Multi-omics identifies regulatory hubs coordinating growth-metabolism trade-offs under nitrogen deficiency in an invasive Mikania micrantha

Multi-omics identifies regulatory hubs coordinating growth-metabolism trade-offs under nitrogen deficiency in an invasive Mikania micrantha | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics identifies regulatory hubs coordinating growth-metabolism trade-offs under nitrogen deficiency in an invasive Mikania micrantha Jiyue Wang, Qinghong Duan, Zengling Liu, Hongxia Sheng, Xiuhuan Meng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9155517/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Aims Nitrogen availability is a critical determinant of plant invasion success, yet how invasive species respond to nitrogen deficiency remains poorly understood. This study aimed to investigate the physiological and molecular responses of the globally invasive vine Mikania micrantha to nitrogen deficiency. Methods Integrative transcriptomic and metabolomic analyses were performed on M. micrantha under nitrogen deprivation. Physiological parameters including growth, root development, photosynthetic capacity, and nitrogen accumulation were assessed. Differentially expressed genes and accumulated metabolites were identified, and multi-omics network analysis was conducted to uncover central regulatory hubs. Results Nitrogen deprivation severely suppressed growth, root development, photosynthetic capacity, and nitrogen accumulation in M. micrantha . A total of 119 differentially accumulated metabolites and 3,409 differentially expressed genes were identified. Galactose metabolism and phenolic acid biosynthesis pathways were up-regulated, while photosynthesis and nucleotide metabolism were repressed. Multi-omics network analysis revealed two central regulators: the transcription factor HHO3-like negatively controlled 3,4-dimethoxycinnamic acid accumulation and positively regulated growth and photosynthetic traits; the probable zinc transporter 10 negatively associated with verbascose accumulation and positively influenced shoot biomass. Both genes were down-regulated under nitrogen deficiency, driving accumulation of growth-inhibitory secondary metabolites. Conclusions M. micrantha exhibits a high-nitrogen-demand strategy. Nitrogen deficiency triggers a resource allocation shift from growth to stress metabolism via specific regulatory hubs, thereby constraining its invasive potential in nitrogen-poor environments. M. micrantha nitrogen deficiency multi-omics plant invasion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Mikania micrantha Kunth, commonly known as mile-a-minute weed, is a perennial vine of the Asteraceae family, native to tropical America (Clements & Kato-Noguchi, 2025). Recognized as one of the world's 100 worst invasive alien species (Lowe et al., 2000), it has aggressively colonized vast areas of tropical and subtropical Asia and the Pacific Islands since its introduction. Its invasion leads to the formation of dense monocultures that smother native vegetation and crops, resulting in severe biodiversity loss and substantial economic damage (Day et al., 2016; Kaur et al., 2012). For instance, within China’s Pearl River Delta alone, annual economic losses are estimated at billions of RMB (Han et al., 2017). The remarkable invasion success of M. micrantha is attributed to a synergy of multiple traits, including extraordinarily rapid growth (up to 20 cm day⁻¹), prolific seed production and efficient wind/water dispersal, vigorous vegetative propagation via adventitious roots, and high genetic and epigenetic variability that underpin its adaptability to diverse environments (Huang et al., 2015; Liu et al., 2020; Shen et al., 2021). Recent genomic and ecological studies have begun to unravel the mechanisms behind this invasiveness. Population genetic analyses suggest that its invasion in Asia likely involved multiple introductions, admixture events, and founder effects, creating genetically diverse populations capable of rapid local adaptation (Yang et al., 2017; Ji et al., 2021; Banerjee et al., 2020). Furthermore, its interaction with biotic factors, such as altered herbivore and pathogen pressure in the introduced range (enemy release hypothesis), and abiotic factors, particularly climate change which may expand its suitable habitat, play significant roles in its spread (Mitchell & Power, 2003; Xie et al., 2025). Physiologically, M. micrantha possesses unique adaptations like nighttime CO₂ fixation and highly efficient stem photosynthesis, which fuel its rapid growth (Liu et al., 2020; Cai et al., 2023). Its climbing habit enables it to quickly overtop and smother trees, a process driven by specific growth patterns and hormonal responses (Chen et al., 2024), and it also exhibits notable drought tolerance (Cai et al., 2025). A particularly intriguing aspect of its invasion strategy is its interaction with soil nutrient cycles, especially nitrogen (N). The genome of M. micrantha reveals not only recent whole-genome duplication and transposable element expansion but also insights into its metabolic capabilities (Liu et al., 2020). Critically, it has been suggested that metabolites from M. micrantha can enhance N availability by positively influencing the abundance and activity of N-cycling microorganisms in the soil (Liu et al., 2020). This aligns with field observations that M. micrantha invasion significantly alters soil microbial communities, increasing the abundance of ammonia-oxidizing bacteria, accelerating nitrification rates, and elevating soil nitrate (NO 3 ⁻) concentrations (Li et al., 2006; Chen et al., 2009; Yu et al., 2021). These changes create a feedback loop that potentially improves nutrient conditions for the invader itself, a common mechanism in successful plant invasions (Gioria et al., 2023). Cai et al.,(2025) recently demonstrated that under increasing global nitrogen deposition–particularly the sustained rise in NO 3 − -N deposition–M. micrantha may achieve rapid growth through the regulation of NO 3 − -N uptake-related transcription factors (e.g., HY5) and transporters (CLC, SCLA/C, NPF), as well as the modulated expression of key nitrogen assimilation genes (NR, GS, GOGAT). These molecular adjustments enhance nitrogen use efficiency under NO 3 − -N-dominated conditions and may accelerate the spread of this invasive species. However, that study focused exclusively on NO 3 − -N enrichment, leaving responses to nitrogen deficiency largely unexplored.To address this gap, the present study investigates the physiological and molecular responses of M. micrantha to varying nitrogen supply levels (zero, half, and full nitrogen) under hydroponic conditions. We integrate analyses of growth performance, nutrient acquisition, photosynthesis, chlorophyll fluorescence, transcriptome, and metabolome. This multi-omics approach aims to uncover the regulatory networks and