Bibliographic record
Abstract
Cellular function is dependent upon the correct translation of genomic information encoded in DNA into functioning products, usually proteins. Prior to protein translation, DNA is transcribed into messenger RNA (mRNA). In eukaryotes such as human, this process must almost always undergo an intermediate step, termed pre-mRNA splicing, in which non-protein-coding regions (introns) are removed from the mRNA, yielding mature message (exons). Defects in pre-mRNA splicing are responsible for various human disorders including retinitis pigmentosa, spinal muscular atrophy, and myotonic dystrophy. In order to work towards a cure for these diseases, it is necessary to understand how pre-mRNA splicing works normally. One potentially useful tool for this is small molecule inhibitors of splicing, which have previously been shown to inhibit catalytic RNAs the additional benefit of being candidates for therapeutics. Only two papers (Hertweck et al., 2003; Kaida et al. 2007) have explored the effects of small molecules on nuclear splicing. Previous work has investigated inhibition of human splicing. In this work, I have examined the effect of small molecules on yeast splicing in order to make use of the powerful genetic and biochemical tools available for yeast. The main goal of this thesis was to identify small molecule inhibitors of yeast pre-mRNA splicing and to characterize the step at which they exert their inhibitory effects. Thirty-two different small molecules were tested. Ten of them were found to completely inhibit pre-mRNA splicing. IC₅₀ values were measured for each of the inhibitory small molecules, and neomycin was found to be the strongest inhibitor with an IC₅₀ of 80~M, while cefoperazone was the weakest inhibitor with an IC50 of 6.1 mM. Native gel analysis was used to establish the step at which splicing was inhibited. Four of the ten inhibitors showed a complete block in spliceosome assembly with accumulation of spliceosomal complex H; one accumulated spliceosomal complex A; two accumulated both spliceosomal complexes A and B; and three accumulated spliceosomal complexes B and C. I anticipate that these inhibitors will be useful tools for the studying the mechanism of pre-mRNA splicing. By characterizing the splicing complexes that accumulate in the presence of these inhibitors it will be possible to map out the path by which splicing complexes assemble. Furthermore, several of the inhibitors are previously uncharacterized, and consequently have potential to be useful in a variety of other context. In the long term, these inhibitors may lead to novel therapies for splicing related diseases.
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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) | 0.001 | 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 | 0.001 |
| 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".