Identification and Characterization of Novel snRNA Processing Regulators in C. elegans
Bibliographic record
Abstract
Splicing of messenger RNA is a hallmark of eukaryotic cells. snRNA molecules are critical for precise splicing as they recognize and target the spliceosome to specific sequences of precursor mRNA that require splicing. The Integrator complex has been implicated in the processing and maturation of these snRNA molecules. Perturbations to splicing can have deleterious consequences, leading to various human diseases including cancer. Using the model organism C. elegans I identified several novel snRNA processing regulators. In Chapter 2, using a reverse genetic screen I found that the Argonaute CSR-1 plays a pivotal role in snRNA processing. Loss of CSR-1 caused increased levels of snRNA misprocessing that was dependent on the catalytic activity of the CSR-1b isoform. RNA-seq studies revealed the transcriptome is altered similarly during both CSR-1 or INTS-4 knockdown, indicating these proteins affect similar biological pathways. Additionally, members of the nuclear pore complex NPP-1, NPP-3, and NPP-6 were also confirmed to cause snRNA misprocessing through qPCR analysis. In Chapter 3 I isolated additional snRNA processing regulators through a forward genetic screen and determined to which degree they cause misprocessing. SNP mapping identified mutations in rde-11, rde-1, sago-2, and rsd-2, all components of the RNAi pathway, causing snRNA misprocessing. Complementation analysis confirmed the identified mutations were causing the misprocessing defect. Finally, in Chapter 3, I examined the effects these novel regulators have on maintaining a normal lifespan. The catalytic activity of CSR-1b was found to be essential in promoting a healthy lifespan. Depletion of any of the RNAi components identified in Chapter 2 all caused decreases in average lifespan, as did the majority of nuclear pore proteins. Overall these results highlight the importance of transcriptome regulation and integrity in maintaining a healthy lifespan.
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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 | 0.001 | 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 | 0.001 |
| 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".