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Record W2904894377 · doi:10.1093/aob/mcy202

Nitrate nutrition influences multiple factors in order to increase energy efficiency under hypoxia in Arabidopsis

2018· article· en· W2904894377 on OpenAlexaff
Aakanksha Wany, Alok Kumar Gupta, Aprajita Kumari, Sonal Mishra, Namrata Singh, Sonika Pandey, Rhythm Vanvari, Abir U. Igamberdiev, Alisdair R. Fernie, Kapuganti Jagadis Gupta

Bibliographic record

VenueAnnals of Botany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsMemorial University of Newfoundland
FundersScience and Engineering Research Board
KeywordsBiologyNitrate reductaseAlternative oxidaseHypoxia (environmental)Nitric oxideNitrateRespirationArabidopsisAmmoniumBiochemistryNitrite reductaseBotanyOxygenEcologyChemistryEndocrinologyGene

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Nitrogen (N) levels vary between ecosystems, while the form of available N has a substantial impact on growth, development and perception of stress. Plants have the capacity to assimilate N in the form of either nitrate (NO3-) or ammonium (NH4+). Recent studies revealed that NO3- nutrition increases nitric oxide (NO) levels under hypoxia. When oxygen availability changes, plants need to generate energy to protect themselves against hypoxia-induced damage. As the effects of NO3- or NH4+ nutrition on energy production remain unresolved, this study was conducted to investigate the role of N source on group VII transcription factors, fermentative genes, energy metabolism and respiration under normoxic and hypoxic conditions. METHODS: We used Arabidopsis plants grown on Hoagland medium with either NO3- or NH4+ as a source of N and exposed to 0.8 % oxygen environment. In both roots and seedlings, we investigated the phytoglobin-nitric oxide cycle and the pathways of fermentation and respiration; furthermore, NO levels were tested using a combination of techniques including diaminofluorescein fluorescence, the gas phase Griess reagent assay, respiration by using an oxygen sensor and gene expression analysis by real-time quantitative reverse transcription-PCR methods. KEY RESULTS: Under NO3- nutrition, hypoxic stress leads to increases in nitrate reductase activity, NO production, class 1 phytoglobin transcript abundance and metphytoglobin reductase activity. In contrast, none of these processes responded to hypoxia under NH4+ nutrition. Under NO3- nutrition, a decreased total respiratory rate and increased alternative oxidase capacity and expression were observed during hypoxia. Data correlated with decreased reactive oxygen species and lipid peroxidation levels. Moreover, increased fermentation and NAD+ recycling as well as increased ATP production concomitant with the increased expression of transcription factor genes HRE1, HRE2, RAP2.2 and RAP2.12 were observed during hypoxia under NO3- nutrition. CONCLUSIONS: The results of this study collectively indicate that nitrate nutrition influences multiple factors in order to increase energy efficiency under hypoxia.

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 score0.002
Threshold uncertainty score0.004

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 head0.042
GPT teacher head0.266
Teacher spread0.224 · 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

Citations42
Published2018
Admission routes1
Has abstractyes

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