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Record W3213940119 · doi:10.1080/19490976.2021.1994832

E.coli Nissle increases transcription of flagella assembly and formate hydrogenlyase genes in response to colitis

2021· article· en· W3213940119 on OpenAlexafffund
Louis Dacquay, Derek S. Tsang, Donny Chan, John Parkinson, Dana J. Philpott, David R. McMillen

Bibliographic record

VenueGut Microbes · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyDownregulation and upregulationTranscriptomeTranscriptional regulationGeneMicrobiologyEscherichia coliPopulationNitric oxideInflammationMotilityProbioticTranscription (linguistics)Gene expressionBacteriaImmunologyCell biologyGeneticsMedicine

Abstract

fetched live from OpenAlex

Escherichia coli Nissle (EcN), a probiotic bacterium, has been employed in treating inflammatory bowel disease, but the nature of its therapeutic effect is not fully understood. Intestinal inflammation alters the environment, exposing the microbial population to new stresses and eliciting transcriptional responses. We administered EcN to germ-free mice and then compared its transcriptional response between DSS-treated and untreated conditions using RNA-seq analysis to identify 187 differentially expressed genes (119 upregulated, 68 downregulated) and verifying a subset with qRT-PCR. The upregulated genes included many involved in flagella biosynthesis and motility, as well as several members of the formate hydrogenlyase complex. Despite prior evidence that these pathways are both transcriptionally regulated by nitric oxide, in vitro tests did not establish that nitric oxide exposure alone elicited the transcriptional response. The results provide new information on the transcriptional response of EcN to inflammation and establish a basis for further investigation of its anti-inflammatory activity.

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 score0.068
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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