Generation of prolactin-inducible protein (Pip) knockout mice by CRISPR/Cas9-mediated gene engineering
Bibliographic record
Abstract
Prolactin-inducible protein (PIP) is a multifunctional glycoprotein that is highly expressed and found in the secretions of apocrine glands such as salivary, lacrimal, and sweat glands including the mammary glands. PIP has been implicated in various diseases, including breast cancer, gross cystic disease of the breast, keratoconus of the eye, and the autoimmune Sjögren’s syndrome. Here we have generated a Pip knockout (KO) mouse using the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRSPR-associated (Cas)9 system. The Cas9 protein and two single guide RNAs targeting specific regions for both exons 1 and 2 of the Pip gene were microinjected into mouse embryos. The deletions and insertions promoted by CRISPR/Cas9 system on the Pip gene successfully disrupted Pip protein coding, as confirmed by PCR genotyping, sequencing, and ultimately Western blot analysis. This mouse model was generated in the inbred C57Bl/6J mouse, which exhibits lower genetic variation. This novel CRISPR Pip KO mouse model will not only be useful for future studies to interrogate the multifunctional role of PIP in physiological processes but will facilitate a broader understanding of the function of PIP in vivo while providing unprecedented insight into its role in a spectrum of diseases attributed to the deregulation of the PIP gene.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".