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Record W1997119725 · doi:10.1021/jf073012d

Proteome Changes in Leaves of Brassica napus L. as a Result of Sclerotinia sclerotiorum Challenge

2008· article· en· W1997119725 on OpenAlexaff
Yue Liang, Sanjeeva Srivastava, Muhammad Hafizur Rahman, Stephen E. Strelkov, Nat N. V. Kav

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsUniversity of Alberta
FundersDirectorate for Biological Sciences
KeywordsSclerotinia sclerotiorumBrassicaSclerotiniaProteomeBiologyBotanyLeptosphaeria maculansAgronomyBioinformatics

Abstract

fetched live from OpenAlex

Sclerotinia stem rot, caused by the necrotrophic fungal pathogen Sclerotinia sclerotiorum, is a serious disease of canola (Brassica napus L.). To increase the understanding of the B. napus- S. sclerotiorum interaction, proteins potentially involved in mediating this interaction were identified and characterized. Upon infection of canola leaves by S. sclerotiorum, necrosis of host leaves was observed by 12 h and rapidly progressed during the later time points. These morphological observations were supported by microscopic study performed at different time points after pathogen challenge. Leaf proteins were extracted and analyzed by 2-DE, which revealed the modulation of 32 proteins (12 down- and 20 up-regulated). The identities of these proteins were established by ESI-q-TOF MS/MS and included proteins involved in photosynthesis and metabolic pathways, protein folding and modifications, hormone signaling, and antioxidant defense. Gene expression analysis of selected genes was performed by qRT-PCR, whereas the elevated levels of the antioxidant enzymes peroxidase and superoxide dismutase were validated by enzyme assays. To the authors' best knowledge, this is the first proteomics-based investigation of B. napus-S. sclerotiorum interaction, and the roles of many of the proteins identified are discussed within the context of this pathosystem.

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 score0.256
Threshold uncertainty score0.204

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 head0.020
GPT teacher head0.187
Teacher spread0.167 · 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

Citations66
Published2008
Admission routes1
Has abstractyes

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