The COP9 signalosome (CSN) is a highly conserved eukaryotic protein complex which regulates the cullin RING family of ubiquitin ligases and carries out a deneddylase activity that resides in subunit 5 (CSN5). Whereas CSN activity is essential for the development of higher eukaryotes, several unicellular fungi including the budding yeast Saccharomyces cerevisiae can survive without a functional CSN. Nevertheless, the budding yeast CSN is biochemically active and deletion mutants of each of its subunits exhibit deficiency in cullins deneddylation, although the biological context of this activity is still unknown in this organism. To further characterize CSN function in budding yeast, we present here a transcriptomic and proteomic analysis of a S. cerevisiae strain deleted in the CSN5/RRI1 gene (hereafter referred to as CSN5), coding for the only canonical subunit of the complex. We show that Csn5 is involved in modulation of the genes controlling amino acid and lipid metabolism and especially ergosterol biosynthesis. These alterations in gene expression correlate with the lower ergosterol levels and increased intracellular zinc content which we observed in csn5 null mutant cells. We show that some of these regulatory effects of Csn5, in particular the control of isoprenoid biosynthesis, are conserved through evolution, since similar transcriptomic and/or proteomic effects of csn5 mutation were previously observed in other eukaryotic organisms such as Aspergillus nidulans, Arabidopsis thaliana and Drosophila melanogaster. Our results suggest that the diverged budding yeast CSN is more conserved than was previously thought.
Background:The COP9 signalosome is a conserved eukaryotic protein complex that regulates protein degradation. Result: We identified pairwise and combinatorial interactions necessary for the formation of a CSN5-containing subcomplex that binds RBX1. Conclusion: Three distinct types of protein-protein interactions stabilize the COP9 signalosome. Significance: This structure enables binding of RBX1 and supports the significance of detected CSN subcomplexes and monomers.
This study presents PhysioNauts Team's contribution to the PhysioNet/CinC Challenge 2021 on ECG classification for variable leads. Three types of labels were identified: those affecting cardiac rhythm, ECG morphology or both. The full model integrated handcrafted rhythm features and deep learning features into a residual neural network (ResNet) with a squeeze and excitation module and a wide 10-neuron single-layer fully connected (FC) branch to leverage the learning of both feature types. The ResNet inputs were ECG segments of 4096 samples downsampled to 257 Hz. The FC inputs were standard rhythm features extracted from the RR-series. Class imbalance was mitigated by selecting only a third of normal sinus rhythm and sinus bradycardia recordings. Moreover, threshold optimization was performed based on a grid search and the Nelder-Mead method to maximize the Challenge metric (CM). Our entry failed on the UMich test data, so it was not officially ranked and it didn't receive official scores on the full test set. The CMs obtained in the unofficial entry were 0.613, 0.585, 0.603, 0,594, and 0.582 on 12-lead, 6-lead, 4-lead, 3-lead, 2-lead, respectively.
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