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Record W4413140938 · doi:10.1093/femsec/fiaf082

Antimicrobiota vaccine induces lysine-mediated modulation of tick immunity affecting <i>Borrelia</i> colonization

2025· article· en· W4413140938 on OpenAlexaff
Lourdes Mateos‐Hernández, Lianet Abuin‐Denis, Alejandra Wu‐Chuang, Apolline Maître, Helena Roháčková, Ryan O. M. Rego, Elianne Piloto‐Sardiñas, James J. Valdés, Stefania Porcelli, Aurélie Heckmann, Sara Moutailler, Covadonga Lucas‐Torres, Martin Moos, Stanislav Opekar, Myriam Kratou, Dasiel Obregón, Alejandro Cabezas‐Cruz

Bibliographic record

VenueFEMS Microbiology Ecology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Guelph
FundersCollectivité de CorseUniversité Paris-Est Créteil Val-de-MarneAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailAgence Nationale de la Recherche
KeywordsBiologyTickMicrobiologyBorreliaPathogenImmunityMicrobiomeTick-borne diseaseMetaboliteVirologyAntibodyImmune systemImmunologyBorrelia burgdorferiBioinformatics

Abstract

fetched live from OpenAlex

Tick microbiota influences Borrelia colonization, but changes in the microbiota-derived metabolite and how this affects tick physiology and vector competence is unclear. We investigated whether microbiota-induced metabolite modifications influence tick physiology and pathogen transmission. Using an antimicrobiota vaccine (live Escherichia coli) to immunize mice, we generated host antibodies that modulated the tick microbiome, decreasing bacterial abundance and increasing lysine levels in ticks. Elevated lysine correlated with increased tick weight. Lysine supplementation experiments enhanced defensin expression with DefMT6 exhibiting anti-Borrelia activity, reducing pathogen load in ticks. Our findings demonstrate that antimicrobiota vaccines induce metabolite changes, affecting tick physiology, immunity, and vector competence. These insights open new avenues for developing microbiota-targeted strategies to control tick-borne diseases.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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.

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

Citations4
Published2025
Admission routes1
Has abstractyes

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