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Record W2756267254 · doi:10.1038/s41467-017-00625-1

A secreted antibacterial neuropeptide shapes the microbiome of Hydra

2017· article· en· W2756267254 on OpenAlexfundno aff
René Augustin, Katja Schröder, Andrea P. Murillo Rincón, Sebastian Fraune, Friederike Anton‐Erxleben, Eva-Maria Herbst, Jörg Wittlieb, Martin Schwentner, Joachim Grötzinger, Trudy M. Wassenaar, Thomas C. G. Bosch

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
FundersExzellenzclusters EntzündungsforschungDeutsche ForschungsgemeinschaftCanadian Institute for Advanced Research
KeywordsLernaean HydraNeuropeptideBiologyMicrobiomeOrganismModel organismAntimicrobial peptidesCell biologyMicrobiologyReceptorBiochemistryBioinformaticsAntimicrobialGeneticsGene

Abstract

fetched live from OpenAlex

Colonization of body epithelial surfaces with a highly specific microbial community is a fundamental feature of all animals, yet the underlying mechanisms by which these communities are selected and maintained are not well understood. Here, we show that sensory and ganglion neurons in the ectodermal epithelium of the model organism hydra (a member of the animal phylum Cnidaria) secrete neuropeptides with antibacterial activity that may shape the microbiome on the body surface. In particular, a specific neuropeptide, which we call NDA-1, contributes to the reduction of Gram-positive bacteria during early development and thus to a spatial distribution of the main colonizer, the Gram-negative Curvibacter sp., along the body axis. Our findings warrant further research to test whether neuropeptides secreted by nerve cells contribute to the spatial structure of microbial communities in other organisms.Certain neuropeptides, in addition to their neuromodulatory functions, display antibacterial activities of unclear significance. Here, the authors show that a secreted neuropeptide modulates the distribution of bacterial communities on the body surface during development of the model organism Hydra.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.829

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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 designObservational
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

Citations125
Published2017
Admission routes1
Has abstractyes

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