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Record W2093932681 · doi:10.1128/jb.00733-08

Lsr2 of <i>Mycobacterium</i> Represents a Novel Class of H-NS-Like Proteins

2008· article· en· W2093932681 on OpenAlexafffund
Blair R. G. Gordon, Robin Imperial, Linru Wang, William Wiley Navarre, Jun Liu

Bibliographic record

VenueJournal of Bacteriology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiologyComplementationMycobacterium smegmatisPhenotypeGeneticsPsychological repressionEscherichia coliGeneMicrobiologyMolecular biologyGene expressionMycobacterium tuberculosis

Abstract

fetched live from OpenAlex

Lsr2 is a small, basic protein present in Mycobacterium and related actinomycetes. Our previous in vitro biochemical studies showed that Lsr2 is a DNA-bridging protein, a property shared by H-NS-like proteins in gram-negative bacteria. Here we present in vivo evidence based on genetic complementation experiments that Lsr2 is a functional analog of H-NS, the first such protein identified in gram-positive bacteria. We show that lsr2 can complement the phenotypes related to hns mutations in Escherichia coli, including beta-glucoside utilization, mucoidy, motility, and hemolytic activity. We also show that Lsr2 binds specifically to H-NS-regulated genes and the repression of hlyE by Lsr2 can be partially eliminated by overexpression of slyA, suggesting that the molecular mechanisms of Lsr2 repression and depression are similar to those of H-NS. The functional equivalence of these two proteins is further supported by the ability of hns to complement the lsr2 phenotype in Mycobacterium smegmatis. Taken together, our results demonstrate unequivocally that Lsr2 is an H-NS-like protein.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.016
GPT teacher head0.251
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations133
Published2008
Admission routes2
Has abstractyes

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