Failure to Thrive: Impaired BDNF Transport along the Cortical-Striatal Axis in Mouse Q140 Neurons of Huntington's Disease.

Boosting trophic support to striatal neurons by increasing levels of brain-derived neurotrophic factor (BDNF) has been considered as a target for therapeutic intervention for several neurodegenerative diseases, including Huntington's disease (HD). To aid in the implementation of such a strategy, a thorough understanding of BDNF cortical-striatal transport is critical to help guide its strategic delivery. In this manuscript, we investigate the dynamic behavior of BDNF transport along the cortical-striatal axis in Q140 primary neurons, a mouse model for HD. We examine this by using single-molecule labeling of BDNF conjugated with quantum dots (QD-BDNF) to follow the transport along the cortical-striatal axis in a microfluidic chamber system specifically designed for the co-culture of cortical and striatal primary neurons. Using this approach, we observe a defect of QD-BDNF transport in Q140 neurons. Our study demonstrates that QD-BDNF transport along the cortical-striatal axis involves the impairment of anterograde transport within axons of cortical neurons, and of retrograde transport within dendrites of striatal neurons. One prominent feature we observe is the extended pause time of QD-BDNF retrograde transport within Q140 striatal dendrites. Taken together, these finding support the hypothesis that delinquent spatiotemporal trophic support of BDNF to striatal neurons, driven by impaired transport, may contribute to the pathogenesis of HD, providing us with insight into how a BDNF supplementation therapeutic strategy may best be applied for HD.

In Silico Protein Folding Prediction of COVID-19 Mutations and Variants.

With its fast-paced mutagenesis, the SARS-CoV-2 Omicron variant has threatened many societies worldwide. Strategies for predicting mutagenesis such as the computational prediction of SARS-CoV-2 structural diversity and its interaction with the human receptor will greatly benefit our understanding of the virus and help develop therapeutics against it. We aim to use protein structure prediction algorithms along with molecular docking to study the effects of various mutations in the Receptor Binding Domain (RBD) of the SARS-CoV-2 and its key interactions with the angiotensin-converting enzyme 2 (ACE-2) receptor. The RBD structures of the naturally occurring variants of SARS-CoV-2 were generated from the WUHAN-Hu-1 using the trRosetta algorithm. Docking (HADDOCK) and binding analysis (PRODIGY) between the predicted RBD sequences and ACE-2 highlighted key interactions at the Receptor-Binding Motif (RBM). Further mutagenesis at conserved residues in the Original, Delta, and Omicron variants (P499S and T500R) demonstrated stronger binding and interactions with the ACE-2 receptor. The predicted T500R mutation underwent some preliminary tests in vitro for its binding and transmissibility in cells; the results correlate with the in-silico analysis. In summary, we suggest conserved residues P499 and T500 as potential mutation sites that could increase the binding affinity and yet do not exist in nature. This work demonstrates the use of the trRosetta algorithm to predict protein structure and future mutations at the RBM of SARS-CoV-2, followed by experimental testing for further efficacy verification. It is important to understand the protein structure and folding to help develop potential therapeutics.