Development of <i>Escherichia coli</i> Asparaginase II for Immunosensing: A Trade-Off between Receptor Density and Sensing Efficiency
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The clinical success of Escherichia coli l -asparaginase II (EcAII) as a front line chemotherapeutic agent for acute lymphoblastic leukemia (ALL) is often compromised because of its silent inactivation by neutralizing antibodies. Timely detection of silent immune response can rely on immobilizing EcAII, to capture and detect anti-EcAII antibodies. Having recently reported the use of a portable surface plasmon resonance (SPR) sensing device to detect anti-EcAII antibodies in undiluted serum from children undergoing therapy for ALL (Aubé et al., ACS Sensors 2016, 1 (11), 1358–1365), here we investigate the impact of the quaternary structure and the mode of immobilization of EcAII onto low-fouling SPR sensor chips on the sensitivity and reproducibility of immunosensing. We show that the native tetrameric structure of EcAII, while being essential for activity, is not required for antibody recognition because monomeric EcAII is equally antigenic. By modulating the mode of immobilization, we observed that low-density surface coverage obtained upon covalent immobilization allowed each tetrameric EcAII to bind up to two antibody molecules, whereas high-density surface coverage arising from metal chelation by N- or C-terminal histidine-tag reduced the sensing efficiency to less than one antibody molecule per tetramer. Nonetheless, immobilization of EcAII by metal chelation procured up to 10-fold greater surface coverage, thus resulting in increased SPR sensitivity and allowing reliable detection of lower analyte concentrations. Importantly, only metal chelation achieved highly reproducible immobilization of EcAII, providing the sensing reproducibility that is required for plasmonic sensing in clinical samples. This report sheds light on the impact of multiple factors that need to be considered to optimize the practical applications of plasmonic sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".