metabolic reprogramming underlying nitrogen limitation tolerance, thereby completing the full spectrum of nitrogen response strategies in this invasive weed. We hypothesize that nitrogen availability serves as a key environmental cue modulating the expression of genes and accumulation of metabolites associated with rapid growth, clonal reproduction, and potentially the synthesis of allelopathic compounds–thereby directly influencing the invasive capacity of M. micrantha . Materials and Methods Plant Material and Growth Conditions M. micrantha Kunth stems were collected from a naturalized population in Guangdong Province, South China (23°26′ N, 116°35′ E). Uniform apical stem segments (8 cm in length, each bearing three fully expanded leaves) were excised from healthy stock plants, surface-sterilized with 0.5% sodium hypochlorite for 2 min, and rinsed thoroughly with deionized water. The segments were directly transplanted into opaque 2‑L hydroponic containers (four segments per container) filled with modified Hoagland nutrient solution. The basal nutrient solution comprised: 1.25 mM K 2 SO 4 , 0.5 mM KH 2 PO 4 , 2 mM CaCl 2 , 0.5 mM MgSO 4 , 20 µM Fe‑EDTA, 10 µM H 3 BO 3 , 2 µM MnSO 4 1 µM ZnSO 4 , 0.5 µM CuSO 4 and 0.1 µM (NH 4 ) 6 Mo 7 O 24 . Nitrogen Treatments Four nitrogen (N) supply levels were established by modifying the N concentration in the Hoagland solution. Nitrogen was supplied as NH 4 NO 3 at the following concentrations: total nitrogen (TN, 7.5 mM N, standard Hoagland N level), half nitrogen (HN, 3.75 mM N), and zero nitrogen (UN, 0 mM N, N‑free Hoagland solution). Deionized water without any nutrients served as an additional control treatment (H 2 O). The pH of all solutions was adjusted to 5.8 ± 0.1.Each treatment was replicated six times (six independent hydroponic containers) with five plants per replicate, resulting in 30 individuals per treatment. Solutions were renewed every 5 days and continuously aerated. The experiment was conducted in a controlled greenhouse (28/22°C day/night, 14‑h photoperiod, 600 µmol photons m⁻² s⁻¹, 70% relative humidity). Measurements of Phenotypic and Physiological Parameters After 30 days of treatment, the following measurements were performed. Phenotypic Parameters: Ten randomly selected plants per treatment were used for destructive sampling. Plant height(PH), stem diameter(SD), number of leaf (NL), number of branching (NB), fresh weight of shoots (SFW) and roots (RFW), and dry weight of shoots (SFW) and roots (RDW) were recorded. Root system architecture was analyzed using a root scanner (Epson Perfection V800, Japan), and total root length (TRL), root Surfarea(RSA), root Tips(RT), root AvgDiam(RAD), root ProjArea(RPA), root Volume(RV), root Forks(RF), root Crossings(RC) were determined. Leaf area (LA) were measured using the same scanner and associated software. Root Activity: Root activity was assessed using the triphenyltetrazolium chloride (TTC) reduction method. Total Nitrogen Content in Plant(TNP): Oven-dried (80°C to constant weight) shoot and root samples were ground to a fine powder. The total nitrogen concentration was determined using a Continuous Flow Analyzer (AA3, SEAL Analytical, Germany). Photosynthetic Pigment Content: Fresh leaf samples (0.1 g) were extracted with 95% ethanol in the dark. The absorbance of the extracts was measured at 665, 649, and 470 nm using a spectrophotometer. Concentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl t), and carotenoids (Car) were calculated according to Lichtenthaler (1987). The Chl a/b ratio was also derived. Gas Exchange Measurements: Net photosynthetic rate and related parameters were measured on the youngest, fully expanded, sun-exposed leaves on clear days between 9:00 and 11:00 AM using a portable photosynthesis system (LI-6800, LI-COR Biosciences, USA). The recorded parameters included: net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO 2 concentration (Ci), stomatal conductance (Gs), boundary layer conductance (gbw), total conductance to CO 2 (gtc) and water vapor (gtw), leaf temperature (Tleaf), chamber relative humidity (RHcham), and leaf-to-air vapor pressure deficit (VPDleaf). Instantaneous water-use efficiency (WUE) was calculated as Pn/Tr. Stomatal limitation (LS) was calculated as (Ca − Ci) / Ca, where Ca is the ambient CO 2 concentration (set at 400 µmol mol⁻¹ during measurements). Light Response Curves: Light-response curves (LRCs) of photosynthesis were generated using the LI-6800 automated program. After initial acclimation to a saturating light intensity of 750 µmol m⁻² s⁻¹ until a stable photosynthetic rate was achieved (~ 5 min), A was measured at a sequentially decreasing series of photosynthetic photon flux density (PPFD) levels: 1800, 1500, 1200, 1000, 800, 600, 400, 200, 150, 100, 50, 25, and 0 µmol m⁻² s⁻¹. Chlorophyll Fluorescence: Chlorophyll fluorescence parameters were measured at 9:00 PM using a pulse-amplitude modulation fluorometer integrated into the LI-6800 system. After 30 min of dark adaptation, the minimum (Fo) and maximum (Fm) fluorescence yields were measured to calculate the maximum quantum yield of PSII (Fv/Fm = (Fm − Fo)/Fm). Under actinic light (300 µmol m⁻² s⁻¹), steady-state (Fs), maximum (Fm′), and minimum (F'o) fluorescence yields in light-adapted leaves were recorded. These values were used to calculate electron transport rate (ETR), photochemical quenching (qP), and non-photochemical quenching (NPQ). The partitioning of absorbed light energy was estimated as follows: the percentage allocated to photochemistry (P) = qP × F'v/F'm × 100%; to regulated thermal dissipation (D) = (1 − F'v/F'm) × 100%; and to non-regulated dissipation (Ex) = (1 − qP) × F'v/F'm × 100%. Transcriptomic and Metabolomic Profiling The seedlings subjected to the TN and UN treatments were sampled, immediately frozen in liquid nitrogen, and stored at − 80°C. For each treatment, three independent biological replicates were collected. Transcriptome Sequencing: Total RNA was extracted, assessed for quality, and used for library construction. Sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina, USA) to generate paired-end 150 bp reads. Raw RNA-seq reads were processed for quality control using fastp (v0.23.2) to remove adapter sequences and low-quality reads. Clean reads were aligned to the Mikania micrantha reference genome using HISAT2 (v2.2.1) with default parameters. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM) using StringTie (v2.2.1). Differential expression analysis between treatments was performed using DESeq2 (v1.38.3) with thresholds of |log 2 (fold change)|≥1 and adjusted P-value < 0.05. Alternative splicing events were identified and quantified using rMATS (v4.1.2). Transcription factors were predicted using iTAK software against the PlantTFDB database. Weighted gene co-expression network analysis (WGCNA) was conducted using the WGCNA R package to identify modules correlated with nitrogen treatments. