MétaCan
Menu
← Back to cohort
Record W4398768425 · doi:10.1038/s41467-024-48847-4

Phosphorus deficiency alleviates iron limitation in Synechocystis cyanobacteria through direct PhoB-mediated gene regulation

2024· article· en· W4398768425 on OpenAlexaff
Guo‐Wei Qiu, Y. X. Wang, Xing Qi, Da Huang, Guo‐Zheng Dai, Huazhong Shi, Neil M. Price, Bao‐Sheng Qiu

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMcGill University
FundersInstitute of HydrobiologyCentral China Normal UniversityChina Postdoctoral Science FoundationChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsCyanobacteriaPhosphorusSynechocystisBiologyNutrientPhosphorus deficiencyPhytoplanktonEcologyChemistryBacteriaGenetics

Abstract

fetched live from OpenAlex

Iron and phosphorus are essential nutrients that exist at low concentrations in surface waters and may be co-limiting resources for phytoplankton growth. Here, we show that phosphorus deficiency increases the growth of iron-limited cyanobacteria (Synechocystis sp. PCC 6803) through a PhoB-mediated regulatory network. We find that PhoB, in addition to its well-recognized role in controlling phosphate homeostasis, also regulates key metabolic processes crucial for iron-limited cyanobacteria, including ROS detoxification and iron uptake. Transcript abundances of PhoB-targeted genes are enriched in samples from phosphorus-depleted seawater, and a conserved PhoB-binding site is widely present in the promoters of the target genes, suggesting that the PhoB-mediated regulation may be highly conserved. Our findings provide molecular insights into the responses of cyanobacteria to simultaneous iron/phosphorus nutrient limitation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.242
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNature Communications→Same topicMarine and coastal ecosystems→French-language works237,207→