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Record W4416131181 · doi:10.1093/plphys/kiaf575

IPTG- and estradiol-inducible gene expression systems in the unicellular red alga <i>Cyanidioschyzon merolae</i>

2025· article· en· W4416131181 on OpenAlexfundno aff
Takayuki Fujiwara, Shunsuke Hirooka, Shota Yamashita, Shin‐ya Miyagishima

Bibliographic record

VenuePLANT PHYSIOLOGY · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersInstitute of GeneticsJST-Mirai ProgramJapan Science and Technology AgencyJapan Society for the Promotion of ScienceTokyo Metropolitan University
KeywordsChloroplastGene expressionGeneGene knockdownRegulation of gene expressionPhotosynthesisGenomeModel organism

Abstract

fetched live from OpenAlex

The genetically tractable unicellular red alga Cyanidioschyzon merolae has a remarkably simple genome (4,775 nucleus-encoded proteins) and cellular architecture. It contains only a single set of most membranous organelles, making it a valuable tool for elucidating the fundamental mechanisms of photosynthetic eukaryotes. However, as in other genetically tractable eukaryotic algae, previously developed systems for inducible gene expression rely on environmental stimuli such as heat shock or ammonium depletion, which impact cellular physiology and thus limit their usage. To overcome this issue, we developed IPTG- and estradiol-inducible gene expression systems in C. merolae in which the addition of these chemicals itself has no impact on cellular growth or the transcriptome. Additionally, we established IPTG- and estradiol-inducible protein knockdown systems and successfully degraded the endogenous chloroplast division protein DRP5B using the estradiol-inducible system. These systems facilitate functional genomic analyses in C. merolae, especially for understanding physiological mechanisms and their interactions in photosynthetic eukaryotes.

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 score0.020
Threshold uncertainty score0.463

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)0.0000.000

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 head0.008
GPT teacher head0.215
Teacher spread0.206 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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