Multiple levels of gene regulation mediate differentiation of the intracellular pathogen <i>Leishmania</i>
Bibliographic record
Abstract
For many years, mRNA abundance has been used as the surrogate measure of gene expression in biological systems. However, recent genome-scale analyses in both bacteria and eukaryotes have revealed that mRNA levels correlate with steady-state protein abundance for only 50-70% of genes, indicating that translation and post-translation processes also play important roles in determining gene expression. What is not yet clear is whether dynamic processes such as cell cycle progression, differentiation, or response to environmental changes change the relationship between mRNA and protein abundance. Here, we describe a systems approach to interrogate promastigote-to-amastigote differentiation in the obligatory intracellular parasitic protozoan Leishmania donovani. Our results indicate that regulation of mRNA levels plays a major role early in the differentiation process, while translation and post-translational regulation are more important in the latter part. In addition, it appears that the differentiation signal causes a transient global increase in the rate of protein synthesis, which is subsequently down-regulated by phosphorylation of α-subunit of translation initiation factor 2. Thus, Leishmania dynamically changes the relationship between mRNA and protein abundance as it adapts to new environmental circumstances. It is likely that similar mechanisms play a more important role than previously recognized in regulation of gene expression in other organisms.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".