Global identification of conserved post-transcriptional regulatory programs in trypanosomatids
Bibliographic record
Abstract
While regulatory programs are extensively studied at the level of transcription, elements that are involved in regulation of post-transcriptional processes are largely unknown, and methods for systematic identification of these elements are in early stages. Here, using a novel computational framework, we have integrated sequence information with several functional genomics data sets to characterize conserved regulatory programs of trypanosomatids, a group of eukaryotes that almost entirely rely on post-transcriptional processes for regulation of mRNA abundance. This analysis revealed a complex network of linear and structural RNA elements that potentially govern mRNA abundance across different life stages and environmental conditions. Furthermore, we show that the conserved regulatory network that we have identified is responsive to chemical perturbation of several biological functions in trypanosomatids. We have further characterized one of the most abundant regulatory RNA elements that we discovered, an AU-rich element (ARE) that can be found in 3' untranslated region of many trypanosomatid genes. Using bioinformatics approaches as well as in vitro and in vivo experiments, we have identified three ELAV-like homologs, including the developmentally critical protein TbRBP6, which regulate abundance of a large number of trypanosomatid ARE-containing transcripts. Together, these studies lay out a roadmap for characterization of mechanisms that modulate development and metabolic pathways in trypanosomatids.
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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".