<i>Rickettsia typhi</i> peptidoglycan mapping with data-dependent tandem mass spectrometry
Bibliographic record
Abstract
Abstract Rickettsia species are diverse Gram-negative obligate intracellular bacteria often pervasive in numerous invertebrates, as well as fungal, nematode and microeukaryotic hosts. Certain species are etiological agents for well-known arthropod-borne illnesses; e.g., R. rickettsii (Rocky Mountain Spotted Fever), R. prowazekii (Epidemic Typhus), and R. typhi , (Endemic Typhus). Living freely in eukaryotic cytosol presumably exposes rickettsiae to host cell immune receptors, particularly those recognizing bacterial cell envelope glycoconjugates. However, the mechanics of host recognition of rickettsiae remain poorly defined. As rickettsiae synthesize a canonical Gram-negative cell envelope that includes peptidoglycan (PGN) and lipopolysaccharide (LPS), structural insight on these macromolecules is important for deciphering host responses to these pathogens. In this work, PGN from R. typhi was digested and the resultant subunits were analyzed by two different, albeit complementary, sample preparation methods. Both approaches were subsequently subjected to liquid chromatography/mass spectrometry analysis to infer PGN structure. R. typhi PGN was determined to be similar to most other Gram-negative bacteria, with mDAP-type muropeptide subunits. However, additional alanine residues were observed elongating the muropeptide stems, rather than the glycine residues usually observed in Gram-negative bacterial PGN. Despite this deviation, R. typhi contains a murein layer that is predicted to agonize host cellular PGN receptors and be susceptible to PGN-targeting antimicrobials. This same structure is likely synthesized by all Rickettsia species, as bioinformatics and comparative genomics analyses indicate the biosynthesis of PGN is highly conserved. Determining how host cells process this canonical glycoconjugate during infection is crucial for identifying factors behind rickettsial pathogenesis, including immunoavoidance or proinflammatory mechanisms possibly employed by rickettsiae with varying pathogenic potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".