Understanding GroEL and DnaK Stress Response Proteins as Antigens for Bacterial Diseases
Bibliographic record
Abstract
Bacteria do not simply express a constitutive panel of proteins but they instead undergo dynamic changes in their protein repertoire in response to changes in nutritional status and when exposed to different environments. These differentially expressed proteins may be suitable to use for vaccine antigens if they are virulence factors. Immediately upon entry into the host organism, bacteria are exposed to a different environment, which includes changes in temperature, osmotic pressure, pH, etc. Even when an organism has already penetrated the blood or lymphatics and it then enters another organ or a cell, it can respond to these new conditions by increasing the expression of virulence factors to aid in bacterial adherence, invasion, or immune evasion. Stress response proteins such as heat shock proteins and chaperones are some of the proteins that undergo changes in levels of expression and/or changes in cellular localization from the cytosol to the cell surface or the secretome, making them potential immunogens for vaccine development. Herein we highlight literature showing that intracellular chaperone proteins GroEL and DnaK, which were originally identified as playing a role in protein folding, are relocated to the cell surface or are secreted during invasion and therefore may be recognized by the host immune system as antigens. In addition, we highlight literature showcasing the immunomodulation effects these proteins can have on the immune system, also making them potential adjuvants or immunotherapeutics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".