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Interactions among Genetic Background, Anesthetic Agent, and Oxygen Concentration Shape Blunt Traumatic Brain Injury Outcomes in Drosophila melanogaster.

International journal of molecular sciences | 2020

Following traumatic brain injury (TBI), the time window during which secondary injuries develop provides a window for therapeutic interventions. During this time, many TBI victims undergo exposure to hyperoxia and anesthetics. We investigated the effects of genetic background on the interaction of oxygen and volatile general anesthetics with brain pathophysiology after closed-head TBI in the fruit fly Drosophila melanogaster. To test whether sevoflurane shares genetic risk factors for mortality with isoflurane and whether locomotion is affected similarly to mortality, we used a device that generates acceleration-deceleration forces to induce TBI in ten inbred fly lines. After TBI, we exposed flies to hyperoxia alone or in combination with isoflurane or sevoflurane and quantified mortality and locomotion 24 and 48 h after TBI. Modulation of TBI-induced mortality and locomotor impairment by hyperoxia with or without anesthetics varied among fly strains and among combinations of agents. Resistance to increased mortality from hyperoxic isoflurane predicted resistance to increased mortality from hyperoxic sevoflurane but did not predict the degree of locomotion impairment under any condition. These findings are important because they demonstrate that, in the context of TBI, genetic background determines the latent toxic potentials of oxygen and anesthetics.

Pubmed ID: 32967238 RIS Download

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: R01GM134107
  • Agency: NCATS NIH HHS, United States
    Id: UL1 TR002373
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM134107

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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