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Record W2794918098 · doi:10.3389/fmicb.2018.00629

Stem Cell Transcription Factor FoxO Controls Microbiome Resilience in Hydra

2018· article· en· W2794918098 on OpenAlexfundno aff
Benedikt M. Mortzfeld, Jan Taubenheim, Sebastian Fraune, Alexander Klimovich, Thomas C. G. Bosch

Bibliographic record

VenueFrontiers in Microbiology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
FundersExzellenzclusters EntzündungsforschungDeutsche ForschungsgemeinschaftCanadian Institute for Advanced Research
KeywordsBiologyLernaean HydraTranscription factorMicrobiomeEpigeneticsLongevityHolobiontInnate immune systemCell biologyForkhead Transcription FactorsRegulatorGeneticsModel organismImmune systemGeneBacteria

Abstract

fetched live from OpenAlex

The aging process is considered to be the result of accumulating cellular deterioration in an individual organism over time. It can be affected by the combined influence of genetic, epigenetic and environmental factors including life-style associated events. In the non-senescent freshwater polyp Hydra, one of the classical model systems for evolutionary developmental biology and regeneration, transcription factor FoxO modulates both stem cell proliferation and innate immunity. This provides strong support for the role of FoxO as a critical rate-of-aging regulator. However, how environmental factors interact with FoxO remains unknown. Here we find that deficiency in FoxO signaling in Hydra leads to dysregulation of antimicrobial peptide (AMP) expression and that FoxO loss-of-function polyps are impaired in selection for bacteria resembling the native microbiome and more susceptible to colonization of foreign bacteria. These findings reveal a key role of FoxO signaling in the communication between host and microbiota and embed the evolutionary conserved longevity factor FoxO into the holobiont concept.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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 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

Citations30
Published2018
Admission routes1
Has abstractyes

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