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Record W4414536975 · doi:10.1101/2025.09.24.678341

Induced pathogenicity toward open-ocean diatoms by a newly isolated filterable bacterium <i>Ekhidna algicida</i> sp. nov.

2025· preprint· en· W4414536975 on OpenAlexaff
Shiri Graff van Creveld, Sacha Coesel, Ellen Lavoie, Vaughn Iverson, Rhonda Morales, Megan J. Schatz, Alexandra E. Jones-Kellett, Jesse McNichol, R. M. Key, Jed A. Fuhrman, Bryndan P. Durham, E. Virginia Armbrust

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsSt. Francis Xavier University
FundersNational Science Foundation
KeywordsAxenicBacteriaMarine bacteriophagePhytoplanktonPelagic zoneDiatomPathogenStrain (injury)Pacific ocean

Abstract

fetched live from OpenAlex

Abstract Phytoplankton are the base of marine food webs. They form intricate interactions with heterotrophic bacteria ranging from mutualistic to pathogenic that together impact oceanic carbon and nutrient cycling. Our understanding of these interactions in marine environments remains primarily limited to laboratory-based studies of model organisms. Here, we report the discovery and characterization of Ekhidna algicida sp. nov. strain To15, isolated from the oligotrophic Pacific Ocean (16°N, 140°W) based on its algicidal effect on the pelagic diatom Thalassiosira oceanica . Subsequent co-culture experiments demonstrate that E. algicida is lethal within days to a diverse array of diatoms, with the effect mediated by bacterial exudates that remain algicidal on their own against axenic T. oceanica cultures. Twenty additional algicidal Ekhidna strains were subsequently isolated from the Pacific Ocean. Our findings reveal E. algicida as a potentially widespread pathogen of diatoms, that can alter microbial community composition dynamics in pelagic ecosystems. Teaser A newly discovered Pacific Ocean bacterium can kill diatoms, revealing a hidden pathogenic role in open-ocean ecosystems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0040.006
Research integrity0.0010.002
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 head0.021
GPT teacher head0.254
Teacher spread0.234 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

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