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Record W2968431347 · doi:10.3390/microorganisms7080261

Assessing Transcriptional Responses to Light by the Dinoflagellate Symbiodinium

2019· article· en· W2968431347 on OpenAlexafffund
Bahareh Zaheri, Steve Dagenais-Bellefeuille, Bo Song, David Morse

Bibliographic record

VenueMicroorganisms · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDinoflagellateBiologyGeneTranscription (linguistics)Gene expressionTranscriptomeRNAGeneticsSymbiodiniumBotany

Abstract

fetched live from OpenAlex

The control of transcription is poorly understood in dinoflagellates, a group of protists whose permanently condensed chromosomes are formed without histones. Furthermore, while transcriptomes contain a number of proteins annotated as transcription factors, the majority of these are cold shock domain proteins which are also known to bind RNA, meaning the number of true transcription factors is unknown. Here we have assessed the transcriptional response to light in the photosynthetic species Symbiodinium kawagutii. We find that three genes previously reported to respond to light using qPCR do not show differential expression using northern blots or RNA-Seq. Interestingly, global transcript profiling by RNA-Seq at LD 0 (dawn) and LD 12 (dusk) found only seven light-regulated genes (FDR = 0.1). qPCR using three randomly selected genes out of the seven was only able to validate differential expression of two. We conclude that there is likely to be less light regulation of gene expression in dinoflagellates than previously thought and suggest that transcriptional responses to other stimuli should also be more thoroughly evaluated in this class of organisms.

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 categoriesInsufficient payload (model declined to judge)
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 score0.387
Threshold uncertainty score0.991

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.010

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 head0.010
GPT teacher head0.234
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2019
Admission routes2
Has abstractyes

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