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Record W2607096728 · doi:10.1126/sciadv.1602552

Microbial arms race: Ballistic “nematocysts” in dinoflagellates represent a new extreme in organelle complexity

2017· article· en· W2607096728 on OpenAlexafffund
Gregory S. Gavelis, Kevin C. Wakeman, Urban Tillmann, Christina Ripken, Satoshi Mitarai, María Herranz, Suat Özbek, Thomas W. Holstein, Patrick J. Keeling, Brian S. Leander

Bibliographic record

VenueScience Advances · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsUniversity of British Columbia
FundersCanadian Institute for Advanced Research
KeywordsCnidocyteOrganelleBiologyArms raceExtreme environmentAstrobiologyEvolutionary biologyBacteriaEcologyCell biologyGeneticsHistory

Abstract

fetched live from OpenAlex

We examine the origin of harpoon-like secretory organelles (nematocysts) in dinoflagellate protists. These ballistic organelles have been hypothesized to be homologous to similarly complex structures in animals (cnidarians); but we show, using structural, functional, and phylogenomic data, that nematocysts evolved independently in both lineages. We also recorded the first high-resolution videos of nematocyst discharge in dinoflagellates. Unexpectedly, our data suggest that different types of dinoflagellate nematocysts use two fundamentally different types of ballistic mechanisms: one type relies on a single pressurized capsule for propulsion, whereas the other type launches 11 to 15 projectiles from an arrangement similar to a Gatling gun. Despite their radical structural differences, these nematocysts share a single origin within dinoflagellates and both potentially use a contraction-based mechanism to generate ballistic force. The diversity of traits in dinoflagellate nematocysts demonstrates a stepwise route by which simple secretory structures diversified to yield elaborate subcellular weaponry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.410

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.303
Teacher spread0.259 · 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 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

Citations52
Published2017
Admission routes2
Has abstractyes

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