Immobilized Metal Affinity Chromatography Co-Purifies TGF-β1 with Histidine-Tagged Recombinant Extracellular Proteins
Bibliographic record
Abstract
Extracellular recombinant proteins are commonly produced using HEK293 cells as histidine-tagged proteins facilitating purification by immobilized metal affinity chromatography (IMAC). Based on gel analyses, this one-step purification typically produces proteins of high purity. Here, we analyzed the presence of TGF-β1 in such IMAC purifications using recombinant extracellular fibrillin-1 fragments as examples. Analysis of various purified recombinant fibrillin-1 fragments by ELISA consistently revealed the presence of picomolar concentrations of active and latent TGF-β1, but not of BMP-2. These quantities of TGF-β1 were not detectable by Western blotting and mass spectrometry. However, the amounts of TGF-β1 were sufficient to consistently trigger Smad2 phosphorylation in fibroblasts. The purification mechanism was analyzed to determine whether the presence of TGF-β1 in these protein preparations represents a specific or non-specific co-purification of TGF-β1 with fibrillin-1 fragments. Control purifications using conditioned medium from non-transfected 293 cells yielded similar amounts of TGF-β1 after IMAC. IMAC of purified TGF-β1 and the latency associated peptide showed that these proteins bound to the immobilized nickel ions. These data clearly demonstrate that TGF-β1 was co-purified by specific interactions with nickel, and not by specific interactions with fibrillin-1 fragments. Among various chromatographic methods tested for their ability to eliminate TGF-β1 from fibrillin-1 preparations, gel filtration under high salt conditions was highly effective. As various recombinant extracellular proteins purified in this fashion are frequently used for experiments that can be influenced by the presence of TGF-β1, these findings have far-reaching implications for the required chromatographic schemes and quality controls.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".