<i>Leishmania donovani</i> GP63 paralogues cooperatively orchestrate visceral infection and persistence
Bibliographic record
Abstract
ABSTRACT Highly plastic genome of Leishmania is thought to underlie its remarkable ability to adapt to diverse host microenvironments, yet how this plasticity translates into dermal versus visceral persistence remains unclear. GP63, a zinc metalloprotease, is a well-established determinant of promastigote infectivity, particularly in cutaneous leishmaniasis. Despite this, its role in amastigote stage and in visceral disease caused by Leishmania donovani remains undefined. Here, using an integrated approach combining phylogenetics, CRISPR-mediated genome editing, and biochemical analyses, we demonstrate that GP63 paralogues in L. donovani have undergone independent evolutionary diversification to support visceral infection. While GP63-paralogues encoded on chromosome 10, previously implicated in cutaneous manifestation caused by L. major , were found severely truncated and dispensable in L. donovani, two distinct GP63-paralogues encoded on chromosome 28 and 31 were found ‘essential’ to establish visceral infection. LdGP63_31 facilitates promastigote attachment to macrophages by promoting lipid raft engagement and complement inactivation, thereby enabling host entry and subsequent amastigote differentiation. LdGP63_28 is essential for intracellular amastigote survival by suppressing host inflammatory pyroptosis. Structural and enzymatic analyses revealed preferential host localization and substrate specificities, possibly resulting from distinct amino acid substitution within conserved motifs of these proteases, indicating independent evolutionary adaptations. Importantly, loss of the LdGP63_28 more severely impaired amastigote reinfection than loss of its counterpart LdGP63_31. Together, these findings reveal coordinated functional specialization of GP63-paralogues ensuring visceral adaptation.
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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) | 0.001 | 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".