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Background: Axonal degeneration and defects in neuromuscular neurotransmission represent a pathological hallmark
in spinal muscular atrophy (SMA) and other forms of motoneuron disease. These pathological changes do not
only base on altered axonal and presynaptic architecture, but also on alterations in dynamic movements of organelles
and subcellular structures that are not necessarily reflected by static histopathological changes. The dynamic interplay
between the axonal endoplasmic reticulum (ER) and ribosomes is essential for stimulus-induced local translation
in motor axons and presynaptic terminals. However, it remains enigmatic whether the ER and ribosome crosstalk is
impaired in the presynaptic compartment of motoneurons with Smn (survival of motor neuron) deficiency that could
contribute to axonopathy and presynaptic dysfunction in SMA.
Methods: Using super-resolution microscopy, proximity ligation assay (PLA) and live imaging of cultured motoneurons
from a mouse model of SMA, we investigated the dynamics of the axonal ER and ribosome distribution and
activation.
Results: We observed that the dynamic remodeling of ER was impaired in axon terminals of Smn-deficient motoneurons.
In addition, in axon terminals of Smn-deficient motoneurons, ribosomes failed to respond to the brain-derived
neurotrophic factor stimulation, and did not undergo rapid association with the axonal ER in response to extracellular
stimuli.
Conclusions: These findings implicate impaired dynamic interplay between the ribosomes and ER in axon terminals
of motoneurons as a contributor to the pathophysiology of SMA and possibly also other motoneuron diseases.
Background
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.
Results
Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.
Conclusions
Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.
SARS-CoV-2 variants such as the delta or omicron variants, with higher transmission rates, accelerated the global COVID-19 pandemic. Thus, novel therapeutic strategies need to be deployed. The inhibition of acid sphingomyelinase (ASM), interfering with viral entry by fluoxetine was reported. Here, we described the acid ceramidase as an additional target of fluoxetine. To discover these effects, we synthesized an ASM-independent fluoxetine derivative, AKS466. High-resolution SARS-CoV-2–RNA FISH and RTqPCR analyses demonstrate that AKS466 down-regulates viral gene expression. It is shown that SARS-CoV-2 deacidifies the lysosomal pH using the ORF3 protein. However, treatment with AKS488 or fluoxetine lowers the lysosomal pH. Our biochemical results show that AKS466 localizes to the endo-lysosomal replication compartments of infected cells, and demonstrate the enrichment of the viral genomic, minus-stranded RNA and mRNAs there. Both fluoxetine and AKS466 inhibit the acid ceramidase activity, cause endo-lysosomal ceramide elevation, and interfere with viral replication. Furthermore, Ceranib-2, a specific acid ceramidase inhibitor, reduces SARS-CoV-2 replication and, most importantly, the exogenous supplementation of C6-ceramide interferes with viral replication. These results support the hypotheses that the acid ceramidase is a SARS-CoV-2 host factor.