Multiomics Analysis Provides Insight into the Laboratory Evolution of <i>Escherichia coli</i> toward the Metabolic Usage of Fluorinated Indoles
Bibliographic record
Abstract
Abstract Organofluorine compounds are known to be toxic to a broad variety of living beings in different habitats, and chemical fluorination has been historically exploited by mankind for the development of therapeutic drugs or agricultural pesticides. On the other hand, several studies so far have demonstrated that, under appropriate conditions, living systems (in particular bacteria) can tolerate the presence of fluorinated molecules (e.g., amino acids analogues) within their metabolism and even repurpose them as alternative building blocks for the synthesis of cellular macromolecules such as proteins. Understanding the molecular mechanism behind these phenomena would greatly advance approaches to the biotechnological synthesis of recombinant proteins and peptide drugs. However, information about the metabolic effects of long-term exposure of living cells to fluorinated amino acids remains scarce. Hereby, we report the long-term propagation of Escherichia coli (E. coli) in an artificially fluorinated habitat that yielded two strains naturally adapted to live on fluorinated amino acids. In particular, we applied selective pressure to force a tryptophan (Trp)-auxotrophic strain to use either 4- or 5-fluoroindole as essential precursors for the in situ synthesis of Trp analogues, followed by their incorporation in the cellular proteome. We found that full adaptation to both fluorinated Trp analogues requires a low number of genetic mutations but is accompanied by large rearrangements in regulatory networks, membrane integrity, and quality control of protein folding. These findings highlight the cellular mechanisms behind the adaptation to unnatural amino acids and provide the molecular foundation for bioengineering of novel microbial strains for synthetic biology and biotechnology.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".