A SELDI‐TOF MS procedure for the detection, quantitation, and preliminary characterization of low‐molecular‐weight recombinant proteins expressed in transgenic plants
Bibliographic record
Abstract
We describe a SELDI-TOF MS procedure for the rapid detection and quantitation of low-molecular-weight recombinant proteins expressed in plants. Transgenic lines of potato (Solanum tuberosum L.) expressing the clinically useful protein bovine aprotinin or the cysteine protease inhibitor corn cystatin II were generated by Agrobacterium tumefaciens-mediated transformation, and then used as test material for the analyses. Real-time RT-PCR amplifications and detection of the recombinant proteins by immunoblotting were first conducted for transformed potato lines accumulating the proteins in different cell compartments. Both proteins were found at varying levels in leaves, depending on their final cellular destination and transgene expression rate. These conclusions drawn from standard immunodetection assays were easily confirmed by SELDI-TOF MS comparative profiling, after immobilizing the leaf proteins of control and transformed lines on protein biochips for weak cationic exchange. This procedure, carried out in less than 2 h, allows for the rapid comparison of recombinant protein levels in transgenic plant lines. The molecular weight of immobilized proteins can also be determined directly from the MS spectra, thus providing a simple way to assess the structural integrity and homogeneity of recombinant proteins in planta, and to identify the most suitable cellular compartments for their heterologous production.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".