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Record W4403777786 · doi:10.1111/pce.15216

Quantitative Proteomic Analysis of <i>Brassica Napus</i> Reveals Intersections Between Nutrient Deficiency Responses

2024· article· en· W4403777786 on OpenAlexafffund
Lauren E. Grubb, Sabine Scandola, Devang Mehta, Ibrahim Khodabocus, R. Glen Uhrig

Bibliographic record

VenuePlant Cell & Environment · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaBrassicaNutrientBiologyAgronomyPhosphorusCultivarCropChemistryEcology

Abstract

fetched live from OpenAlex

Macronutrients such as nitrogen (N), phosphorus (P), potassium (K) and sulphur (S) are critical for plant growth and development. Field-grown canola (Brassica napus L.) is supplemented with fertilizers to maximize plant productivity, while deficiency in these nutrients can cause significant yield loss. A holistic understanding of the interplay between these nutrient deficiency responses in a single study and canola cultivar is thus far lacking, hindering efforts to increase the nutrient use efficiency of this important oil seed crop. To address this, we performed a comparative quantitative proteomic analysis of both shoot and root tissue harvested from soil-grown canola plants experiencing either nitrogen, phosphorus, potassium or sulphur deficiency. Our data provide critically needed insights into the shared and distinct molecular responses to macronutrient deficiencies in canola. Importantly, we find more conserved responses to the four different nutrient deficiencies in canola roots, with more distinct proteome changes in aboveground tissue. Our results establish a foundation for a more comprehensive understanding of the shared and distinct nutrient deficiency response mechanisms of canola plants and pave the way for future breeding efforts.

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 score0.016
Threshold uncertainty score0.634

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePlant Cell & EnvironmentSame topicNitrogen and Sulfur Effects on BrassicaFrench-language works237,207