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Currently, the mechanism of action for anesthetics is widely accepted as unknown. Millions of patients undergo surgical procedures that rely on the effectiveness of these drugs for a successful onset and offset, yet doctors, anesthesiologists, and researchers still cannot definitively tell patients why these drugs are effective. By studying the metabolic changes that occur due to the introduction of anesthesia, researchers could ultimately determine the major differences between sleep and anesthesia. (Zhang et al., 2015; Icaza et al., 2009) It has been shown that individuals that experience anesthesia during their lifetime are at a higher risk for numerous health issues in the long term (i.e. memory deficits, heart, liver, and kidney problems). So the question on how anesthesia changes the chemical composition of brain tissue can be answered by metabolomic studies. (Makaryus et al., 2011; Jacob et al., 2012; Kawaguchi et al., 2010; Overmyer, Thonusin, Qi, Burant, & Evans, 2015)
Available methods for performing metabolomic studies on effects of anesthetics on the brain include in vivo (Makaryus et al., 2011; Jacob et al., 2012) and ex vivo (Kawaguchi et al., 2010; Overmyer et al., 2015) measurements of numerous metabolites. The major limitation of in vivo sampling is that sample collection is limited by temporal, spatial, and chemical resolution due to a required sample size and complications with the instruments’ sensitivity. (Robinson & Justic, 2013) In the latter case control, non-anesthetized samples can be collected, but there can be challenges with accurately microdissecting brain regions for tissue homogenization and measurement outside the organism, i.e., ex vivo. (Kawaguchi et al., 2010) Additionally, there can be substantial effects of euthanasia and postmortem delays on several metabolic pathways in all tissues. (Overmyer et al., 2015) There are still many questions about sampling the brain to determine dynamically relevant information.
A novel computational method called Transcriptome-To-Metabolome™ (TTM™) offers a solution to these limitations in that the samples used for generating the transcriptomes are reanimated, in silico. (Phelix & Feltus, 2015) This TTM™ technology was used to test metabolomic effects of isoflurane on the basolateral amygdala known for its role in anesthetic-induced amnesia (Alkine & Nathan, 2005; McLott, Jurecic, Hemphill, & Dunn, 2013) and cortex known to have metabolic differences. (Pontieri, Conti, Zacchi, Fieschi, & Orzi, 1999)