Haiyan Wu

In mitigating agricultural nitrous oxide (N₂O) emissions, partially substituting chemical nitrogen (N) fertilizers with organic amendments is widely advocated for; however, the effectiveness of this approach varies, and the optimal substitution ratio remains unclear. This study employed a long-term field experiment in a maize cropping system, integrating in situ N₂O flux monitoring, soil analysis, and metagenomic sequencing to investigate the effects of substituting synthetic N fertilizer with sheep manure at different ratios (one-sixth, one-third, and full substitution) on cumulative N₂O emissions and underlying microbial mechanisms. The results showed that substituting one-third of chemical N with sheep manure (OF2) most effectively reduced cumulative N₂O emissions, by 18.64% (2021) and 47.00% (2022), compared to conventional chemical fertilization (NPK). This treatment optimized key soil properties, significantly lowering soil nitrate (NO₃⁻-N) and ammonium (NH₄⁺-N) concentrations while increasing soil organic matter. Metagenomic analysis revealed that the mitigation was primarily driven by a coordinated shift in the nitrogen-cycling microbial functional gene network: OF2 significantly enhanced the abundance of N₂O reductase genes (nosZ clades I and II) and increased the nosZ/(nirK + nirS) gene ratio, thereby strengthening the genetic potential for complete denitrification (N₂O → N₂). Partial least squares path modeling confirmed that soil properties indirectly reduced N₂O emissions, primarily by regulating the microbial community and functional gene abundances. This study provides mechanistic evidence that partial organic substitution (with one-third sheep manure) mitigates N₂O emissions by optimizing the soil habitat and reprogramming the functional gene network of nitrogen-cycling microbes, with a key emphasis on enhancing the N₂O reduction pathway.

Maize stalk lodging causes substantial yield losses worldwide. Although stalk strength is a genetically determined trait, its molecular mechanisms—particularly the dynamic changes during key developmental stages—remain inadequately characterized due to limitations of single-omics approaches. This study employed an integrated transcriptomic and metabolomic analysis strategy to compare stalk tissues from three maize genotypes with contrasting lodging resistance: the highly resistant inbred line PHB1M, the susceptible inbred line Chang 7-2, and their recombinant inbred line 23NWZ561 (abbreviated as P, C, and Z, respectively). Dynamic sampling of all three genotypes was conducted at both grain-filling and maturity stages, with simultaneous measurement of physiological traits related to stalk strength. Phenotypic analysis revealed that the resistant genotype PHB1M exhibited superior rind penetration strength, cell wall composition (cellulose, hemicellulose, and lignin) content, and vascular bundle development. Multi-omics analysis indicated that the molecular basis of lodging resistance is primarily established during the maturity stage. The transcriptomic and metabolomic profiles of the recombinant inbred line Z shifted from clustering with the susceptible parent C at the grain-filling stage to grouping with the resistant parent P at maturity. Key pathways including phenylpropanoid biosynthesis were significantly enriched specifically at maturity, accompanied by upregulation of related genes (PAL, HCT, CCR) and accumulation of metabolites such as lignin precursors in PHB1M. Integrated analysis identified a core co-expression network within the phenylpropanoid pathway comprising three genes and three metabolites. This study systematically demonstrates that lodging resistance in maize is regulated by transcriptional and metabolic reprogramming during late stalk developmental stages, particularly at maturity, where enhanced activation of the phenylpropanoid biosynthesis pathway plays a central role. These findings provide valuable candidate genes and metabolic markers for breeding lodging-resistant maize varieties.

This research investigates the flavonoid biosynthesis pathways of two ecotypes of Leymus chinensis, distinguished by their gray-green (GG) and yellow-green (YG) leaf colors, to uncover the molecular bases of their adaptability to different environmental conditions. By integrating comprehensive transcriptomic and metabolomic analyses, we identified 338 metabolites, with 161 showing differential expression—124 upregulated and 37 downregulated. The transcriptomic data revealed substantial variation, with 50,065 genes differentially expressed between the ecotypes, suggesting complex genetic regulation of the flavonoid biosynthesis pathways involving 20 enzyme-coding genes. KEGG pathway enrichment analysis further highlighted the involvement of 26 genes in the synthesis of four distinct types of flavonoid metabolites, indicating the sophisticated modulation of these pathways. Our results demonstrate that the GG and YG ecotypes of Leymus chinensis exhibit distinct flavonoid profiles and gene expression patterns, with the GG ecotype showing a higher accumulation of quercetin and kaempferol (increased by 25% and 33%, respectively, compared to YG), suggesting enhanced antioxidant capacity. Conversely, the YG ecotype displayed a broader spectrum of flavonoid metabolites, possibly indicating an adaptive strategy favoring diverse ecological interactions. Our results show that the GG and YG ecotypes of Leymus chinensis exhibit distinct flavonoid profiles and gene expression patterns, suggesting divergent adaptive strategies to environmental stress. This study highlights the crucial role of flavonoid metabolites in plant adaptation strategies, enhancing our understanding of plant resilience and adaptability. The distinct metabolic profiles observed suggest that the GG ecotype may be better equipped to handle oxidative stress, while the YG ecotype could be predisposed to broader ecological interactions. This emphasizes the value of applying machine learning in predicting plant adaptability, providing a new perspective for the future exploration of how plants adapt to environmental challenges. Meanwhile, the information gleaned from this nuanced study offers a foundation for future investigations into the genetic and environmental factors involved in plant adaptation.