In vivo associations of <i>Escherichia coli</i> NarJ with a peptide of the first 50 residues of nitrate reductase catalytic subunit NarG
Bibliographic record
Abstract
The catalytic subunit of many Escherichia coli redox enzymes bares a twin-arginine translocation (Tat)-dependent signal peptide in its precursor, which directs the redox enzyme complex to this Sec-independent pathway. NarG of the E. coli nitrate reductase NarGHI complex possesses a vestige twin-arginine motif at its N terminus. During the cofactor insertion, and assembly and folding of the NarG-NarH complex, a chaperone protein, NarJ, is thought to interact with the N terminus and an unknown second site of NarG. Our previous in vitro study provided evidence that NarJ's role shows some Tat system dependence. In this work, we investigated the associations of NarJ with a peptide of the first 50 residues of NarG (NarG50) in living cells. Two approaches were used: the Förster resonance energy transfer (FRET) based on yellow fluorescent protein - cyan fluorescent protein (YFP-CFP) and the bimolecular fluorescence complementation (BiFC). Compared with the wild-type (WT) E. coli cotransformants expressing both NarJ-YFP and NarG50-CFP, tat gene mutants gave an apparent FRET efficiency (Eapp) that was on the order of 25%-40% lower. These experiments implied a Tat system dependency of the in vivo associations between NarJ and the NarG50 peptide. In the BiFC assay, a 4-fold lower specific fluorescence intensity was observed for the E. coli WT cotransformants expressing both NarJ-Yc and NarG50-Yn than for its tat mutants, again suggesting a Tat dependence of the interactions. Fluorescence microscopy showed a "dot" /unipolar distribution of the reassembled YFP-NarJ:NarG50 both in WT and tat mutants, demonstrating a distinct localization of the interaction. Thus, although the degree of the interaction shows Tat dependence, the cell localization is less so. Taken together, these data further support that NarJ's activity on NarG may be assisted by the Tat system.
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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 | 0.001 |
| 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".