Multivalent Nanobodies for Potent and Broad Neutralization of <i>Staphylococcus aureus</i> Toxins
Bibliographic record
Abstract
Abstract Staphylococcus aureus is a leading cause of lethal bacteremia and pneumonia, which are driven by potent virulence factors such as T-cell superantigens and alpha hemolysin. S. aureus has among the highest rates of antibiotic resistance, yet no vaccines or alternative therapies are available despite decades of research. Here, we developed a repertoire of potent, high affinity nanobodies (Nbs) targeting key toxins in S. aureus infection, including superantigens (SAgs) SEB, SEC, TSST-1, and Hla. Comprehensive cryo-EM and AlphaFold3 analyses of these Nbs, which were elicited with clinical cocktail vaccines, revealed diverse neutralizing epitopes and mechanisms that provide strategic insights for immunotherapy and vaccine design. Guided by these findings, we engineered highly stable, multivalent, and multifunctional Nb constructs. These constructs included an aerosolizable trimeric Nb with enhanced neuralization activity against Hla and SEC, and an ultrapotent decameric Nb-IgG-Fc fusion construct against a wide range of major toxins in S. aureus sepsis (SEB, SEC, TSST-1, and Hla). These multifunctional Nbs demonstrated promising protective activity in murine models of pneumonia and sepsis, underscoring their potential as versatile immunotherapies that address the complex virulence profiles of S. aureus . Our work lays a foundation for precision immunotherapies beyond current treatment options to combat complex bacterial infections with multiple virulence mechanisms. Significance statement S. aureus is among the most common, antibiotic-resistant, and deadly causes of bacterial infections. We developed nanobodies against clinically significant virulence factors in S. aureus sepsis and pneumonia, including superantigens (SAgs) SEB, SEC, and TSST-1 as well as pore forming toxin Hla. These nanobodies displayed complete and potent neutralization of each toxin, exploiting a wide variety neutralizing mechanisms. Structural investigation of these diverse neutralizing nanobodies, which were elicited in llamas using clinically investigated cocktail vaccines, highlighted the importance of disrupting SAg interaction with TCR or MHCII and potential flaws in targeting poorly neutralizing conserved SAg epitopes using vaccine cocktails. Nb leads against each toxin were combined in different multivalent configurations, including an aerosolizable trimeric Nb and a half-life extended decameric Nb IgG Fc fusion construct. This work highlights multivalent nanobodies as a comprehensive yet therapeutically precise drug platform that addresses the complex virulence profiles of bacterial infectious diseases.
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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".