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Record W4288077856 · doi:10.1101/2022.07.25.501442

A mucin-regulated adhesin determines the intestinal biogeography and inflammatory character of a bacterial symbiont

2022· preprint· en· W4288077856 on OpenAlexaff
T. Jarrod Smith, Deepika Sundarraman, Ellie Melançon, Laura Desban, R. Parthasarathy, Karen Guillemin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institutes of Health
KeywordsBacterial adhesinMucinBiologyMucusMicrobiologyAkkermansia muciniphilaGlycanCell biologyBacteriaGlycocalyxEscherichia coliGlycoproteinGeneticsGeneBiochemistryEcology

Abstract

fetched live from OpenAlex

SUMMARY In a healthy gut, microbes are often aggregated with host mucus, yet the molecular basis for this organization and its impact on intestinal health are unclear. Mucus is a viscous physical barrier separating resident microbes from epithelia, but also provides glycan cues that regulate microbial behaviors. Using experimental evolution, we discovered a mucin-sensing pathway in an Aeromonas symbiont of zebrafish, Aer01. In response to the mucin-associated glycan N-acetylglucosamine, a sensor kinase regulates expression of a mucin-binding adhesin we named MbpA. When MbpA is disrupted, Aer01 colonizes to normal levels, but is largely planktonic and elicits increased intestinal inflammation, traits which are reversed by increasing cell surface MpbA. MbpA-like adhesins are common in human-associated bacteria and expression of an Akkermansia muciniphila MbpA-like adhesin in MbpA-deficient Aer01 restored lumenal aggregation and reversed its pro-inflammatory character. Our work demonstrates how resident bacteria use mucin glycans to modulate behaviors congruent with host health.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.222
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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