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Record W4411079989 · doi:10.1101/2025.06.05.25329059

Bridging Pleiotropic Mechanisms in Leprosy Type-1 Reactions and Neurodegenerative diseases

2025· preprint· en· W4411079989 on OpenAlexaff
Vinicius M. Fava, Jônatas Perico, Marianna Orlova, Monica Dallmann-Sauer, Yong Zhong Xu, Nguyen Van Thuc, Vu Hong Thai, Andrea de Faria Fernandes Belone, Ana Carla Pereira Latini, Erwin Schurr

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMichael J. Fox Foundation for Parkinson's Research
KeywordsLeprosyBridging (networking)DiseaseNeuroscienceMedicineBiologyImmunologyComputer sciencePathology

Abstract

fetched live from OpenAlex

ABSTRACT Leprosy is an infectious disease that affects predominantly 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) risk genes. In this study, we further explored the genetic overlap between PD and T1R and expanded the evaluation of pleiotropic effects between T1R and other neurodegenerative disorders. We first replicated the association of PD-risk rare variants in PRKN with T1R in Vietnamese leprosy patients ( P = 0.04; OR = 3.1). Additionally, we discovered large PRKN structural variants only in T1R-affected participants when compared to T1R-free 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 effects via LRRK2/GAK . When testing an additional 94 genes associated with neurodegenerative diseases other than PD, we identified variants in the amyotrophic lateral sclerosis (ALS) disease-risk gene TBK1 associated with T1R ( P = 0.004; OR = 12.9). All TBK1 mutations in T1R-affected participants located to the ubiquitin-like TBK1 domain. PRKN , PINK1 and TBK1 play a critical role in autophagy and in the host immune response to Mycobacteria . Our results highlight shared biological processes between leprosy and neurodegenerative diseases, which may indicate candidate drugs for repurposing for a more favorable T1R management. AUTHORS SUMMARY In this study, we investigated the shared genetic component between hyperinflammatory processes in leprosy and neurodegenerative diseases. A subset of leprosy patients develop sudden episodes of severe inflammation, known as type-1 reactions (T1R), which frequently lead to permanent nerve injury. Here, we aimed to identify key genetic mediators of nerve damage shared with diseases of the central nervous system and T1R, which affects the peripheral nervous system. Previous studies showed Parkinson’s disease (PD) risk genes associated with T1R. To explore this further, we analyzed 24 well-established PD-risk genes in over 800 Vietnamese leprosy patients. We found that rare mutations in PRKN and PINK1 were more frequent in individuals experiencing T1R. These genes are involved in mitochondrial quality control and responses to intracellular pathogens. Inversely, the LRRK2 and GAK PD-risk genes were associated with protection from T1R. This pattern suggested two independent biological processes influencing T1R susceptibility. Our findings highlight a shared genetic component between PD and T1R and suggest that therapies developed for PD may be repurposed for T1R management. Our results underscore the value of cross-disease research to uncover new therapeutic strategies, particularly for neglected diseases with limited treatments or therapies with frequent adverse effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.321
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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