Transcriptional profiling of macrophages reveals distinct parasite stage-driven signatures during early infection by Leishmania donovani
Bibliographic record
Abstract
Macrophages undergo swift changes in mRNA abundance upon pathogen invasion. Herein we describe early remodelling of the macrophage transcriptome during infection by amastigotes or promastigotes of Leishmania donovani. Approximately 10-16% of host mRNAs were differentially modulated in L. donovani-infected macrophages when compared to uninfected controls. This response was partially stage-specific as a third of changes in mRNA abundance were either exclusively driven by one of the parasite forms or significantly different between them. Gene ontology analyses identified categories associated with immune functions (e.g. antigen presentation and leukocyte activation) among significantly downregulated mRNAs during amastigote infection while cytoprotective-related categories (e.g. DNA repair and apoptosis inhibition) were enriched in upregulated transcripts. Interestingly a combination of upregulated (e.g. cellular response to IFNβ) and repressed (e.g. leukocyte activation, chemotaxis) immune-related transcripts were overrepresented in the promastigote-infected dataset. In addition, Ingenuity Pathway Analysis (IPA) associated specific mRNA subsets with a number of upstream transcriptional regulators predicted to be modulated in macrophages infected with L. donovani amastigotes (e.g. STAT1 inhibition) or promastigotes (e.g. NRF2, IRF3, and IRF7 activation). Overall, our results indicate that early parasite stage-driven transcriptional remodelling in macrophages contributes to orchestrate both protective and deleterious host cell responses during L. donovani 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) | 0.001 | 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".