Characterization of Sec-Translocon-Dependent Extracytoplasmic Proteins of <i>Rickettsia typhi</i>
Bibliographic record
Abstract
As obligate intracellular, vector-borne bacteria, rickettsiae must adapt to both mammalian and arthropod host cell environments. Deciphering the molecular mechanisms of the interactions between rickettsiae and their host cells has largely been hindered by the genetic intractability of these organisms; however, research in other gram-negative pathogens has demonstrated that many bacterial determinants of attachment, entry, and pathogenesis are extracytoplasmic proteins. The annotations of several rickettsial genomes indicate the presence of homologs of the Sec translocon, the major route for bacterial protein secretion from the cytoplasm. For Rickettsia typhi, the etiologic agent of murine typhus, homologs of the Sec-translocon-associated proteins LepB, SecA, and LspA have been functionally characterized; therefore, the R. typhi Sec apparatus represents a mechanism for the secretion of rickettsial proteins, including virulence factors, into the extracytoplasmic environment. Our objective was to characterize such Sec-dependent R. typhi proteins in the context of a mammalian host cell infection. By using the web-based programs LipoP, SignalP, and Phobius, a total of 191 R. typhi proteins were predicted to contain signal peptides targeting them to the Sec translocon. Of these putative signal peptides, 102 were tested in an Escherichia coli-based alkaline phosphatase (PhoA) gene fusion system. Eighty-four of these candidates exhibited signal peptide activity in E. coli, and transcriptional analysis indicated that at least 54 of the R. typhi extracytoplasmic proteins undergo active gene expression during infections of HeLa cells. This work highlights a number of interesting proteins possibly involved in rickettsial growth and virulence in mammalian cells.
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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) | 0.000 | 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".