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using clusterProfiler (v4.6.0) with a significance threshold of adjusted P-value < 0.05.2.) Metabolomic Profiling: Metabolites were extracted from the same tissues and analyzed using a UPLC-MS/MS system (Waters, USA) coupled with a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, USA) in both positive and negative ionization modes. Metabolomic data processing and analysis were performed following the MetMiner pipeline as described by Wang et al., (2025). Briefly, raw LC-MS data were converted to mzXML format and processed using XCMS for peak detection, retention time correction, and alignment. Metabolite annotation was achieved by matching MS/MS spectra against an in-house plant-specific mass spectrometry database integrated in MetMiner. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied for multivariate statistical analysis. Differential metabolites were identified based on variable importance in projection (VIP) score > 1 from OPLS-DA and Student's t-test P-value < 0.05. Metabolite classification and KEGG enrichment analysis were conducted using the MDAtoolkits embedded in MetMiner. Statistical Analysis All phenotypic and physiological data are presented as mean ± standard error (SE). One-way analysis of variance (ANOVA) followed by Tukey’s Honestly Significant Difference (HSD) post-hoc test (p < 0.05) was performed to assess significant differences among the four N treatments using SPSS software (version 26.0, IBM, USA). Figures were generated using GraphPad Prism 9.0. For multi-omics data, differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were identified based on thresholds of |log₂(fold change)| > 1 and a false discovery rate (FDR) < 0.05. Integrated analysis of transcriptomic and metabolomic data was conducted to elucidate the regulatory network responding to N deprivation. Results and discussion Phenotypic and Growth Responses to Nitrogen Deficiency Nitrogen supply levels significantly modulated the growth performance and root system architecture of M micrantha . In general, all phenotypic parameters exhibited a declining trend with decreasing nitrogen availability(Fig. 1 ). Specifically, PH, SD, SFW, RFW, SDW, and RDW were progressively reduced from TN to HN and UN treatments. Notably, the UN treatment resulted in a complete failure of root system development, precluding any root architecture analysis for this group. While branching nodes (BN) and SDW in the UN treatment did not differ significantly from the control (CK), they were lower than in TN and HN. NL remained statistically comparable among TN, HN, and CK, and LA showed no significant difference between TN and UN. For the root parameters measured in TN, HN, and CK, a consistent pattern of reduction with decreasing nitrogen was observed across most metrics, including TRL, RSA, RV, and RC. However, RAD and RT deviated from this trend, suggesting a differential sensitivity to nitrogen limitation in specific root architectural traits. Physiological Responses: Photosynthesis and Nitrogen Accumulation Concentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl t), and carotenoids (Car) exhibited a progressive decline with decreasing nitrogen availability, with the UN treatment showing significantly lower values than CK (Fig. 2 ). Similarly, root activity (RA) and total nitrogen content in plants (TNP) followed the same monotonic decreasing trend across treatments, from TN through HN to UN. All measured parameters in the UN treatment were significantly suppressed compared to CK, indicating that complete nitrogen deprivation severely impairs both photosynthetic pigment biosynthesis and root physiological function. Gas exchange parameters, including net photosynthetic rate (Pn), transpiration rate (Tr), boundary layer conductance (gbw), stomatal limitation (LS), and water-use efficiency (WUE), consistently followed the pattern TN > HN > CK > UN, with all parameters under UN treatment significantly lower than CK (Fig. 3 A). This hierarchical response demonstrates a linear positive relationship between nitrogen supply and photosynthetic capacity. Conversely, total conductance to water vapor (gtw) peaked under HN treatment, with no significant difference between TN and CK, while UN remained substantially depressed. Stomatal conductance (Gs) under UN was significantly reduced relative to TN and HN but did not differ from CK. The total conductance to CO₂ (gtc) was significantly impaired under UN compared to TN and HN, yet only HN exhibited significantly higher gtc than CK. Chlorophyll Fluorescence and PSII Performance Fluorescence parameters revealed pronounced photosystem II (PSII) dysfunction under nitrogen deprivation (Fig. 3 B). Minimum fluorescence (Fo), maximum fluorescence (Fm), maximum fluorescence in light-adapted state (Fm′), variable fluorescence in light-adapted state (Fv′), and steady-state fluorescence (Fs) were all significantly depressed under UN relative to all other treatments. Maximum quantum efficiency of PSII (Fv/Fm) under UN was significantly lower than TN and HN but statistically indistinguishable from CK. Potential PSII activity (Fv/Fo), effective quantum yield (Fv′/Fm′), electron transport rate (ETR), and photochemical quenching (qP) under UN were significantly reduced only compared to TN, with no differences detected relative to HN or CK. These patterns collectively indicate that complete nitrogen deprivation imposes severe constraints on both stomatal and non-stomatal components of photosynthesis, while partial nitrogen supply (HN) enables partial compensation through enhanced conductance parameters. Metabolomic Reprogramming under Nitrogen Deficiency Metabolomic profiling revealed pronounced nitrogen deficiency-induced metabolic reprogramming in M. micrantha . Principal component analysis demonstrated clear separation between TN and UN treatments, indicating extensive reconfiguration of the metabolic landscape under nitrogen deprivation (Fig. 4 A). A total of 119 significantly differential metabolites (DMs, P < 0.05) were identified, of which 43 were down-regulated under UN. Phenolic acids (23 metabolites, 19.30%) and flavonoids (14 metabolites, 11.70%) constituted the two largest DM classes, collectively accounting for nearly one-third of all differentially accumulated compounds (SFig. 1A). The top 10 up- and down-regulated DMs encompassed diverse chemical categories, including lipids, phenolic acids, flavonoids, amino acid derivatives, alkaloids, lignans, and coumarins (Fig. 4 B). KEGG enrichment analysis identified five significantly enriched pathways (P < 0.005): galactose metabolism (most highly enriched), ABC transporters, caffeic acid derivative biosynthesis, glutathione metabolism, and nucleotide metabolism (Fig. 4 C). Galactose metabolism exhibited a strongly coordinated down-regulation pattern (DA = -0.73), whereas nucleotide