An<i>In Vivo</i>High-Throughput Screening Approach Targeting the Type IV Secretion System Component VirB8 Identified Inhibitors of<i>Brucella abortus</i>2308 Proliferation
Bibliographic record
Abstract
As bacterial pathogens develop resistance against most currently used antibiotics, novel alternatives for treatment of microbial infectious diseases are urgently needed. Targeting bacterial virulence functions in order to disarm pathogens represents a promising alternative to classical antibiotic therapy. Type IV secretion systems, which are multiprotein complexes in the cell envelope that translocate effectors into host cells, are critical bacterial virulence factors in many pathogens and excellent targets for such "antivirulence" drugs. The VirB8 protein from the mammalian pathogen Brucella was chosen as a specific target, since it is an essential type IV secretion system component, it participates in multiple protein-protein interactions, and it is essential for the assembly of this translocation machinery. The bacterial two-hybrid system was adapted to assay VirB8 interactions, and a high-throughput screen identified specific small-molecule inhibitors. VirB8 interaction inhibitors also reduced the levels of VirB8 and of other VirB proteins, and many of them inhibited virB gene transcription in Brucella abortus 2308, suggesting that targeting of the secretion system has complex regulatory effects in vivo. One compound strongly inhibited the intracellular proliferation of B. abortus 2308 in a J774 macrophage infection model. The results presented here show that in vivo screens with the bacterial two-hybrid assay are suited to the identification of inhibitors of Brucella type IV secretion system function.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".