Extracellular Secretion and Simple Purification of Bacterial Collagen from <i>Escherichia coli</i>
Bibliographic record
Abstract
Because of structural similarities with type-I animal collagen, recombinant bacterial collagen-like proteins have been progressively used as a source of collagen for biomaterial applications. However, the intracellular expression combined with current costly and time-consuming chromatography methods for purification makes the large-scale production of recombinant bacterial collagen challenging. Here, we report the use of an adapted secretion pathway, used natively by Escherichia coli to secrete curli fibers, for extracellular secretion of the bacterial collagen. We confirmed that a considerable fraction of expressed collagen (∼70%) is being secreted freely into the extracellular medium, with an initial purity of ∼50% in the crude culture supernatant. To simplify the purification of extracellular collagen, we avoided cell lysis and used cross-flow filtration or acid precipitation to concentrate the voluminous supernatant and separate the collagen from impurities. We confirmed that the secreted collagen forms triple helical structures, using Sirius Red staining and circular dichroism. We also detected collagen biomarkers via Raman spectroscopy, further supporting that the recombinant collagen forms a stable triple helical conformation. We further studied the effect of the isolation methods on the morphology and secondary structure, concluding that the final collagen structure is process-dependent. Overall, we show that the curli secretion system can be adapted for extracellular secretion of the bacterial collagen, eliminating the need for cell lysis, which simplifies the collagen isolation process and enables a simple cost-effective method with potential for scale-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".