ArticleFunctional & integrative genomics2025
Joint multi-omics analysis reveals the response mechanism in rapeseed (Brassica Rapa L.) under low nitrogen stress.
Article in Functional & integrative genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Efficiency of nitrogen and phosphorus cycling in paddy soils is directly driven by functional gene-microbe co-occurrence networks and indirectly controlled by soil physicochemical properties.World journal of microbiology & biotechnology · 2026Article
- Roles of Metabolites Unveiled by Metabolomics inMetabolites · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nitrogen is fundamental macronutrient that regulates plant growth by modulating a wide array of physiological and metabolic processes. However, the excessive application of nitrogen fertilizers has led to substantial environmental concerns, emphasizing the need for sustainable nitrogen management in agriculture. In this study, an integrative analysis encompassing morphological, physiological, transcriptomic, and metabolomic approaches was employed to investigate the adaptive responses of Brassica rapa (rapeseed) under contrasting nitrogen regimes. Morphological assessments demonstrated significant enhancements in root length and surface area under low nitrogen conditions, while photosynthesis traits particularly chlorophyll content were markedly reduced, underscoring nitrogen`s essential role in photosynthetic efficiency. Enzymatic activity assays revealed tissue-specific responses: roots exhibited elevated enzymatic activities under nitrogen deficiency, indicative of compensatory nitrogen uptake strategies, whereas leaves showed a decline in enzymatic functions, reflecting Limited nitrogen availability. Transcriptomic profiling identified 1,481 and 1,917 differentially expressed genes (DEGs) in roots and leaves, respectively, which were primarily associated with photosynthesis, amino acid metabolism, and oxidative stress responses. These transcriptomic shifts were corroborated by metabolic profiling, which reveled significant alterations in metabolites involved in amino acid metabolism, phenylpropanoid biosynthesis, and energy production pathways. Integration of transcriptomic and metabolomic datasets through Weighted Gene Co-expression Network Analysis (WGCNA) identified key gene metabolite modules implicated in nitrogen stress adaptation. Quantitative RT-PCR validation of selected DEGs confirmed the RNA-Seq expression patterns, further substantiating the reliability of the transcriptomic data. Collectively, this comprehensive multi-omics investigation elucidates the molecular basis of B. rapa`s adaptation to nitrogen deficient conditions, providing valuable insights for enhancing nitrogen use efficiency and guiding sustainable crop management strategies.
Indexed as
Identifiers
40996567What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.