Publications by authors named "Joseph McGaunn"

Article Synopsis
  • * The study investigates how the balance between two mTOR complexes in Sertoli cells—mTORC1 and mTORC2—affects age-related changes in the sperm epigenome by using genetic modifications in mice.
  • * Findings show that suppressing mTORC2 accelerated sperm epigenetic aging, while suppressing mTORC1 led to improvements in the sperm epigenome, suggesting potential therapeutic avenues for enhancing sperm quality in older men.
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Reversing age-associated taurine loss improves mouse longevity and monkey health.

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Article Synopsis
  • Velcrin compounds can kill certain cancer cells by causing two proteins, PDE3A and SLFN12, to work together, but we don’t fully understand how this happens yet.
  • SLFN12 targets and breaks down a specific type of RNA called tRNA(TAA), especially when treated with Velcrin.
  • This breaking down of tRNA(TAA) by SLFN12 leads to a stop in making proteins in the cells, which may help trigger cell death, a process called apoptosis.
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DNMDP and related compounds, or velcrins, induce complex formation between the phosphodiesterase PDE3A and the SLFN12 protein, leading to a cytotoxic response in cancer cells that express elevated levels of both proteins. The mechanisms by which velcrins induce complex formation, and how the PDE3A-SLFN12 complex causes cancer cell death, are not fully understood. Here, we show that PDE3A and SLFN12 form a heterotetramer stabilized by binding of DNMDP.

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Targeted methods that dominated toxicological research until recently did not allow for screening of all molecular changes involved in toxic response. Therefore, it is difficult to infer if all major mechanisms of toxicity have already been discovered, or if some of them are still overlooked. We used data on 591,084 unique chemical-gene interactions to identify genes and molecular pathways most sensitive to chemical exposures.

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A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction.

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