Control of Autophagosome Axonal Retrograde Flux by Presynaptic Activity Unveiled Using Botulinum Neurotoxin Type A

Botulinum neurotoxin type A (BoNT/A) is a highly potent neurotoxin that elicits flaccid paralysis by enzymatic cleavage of the exocytic machinery component SNAP25 in motor nerve terminals. However, recent evidence suggests that the neurotoxic activity of BoNT/A is not restricted to the periphery, but also reaches the CNS after retrograde axonal transport. Because BoNT/A is internalized in recycling synaptic vesicles, it is unclear which compartment facilitates this transport. Using live-cell confocal and single-molecule imaging of rat hippocampal neurons cultured in microfluidic devices, we show that the activity-dependent uptake of the binding domain of the BoNT/A heavy chain (BoNT/A-Hc) is followed by a delayed increase in retrograde axonal transport of BoNT/A-Hc carriers. Consistent with a role of presynaptic activity in initiating transport of the active toxin, activity-dependent uptake of BoNT/A in the terminal led to a significant increase in SNAP25 cleavage detected in the soma chamber compared with nonstimulated neurons. Surprisingly, most endocytosed BoNT/A-Hc was incorporated into LC3-positive autophagosomes generated in the nerve terminals, which then underwent retrograde transport to the cell soma, where they fused with lysosomes both in vitro and in vivo. Blocking autophagosome formation or acidification with wortmannin or bafilomycin A1, respectively, inhibited the activity-dependent retrograde trafficking of BoNT/A-Hc. Our data demonstrate that both the presynaptic formation of autophagosomes and the initiation of their retrograde trafficking are tightly regulated by presynaptic activity

Computing linkage disequilibrium aware genome embeddings using autoencoders

Motivation The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. Results We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block’s genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy—i.e. minimal information loss—while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data