Characterization of the relationship between two RBM5 family members
Bibliographic record
Abstract
RNA binding proteins (RBPs) control all aspects of RNA metabolism, and a single RBP can \nhave numerous downstream effects. Alterations to their expression and/or function can, \ntherefore, have remarkable consequences. For instance, decreased levels of the RNA binding \nmotif domain (RBM) protein RBM5 are associated with increased risk of a number of cancer \ntypes, and RBM10 mutations can be lethal. Although these consequences are quite severe, little is \nknown regarding the range of processes and events influenced by these two homologous RBPs. \nIn fact, previous RBM5 and RBM10 functional studies were largely focused only on their \nabilities to promote two processes; apoptosis and cell cycle arrest. Potentially by control of these \nprocesses, RBM5 and RBM10 were shown to influence one event: differentiation. The objectives \nof this study were to identify all cellular processes and events enriched by changes in RBM5 \nand/or RBM10 expression in a particular cultured cell line, and to determine the extent of \nfunctional overlap for RBM5 and RBM10 in these cells. Towards these goals, a list of RBM5 and \nRBM10 mRNA targets and differentially expressed genes was determined using next generation \nsequencing techniques. Our data suggest that RBM5 and RBM10 do influence a wide range of \ncellular processes and events. Although there is overlap in RBM5 and RBM10 mRNA targets and \ndifferentially expressed genes, these RBPs can have antagonistic functions; for example our data \nsuggest that RBM5 prevents the transformed state, whereas RBM10 actually promotes it in an \nRBM5-null environment. Furthermore, we present a working model by which RBM5 may \nregulate RBM10’s protransformatory function. Finally, we demonstrate a relationship between \nRBM5 and RBM10 in non-transformed cells. The results presented herein provide insight not \nonly into the roles and regulation of RBM5 and RBM10, but of RBPs in general. Taken together, \nthe results presented in the four papers included in this thesis expand the knowledge base of \nRBM5 and RBM10, which provides insight into the disease states associated with their disrupted \nexpression or function. Our findings are thus relevant to a wide range of scientific fields \nincluding molecular, developmental and cancer biology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".