Metal-Selective DNA-Binding Response of <i>Escherichia coli</i> NikR
Bibliographic record
Abstract
The NikR transcription factor from Escherichia coli is a Ni(II)-dependent repressor that regulates production of the nickel ion transporter encoded by the nik operon. In the previous paper in this issue (Wang, S. C., Dias, A., Bloom, S. L., and Zamble, D. B. (2004) Selectivity of Metal Binding and Metal-Induced Stability of Escherichia coli NikR, Biochemistry 43, 10018-10028) we demonstrated that NikR can bind 1 equiv of Ni(II) or several other divalent transition metals with similar affinities, but that the Ni(II)-loaded protein is less susceptible to thermal or chemical denaturation than other divalent metal complexes. Here, we investigate the metal selectivity of the DNA-binding activity of NikR. Stoichiometric nickel induces binding of nanomolar NikR to the recognition sequence in the nik promoter, but single equivalents of other divalent metals such as Cd(II), Co(II), and Cu(II) also induce a similar DNA-binding affinity. In the presence of excess nickel, DNA-binding experiments indicate that NikR binds to the nik promoter as a tetramer with much higher affinity (20 pM), and it is this response that is selective for nickel. The DNA binding induced by an excess of other divalent metals is weaker, and is enhanced by the addition of stoichiometric nickel. Nickel titrations into a DNA-binding assay reveal a nickel affinity of 30 nM for a second metal-binding site, and in the presence of 30 nM metal only nickel induces detectable DNA binding by Ni(II)-NikR. These experiments support the hypothesis that there are two metal-binding sites and that both contribute to the nickel-selective DNA-binding response. A model for the in vivo activity of NikR is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".