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Record W2138474183 · doi:10.1128/jb.00748-10

Deficiency in<scp>l</scp>-Serine Deaminase Interferes with One-Carbon Metabolism and Cell Wall Synthesis in<i>Escherichia coli</i>K-12

2010· article· en· W2138474183 on OpenAlexaff
Xiao Zhang, Ziad W. El‐Hajj, E. B. Newman

Bibliographic record

VenueJournal of Bacteriology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsConcordia University
Fundersnot available
KeywordsSerineBiologyBiochemistryAlanineEscherichia coliGlycineAmino acidOperonLysisMutantMetabolismCell divisionBiosynthesisCellEnzymeGene

Abstract

fetched live from OpenAlex

Escherichia coli K-12 provided with glucose and a mixture of amino acids depletes L-serine more quickly than any other amino acid even in the presence of ammonium sulfate. A mutant without three 4Fe4S L-serine deaminases (SdaA, SdaB, and TdcG) of E. coli K-12 is unable to do this. The high level of L-serine that accumulates when such a mutant is exposed to amino acid mixtures starves the cells for C(1) units and interferes with cell wall synthesis. We suggest that at high concentrations, L-serine decreases synthesis of UDP-N-acetylmuramate-L-alanine by the murC-encoded ligase, weakening the cell wall and producing misshapen cells and lysis. The inhibition by high L-serine is overcome in several ways: by a large concentration of L-alanine, by overproducing MurC together with a low concentration of L-alanine, and by overproducing FtsW, thus promoting septal assembly and also by overexpression of the glycine cleavage operon. S-Adenosylmethionine reduces lysis and allows an extensive increase in biomass without improving cell division. This suggests that E. coli has a metabolic trigger for cell division. Without that reaction, if no other inhibition occurs, other metabolic functions can continue and cells can elongate and replicate their DNA, reaching at least 180 times their usual length, but cannot divide.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.487
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.204
Teacher spread0.197 · 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 teacher head, 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

Citations62
Published2010
Admission routes1
Has abstractyes

Explore more

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