Stress-Induced Proteasome Sub-Cellular Translocation in Cardiomyocytes Causes Altered Intracellular Calcium Handling and Arrhythmias.

The ubiquitin-proteasome system (UPS) is an essential mechanism responsible for the selective degradation of substrate proteins via their conjugation with ubiquitin. Since cardiomyocytes have very limited self-renewal capacity, as they are prone to protein damage due to constant mechanical and metabolic stress, the UPS has a key role in cardiac physiology and pathophysiology. While altered proteasomal activity contributes to a variety of cardiac pathologies, such as heart failure and ischemia/reperfusion injury (IRI), the environmental cues affecting its activity are still unknown, and they are the focus of this work. Following a recent study by Ciechanover's group showing that amino acid (AA) starvation in cultured cancer cell lines modulates proteasome intracellular localization and activity, we tested two hypotheses in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs, CMs): (i) AA starvation causes proteasome translocation in CMs, similarly to the observation in cultured cancer cell lines; (ii) manipulation of subcellular proteasomal compartmentalization is associated with electrophysiological abnormalities in the form of arrhythmias, mediated via altered intracellular Ca2+ handling. The major findings are: (i) starving CMs to AAs results in proteasome translocation from the nucleus to the cytoplasm, while supplementation with the aromatic amino acids tyrosine (Y), tryptophan (W) and phenylalanine (F) (YWF) inhibits the proteasome recruitment; (ii) AA-deficient treatments cause arrhythmias; (iii) the arrhythmias observed upon nuclear proteasome sequestration(-AA+YWF) are blocked by KB-R7943, an inhibitor of the reverse mode of the sodium-calcium exchanger NCX; (iv) the retrograde perfusion of isolated rat hearts with AA starvation media is associated with arrhythmias. Collectively, our novel findings describe a newly identified mechanism linking the UPS to arrhythmia generation in CMs and whole hearts.

Multidimensional co-segmentation of longitudinal brain MRI ensembles in the presence of a neurodegenerative process.

MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomical priors that provide top-down information to aid segmentation are inadequate in the presence of abnormalities. This problem is further complicated for longitudinal data capturing impaired brain development or neurodegenerative conditions, since the dynamic of brain atrophies has to be considered as well. For these cases, the absence of compatible annotated training examples renders the commonly used multi-atlas or machine-learning approaches impractical. We present a novel segmentation approach that accounts for the lack of labeled data via multi-region multi-subject co-segmentation (MMCoSeg) of longitudinal MRI sequences. The underlying, unknown anatomy is learned throughout an iterative process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions, which share common boundaries, and by the segmentation of corresponding regions, in the other jointly segmented images. A 4D multi-region atlas that models the spatio-temporal deformations and can be adapted to different subjects undergoing similar degeneration processes is reconstructed concurrently. An inducible mouse model of p25 accumulation (the CK-p25 mouse) that displays key pathological hallmarks of Alzheimer disease (AD) is used as a gold-standard to test the proposed algorithm by providing a conditional control of rapid neurodegeneration. Applying the MMCoSeg to a cohort of CK-p25 mice and littermate controls yields promising segmentation results that demonstrate high compatibility with expertise manual annotations. An extensive comparative analysis with respect to current well-established, atlas-based segmentation methods highlights the advantage of the proposed approach, which provides accurate segmentation of longitudinal brain MRIs in pathological conditions, where only very few annotated examples are available.