Expanded roles of lactate-sensing LldR in transcription regulation of the Escherichia coli K-12 genome: lactate utilisation and acid resistance
Bibliographic record
Abstract
LldR is a lactate-responsive transcription factor (TF) that transcriptionally regulates the lldPRD operon consisting of lactate permease and lactate dehydrogenase. The lldPRD operon facilitates the utilisation of lactic acid in bacteria. However, the role of LldR in whole genomic transcriptional regulation, and the mechanism involved in adaptation to lactate remains unclear. We used genomic SELEX (gSELEX) to comprehensively analyse the genomic regulatory network of LldR to understand the overall regulatory mechanism of lactic acid adaptation of the model intestinal bacterium Escherichia coli . In addition to the involvement of the lldPRD operon in utilising lactate as a carbon source, genes related to glutamate-dependent acid resistance and altering the composition of membrane lipids were identified as novel targets of LldR. A series of in vitro and in vivo regulatory analyses led to the identification of LldR as an activator of these genes. Furthermore, the results of lactic acid tolerance tests and co-culture experiments with lactic acid bacteria suggested that LldR plays a significant role in adapting to the acid stress induced by lactic acid. Therefore, we propose that LldR is an l-/d-lactate sensing TF for utilising lactate as a carbon source and for resistance to lactate-induced acid stress in intestinal bacteria.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".