Effect of Drug Transporter Genes on Cysteine Export and Overproduction in <i>Escherichia coli</i>
Bibliographic record
Abstract
L-cysteine is an important amino acid in terms of its industrial applications. We previously found a marked production of L-cysteine from glucose in recombinant Escherichia coli cells expressing an altered cysE gene encoding feedback inhibition-insensitive serine acetyltransferase. Also, a lower level of cysteine desulfhydrase (CD) activity, which is involved in L-cysteine degradation, increased L-cysteine productivity in E. coli. The use of an L-cysteine efflux system could be promising for breeding L-cysteine overproducers. In addition to YdeD and YfiK, which have been reported previously as L-cysteine exporter proteins in E. coli, we analyzed the effects of 33 putative drug transporter genes in E. coli on L-cysteine export and overproduction. Overexpression of the acrD, acrEF, bcr, cusA, emrAB, emrKY, ybjYZ, and yojIH genes reversed the growth inhibition of tnaA (the major CD gene)-disrupted E. coli cells by L-cysteine. We also found that overexpression of these eight genes reduces intracellular L-cysteine levels after cultivation in the presence of L-cysteine. Amino acid transport assays showed that Bcr overexpression conferring bicyclomycin and tetracycline resistance specifically promotes L-cysteine export driven by energy derived from the proton gradient. When a tnaA-disrupted E. coli strain expressing the altered cysE gene was transformed with a plasmid carrying the bcr gene, the transformant exhibited more L-cysteine production than cells carrying the vector only. A reporter gene assay suggested that the bcr gene is constitutively expressed at a substantial level. These results indicate that the multidrug transporter Bcr in the major facilitator family is involved in L-cysteine export and overproduction in genetically engineered E. coli cells.
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 | 0.001 | 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".