Synaptic actions of amyotrophic-lateral-sclerosis-associated G85R-SOD1 in the squid giant synapse

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Song, Y. Synaptic actions of amyotrophic-lateral-sclerosis-associated G85R-SOD1 in the squid giant synapse. Eneuro, (2020): ENEURO.0369-19.2020, doi: 10.1523/ENEURO.0369-19.2020.Altered synaptic function is thought to play a role in many neurodegenerative diseases, but little is known about the underlying mechanisms for synaptic dysfunction. The squid giant synapse (SGS) is a classical model for studying synaptic electrophysiology and ultrastructure, as well as molecular mechanisms of neurotransmission. Here, we conduct a multidisciplinary study of synaptic actions of misfolded human G85R-SOD1 causing familial Amyotrophic Lateral Sclerosis (fALS). G85R-SOD1, but not WT-SOD1, inhibited synaptic transmission, altered presynaptic ultrastructure, and reduced both the size of the Readily Releasable Pool (RRP) of synaptic vesicles and mobility from the Reserved Pool (RP) to the RRP. Unexpectedly, intermittent high frequency stimulation (iHFS) blocked inhibitory effects of G85R-SOD1 on synaptic transmission, suggesting aberrant Ca2+ signaling may underlie G85R-SOD1 toxicity. Ratiometric Ca2+ imaging showed significantly increased presynaptic Ca2+ induced by G85R-SOD1 that preceded synaptic dysfunction. Chelating Ca2+ using EGTA prevented synaptic inhibition by G85R-SOD1, confirming the role of aberrant Ca2+ in mediating G85R-SOD1 toxicity. These results extended earlier findings in mammalian motor neurons and advanced our understanding by providing possible molecular mechanisms and therapeutic targets for synaptic dysfunctions in ALS as well as a unique model for further studies.Grass Foundation, HHMI, MGH Jack Satter Foundation, Harvard University ALS and Alzheimer's Endowed Research Fund, Harvard Brain Science Initiative

Variational Reasoning for Question Answering with Knowledge Graph

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets

From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning

Video captioning in essential is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, etc. In this paper we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods, that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning, and generate multiple sentences to describe a video considering different random factors. Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks

ALS-linked FUS exerts a gain of toxic function involving aberrant p38 MAPK activation

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 7 (2017): 115, doi:10.1038/s41598-017-00091-1.Mutations in Fused in Sarcoma/Translocated in Liposarcoma (FUS) cause familial forms of amyotrophic lateral sclerosis (ALS), a neurodegenerative disease characterized by progressive axonal degeneration mainly affecting motor neurons. Evidence from transgenic mouse models suggests mutant forms of FUS exert an unknown gain-of-toxic function in motor neurons, but mechanisms underlying this effect remain unknown. Towards this end, we studied the effect of wild type FUS (FUS WT) and three ALS-linked variants (G230C, R521G and R495X) on fast axonal transport (FAT), a cellular process critical for appropriate maintenance of axonal connectivity. All ALS-FUS variants impaired anterograde and retrograde FAT in squid axoplasm, whereas FUS WT had no effect. Misfolding of mutant FUS is implicated in this process, as the molecular chaperone Hsp110 mitigated these toxic effects. Interestingly, mutant FUS-induced impairment of FAT in squid axoplasm and of axonal outgrowth in mammalian primary motor neurons involved aberrant activation of the p38 MAPK pathway, as also reported for ALS-linked forms of Cu, Zn superoxide dismutase (SOD1). Accordingly, increased levels of active p38 MAPK were detected in post-mortem human ALS-FUS brain tissues. These data provide evidence for a novel gain-of-toxic function for ALS-linked FUS involving p38 MAPK activation.We are grateful for funding from NIH/NINDS (R01 NS078145, R01 NS090352, and R21 NS091860 to D.A.B., R01 NS066942A and R21 NS096642 to G.M., R01NS023868 and R01NS041170 to S.T.B.), the ALS Therapy Alliance/CVS Pharmacy (to D.A.B. and G.M.) and the ALS Association (to C.F. and J.M.)