been designed, developed, and validated, able to execute the required analysis, including data pre-processing, data co-registration, reflectance calibration, canopy extraction and water stress estimation. Experiments were performed at two viticultural regions in Greece, for several vine parcels of four different vine varieties, Sauvignon Blanc, Merlot, Syrah and Xinomavro. The performed qualitative and quantitative assessment indicated that a single model for the estimation of water stress across all studied vine varieties was not able to be established (r2 < 0.30). Relatively high correlation rates (r2 > 0.80) were achieved per variety and per individual variety clone. The overall root mean square error (RMSE) for the estimated canopy water stress was less than 29 mmol m−2 s−1, spanning from no-stress to severe canopy stress levels. Overall, experimental results and validation indicated the quite high potentials of the proposed instrumentation and methodology.

Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object detection tasks despite the huge amount of the concurrently acquired video streams. In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate system on the hypercube image plane. Regarding the object detection, a detector-agnostic procedure has been developed, integrating both unsupervised (background subtraction) and supervised (deep learning convolutional neural networks) techniques for validation purposes. The detected and verified targets are extracted through the fusion and data association steps based on temporal spectral signatures of both target and background. The quite promising experimental results in challenging indoor and outdoor scenes indicated the robust and efficient performance of the developed methodology under different conditions like fog, smoke, and illumination changes.

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.

Dominant mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most prevalent cause of Parkinson's disease, however, little is known about the biological function of LRRK2 protein. LRRK2 is expressed in neural precursor cells suggesting a role in neurodevelopment.In the present study, differential gene expression profiling revealed a faster silencing of pluripotency-associated genes, like Nanog, Oct4, and Lin28, during retinoic acid-induced neuronal differentiation of LRRK2-deficient mouse embryonic stem cells compared to wildtype cultures. By contrast, expression of neurotransmitter receptors and neurotransmitter release was increased in LRRK2+/- cultures indicating that LRRK2 promotes neuronal differentiation. Consistently, the number of neural progenitor cells was higher in the hippocampal dentate gyrus of adult LRRK2-deficient mice. Alterations in phosphorylation of the putative LRRK2 substrates, translation initiation factor 4E binding protein 1 and moesin, do not appear to be involved in altered differentiation, rather there is indirect evidence that a regulatory signaling network comprising retinoic acid receptors, let-7 miRNA and downstream target genes/mRNAs may be affected in LRRK2-deficient stem cells in culture.Parkinson's disease-linked LRRK2 mutations that associated with enhanced kinase activity may affect retinoic acid receptor signaling during neurodevelopment and/or neuronal maintenance as has been shown in other mouse models of chronic neurodegenerative diseases.