A Rationally Designed Bovine IgA Fc Scaffold Enhances in planta Accumulation of a VHH-Fc Fusion Without Compromising Binding to Enterohemorrhagic E. coli
Bibliographic record
Abstract
We previously isolated a single domain antibody (VHH) that binds Enterohemorrhagic Escherichia coli (EHEC) with the end-goal being the enteromucosal passive immunization of cattle herds. To improve the yield of a chimeric fusion of the VHH with an IgA Fc, we employed two rational design strategies, supercharging and introducing de novo disulfide bonds, on the bovine IgA Fc component of the chimera. After mutagenizing the Fc, we screened for accumulation levels after transient transformation in Nicotiana benthamiana leaves. We identified and characterized five supercharging and one disulfide mutant, termed ‘(5 + 1)Fc’, that improve accumulation in comparison to the native Fc. Combining all these mutations is associated with a 32-fold increase of accumulation for the Fc alone, from 23.9 mg/kg fresh weight (FW) to 599.5 mg/kg FW, as well as a twenty-fold increase when fused to a VHH that binds EHEC, from 12.5 mg/kg FW tissue to 236.2 mg/kg FW. Co-expression of native or mutated VHH-Fc with bovine joining chain (JC) and bovine secretory component (SC) followed by co-immunoprecipitation suggests that the stabilizing mutations do not interfere with the capacity of VHH-Fc to assemble with JC and FC into a secretory IgA. Both the native and the mutated VHH-Fc similarly neutralized the ability of four of the seven most prevalent EHEC strains (O157:H7, O26:H11, O111:Hnm, O145:Hnm, O45:H2, O121:H19 and O103:H2), to adhere to HEp-2 cells as visualized by immunofluorescence microscopy and quantified by fluorometry. These results collectively suggest that supercharging and disulfide bond tethering on a Fc chain can effectively improve accumulation of a VHH-Fc fusion without impacting VHH functionality.
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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) | 0.000 | 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 | 0.000 |
| 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 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".