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Delivery of genetic medicines for muscular dystrophies.

Cell Rep Med

December 2024

Laboratory of Precision Nanomedicine, The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel-Aviv, Israel; Department of Materials Sciences and Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel; Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, Israel; Cancer Biology Research Center, Tel Aviv University, Tel Aviv, Israel. Electronic address:

Muscular dystrophies are a group of heterogenic disorders characterized by progressive muscle weakness, the most common of them being Duchenne muscular dystrophy (DMD). Muscular dystrophies are caused by mutations in over 50 distinct genes, and many of them are caused by different genetic mechanisms. Currently, none of these diseases have a cure.

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Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

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[This corrects the article DOI: 10.1016/j.fochms.

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It is well known that activation of NMDA receptors can trigger long-term synaptic depression (LTD) and that a morphological correlate of this functional plasticity is spine retraction and elimination. Recent studies have led to the surprising conclusion that NMDA-induced spine shrinkage proceeds independently of ion flux and requires the initiation of protein synthesis, highlighting an unappreciated contribution of mRNA translation to non-ionotropic NMDAR signaling. Here we used NMDA-induced spine shrinkage in slices of mouse hippocampus as a readout to investigate this novel modality of synaptic transmission.

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Connections between the mechanical properties of DNA and biological functions have been speculative due to the lack of methods to measure or predict DNA mechanics at scale. Recently, a proxy for DNA mechanics, cyclizability, was measured by loop-seq and enabled genome-scale investigation of DNA mechanics. Here, we use this dataset to build a computational model predicting bias-corrected intrinsic cyclizability, with near-perfect accuracy, solely based on DNA sequence.

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