<i>Francisella </i><i>t</i><i>ularensis</i> Proteome: Low Levels of ASB-14 Facilitate the Visualization of Membrane Proteins in Total Protein Extracts
Bibliographic record
Abstract
Proteomic analysis of bacterial pathogens isolated from in vivo sources, such as infected tissues, provides many challenges not the least of which is the limited quantity of sample available for analysis. It is, therefore, highly desirable to develop a one-step cellular lysis and protein solubilization method that minimizes protein losses and allows the maximum possible coverage of the proteome. Here, we have used standard sample buffer constituents including urea, thiourea and DTT, but varied the detergent composition of the buffers in order to achieve the best quality of gels and the greatest spot resolution. We found that the most efficient solubilizing solution in this case consisted of 7 M urea, 2 M thiourea, 1% DTT, 0.5% amidosulfobetaine-14 (ASB-14) and 4% 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS). Inclusion of low levels of ASB-14 in solutions allowed visualization of a subset of 24 new protein spots in the Live Vaccine Strain (LVS) of Francisella tularensis and 21 spots in a virulent A-strain of the pathogen. Further investigation showed that 15 of the 24 enriched LVS spots were membrane or membrane-associated proteins suggesting that the optimized lysis and solubilization solution aids in the detection of more hydrophobic proteins. This methodology is now being applied to the analysis of Francisella obtained from in vivo sources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".