Deep proteome coverage advances knowledge of Treponema pallidum protein expression profiles during infection
Bibliographic record
Abstract
Comprehensive proteome-wide analysis of the syphilis spirochete, Treponema pallidum ssp. pallidum, is technically challenging due to high sample complexity, difficulties with obtaining sufficient quantities of bacteria for analysis, and the inherent fragility of the T. pallidum cell envelope which further complicates proteomic identification of rare T. pallidum outer membrane proteins (OMPs). The main aim of the present study was to gain a deeper understanding of the T. pallidum global proteome expression profile under infection conditions. This will corroborate and extend genome annotations, identify protein modifications that are unable to be predicted at the genomic or transcriptomic levels, and provide a foundational knowledge of the T. pallidum protein expression repertoire. Here we describe the optimization of a T. pallidum-specific sample preparation workflow and mass spectrometry-based proteomics pipeline which allowed for the detection of 77% of the T. pallidum protein repertoire under infection conditions. When combined with prior studies, this brings the overall coverage of the T. pallidum proteome to almost 90%. These investigations identified 27 known/predicted OMPs, including potential vaccine candidates, and detected expression of 11 potential OMPs under infection conditions for the first time. The optimized pipeline provides a robust and reproducible workflow for investigating T. pallidum protein expression during infection. Importantly, the combined results provide the deepest coverage of the T. pallidum proteome to date.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".