Overexpression of the HspL Promotes<i>Agrobacterium tumefaciens</i>Virulence in<i>Arabidopsis</i>Under Heat Shock Conditions
Bibliographic record
Abstract
Agrobacterium tumefaciens transfers a specific DNA fragment from the resident tumor-inducing (Ti) plasmid and effector virulence (Vir) proteins to plant cells during infection. A. tumefaciens VirB1-11 and VirD4 proteins assemble as the type IV secretion system (T4SS), which mediates transfer of the T-DNA and effector Vir protein into plant cells, thus resulting in crown gall disease in plants. Previous studies revealed that an α-crystallin-type, small heat-shock protein (HspL) is a more effective VirB8 chaperone than three other small heat-shock proteins (HspC, HspAT1, and HspAT2). Additionally, HspL contributes to efficient T4SS-mediated DNA transfer and tumorigenesis under room-temperature growth. In this study, we aimed to characterize the impact of HspL on Agrobacterium-mediated transformation efficiency under heat-shock treatment. During heat shock, transient transformation efficiency and VirB8 protein accumulation were lower in the hspL deletion mutant than in the wild type. Overexpression of HspL in A. tumefaciens enhanced the transient transformation efficiency in root explants of both susceptible and recalcitrant Arabidopsis ecotypes. In addition, the reduced transient transformation efficiency during heat stress was recovered by overexpression of HspL in A. tumefaciens. HspL may help maintain VirB8 homeostasis and elevate Agrobacterium-mediated transformation efficiency under both heat-shock and nonheat-shock growth.
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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) | 0.001 | 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".