A Novel Single Domain Antibody Targeting FliC Flagellin of Salmonella enterica for Effective Inhibition of Host Cell Invasion
Bibliographic record
Abstract
The enteric pathogen, Salmonella enterica is a major cause of human gastroenteritis globally and with increasing bacterial resistance to antibiotics, alternative solutions are urgently needed. Single domain antibodies (sdAbs), the smallest antibody fragments that retain antigen binding specificity and affinity, are derived from variable heavy-chain only fragments (VHH) of camelid heavy-chain-only immunoglobulins. SdAbs typically contain a single disulfide bond simplifying recombinant protein production in microbial systems. These factors make sdAbs ideally suited for the development of effective anti-bacterial therapeutics. To this end, we generated an anti-Salmonella VHH library from which we screened for high affinity sdAbs. We present a novel sdAb (Abi-Se07) that targets the Salmonella virulence factor, FliC, required for bacterial motility and invasion of host cells. We demonstrate that Abi-Se07 bound FliC with a KD of 16.2 ± 0.1 nM. In addition, Abi-Se07 exhibited cross-serovar binding to both whole cells of S. enterica serovar Typhimurium, Heidelberg, and Hadar. Abi-Se07 significantly inhibited bacterial motility and significantly reduced S. enterica colonization in a more native environment of chicken jejunum epithelium. Taken together, we have identified a novel anti-Salmonella sdAb and discuss future efforts toward therapeutic development.
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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) | 0.001 | 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".