metabolism was the sole pathway showing net up-regulation (DA = + 0.27) in the TN vs. UN comparison. Correlation analysis of the eight DMs mapping to these pathways revealed a tightly interconnected module centred on 2,2-dimethylsuccinic acid, which displayed significant positive correlations with three phenolic acids (3,4-dimethoxycinnamic acid, 3-O-caffeoylshikimic acid, and 4-O-(4'-O-alpha-D-glucopyranosyl)caffeoylquinic acid), two terpenoids (zaluzanin C, verbascose), and the amino acid derivative N-acetyl-tryptophan (Fig. 4 D). Transcriptomic Reprogramming under Nitrogen Deficiency Comparative transcriptomic analysis of TN and UN treatments revealed significant transcriptional reprogramming in M. micrantha seedlings in response to nitrogen deprivation. In total, 3,409 differentially expressed genes (DEGs) were identified in the TN vs. UN comparison (SFig. 1B). Gene set enrichment analysis (GSEA) demonstrated that under nitrogen sufficiency (TN), M. micrantha actively engages in defense-related processes, with top-ranked GO terms including "defense response to fungus," "response to chitin," and "response to organonitrogen compound" (Fig. 5 A). Conversely, only "organelle fission" was induced under UN, suggesting cellular remodeling as a specific starvation response. KEGG enrichment analysis revealed profound metabolic reconfiguration (Fig. 5 B). The significant upregulation of "Photosynthesis" and "Photosynthesis proteins" pathways under TN (i.e., repressed under UN) is a hallmark response to nitrogen limitation, restricting the synthesis of chlorophyll and photosynthetic enzymes. The upregulation of "alpha-Linolenic acid metabolism" under TN suggests that nitrogen-starved plants may down-regulate jasmonate precursor biosynthesis to conserve resources. In stark contrast, pathways for "Flavonoid biosynthesis" and "Selenocompound metabolism" were up-regulated under UN, representing a strategic investment in constitutive chemical defense when resources for rapid growth are unavailable. At the post-transcriptional level, nitrogen deficiency induced substantial alternative splicing, with 646 mutually exclusive exons (MEX) and 1,881 skipped exon (SE) events identified. This was accompanied by the differential expression of 1,845 transcription factors (TFs), predominantly from the bHLH, NAC, ERF, MYB-related, WRKY, and C2H2 families (SFig. 1C), pointing to their central role in orchestrating the nitrogen-responsive transcriptional network. In parallel, 840 novel transcripts were identified, further expanding the repertoire of nitrogen-responsive transcriptional units. Multi-Omics Integration Identifies Regulatory Hubs Integrative multi-omics analysis revealed two key regulatory modules linking nitrogen deficiency-responsive genes, metabolites, and phenotypic traits in M. micrantha (Fig. 6 ). The transcription factor gene HHO3-like (R6Q59_002673) exhibited strong negative correlations with the phenolic acid metabolite 3,4-dimethoxycinnamic acid, and positive correlations with three growth parameters (PH, RDW, SD) and eight physiological traits, including Car, Chl a, Chl t, Fo, Pn, gbw, RA, and TNP. Under nitrogen deficiency (UN), HHO3-like expression was significantly down-regulated (log 2 FC = 2.80), coinciding with elevated 3,4-dimethoxycinnamic acid accumulation and concomitant reductions in all positively correlated traits. Similarly, the probable zinc transporter 10 gene (R6Q59_005338) showed strong negative association with the raffinose family oligosaccharide verbascose and positive association with SDW. Under UN treatment, this transporter was markedly suppressed (log2FC = 15.99), leading to verbascose accumulation and reduced SDW. These coordinated responses demonstrate that nitrogen deprivation simultaneously represses key transcriptional regulators and facilitates accumulation of specific secondary metabolites that collectively inhibit growth, photosynthetic capacity, pigment biosynthesis, root function, and nitrogen acquisition. Discussion Nitrogen Dependency as an Ecological Constraint on Invasiveness The profound suppression of M. micrantha growth and the complete inhibition of root development under nitrogen-free conditions underscore this species' high dependency on exogenous nitrogen, particularly for its rapid invasive growth. The optimal phenotypic responses observed under TN are consistent with recent findings demonstrating that increasing NO 3 − -N concentrations significantly promote M. micrantha growth, with optimal responses observed at 5 mM NO 3 − -N, concomitant with up-regulation of nitrate transporter genes and nitrogen metabolism enzymes. The inability of the UN treatment to initiate roots highlights a critical nitrogen threshold for fundamental developmental processes. This may be explained by systemic signaling pathways where nitrogen satiety or deficiency modulates root architecture. Recent work in Arabidopsis has elucidated that high nitrogen conditions induce peptides like LOHN1 that suppress lateral root development, whereas under severe nitrogen deprivation, the lack of nitrogen itself may fail to trigger necessary signaling cascades involving NLP7 transcription factors that coordinate root growth by integrating cytokinin and reactive oxygen species signals (Ito et al., 2025). The pronounced photosynthetic depression under UN treatment reflects a fundamental constraint in M. micrantha 's nitrogen acquisition strategy. Recent evidence demonstrates that this invader exhibits strong preference for nitrate (NO 3 − ) over ammonium, with nitrate transporter and assimilation genes constitutively upregulated under nitrogen-sufficient conditions. Complete nitrogen deprivation therefore disrupts not only substrate availability for Rubisco and photochemical machinery but also the expression of nitrate-specific uptake and assimilation pathways upon which M. micrantha has evolved dependency. The observation that UN-treated plants performed significantly worse than water-only controls (CK) is particularly striking. This suggests that abrupt, complete withdrawal of nitrogen after initial exposure triggers a more severe stress response than chronic complete nutrient deprivation, potentially involving regulatory imbalances in nitrogen starvation signaling pathways (Qi et al., 2025). The partial compensation observed under HN treatment, particularly the elevated gtw and gtc, aligns with findings in low-nitrogen-tolerant genotypes that maintain photosynthetic function through optimized nitrogen allocation to electron transport and carboxylation components (Qi et al., 2025). However, M. micrantha 's failure to sustain Pn and WUE under HN relative to TN indicates that this species, despite its invasive success, requires relatively high nitrogen inputs to achieve its characteristically superior photosynthetic performance. This nitrogen-dependent physiological trade-off may constrain its invasive spread into severely