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Record W2918007417 · doi:10.1186/s12870-019-1633-1

Soybean (Glycine max L. Merr.) seedlings response to shading: leaf structure, photosynthesis and proteomic analysis

2019· article· en· W2918007417 on OpenAlexaff
Yuanfang Fan, Junxu Chen, Zhonglin Wang, Tingting Tan, Shenglan Li, Jiafeng Li, Beibei Wang, Jiawei Zhang, Yajiao Cheng, Xiaoling Wu, Wenyu Yang, Feng Yang

Bibliographic record

VenueBMC Plant Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsMinistry of Agriculture
FundersProgram on Industrial Technology System of National SoybeanNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsBiologyPhotosynthesisGlycineBotanyShadingCanopyAmino acidBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Intercropping and close planting are important cultivation methods that increase soybean yield in agricultural production. However, plant shading is a major abiotic stress factor that influences soybean growth and development. Although shade affects leaf morphological parameters and decreases leaf photosynthesis capacity, information on the responses of soybean leaf photosynthesis to shading at proteomic level is still lacking. RESULTS: Compared with leaves under normal light (CK) treatment, leaves under shading treatment exhibited decreased palisade and spongy tissue thicknesses but significantly increased cell gap. Although shade increased the number of the chloroplast, the thickness of the grana lamella and the photosynthetic pigments per unit mass, but the size of the chloroplast and starch grains and the rate of net photosynthesis decreased compared with those of under CK treatment. A total of 248 differentially expressed proteins, among which 138 were upregulated, and 110 were downregulated, in soybean leaves under shading and CK treatments were detected via isobaric tags for relative and absolute quantification labeling in the three biological repeats. Differentially expressed proteins were classified into 3 large and 20 small groups. Most proteins involved in porphyrin and chlorophyll metabolism, photosynthesis-antenna proteins and carbon fixation in photosynthetic organisms were upregulated. By contrast, proteins involved in photosynthesis were downregulated. The gene family members corresponding to differentially expressed proteins, including protochlorophyllide reductase (Glyma06g247100), geranylgeranyl hydrogenase (Ggh), LHCB1 (Lhcb1) and ferredoxin (N/A) involved in the porphyrin and chlorophyll metabolism, photosynthesis-antenna proteins and photosynthesis pathway were verified with real-time qPCR. The results showed that the expression patterns of the genes were consistent with the expression patterns of the corresponding proteins. CONCLUSIONS: This study combined the variation of the soybean leaf structure and differentially expressed proteins of soybean leaves under shading. These results demonstrated that shade condition increased the light capture efficiency of photosystem II (PSII) in soybean leaves but decreased the capacity from PSII transmitted to photosystem II (PSI). This maybe the major reason that the photosynthetic capacity was decreased in shading.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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)0.0000.000

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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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 designObservational
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

Citations109
Published2019
Admission routes1
Has abstractyes

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