Investigating the Integration of Alternative Splicing and Transcriptional Regulation in Mammalian Gene Expression
Bibliographic record
Abstract
Alternative splicing functions to generate proteomic diversity and to regulate gene expression in higher eukaryotes. Genome-wide analyses suggest that alternative splicing and transcription typically regulate different gene sets to achieve cell- and tissue-type specificity. However, within individual cell-types, most alternative splicing events occur co-transcriptionally and are impacted by the transcriptional machinery. Despite many focused studies on co-transcriptional regulation of alternative splicing, its mechanisms and functions in regulation of gene expression are still poorly understood.\nTo investigate relationships between transcription and alternative splicing, I performed microarray profiling of alternative splicing and transcript levels during activation of a T cell line. This experiment revealed that different sets of genes and associated functional categories are regulated by alternative splicing and transcription during T cell activation. I next employed inhibitors of RNA polymerase II (Pol II) elongation and microarray profiling to identify genes with coupled changes in splicing and transcript levels when transcription is impeded in activated T cell. Genes that were affected at both levels were significantly enriched in RNA binding and processing functions, and generally displayed increased alternative exon inclusion and decreased transcript levels when transcription elongation was disrupted. Similar effects were observed when transcription was driven by mutant polymerases with reduced elongation activity, and when cells were subjected to stress treatments. Many of the elongation inhibition-sensitive exons from the affected genes introduce premature termination codons into the mRNA, resulting in spliced mRNAs that are substrates of the nonsense-mediated decay pathway and further reduction in mRNA levels. ChIP-Seq experiment demonstrated that Pol II occupancy specifically increased in introns flanking the affected exons. These results provide evidence that a physiological function of transcription elongation-coupled alternative splicing regulation is to regulate the levels of RNA processing factors under conditions that reduce elongation activity, including cell stress. In summary, my thesis research has provided new insights into the integration of transcription and splicing control. While these two regulatory levels can control different gene sets during the activation of T cells, within a given cell type, they are closely coupled to control specific alternative splicing events that appear to coordinate mRNA and RNA processing factors levels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".