Functional proteomic profiling reveals KLK13 and TMPRSS11D as active proteases in the lower female reproductive tract
Bibliographic record
Abstract
Background : Cervical-vaginal fluid (CVF) hydrates the mucosa of the lower female reproductive tract and is known to contain numerous proteases. The low pH of CVF (4.5 or below in healthy women of reproductive age) is a uniquely human attribute and poses a challenge for the proteolytic functioning of the proteases identified in this complex biological fluid. Despite the abundance of certain proteases in CVF, the proteolytic activity and function of proteases in CVF is not well characterized. Methods : In the present study, we employed fluorogenic substrate screening to investigate the influence of pH and inhibitory compounds on the proteolytic activity in CVF. Activity-based probe (ABP) proteomics has evolved as a powerful tool to investigate active proteases within complex proteomes and a trypsin-specific ABP was used to identify active proteases in CVF. Results : Serine proteases are among the most abundant proteins in the CVF proteome. Labeling human CVF samples with the trypsin-specific ABP revealed serine proteases transmembrane protein serine 11D and kallikrein-related peptidase 13 as active proteases in CVF. Furthermore, we demonstrated that the proteolytic activity in CVF is highly pH-dependent with an almost absolute inhibition of trypsin-like proteolytic activity at physiological pH levels. Conclusions : These findings provide a framework to understand proteolytic activity in CVF. Furthermore, the present results provide clues for a novel regulatory mechanism in which fluctuations in CVF pH have the potential to control the catalytic activity in the lower female reproductive tract.
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) | 0.000 | 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 | 0.000 |
| 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".