Genomic adaptation to disease: A role for DNA demethylation of microRNA regulation in cancer and chronic neuropathic pain
Bibliographic record
Abstract
Cancer and chronic pain are two common pathologies affecting millions of patients worldwide. Much like most other disease states, they can be determined genetically, environmentally, or both. Unlike the static genome, the epigenome is responsible for interpreting environmental interactions and is often altered in disease states. A subset of epigenetic modifications, known as DNA methylation, is capable of mediating gene silencing. In this thesis, two cases will be explored probing the nature of DNA methylation in cancer and in peripheral neuropathy. Aberrant DNA methylation is a common hallmark of cancer often resulting in the methylation of tumor suppressors and the demethylation of oncogenes. The identity of a DNA methylase, however, remains elusive. One candidate, methyl binding domain 2 (MBD2), has been previously characterized as a demethylase and also functions as a transcriptional repressor. One possible explanation for its role as a repressor may involve the direct activation of a repressor which can then mediate silencing. An attractive class of genes for this model are microRNAs, which are capable of binding several targets in the cell and mediate their silencing. We therefore test the hypothesis that MBD2 is capable of activating a microRNA which is capable of negatively-regulating target genes. In this thesis, we delineate mechanisms that demonstrate MBD2 is capable of binding a microRNA, mir-496, which is then capable of inducing itsiactivation. We further show that mir-496 can mediate a repressive action on three separate genes in the cell that have tumor suppressive roles in cancer. Chronic pain has been shown to alter gene expression and brain anatomy and is often accompanied with comorbidities that affect cognitive processing, sleep and anxiety. Interestingly, these changes have been shown to be reversible following effective treatment of pain, suggesting the mechanisms behind pain may also be reversible, thus prompting the study of pain epigenetics. We therefore proposed to test the hypothesis that the methylome and transcriptome are altered in the brain following peripheral nerve injury. We were able to identify a signature of DNA methylation and transcription specific to the prefrontal cortex and amygdala that accompanied peripheral nerve injury and behavioral signs of neuropathy. Furthermore we were able reverse behavioral signs of neuropathic pain and altered methylation states in the prefrontal cortex with environmental enrichment, demonstrating their reversible nature. Taken together, this thesis explores the role of DNA methylation in two complex diseases: through small scale processes in cancer and through broader changes at the level of the methylome and transcriptome in chronic pain. In identifying these molecular pathways and signatures, we hope to improve the mechanistic understanding of these pathologicaliistates, ultimately resulting in better treatment outcomes for millions of patients worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".