nitrogen-impoverished habitats, consistent with the "nitrogen amplification-preemption" strategy observed in native competitors that suppress M. micrantha through nitrogen cycling optimization. Metabolic Reprogramming: Carbon Reallocation from Growth to Defense The coordinated up-regulation of galactose metabolism under UN observed in M. micrantha seedlings suggests enhanced mobilization of cell wall polysaccharides and soluble sugars. Galactose metabolism is intimately linked to the turnover of cell wall galactans and raffinose family oligosaccharides (RFOs), which serve as carbon reserves and osmoprotectants under stress conditions (Van den Ende, 2013). The accumulation of verbascose—a major RFO—within the positively correlated metabolite network further supports active RFO biosynthesis or retention under nitrogen limitation, likely to maintain carbon balance and protect against oxidative stress. Similar metabolic adjustments have been reported in Panax ginseng under nitrogen deficiency, where increased accumulation of flavonoids, phenolic acids, and lipids was observed alongside down-regulation of terpenoid biosynthesis . The enrichment of caffeic acid derivative biosynthesis and glutathione metabolism pathways reflects enhanced phenylpropanoid flux and antioxidant capacity, respectively. Nitrogen limitation typically redirects carbon from nitrogen-rich primary metabolites toward carbon-rich secondary metabolites, particularly phenolic compounds, as demonstrated in Citrus sinensis where long-term nitrogen deficiency up-regulates the phenylpropanoid pathway and accumulates phenolic acids (Peng et al., 2023). The strong positive correlations between 2,2-dimethylsuccinic acid and multiple phenolic acids, together with the terpenoid zaluzanin C and the amino acid derivative N-acetyl-tryptophan, suggest coordinated carbon reallocation toward stress-protective secondary metabolites under nitrogen deficiency. In contrast, the down-regulation of nucleotide metabolism under UN reflects reduced demand for nitrogen-rich nucleotide biosynthesis under nitrogen-limited conditions, consistent with the general shift from N-rich to C-rich metabolic pools observed in nitrogen-starved plants (Kováčik & Klejdus, 2014; Peng et al., 2023). Recent multi-omics analyses in alpine plants have similarly demonstrated that nitrogen deficiency induces suppression of antioxidant enzymes concurrent with elevated secondary metabolites, with transcriptomic perturbations in nitrogen metabolism and photosynthetic pathways. Collectively, these findings reveal that M. micrantha mounts a multi-faceted metabolic acclimation to nitrogen deficiency: enhancing carbon mobilization via galactose metabolism, boosting phenylpropanoid-mediated stress protection, strengthening antioxidant capacity through glutathione metabolism, and activating transporter-mediated nutrient salvage, while suppressing energetically costly nucleotide biosynthesis. Transcriptional Regulation of Growth-Defense Trade-offs The GSEA results demonstrating that under nitrogen sufficiency, M. micrantha actively engages in defense-related processes is intriguing, as it suggests that nitrogen availability enables the expression of biotic stress pathways, potentially reflecting a resource-dependent priming of defense mechanisms. Similar observations have been reported in Arabidopsis , where nitrogen status modulates the expression of pathogen-responsive genes, with nitrogen-rich conditions often supporting more robust defense responses (Dietrich et al., 2004; Mur et al., 2017). This challenges the traditional growth-defense trade-off model and suggests a more nuanced relationship where resource availability determines the capacity to mount effective defense responses. The identification of HHO3-like as a central negative regulator of 3,4-dimethoxycinnamic acid under nitrogen sufficiency provides mechanistic insight into how nitrogen status modulates phenolic metabolism and growth performance. HHO3 (Homology to HAP2,3,4) belongs to the CDF (Dof-type zinc finger) transcription factor family, members of which integrate nutrient signals with developmental programs in plants (Hussain et al., 2022). The positive correlation between HHO3-like expression and multiple physiological parameters, particularly photosynthetic pigments and efficiency, suggests this transcription factor orchestrates a coordinated growth-promoting program under adequate nitrogen supply. Its down-regulation under nitrogen deficiency would release repression on phenolic acid biosynthesis, consistent with the observed accumulation of 3,4-dimethoxycinnamic acid and related phenylpropanoids. Excessive accumulation of specific phenolic acids can exert negative feedback on growth by competing with lignin biosynthesis for phenylpropanoid precursors or by direct allelopathic effects (Dong & Lin, 2021). The involvement of a zinc transporter gene (probable zinc transporter 10) in the nitrogen response network highlights the intricate coupling between nitrogen and micronutrient metabolism. Zinc transporters are essential for maintaining zinc homeostasis, which critically influences protein synthesis, enzyme activities, and hormone signaling (Marschner, 2012). The strong negative correlation between this transporter and verbascose suggests a previously unrecognized link between zinc status and carbohydrate partitioning under nitrogen stress. The down-regulation of the zinc transporter under nitrogen deficiency could reflect reduced demand for zinc-containing proteins when overall protein synthesis is constrained while simultaneously promoting verbascose accumulation through unknown regulatory mechanisms. Recent studies on nitrogen supply optimization in rice have similarly demonstrated that nitrogen deficiency suppresses pigment biosynthesis and photosynthetic activity while elevating oxidative stress markers, reinforcing the conserved nature of these regulatory responses across species. Ecological and Management Implications The convergence of the two regulatory modules identified in this study—transcriptional control of phenolic metabolism and micronutrient-transport-mediated carbohydrate partitioning—reveals a multilayered adaptive strategy in M. micrantha under nitrogen limitation. Rather than simply reducing growth, nitrogen deficiency actively reprograms gene expression to favor accumulation of specific secondary metabolites that may serve protective functions but simultaneously impose growth costs. This trade-off mirrors patterns observed in other species where nutrient stress triggers metabolic reconfiguration toward defense compound synthesis at the expense of primary metabolism (Sharma et al., 2019; Dong & Lin, 2021). These findings have important implications for understanding and potentially managing M. micrantha invasion. The strong association between nitrogen availability and invasive success suggests that nitrogen-poor habitats