Enzymatic Modification of Lipid A by ArnT Protects Bordetella bronchiseptica against Cationic Peptides and Is Required for Transmission
Bibliographic record
Abstract
Pathogen transmission cycles require many steps: initial colonization, growth and persistence, shedding, and transmission to new hosts. Alterations in the membrane components of the bacteria, including lipid A, the membrane anchor of lipopolysaccharide, could affect any of these steps via its structural role protecting bacteria from host innate immune defenses, including antimicrobial peptides and signaling through Toll-like receptor 4 (TLR4). To date, lipid A has been shown to affect only the within-host dynamics of infection, not the between-host dynamics of transmission. Here, we investigate the effects of lipid A modification in a mouse infection and transmission model. Disruption of the Bordetella bronchiseptica locus (BB4268) revealed that ArnT is required for addition of glucosamine (GlcN) to B. bronchiseptica lipid A. ArnT modification of lipid A did not change its TLR4 agonist activity in J774 cells, but deleting arnT decreased resistance to killing by cationic antimicrobial peptides, such as polymyxin B and β-defensins. In the standard infection model, mutation of arnT did not affect B. bronchiseptica colonization, growth, persistence throughout the respiratory tract, recruitment of neutrophils to the nasal cavity, or shedding of the pathogen. However, the number of bacteria necessary to colonize a host (50% infective dose [ID50]) was 5-fold higher for the arnT mutant. Furthermore, the arnT mutant was defective in transmission between hosts. These results reveal novel functions of the ArnT lipid A modification and highlight the sensitivity of low-dose infections and transmission experiments for illuminating aspects of infectious diseases between hosts. Factors such as ArnT can have important effects on the burden of disease and are potential targets for interventions that can interrupt transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".