Analysis of Protein-protein Interactions and Co-localization Between Components of Gap, Tight, and Adherens Junctions in Murine Mammary Glands
Bibliographic record
Abstract
Cell-cell interactions play a pivotal role in preserving tissue integrity and the barrier between the different compartments of the mammary gland. These interactions are provided by junctional proteins that form nexuses between adjacent cells. Junctional protein mislocalization and reduced physical associations with their binding partners can result in the loss of function and, consequently, to organ dysfunction. Thus, identifying protein localization and protein-protein interactions (PPIs) in normal and disease-related tissues is essential to finding new evidences and mechanisms leading to the progression of diseases or alterations in developmental status. This manuscript presents a two-step method to evaluate PPIs in murine mammary glands. In protocol section 1, a method to perform co-immunofluorescence (co-IF) using antibodies raised against the proteins of interest, followed by secondary antibodies labeled with fluorochromes, is described. Although co-IF allows for the demonstration of the proximity of the proteins, it does make it possible to study their physical interactions. Therefore, a detailed protocol for co-immunoprecipitation (co-IP) is provided in protocol section 2. This method is used to determine the physical interactions between proteins, without confirming whether these interactions are direct or indirect. In the last few years, co-IF and co-IP techniques have demonstrated that certain components of intercellular junctions co-localize and interact together, creating stage-dependent junctional nexuses that vary during mammary gland development.
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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 | 0.001 | 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 | 0.001 |
| 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".