Systems genetic analysis of addiction-associated traits
Substance abuse disorders are heritable neuropsychiatric disorders with largely unknown genetic etiology. Distinct genetic factors likely contribute to the different stages and behaviors of addiction, including initial sensitivity to the subjective and physiological effects of drugs and physiologica...
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ndltd-bu.edu-oai-open.bu.edu-2144-235612019-01-08T15:42:16Z Systems genetic analysis of addiction-associated traits Goldberg, Lisa Neurosciences Substance abuse disorders are heritable neuropsychiatric disorders with largely unknown genetic etiology. Distinct genetic factors likely contribute to the different stages and behaviors of addiction, including initial sensitivity to the subjective and physiological effects of drugs and physiological and psychological measures of withdrawal. Mammalian model organisms permit a comprehensive approach to gene mapping and to bridging genetic variation with neurobiological mechanisms of addiction-relevant behaviors. The focus of this dissertation is to investigate the genetic basis of the rewarding and aversive properties of opioids, utilizing a systems genetics approach that includes both forward and reverse genetics in combination with transcriptomics and bioinformatics as tools to determine the molecular mechanisms. The first aim of this research is to conduct a genetic linkage mapping study of addiction-associated traits in a reduced complexity cross of two nearly identical B6 substrains (C57BL/6J and C57BL/6NJ). Forward genetic techniques, such as quantitative trait locus (QTL) mapping was utilized to identify novel candidate genes involved in addiction-associated traits. We completed QTL mapping combined with genome-wide gene expression analyses to rapidly identify compelling candidate genes underlying addiction traits. Most notably, we identified a region on distal chromosome 1 that regulates opioid sensitivity and withdrawal. Using striatal expression QTL mapping, transcript/behavior covariance, and convergent haplotype analysis, we identified a strong positional candidate gene, Rgs7. The second aim of this research is to validate novel candidate genes and molecular mechanisms responsible for modulation of opioid reward and aversion. Using behavioral and expression QTL mapping, Csnk1e was previously identified as a candidate gene for psychostimulant sensitivity. Here, we utilized Csnk1e knockout mice to confirm the effect of Csnk1e deletion on opioid sensitivity and extend its role to opioid reward and a natural reward dependent on opioid signaling- sweetened palatable food consumption. Additionally, we have utilized striatal transcriptome analyses to identify potential molecular mechanisms, including aberrant myelination and neurodevelopment of the striatum. In summary, this dissertation research utilizes mouse forward and reverse genetics, in combination with transcriptome and bioinformatics analyses to identify the genetic and neurobiological underpinnings of addiction-associated traits. 2017-08-17T17:31:21Z 2017-08-17T17:31:21Z 2017 2017-07-10T01:14:24Z Thesis/Dissertation https://hdl.handle.net/2144/23561 en_US |
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Neurosciences Goldberg, Lisa Systems genetic analysis of addiction-associated traits |
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Substance abuse disorders are heritable neuropsychiatric disorders with largely unknown genetic etiology. Distinct genetic factors likely contribute to the different stages and behaviors of addiction, including initial sensitivity to the subjective and physiological effects of drugs and physiological and psychological measures of withdrawal. Mammalian model organisms permit a comprehensive approach to gene mapping and to bridging genetic variation with neurobiological mechanisms of addiction-relevant behaviors. The focus of this dissertation is to investigate the genetic basis of the rewarding and aversive properties of opioids, utilizing a systems genetics approach that includes both forward and reverse genetics in combination with transcriptomics and bioinformatics as tools to determine the molecular mechanisms.
The first aim of this research is to conduct a genetic linkage mapping study of addiction-associated traits in a reduced complexity cross of two nearly identical B6 substrains (C57BL/6J and C57BL/6NJ). Forward genetic techniques, such as quantitative trait locus (QTL) mapping was utilized to identify novel candidate genes involved in addiction-associated traits. We completed QTL mapping combined with genome-wide gene expression analyses to rapidly identify compelling candidate genes underlying addiction traits. Most notably, we identified a region on distal chromosome 1 that regulates opioid sensitivity and withdrawal. Using striatal expression QTL mapping, transcript/behavior covariance, and convergent haplotype analysis, we identified a strong positional candidate gene, Rgs7.
The second aim of this research is to validate novel candidate genes and molecular mechanisms responsible for modulation of opioid reward and aversion. Using behavioral and expression QTL mapping, Csnk1e was previously identified as a candidate gene for psychostimulant sensitivity. Here, we utilized Csnk1e knockout mice to confirm the effect of Csnk1e deletion on opioid sensitivity and extend its role to opioid reward and a natural reward dependent on opioid signaling- sweetened palatable food consumption. Additionally, we have utilized striatal transcriptome analyses to identify potential molecular mechanisms, including aberrant myelination and neurodevelopment of the striatum.
In summary, this dissertation research utilizes mouse forward and reverse genetics, in combination with transcriptome and bioinformatics analyses to identify the genetic and neurobiological underpinnings of addiction-associated traits. |
author |
Goldberg, Lisa |
author_facet |
Goldberg, Lisa |
author_sort |
Goldberg, Lisa |
title |
Systems genetic analysis of addiction-associated traits |
title_short |
Systems genetic analysis of addiction-associated traits |
title_full |
Systems genetic analysis of addiction-associated traits |
title_fullStr |
Systems genetic analysis of addiction-associated traits |
title_full_unstemmed |
Systems genetic analysis of addiction-associated traits |
title_sort |
systems genetic analysis of addiction-associated traits |
publishDate |
2017 |
url |
https://hdl.handle.net/2144/23561 |
work_keys_str_mv |
AT goldberglisa systemsgeneticanalysisofaddictionassociatedtraits |
_version_ |
1718812371220168704 |