Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats
Background: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone. Objectives: Investigating metabolomics changes associated with oxycodone exposure can...
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MDPI AG
2025-02-01
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| author | Thao Vu Suneeta Godbole Lieselot L. G. Carrette Lisa Maturin Olivier George Laura M. Saba Katerina Kechris |
| author_facet | Thao Vu Suneeta Godbole Lieselot L. G. Carrette Lisa Maturin Olivier George Laura M. Saba Katerina Kechris |
| author_sort | Thao Vu |
| collection | DOAJ |
| description | Background: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone. Objectives: Investigating metabolomics changes associated with oxycodone exposure can provide insights into biochemical mechanisms of the addiction cycle and prognosis prediction. Methods: Plasma samples from 16 rats at pre-exposure and intoxication time points were profiled on the Metabolon platform. A total of 941 metabolites were characterized. We employed a k-Nearest Neighbor imputation to impute metabolites with low levels of missingness and binarized metabolites with moderate levels of missingness, respectively. Results: Of the 136 binarized metabolites, 6 showed differential abundance (FDR < 0.05), including 5 that were present at pre-exposure but absent at intoxication (e.g., <i>adenine</i>), while <i>linoleamide (18:2n6)</i> exhibited the opposite behavior. Among the 798 metabolites with low levels of missingness, 364 showed significant changes between pre-exposure and intoxication (FDR < 0.01), including <i>succinate</i>, <i>oleamide</i>, and <i>sarcosine</i>. We identified four pathways, including <i>tryptophan metabolism,</i> that were nominally enriched among the metabolites that change with oxycodone exposure (<i>p</i> < 0.05). Furthermore, we identified several metabolites that showed nominal correlations with the Addiction Index (composite of oxycodone behaviors): 17 at pre-exposure and 8 at intoxication. In addition, the changes in abundance between pre-exposure and intoxication time points of 9 metabolites were nominally correlated with the Addiction Index, including <i>sphingomyelins</i>, <i>methylhistidines</i>, and <i>glycerols</i>. Conclusions: In summary, not only were we able to capture oxy-induced changes in metabolic pathways using easily accessible blood samples, but we also demonstrated the potential of blood metabolomics to better understand addiction liability. |
| format | Article |
| id | doaj-art-d5dfe3939105413fbb4285df1e87f346 |
| institution | DOAJ |
| issn | 2218-1989 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj-art-d5dfe3939105413fbb4285df1e87f3462025-08-20T02:44:32ZengMDPI AGMetabolites2218-19892025-02-011529510.3390/metabo15020095Identification of Plasma Metabolites Responding to Oxycodone Exposure in RatsThao Vu0Suneeta Godbole1Lieselot L. G. Carrette2Lisa Maturin3Olivier George4Laura M. Saba5Katerina Kechris6Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USADepartment of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USADepartment of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093, USADepartment of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093, USADepartment of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093, USADepartment of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USADepartment of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USABackground: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone. Objectives: Investigating metabolomics changes associated with oxycodone exposure can provide insights into biochemical mechanisms of the addiction cycle and prognosis prediction. Methods: Plasma samples from 16 rats at pre-exposure and intoxication time points were profiled on the Metabolon platform. A total of 941 metabolites were characterized. We employed a k-Nearest Neighbor imputation to impute metabolites with low levels of missingness and binarized metabolites with moderate levels of missingness, respectively. Results: Of the 136 binarized metabolites, 6 showed differential abundance (FDR < 0.05), including 5 that were present at pre-exposure but absent at intoxication (e.g., <i>adenine</i>), while <i>linoleamide (18:2n6)</i> exhibited the opposite behavior. Among the 798 metabolites with low levels of missingness, 364 showed significant changes between pre-exposure and intoxication (FDR < 0.01), including <i>succinate</i>, <i>oleamide</i>, and <i>sarcosine</i>. We identified four pathways, including <i>tryptophan metabolism,</i> that were nominally enriched among the metabolites that change with oxycodone exposure (<i>p</i> < 0.05). Furthermore, we identified several metabolites that showed nominal correlations with the Addiction Index (composite of oxycodone behaviors): 17 at pre-exposure and 8 at intoxication. In addition, the changes in abundance between pre-exposure and intoxication time points of 9 metabolites were nominally correlated with the Addiction Index, including <i>sphingomyelins</i>, <i>methylhistidines</i>, and <i>glycerols</i>. Conclusions: In summary, not only were we able to capture oxy-induced changes in metabolic pathways using easily accessible blood samples, but we also demonstrated the potential of blood metabolomics to better understand addiction liability.https://www.mdpi.com/2218-1989/15/2/95plasma metabolomicsprescription opioidsoxycodone exposurerodent model |
| spellingShingle | Thao Vu Suneeta Godbole Lieselot L. G. Carrette Lisa Maturin Olivier George Laura M. Saba Katerina Kechris Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats Metabolites plasma metabolomics prescription opioids oxycodone exposure rodent model |
| title | Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats |
| title_full | Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats |
| title_fullStr | Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats |
| title_full_unstemmed | Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats |
| title_short | Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats |
| title_sort | identification of plasma metabolites responding to oxycodone exposure in rats |
| topic | plasma metabolomics prescription opioids oxycodone exposure rodent model |
| url | https://www.mdpi.com/2218-1989/15/2/95 |
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