may serve as refugia from invasion, and that management strategies aimed at reducing nitrogen bioavailability could suppress invasion potential. This aligns with recent work (Zhong et al.,2025) demonstrating that native competitors can suppress M. micrantha through a "nitrogen fixation-nitrogen preemption" mechanism, effectively outcompeting the invader for available nitrogen. Furthermore, the identification of HHO3-like and zinc transporter 10 as regulatory hubs provides potential molecular targets for developing control strategies, whether through genetic approaches or through manipulation of soil nitrogen conditions to favor the expression of growth-suppressing secondary metabolite pathways. Conclusions This study demonstrates that nitrogen deficiency profoundly constrains the invasive capacity of M. micrantha through coordinated suppression of growth, photosynthesis, and nitrogen acquisition, accompanied by extensive metabolic and transcriptional reprogramming. Complete nitrogen deprivation arrests root development and severely impairs photosynthetic performance, revealing this species' high dependency on nitrogen availability. Multi-omics integration identifies two central regulatory modules: HHO3-like transcription factor and probable zinc transporter 10, both positively correlated with growth and physiological performance while negatively regulating accumulation of growth-inhibitory secondary metabolites—3,4-dimethoxycinnamic acid and verbascose, respectively. Their downregulation under nitrogen deficiency drives accumulation of these metabolites, mechanistically linking nitrogen status to growth suppression via metabolic competition or allelopathic effects. These findings establish that M. micrantha employs a high-nitrogen-demand strategy, with nitrogen limitation triggering resource reallocation from growth to stress metabolism, thereby constraining its invasive potential in nitrogen-poor habitats. The identification of these regulatory hubs provides potential molecular targets for managing invasion success through nitrogen manipulation. Declarations Author Contributions : For research articles with seven authors, Jiyue Wang, Qinghong Duan, Zengling Liu, Hongxia Sheng, Xiuhuan Meng, Wenjia Yang and Hongkun Huang. methodology, Jiyue Wang, Qinghong Duan, Zengling Liu, Hongxia Sheng; validation, formal analysis, Jiyue Wang, Qinghong Duan and Zengling Liu; investigation, Jiyue Wang, Qinghong Duan, and Xiuhuan Meng; data curation, Jiyue Wang and Qinghong Duan; writing-original draft preparation, Jiyue Wang and Qinghong Duan; project administration, Hongkun Huangand; funding acquisition,Wenjia Yang. All authors have read and agreed to the published version of the manuscript. Funding This study was supported by the National Key Research and Development Program of China (2023YFC2605204), the Program of Excellent Innovation Talents in Guizhou Province (GCC[2023]071), the Program for Natural Science Research in Guizhou Education Department (QJJ-[2023]-024), Guizhou Key Laboratory of Agricultural Biosecurity[QKHZSYS(2025)024]and the Guiyang Univeristy Multidisciplinary Team Construction Projects in 2025 (Gyxk202506). Declarations of interest None Data availability All data analyzed during this study are included in this published article.Raw sequencing datasets for RNA-Seq have been deposited in the NCBI repository under accession number PRJNA1433878. References Banerjee, A. K., Hou, Z., Lin, Y., Lan, W., Tan, F., Xing, F., Li, G., Guo, W., & Huang, Y. (2020). 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Integrated physiological, biochemical and transcriptomic analyses elucidate the response of alpine plant Lamiophlomis rotata (Benth.) Kudo to Low-Nitrogen stress. BMC plant biology, 25(1), 941. https://doi.org/10.1186/s12870-025-06953-5 Supplementary Files Fig.S1.png Fig.S2.png Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviewers invited by journal 27 Apr, 2026 Editor invited by journal 05 Apr, 2026 Editor assigned by journal 05 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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06:47:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9155517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9155517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805678,"identity":"c2a04ce9-48cd-47b1-a7df-a1949c2db742","added_by":"auto","created_at":"2026-05-08 15:26:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":736137,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic parameters of \u003cem\u003eM. micrantha\u003c/em\u003eunder different nitrogen treatments.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/5e742b95c5d4e4b80e96ccf9.jpeg"},{"id":108601304,"identity":"44edfe50-82bc-4ed6-bb3e-189eab28a26e","added_by":"auto","created_at":"2026-05-06 11:32:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":374160,"visible":true,"origin":"","legend":"\u003cp\u003eRoot Activity and total nitrogen content in plant (A), and photosynthetic pigment concentrations (B) in \u003cem\u003eM. micrantha\u003c/em\u003eunder different nitrogen treatments.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/d1bc3cbed7e3fe403d292640.jpeg"},{"id":108804962,"identity":"b4ed3068-4a8d-40e0-be28-ffb3fee01839","added_by":"auto","created_at":"2026-05-08 15:24:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1016458,"visible":true,"origin":"","legend":"\u003cp\u003eLeaf gas-exchange (A) and chlorophyll fluorescence (B) parameters in M. micrantha under different nitrogen treatments.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/528ce237a4be10d31155272b.png"},{"id":108601307,"identity":"4b199897-4c4c-41a8-b687-23324ad688c2","added_by":"auto","created_at":"2026-05-06 11:32:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":420198,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic profiling of \u003cem\u003eM. micrantha\u003c/em\u003ein response to nitrogen treatments. (A) Principal component analysis (PCA) score plot showing metabolic separation among treatments. (B) Top 20 differentially accumulated metabolites (DMs) ranked by fold change and significance. (C) KEGG pathway enrichment analysis of DMs; bubble size represents metabolite count, color intensity indicates enrichment significance. (D) Correlation network among eight key DMs; line thickness reflects correlation strength, yellow and blue lines indicate positive and negative correlations, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/58946c0e94842a0cf07547be.png"},{"id":108805433,"identity":"33a9e0d3-6b44-4004-b21b-c55ea3b79739","added_by":"auto","created_at":"2026-05-08 15:25:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":770291,"visible":true,"origin":"","legend":"\u003cp\u003eGene set enrichment analysis (GSEA) in the TN vs. UN comparison. (A) Top enriched Gene Ontology (GO) terms. The colored curve represents the enrichment score (ES) across the ranked gene list, with the peak value indicating the maximum ES for each term. Vertical colored bars below the x-axis denote the positions of genes belonging to the term within the ranked list; bar colors reflect log₂(fold change) values (pink: higher expression in experimental group; blue: higher expression in control group). Uniform bar distribution indicates no significant directional change. The gray-shaded area in the lower panel shows the distribution of rank values (derived from log₂FC) for all genes. (B) Top enriched KEGG pathways, with visualization elements as described in (A).