Overview of transcriptomic analysis of all human proteases, non-proteolytic homologs and inhibitors: Organ, tissue and ovarian cancer cell line expression profiling of the human protease degradome by the CLIP-CHIP™ DNA microarray
Bibliographic record
Abstract
The protease degradome is defined as the complete repertoire of proteases and inhibitors, and their nonfunctional homologs present in a cell, tissue or organism at any given time. We review the tissue distribution of virtually the entire degradome in 23 different human tissues and 6 ovarian cancer cell lines. To do so, we developed the CLIP-CHIP™, a custom microarray based on a 70-mer oligonucleotide platform, to specifically profile the transcripts of the entire repertoire of 473 active human proteases, 156 protease inhibitors and 92 non-proteolytically active homologs known at the design date using one specific 70-mer oligonucleotide per transcript. Using the CLIP-CHIP™ we mapped the expression profile of proteases and their inhibitors in 23 different human tissues and 6 ovarian cancer cell lines in 104 sample datasets. Hierarchical cluster analysis showed that expression profiles clustered according to their anatomic locations, cellular composition, physiologic functions, and the germ layer from which they are derived. The human ovarian cancer cell lines cluster according to malignant grade. 110 proteases and 42 inhibitors were tissue specific (1 to 3 tissues). Of these 110 proteases 69% (74) are mainly extracellular, 30% (34) intracellular and 1% intramembrane. Notably, 35% (197/565) of human proteases and 30% (47/156) of inhibitors were ubiquitously expressed in all 23 tissues; 27% (155) of proteases and 21% (32) of inhibitors were broadly expressed in 4-20 tissues. Our datasets provide a valuable resource for the community of baseline protease and inhibitor relative expression in normal human tissues and can be used for comparison with diseased tissue, e.g. ovarian cancer, to decipher pathogenesis, and to aid drug development. This article is part of a Special Issue entitled: Proteolysis as a Regulatory Event in Pathophysiology edited by Stefan Rose-John.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".