Expression of MMPs and TIMPs in Mammalian Cells
Bibliographic record
Abstract
Expression of recombinant matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) in mammalian cells is an important step in their functional characterization. Also, transient transfection analysis of promoter constructs driving CAT or luciferase reporter genes is a mainstay of gene regulation studies. One of the critical advantages of mammalian expression over bacterial systems for production of functional proteins is that problems of refolding, which are particularly significant for the MMPs and TIMPs, are generally not encountered. Transient expression in COS cells is very rapid and can be useful in many situations, for instance to demonstrate activity or to compare the characteristics of mutant proteins. It can also generate sufficient quantities of protein for biochemical and cell biological studies, but most often bulk production will require stable expression. In this chapter we will discuss our experiences with transient expression in COS-1 and C3H 10T1/2 mouse fibroblasts, and describe two systems that we have used for constitutive stable expression, namely BHK (Baby Hamster Kidney) and the NS0 myeloma cell line. This chapter complements the information in Chapter 11 by Butler, d’Ortho, and Atkinson who have described stable, tetracycline-regulated expression of MT1-MMP in CHOL cells. Also, the reader should note that many expression vector systems are now available commercially, and we have not attempted here to provide comparisons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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".