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/957c903fb474269ad470f109.png"},{"id":108601311,"identity":"d6921abb-2ef2-4ead-bf74-c88647da7adc","added_by":"auto","created_at":"2026-05-06 11:32:10","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":952988,"visible":true,"origin":"","legend":"\u003cp\u003eGene-metabolite-phenotype/physiology regulatory network\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/c4b1858e4cb4256e61f9c05a.jpeg"},{"id":108809745,"identity":"0f4451cd-741d-4f18-b912-de3b65a202ce","added_by":"auto","created_at":"2026-05-08 15:55:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4690802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/e6cd430a-5c91-4bd4-a7ff-c21918f15ac9.pdf"},{"id":108805888,"identity":"16e878cc-9c3c-4f3b-a2ba-7be29e4ef28f","added_by":"auto","created_at":"2026-05-08 15:27:06","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1634796,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/2a2f354a1dc1ab8ca49b3915.png"},{"id":108601309,"identity":"7eaa5988-3164-41f1-9804-5495328472fe","added_by":"auto","created_at":"2026-05-06 11:32:10","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1790508,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.png","url":"https://assets-eu.researchsquare.com/files/rs-9155517/v1/d077e3f9d8ba60c5877b33b7.png"}],"financialInterests":"","formattedTitle":"Multi-omics identifies regulatory hubs coordinating growth-metabolism trade-offs under nitrogen deficiency in an invasive Mikania micrantha","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eMikania micrantha\u003c/em\u003e Kunth, commonly known as mile-a-minute weed, is a perennial vine of the Asteraceae family, native to tropical America (Clements \u0026amp; Kato-Noguchi, 2025). Recognized as one of the world's 100 worst invasive alien species (Lowe et al., 2000), it has aggressively colonized vast areas of tropical and subtropical Asia and the Pacific Islands since its introduction. Its invasion leads to the formation of dense monocultures that smother native vegetation and crops, resulting in severe biodiversity loss and substantial economic damage (Day et al., 2016; Kaur et al., 2012). For instance, within China\u0026rsquo;s Pearl River Delta alone, annual economic losses are estimated at billions of RMB (Han et al., 2017). The remarkable invasion success of M. micrantha is attributed to a synergy of multiple traits, including extraordinarily rapid growth (up to 20 cm day⁻\u0026sup1;), prolific seed production and efficient wind/water dispersal, vigorous vegetative propagation via adventitious roots, and high genetic and epigenetic variability that underpin its adaptability to diverse environments (Huang et al., 2015; Liu et al., 2020; Shen et al., 2021).\u003c/p\u003e \u003cp\u003eRecent genomic and ecological studies have begun to unravel the mechanisms behind this invasiveness. Population genetic analyses suggest that its invasion in Asia likely involved multiple introductions, admixture events, and founder effects, creating genetically diverse populations capable of rapid local adaptation (Yang et al., 2017; Ji et al., 2021; Banerjee et al., 2020). Furthermore, its interaction with biotic factors, such as altered herbivore and pathogen pressure in the introduced range (enemy release hypothesis), and abiotic factors, particularly climate change which may expand its suitable habitat, play significant roles in its spread (Mitchell \u0026amp; Power, 2003; Xie et al., 2025). Physiologically, \u003cem\u003eM. micrantha\u003c/em\u003e possesses unique adaptations like nighttime CO₂ fixation and highly efficient stem photosynthesis, which fuel its rapid growth (Liu et al., 2020; Cai et al., 2023). Its climbing habit enables it to quickly overtop and smother trees, a process driven by specific growth patterns and hormonal responses (Chen et al., 2024), and it also exhibits notable drought tolerance (Cai et al., 2025).\u003c/p\u003e \u003cp\u003eA particularly intriguing aspect of its invasion strategy is its interaction with soil nutrient cycles, especially nitrogen (N). The genome of \u003cem\u003eM. micrantha\u003c/em\u003e reveals not only recent whole-genome duplication and transposable element expansion but also insights into its metabolic capabilities (Liu et al., 2020). Critically, it has been suggested that metabolites from \u003cem\u003eM. micrantha\u003c/em\u003e can enhance N availability by positively influencing the abundance and activity of N-cycling microorganisms in the soil (Liu et al., 2020). This aligns with field observations that \u003cem\u003eM. micrantha\u003c/em\u003e invasion significantly alters soil microbial communities, increasing the abundance of ammonia-oxidizing bacteria, accelerating nitrification rates, and elevating soil nitrate (NO\u003csub\u003e3\u003c/sub\u003e⁻) concentrations (Li et al., 2006; Chen et al., 2009; Yu et al., 2021). These changes create a feedback loop that potentially improves nutrient conditions for the invader itself, a common mechanism in successful plant invasions (Gioria et al., 2023). Cai et al.,(2025) recently demonstrated that under increasing global nitrogen deposition\u0026ndash;particularly the sustained rise in NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N deposition\u0026ndash;M.\u003cem\u003emicrantha\u003c/em\u003e may achieve rapid growth through the regulation of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N uptake-related transcription factors (e.g., HY5) and transporters (CLC, SCLA/C, NPF), as well as the modulated expression of key nitrogen assimilation genes (NR, GS, GOGAT). These molecular adjustments enhance nitrogen use efficiency under NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N-dominated conditions and may accelerate the spread of this invasive species. However, that study focused exclusively on NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N enrichment, leaving responses to nitrogen deficiency largely unexplored.To address this gap, the present study investigates the physiological and molecular responses of \u003cem\u003eM. micrantha\u003c/em\u003e to varying nitrogen supply levels (zero, half, and full nitrogen) under hydroponic conditions. We integrate analyses of growth performance, nutrient acquisition, photosynthesis, chlorophyll fluorescence, transcriptome, and metabolome. This multi-omics approach aims to uncover the regulatory networks and metabolic reprogramming underlying nitrogen limitation tolerance, thereby completing the full spectrum of nitrogen response strategies in this invasive weed.