Nucleoside transporter gene expression in wild-type and mENT1 knockout miceThis paper is one of a selection of papers published in a Special Issue entitled CSBMCB 53rd Annual Meeting — Membrane Proteins in Health and Disease, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Owing to the overlapping and redundant roles of the seven mammalian nucleoside transporters (NTs), which belong to two protein families (ENTs and CNTs), the physiological importance of individual NTs has been difficult to assess. Mice that have NT genes knocked out can be a valuable tool in gaining an understanding of the NT proteins. We have generated a strain of mice that is homozygous for a disruption mutation between exons 2 and 3 of the mouse equilibrative nucleoside transporter, mENT1. We have undertaken a quantitative survey of NT gene expression in 10 tissues, as well as microarray analysis of heart and kidney, from wild-type and mENT1 knockout mice. Rather than a consistent change in expression of NT genes in all tissues of mENT1 knockout mice, a complex pattern of changes was found. Some genes, such as those encoding mCNT1 and mCNT3 in colon tissue, exhibited increased expression, whereas other genes, such as those encoding mCNT2 and mENT4 in lung tissue, exhibited decreased expression. Although mCNT3 has been shown to be important in human and rat kidney tissue, we were unable to detect mCNT3 transcripts in the kidney of either the wild-type or mENT1 knockout mice, suggesting differences in renal nucleoside resorption between species.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".