Talks
Presentation Type
Oral Presentation
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
Background: Opioid use disorder (OUD) is a chronic and relapsing condition influenced by genetic and environmental factors. OUD is associated with altered gene expression and epigenetic modifications in BA9 and the greater prefrontal cortex affecting regional function and connectivity. OUD may also involve the downregulation of genes related to synaptic transmission and neurogenesis in the amygdala, impairing its adaptability. In addition to the brain, peripheral blood can serve as a source of molecular markers for OUD as it is easily accessible and may reflect systemic and chronic inflammation changes prompted by OUD. Understanding the molecular mechanisms dysregulated by OUD may help identify novel biomarkers and therapeutic targets. This study aims to identify significantly predictive gene signatures using machine learning methods with bulk and miRNA-seq data across these tissues to elucidate their causal relationships with OUD and to explore their interactions with co-regulated factors.
Methods: Raw counts tables were downloaded from the Gene Expression Omnibus (GEO) under accession numbers GSE182321 (BA9 RNA-seq), GSE221515 (BA9 miRNA-seq), GSE198121 (PBMC RNA-seq), GSE198122 (PBMC miRNA-seq), GSE194368 (amygdala RNA-seq). TMM normalized counts were obtained with edgeR and the Min-Max Scaler was used for feature pre-processing. Mutual information-based feature selection (feature importance > 0.001) was used to identify significant mRNA, lncRNA, and miRNA in the tissue types predictive of OUD. Top targeting mRNA and lncRNA of each selected miRNA were retrieved using miRcode and SPMLMI. Selected genes were used in Support-Vector-Machine, Random Forest, and K-Nearest-Neighbor classifiers and 10 fold and leave one out cross validation (LOOCV) was applied to quantify their accuracy in determining OUD state across tissue types.
Results: Several genes of overlapping significance concerned with neurological conditions were confirmed through LOOCV including TRBJ2-5 which may be involved in the modulation of the blood brain barrier and neuro-inflammatory processes. Further, both EGLN1 , playing a crucial role in the cellular response to hypoxia, and miR-223-3p are connected to neuroinflammation. Their large contribution in classifying OUD suggests a connection between altered barrier function, inflammation, and the development or progression of the disorder. LRP6 encodes a receptor involved in the Wnt signaling pathway, which plays a crucial role in neurodevelopment and synaptic plasticity. Dysregulation of the Wnt signaling pathway has been implicated in addiction, suggesting that LRP6 may play a larger role in the development or maintenance of the disorder. In the context of OUD, miR-219a-1-3p may have a regulatory role in modulating synaptic plasticity and neuronal adaptation to opioids since it targets genes involved in neuroplasticity, such as glutamate receptors and neurotrophins. Dysregulation of these targets may contribute to the maladaptive changes observed in the brain during opioid exposure and withdrawal, leading to the development and maintenance of OUD.
Conclusions: This study highlights the significance of high predictive genes of BA9 and the amygdala in OUD, demonstrating their association with altered synaptic plasticity, which contribute to the dysfunction of these brain regions in OUD. Further, the potential of peripheral blood as a source of molecular markers for OUD is made evident, particularly its ability to reflect inflammatory pathway changes caused by OUD.
Recommended Citation
Kumar, Kashish, "Identification of Predictive Gene Signatures and Molecular Interactions Underlying Opioid Use Disorder in Brain Tissues and Peripheral Blood" (2024). Research Symposium. 10.
https://scholarworks.utrgv.edu/somrs/2023/talks/10
Included in
Identification of Predictive Gene Signatures and Molecular Interactions Underlying Opioid Use Disorder in Brain Tissues and Peripheral Blood
Background: Opioid use disorder (OUD) is a chronic and relapsing condition influenced by genetic and environmental factors. OUD is associated with altered gene expression and epigenetic modifications in BA9 and the greater prefrontal cortex affecting regional function and connectivity. OUD may also involve the downregulation of genes related to synaptic transmission and neurogenesis in the amygdala, impairing its adaptability. In addition to the brain, peripheral blood can serve as a source of molecular markers for OUD as it is easily accessible and may reflect systemic and chronic inflammation changes prompted by OUD. Understanding the molecular mechanisms dysregulated by OUD may help identify novel biomarkers and therapeutic targets. This study aims to identify significantly predictive gene signatures using machine learning methods with bulk and miRNA-seq data across these tissues to elucidate their causal relationships with OUD and to explore their interactions with co-regulated factors.
Methods: Raw counts tables were downloaded from the Gene Expression Omnibus (GEO) under accession numbers GSE182321 (BA9 RNA-seq), GSE221515 (BA9 miRNA-seq), GSE198121 (PBMC RNA-seq), GSE198122 (PBMC miRNA-seq), GSE194368 (amygdala RNA-seq). TMM normalized counts were obtained with edgeR and the Min-Max Scaler was used for feature pre-processing. Mutual information-based feature selection (feature importance > 0.001) was used to identify significant mRNA, lncRNA, and miRNA in the tissue types predictive of OUD. Top targeting mRNA and lncRNA of each selected miRNA were retrieved using miRcode and SPMLMI. Selected genes were used in Support-Vector-Machine, Random Forest, and K-Nearest-Neighbor classifiers and 10 fold and leave one out cross validation (LOOCV) was applied to quantify their accuracy in determining OUD state across tissue types.
Results: Several genes of overlapping significance concerned with neurological conditions were confirmed through LOOCV including TRBJ2-5 which may be involved in the modulation of the blood brain barrier and neuro-inflammatory processes. Further, both EGLN1 , playing a crucial role in the cellular response to hypoxia, and miR-223-3p are connected to neuroinflammation. Their large contribution in classifying OUD suggests a connection between altered barrier function, inflammation, and the development or progression of the disorder. LRP6 encodes a receptor involved in the Wnt signaling pathway, which plays a crucial role in neurodevelopment and synaptic plasticity. Dysregulation of the Wnt signaling pathway has been implicated in addiction, suggesting that LRP6 may play a larger role in the development or maintenance of the disorder. In the context of OUD, miR-219a-1-3p may have a regulatory role in modulating synaptic plasticity and neuronal adaptation to opioids since it targets genes involved in neuroplasticity, such as glutamate receptors and neurotrophins. Dysregulation of these targets may contribute to the maladaptive changes observed in the brain during opioid exposure and withdrawal, leading to the development and maintenance of OUD.
Conclusions: This study highlights the significance of high predictive genes of BA9 and the amygdala in OUD, demonstrating their association with altered synaptic plasticity, which contribute to the dysfunction of these brain regions in OUD. Further, the potential of peripheral blood as a source of molecular markers for OUD is made evident, particularly its ability to reflect inflammatory pathway changes caused by OUD.