Prevalence of Slam-dependent hemophores in Gram-negative bacteria
Bibliographic record
Abstract
Abstract Iron acquisition systems are crucial for pathogen growth and survival in iron-limiting host environments. To overcome nutritional immunity, bacterial pathogens evolved to use diverse mechanisms to acquire iron. Here, we examined a heme acquisition system driven by hemophores called HphAs from several Gram-negative bacteria. Structural determination of HphAs revealed a N-terminal clamp-like domain that binds heme and a C-terminal eight-stranded β-barrel domain that shares the same architecture as the Slam-dependent Neisserial surface lipoproteins. The structure of these HphAs is strikingly similar to a novel hemophore discovered by Latham et al. (2019), named hemophilin 1 . The genetic organization of HphAs consist of genes encoding a Slam homolog and a TonB-dependent receptor (TBDR). We investigated the Slam-HphA system in the native organism or the reconstituted system in E. coli cells and found that the efficient secretion of HphA is dependent on Slam. The TBDR also played an important role for heme uptake and conferred specificity for its cognate HphA. Furthermore, bioinformatic analysis of HphA homologs revealed that HphAs are conserved in the alpha, beta, and gammaproteobacteria Together, these results show that HphA presents a new class of hemophores in Gram-negative bacteria and further expands the role of Slams in transporting soluble proteins supporting it role as a type 11 secretion system.
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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 | 0.001 | 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".