Translational profiling of macrophages infected with Leishmania donovani identifies mTOR- and eIF4A-sensitive immune-related transcripts
Bibliographic record
Abstract
The protozoan parasite Leishmania donovani (L. donovani) causes visceral leishmaniasis, a chronic infection which is fatal when untreated. Herein, we investigated whether in addition to altering transcription, L. donovani modulates host mRNA translation to establish a successful infection. Polysome-profiling revealed that one third of protein-coding mRNAs expressed in primary mouse macrophages are differentially translated upon infection with L. donovani promastigotes or amastigotes. Gene ontology analysis identified key biological processes enriched for translationally regulated mRNAs and were predicted to be either activated (e.g. chromatin remodeling and RNA metabolism) or inhibited (e.g. intracellular trafficking and antigen presentation) upon infection. Mechanistic in silico and biochemical analyses showed selective activation mTOR- and eIF4A-dependent mRNA translation, including transcripts encoding central regulators of mRNA turnover and inflammation (i.e. PABPC1, EIF2AK2, and TGF-β). L. donovani survival within macrophages was favored under mTOR inhibition but was dampened by pharmacological blockade of eIF4A. Overall, this study uncovers a vast yet selective reprogramming of the host cell translational landscape early during L. donovani infection, and suggests that some of these changes are involved in host defense mechanisms while others are part of parasite-driven survival strategies. Further in vitro and in vivo investigation will shed light on the contribution of mTOR- and eIF4A-dependent translational programs to the outcome of visceral leishmaniasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".