Biology subjects

Sha, C. M.

Publications and source records attributed to Sha, C. M..

4 recordsLinked to original sources

bioRxiv2024

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells

Misfolded soluble trimeric species of superoxide dismutase 1 (SOD1) are associated with increased death in neuron-like cell models and greater disease severity in amyotrophic lateral sclerosis (ALS) patients compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may be a driver of disease pathology. Here, we uncovered the SOD1 trimer interactome - a map of potential tissue-selective protein binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers, comparing them to those of wild-type SOD1 dimers. We found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We also identified key pathways using transcriptomic data from motor neuron-like cells (NSC-34s) expressing SOD1 trimers. We discovered differential gene expression in cells that express SOD1 trimers with selective enrichment of genes responsible for protein localization to membranes and a global upregulation of cellular senescence pathways. We performed detailed computational and biochemical characterization of protein binding for septin-7, an SOD1 trimer binding partner. We found that septin-7 preferentially binds SOD1 trimers and co-localizes in neuron-like cells. We explore a double-edged sword theory regarding the toxicity of SOD1 trimers. These trimers are implicated in causing dysfunction not only in the central nervous system but also in muscle tissues. Our investigation highlights key protein factors and pathways within each system, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers. SummaryIn amyotrophic lateral sclerosis (ALS), misfolded soluble species of superoxide dismutase 1 (SOD1) are associated with disease severity and, specifically, trimeric forms of SOD1 are toxic in neuron-like cells compared to insoluble aggregates. The role of toxic SOD1 trimers in cells is unknown. Using molecular engineering and pull-down experiments, we found that SOD1 trimers have tissue-selective protein interactions that affect pathways such as dendritic spine morphogenesis and synaptic function in the nerves, energy, and amino acid metabolism in skeletal muscle. We investigated the SOD1 trimer transcriptome to reveal a global upregulation of genes associated with cellular senescence compared to SOD1 dimers. We further validated septin-7, a shared brain and spinal cord protein binding hit, using integrative computational and biochemical approaches, and confirmed that septin-7 binds SOD1 trimers and not native dimers. Taken together, we show evidence that SOD1 trimers play a central role in the convergence of ALS pathophysiology. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/586186v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@35e9d9org.highwire.dtl.DTLVardef@18bbc3corg.highwire.dtl.DTLVardef@f30b02org.highwire.dtl.DTLVardef@57e51b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Quantifying network behavior in the rat prefrontal cortex: a reproducibility crisis

The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, however, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. We piloted the use of dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials, to quantify neural network synchronicity and correlate neuroelectrophysiology with rat behavior. Our results indicate the statistical limitations of existing data, making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis currently impossible until larger and cleaner datasets are available. Significance StatementThe prefrontal cortex is important in decision-making, yet no robust method currently exists to correlate neuron firing in the PFC to behavior. We argue that existing experimental designs are ill-suited to addressing these scientific questions, and we propose a potential method using dynamic time warping to analyze PFC neural electrical activity. We conclude that careful curation of experimental controls is needed to separate true neural signals from noise accurately.

neuroscience↗

bioRxiv2022

Predicting 3D RNA structure from solely the nucleotide sequence using Euclidean distance neural networks

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by base pair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network which predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high accuracy predictions on RNAs up to 100 nucleotides in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline accurately predicts all-atom RNA models solely from the nucleotide sequence.

biophysics↗

bioRxiv2021

NeuralDock: Rapid and conformation-agnostic docking of small molecules

Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock which accelerates the process of high-quality computational docking by a factor of 106, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 hours, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.

biophysics↗