Implication of DNA methylation changes at chromosome 1q21.1 in the brain pathology of Primary Progressive Multiple Sclerosis
Bibliographic record
Abstract
Abstract Multiple Sclerosis (MS) is a heterogeneous inflammatory and neurodegenerative disease of the central nervous system with an unpredictable course toward progressive disability. Understanding and treating progressive MS remains extremely challenging due to the limited knowledge of the underlying mechanisms. We examined the molecular changes associated with primary progressive MS (PPMS) using a cross-tissue (blood and post-mortem brain) and multilayered data (genetic, epigenetic, transcriptomic) from independent cohorts. We identified and replicated hypermethylation of an intergenic region within the chromosome 1q21.1 locus in the blood of PPMS patients compared to other MS patients and healthy individuals. We next revealed that methylation is under genetic control both in the blood and brain. Genetic analysis in the largest to date PPMS dataset yielded evidence of association of genetic variations in the 1q21.1 locus with PPMS risk. Several variants affected both 1q21.1 methylation and the expression of proximal genes ( CHD1L, PRKAB2, FMO5 ) in the brain, suggesting a genetic-epigenetic-transcriptional interplay in PPMS pathogenesis. We addressed the causal link between methylation and expression using reporter systems and dCas9-TET1-induced CpG demethylation in the 1q21.1 region, which resulted in upregulation of CHD1L and PRKAB2 genes in SH-SY5Y neuron-like cells. Independent exploration using unbiased correlation network analysis confirmed the putative implication of CHD1L and PRKAB2 in brain processes in PPMS patients. Thus, several lines of evidence suggest that distinct molecular changes in 1q21.1 locus, known to be important for brain development and disorders, associate with genetic predisposition to high methylation in PPMS patients that regulates the expression of proximal genes. Significance Statement Multiple sclerosis (MS) is a long-lasting neurological disease affecting young individuals that occurs when the body’s natural guard (immune system) attacks the brain cells. There are currently no efficient treatments for the progressive form of MS disease, probably because the mechanisms behind MS progression are still largely unknown. Thus, treatment of progressive MS remains the greatest challenge in managing patients. We aim to tackle this issue using the emerging field called “epigenetics” which has the potential to explain the impact of genetic and environmental risk factors in MS. In this project, by using unique clinical material and novel epigenetic tools, we identified new mechanisms involved in MS progression and putative candidates for targeted epigenetic therapy of progressive MS patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".