The role of Leishmania GP63 in the modulation of innate inflammatory response to Leishmania major infection
Bibliographic record
Abstract
Leishmaniasis is a disease caused by the protozoan parasite Leishmania and is known to affect millions of individuals worldwide. In recent years, we have established the critical role played by Leishmania zinc-metalloprotease GP63 in the modulation of host macrophage signalling and functions, favouring its survival and progression within its host. Leishmania major lacking GP63 was reported to cause limited infection in mice, however, it is still unclear how GP63 may influence the innate inflammatory response and parasite survival in an in vivo context. Therefore, we were interested in analyzing the early innate inflammatory events upon Leishmania inoculation within mice and establish whether Leishmania GP63 influences this initial inflammatory response. Experimentally, L. major WT (L. majorWT), L. major GP63 knockout (L. majorKO), or L. major GP63 rescue (L. majorR) were intraperitoneally inoculated in mice and the inflammatory cells recruited were characterized microscopically and by flow cytometry (number and cell type), and their infection determined. Pro-inflammatory markers such as cytokines, chemokines, and extracellular vesicles (EVs, e.g. exosomes) were monitored and proteomic analysis was performed on exosome contents. Data obtained from this study suggest that Leishmania GP63 does not significantly influence the pathogen-induced inflammatory cell recruitment, but rather their activation status and effector function. Concordantly, internalization of promastigotes during early infection could be influenced by GP63 as fewer L. majorKO amastigotes were found within host cells and appear to maintain in host cells over time. Collectively this study provides a clear analysis of innate inflammatory events occurring during L. major infection and further establish the prominent role of the virulence factor GP63 to provide favourable conditions for host cell infection.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".