Bridging pleiotropic mechanisms in leprosy type-1 reactions and neurodegenerative diseases
Bibliographic record
Abstract
Abstract Leprosy is an infectious disease of the skin and peripheral nervous system. Sudden episodes of hyperinflammation, known as Type 1 Reactions (T1R), are a main contributor to permanent nerve damage in leprosy. The genetic component associated with the neuro-inflammatory phenotype of T1R displays pleiotropic effects with Parkinson’s disease (PD). In this study, we explored the genetic overlap between PD and T1R and expanded the evaluation of pleiotropic effects between T1R and other neurodegenerative disorders. We replicated the association of PD-linked rare variants in PRKN with T1R in Vietnamese leprosy patients. Analysis of 24 PD associated-genes revealed compound effects between rare protein-altering variants and T1R in the interacting genes PRKN/PINK1 ( P = 2.7 –05 ; OR = 4.0) and a combination of rare/low frequency variants in the LRRK2/GAK pair ( P = 6.7 –05 ; OR = 0.54). These findings validated a genetic overlap between T1R and PD with two distinct axes, one of shared risk via PRKN/PINK1 and a second of antagonistic pleiotropic via LRRK2/GAK . When testing an additional 94 genes associated with neurodegenerative diseases we identified variants in the amyotrophic lateral sclerosis disease-linked gene TBK1 associated with T1R ( P = 0.004; OR = 12.9). Our results highlight shared biological processes between leprosy and neurodegenerative diseases, which may indicate candidate drugs for repurposing to improve T1R management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".