Molecular Genetics
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/21632
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Browsing Molecular Genetics by Author "Aryal, Subhash"
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Item Candidate Gene Association Study of Low Back Pain Using SNP Derived Gene Expression Profiling: A PRECISION PAIN Research Registry Study(2019-03-05) Pathak, Gita; Aryal, Subhash; Phillips, Nicole R.; Licciardone, John C.; Bhatnagar, ShwetaCandidate Gene Association Study of Low Back Pain Using SNP Derived Gene Expression Profiling: A PRECISION PAIN Research Registry Study Shweta Bhatnagar, Gita Pathak, Subhash Aryal, Nicole Phillips, John Licciardone Abstract Objective: The Global Burden of Disease Study estimated that 632 million persons worldwide are affected by low back pain (LBP), making it the leading cause of disability worldwide. Furthering our understanding of genetic-based risk for LBP may allow for development of targeted gene therapy for pain which may help mitigate the healthcare and financial burden. Inflammatory genes have been implicated in pain disorders. Variability in how these genes are expressed may determine their association in low back pain. This study aims to predict the expression of candidate genes and their association with pain in participants of the PRECISION Pain Research Registry. Hypothesis: Elevated self-reported pain intensity and disability from LBP is associated with the higher expression of inflammatory genes. Methods: The DNA was collected, extracted, and genotyped using the InfiniumĀ® Global Screening Array (Illumina). Data were filtered based on standard quality control protocols (Anderson et al., 2010). Gene expression data of monocytes from the Multi-Ethnic Study for Atherosclerosis (MESA) was used for gene expression imputation using PrediXcan. Twenty-six candidate genes involved with inflammation and immune response processes (based on Gene Ontology Analysis) were analyzed. The imputed gene expression levels were transformed to dichotomized gene expression levels, over-expressed and under-expressed. The highly expressed gene levels were then tested for association with PRECISION participant outcomes data, including Roland-Morris Disability Score and a pain intensity score using SPSS. Results: Seven genes showed positive correlation between their predicted expression levels and scores on the Roland-Morris Questionnaire and the pain intensity scale for LBP: STAT-1, STAT-2, HLA-A, CD48, CD209, CLEC4G and SLAMF8. Also, as expected, many of these genes demonstrated co-expression patterns due to their common role in immune mediation. Conclusions: The results demonstrate a positive correlation between the increased expression of inflammatory genes and how the subjects perceived and reported LBP. Understanding the relationship between pain and variability in inflammatory genes could play a role in future precision medicine and pain management.Item Examining SLC6A4 Variations in the PRECISION Pain Research Registry(2019-03-05) Phillips, Nicole R.; Aryal, Subhash; Licciardone, John C.; Lopez, JonathanPurpose: Chronic low back pain is the leading cause of disability globally and has been linked to comorbidities such as depression. Common pathways involving serotonin and norepinephrine may play a role in both pain and mood disorders. The SLC6A4 gene, encoding a serotonin reuptake transporter, has been heavily studied with regard to depression. Polymorphisms within the gene are thought to influence the expression of the transporter, thereby modulating serotonin transmission. More recently, this gene has been studied in the context of chronic pain. This study seeks to further our understanding of chronic pain with respect to depression and other outcome measures in participants with chronic low back pain to aid in the development of better treatments. Methods: Participants with chronic low back pain in the PRECISION Pain Research Registry provided DNA samples that were genotyped on the Infinium Global Screening Array (Illumina). Long and short length polymorphisms of the SLC6A4 gene were predicted using an eight single nucleotide polymorphism (SNP) machine learning model. Of the eight, one SNP was collected from the array, and the other seven were imputed. Additionally, the rs25531A [greater than] G SNP was imputed. Using the length of the polymorphism and the rs25531 SNP, subjects were divided into high, intermediate and low expression groups. Participants also reported clinical status measures such as low back pain intensity, back-specific functioning, PROMIS quality of life, levels of pain self-efficacy, and pain catastrophizing. Results: There was no significant association between self-reported depression and expression levels (high, intermediate, low) of SLC6A4. No correlation was found between PROMIS depression scores and SLC6A4 expression. Analyses for depression values were conducted using logistic linear regression and non-parametric ANOVA respectively. There were no observed correlations between transporter expression level and the outcome variables of back-related disability, pain, pain catastrophizing, or pain self-efficacy. Disability was analyzed using ANOVA. Pain, pain catastrophizing, and pain self-efficacy were investigated using non-parametric ANOVA analyses. Conclusion: No correlations were found between serotonin transporter expression level and depression or other outcome variables. Similar previous studies investigating SLC6A4 used homogenous populations. We recommend conducting a larger study that takes into account race.