PINK1 deficiency rewires early immune responses in a mouse model of Parkinson’s disease triggered by intestinal infection
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is characterized by a protracted period of non-motor symptoms, including gastrointestinal (GI) dysfunction, which can precede the development of the cardinal motor deficits by decades. This long prodrome of disease is highly suggestive of immune cell involvement in the initiation of disease, but currently the field lacks robust model systems to study such mechanisms. It has been hypothesized that pathology may be first initiated in the periphery due to environmental triggers, such as pathogens that enter the GI tract. We further speculate that the impact of such pathogens on the immune system could be exacerbated in genetically predisposed individuals. Our group has developed a GI-targeted pathogen-induced PD mouse model system in PINK1 KO mice with Gram-negative bacterial infections and found that T cells are a major player in driving PD-like motor symptoms at late stages following infection. Herein, we now map the initiating immune events at the site of infection at the earliest stages with the goal of shedding light on the earliest mechanisms triggering immune-mediated pathological processes relevant to PD. Using unbiased single cell sequencing, we demonstrate that myeloid cells are the earliest dysregulated immune cell type in PINK1 KO infected mice at 1-week post-infection, followed by a dysregulated T cell response shortly after, at 2 weeks post-infection. We find that these myeloid cells have an enhanced proinflammatory profile, are more mature, and develop enhanced capacity for antigen presentation. Using unbiased prediction analysis, our data suggest that cytotoxic T cells and myeloid cells are particularly poised for interacting with each other, and we identify possible direct cell-cell interaction pathways that might be implicated. Taken together, deciphering the earliest immune mechanisms in the periphery underpinning PD autoimmunity will be instrumental in the development of effective therapeutic targeting strategies before irrevocable neuronal damage ensues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".