Identification and functional analysis of PCNA1 and PCNA-like1 genes of Phaseolus coccineus
Bibliographic record
Abstract
Proliferating cell nuclear antigen (PCNA) is an essential factor in DNA replication and in many other processes in eukaryotic cells. Genetic analysis of Phaseolus coccineus showed the presence of at least two PCNA-like genes in the runner bean genome. Two PCNA genes have previously been found in a few plant species including Arabidopsis, tobacco, and maize. In these species, genes were nearly identical. Two cDNAs of P. coccineus PCNA (PcPCNA1 and PcPCNA-like1) have been identified that differ distinctly from each other. Interestingly, both the genetic organization of PcPCNA1 and PcPCNA-like1 genes and their expression patterns were similar, but these were the only similarities between these genes and their products. The identity between PcPCNA1 and PcPCNA-like1 at the amino acid level was only 54%, with PcPCNA-like1 lacking motifs that are crucial for the activity typical of PCNA. Consequently, these two proteins showed different properties. PcPCNA1 behaved like a typical PCNA protein: it formed a homotrimer and stimulated the activity of human DNA polymerase delta. In addition, PcPCNA1 interacted with a p21 peptide and was recognized by an anti-human PCNA monoclonal antibody PC10. By contrast, PcPCNA-like1 was detected as a monomer and was unable to stimulate the DNA polymerase delta activity. PcPCNA-like1 also could not interact with p21 and was not recognized by the PC10 antibody. Our results suggest that PcPCNA-like1 either is unable to function alone and therefore might be a component of the heterotrimeric PCNA ring or may have other, yet unknown functions. Alternatively, the PcPCNA-like1 gene may represent a pseudogene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".