Elucidating the Role of LRRK2 Kinase Activity in the Innate Immune System
Bibliographic record
Abstract
Mutations in LRRK2 are linked to three distinct diseases: Parkinson's disease (PD), Crohn's disease, and leprosy.The main pathogenic variant of LRRK2 associated with Parkinson's disease is the p.G2019S mutant, which causes increased kinase activity of the protein.Recently, a role for LRRK2 has been implicated in the immune system; however, the exact contribution of the kinase activity in this function remains unknown.We have used mice with a Lrrk2 knock-in p.D1994S mutation, which renders the protein kinase-dead, and three distinct infection paradigms to investigate this role: a systemic, nasal inoculation of reovirus serotype-3 Dearing (T3D); an intracerebral, direct-brain infection of reovirus T3D (both cause encephalitis); and a systemic, peripheral infection by Salmonella typhimurium (causing sepsis).Lrrk2 kinase-dead mice have increased survival compared to wild-type mice following a systemic reovirus T3D infection, with a slight increase in viral titre in the lungs at the early stage of disease.Nevertheless, loss of Lrrk2 kinase activity had no effect on survival from sepsis or bacterial load following i.v.inoculation.Lastly, Lrrk2 kinase-dead mice had the same survival rate as wild-type mice following a direct-brain infection by reovirus T3D.We demonstrated that in the context of both systemic infection models (i.e., viral and bacterial) Lrrk2 kinase is not required for the host's immune response to control virulent pathogens (when compared with wild-type Lrrk2 expression) and may in fact be protective in certain paradigms (i.e., systemic reovirus infection leading to encephalitis).Additionally, we have shown that Lrrk2 seems to exert its immune function predominantly in the periphery rather than the brain, and that the p.G2019S mutation confers a gain-of-function.Taken together, these data will provide important insights into LRRK2 biology, PD pathogenesis, and cause-directed therapies for all three diseases affected by allelic variants at this locus.A very special thank you to additional equally important mentors: Dr. Julianna Tomlinson, Dr. Bojan Shutinoski, and Dr. Earl Brown.Julie, thank you for being an amazing mentor, giving me advice on anything I ask, and always encouraging me to my highest potential.Bojan, thank you for being an incredible teacher and always showing me patience and understanding.Earl, thank you for all the extra time you took to help me understand topics and to answer questions any day of the week.To everyone at the Hayley lab, thank you for being welcoming and inclusive when I found myself at Carleton.To my dear friends at the Schlossmacher lab, thank you for making my time in (and outside of) the lab tremendously enjoyable and memorable.I will cherish our times together for years to come.To my parents, Allan and
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".