\u003c/p\u003e \u003cp\u003eWe hypothesize that nitrogen availability serves as a key environmental cue modulating the expression of genes and accumulation of metabolites associated with rapid growth, clonal reproduction, and potentially the synthesis of allelopathic compounds\u0026ndash;thereby directly influencing the invasive capacity of \u003cem\u003eM. micrantha\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant Material and Growth Conditions\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eM. micrantha\u003c/em\u003e Kunth stems were collected from a naturalized population in Guangdong Province, South China (23°26′ N, 116°35′ E). Uniform apical stem segments (8 cm in length, each bearing three fully expanded leaves) were excised from healthy stock plants, surface-sterilized with 0.5% sodium hypochlorite for 2 min, and rinsed thoroughly with deionized water. The segments were directly transplanted into opaque 2‑L hydroponic containers (four segments per container) filled with modified Hoagland nutrient solution. The basal nutrient solution comprised: 1.25 mM K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 0.5 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 2 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 0.5 mM MgSO\u003csub\u003e4\u003c/sub\u003e, 20 µM Fe‑EDTA, 10 µM H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e, 2 µM MnSO\u003csub\u003e4\u003c/sub\u003e 1 µM ZnSO\u003csub\u003e4\u003c/sub\u003e, 0.5 µM CuSO\u003csub\u003e4\u003c/sub\u003e and 0.1 µM (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e6\u003c/sub\u003eMo\u003csub\u003e7\u003c/sub\u003eO\u003csub\u003e24\u003c/sub\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNitrogen Treatments\u003c/h3\u003e\n\u003cp\u003eFour nitrogen (N) supply levels were established by modifying the N concentration in the Hoagland solution. Nitrogen was supplied as NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e at the following concentrations: total nitrogen (TN, 7.5 mM N, standard Hoagland N level), half nitrogen (HN, 3.75 mM N), and zero nitrogen (UN, 0 mM N, N‑free Hoagland solution). Deionized water without any nutrients served as an additional control treatment (H\u003csub\u003e2\u003c/sub\u003eO). The pH of all solutions was adjusted to 5.8 ± 0.1.Each treatment was replicated six times (six independent hydroponic containers) with five plants per replicate, resulting in 30 individuals per treatment. Solutions were renewed every 5 days and continuously aerated. The experiment was conducted in a controlled greenhouse (28/22°C day/night, 14‑h photoperiod, 600 µmol photons m⁻² s⁻¹, 70% relative humidity).\u003c/p\u003e\n\u003ch3\u003eMeasurements of Phenotypic and Physiological Parameters\u003c/h3\u003e\n\u003cp\u003eAfter 30 days of treatment, the following measurements were performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePhenotypic Parameters: Ten randomly selected plants per treatment were used for destructive sampling. Plant height(PH), stem diameter(SD), number of leaf (NL), number of branching (NB), fresh weight of shoots (SFW) and roots (RFW), and dry weight of shoots (SFW) and roots (RDW) were recorded. Root system architecture was analyzed using a root scanner (Epson Perfection V800, Japan), and total root length (TRL), root Surfarea(RSA), root Tips(RT), root AvgDiam(RAD), root ProjArea(RPA), root Volume(RV), root Forks(RF), root Crossings(RC) were determined. Leaf area (LA) were measured using the same scanner and associated software.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRoot Activity: Root activity was assessed using the triphenyltetrazolium chloride (TTC) reduction method.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTotal Nitrogen Content in Plant(TNP): Oven-dried (80°C to constant weight) shoot and root samples were ground to a fine powder. The total nitrogen concentration was determined using a Continuous Flow Analyzer (AA3, SEAL Analytical, Germany).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePhotosynthetic Pigment Content: Fresh leaf samples (0.1 g) were extracted with 95% ethanol in the dark. The absorbance of the extracts was measured at 665, 649, and 470 nm using a spectrophotometer. Concentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl t), and carotenoids (Car) were calculated according to Lichtenthaler (1987). The Chl a/b ratio was also derived.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGas Exchange Measurements: Net photosynthetic rate and related parameters were measured on the youngest, fully expanded, sun-exposed leaves on clear days between 9:00 and 11:00 AM using a portable photosynthesis system (LI-6800, LI-COR Biosciences, USA). The recorded parameters included: net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO\u003csub\u003e2\u003c/sub\u003e concentration (Ci), stomatal conductance (Gs), boundary layer conductance (gbw), total conductance to CO\u003csub\u003e2\u003c/sub\u003e (gtc) and water vapor (gtw), leaf temperature (Tleaf), chamber relative humidity (RHcham), and leaf-to-air vapor pressure deficit (VPDleaf). Instantaneous water-use efficiency (WUE) was calculated as Pn/Tr. Stomatal limitation (LS) was calculated as (Ca − Ci) / Ca, where Ca is the ambient CO\u003csub\u003e2\u003c/sub\u003e concentration (set at 400 µmol mol⁻¹ during measurements).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLight Response Curves: Light-response curves (LRCs) of photosynthesis were generated using the LI-6800 automated program. After initial acclimation to a saturating light intensity of 750 µmol m⁻² s⁻¹ until a stable photosynthetic rate was achieved (~ 5 min), A was measured at a sequentially decreasing series of photosynthetic photon flux density (PPFD) levels: 1800, 1500, 1200, 1000, 800, 600, 400, 200, 150, 100, 50, 25, and 0 µmol m⁻² s⁻¹.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChlorophyll Fluorescence: Chlorophyll fluorescence parameters were measured at 9:00 PM using a pulse-amplitude modulation fluorometer integrated into the LI-6800 system. After 30 min of dark adaptation, the minimum (Fo) and maximum (Fm) fluorescence yields were measured to calculate the maximum quantum yield of PSII (Fv/Fm = (Fm − Fo)/Fm). Under actinic light (300 µmol m⁻² s⁻¹), steady-state (Fs), maximum (Fm′), and minimum (F'o) fluorescence yields in light-adapted leaves were recorded. These values were used to calculate electron transport rate (ETR), photochemical quenching (qP), and non-photochemical quenching (NPQ).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe partitioning of absorbed light energy was estimated as follows: the percentage allocated to photochemistry (P) = qP × F'v/F'm × 100%; to regulated thermal dissipation (D) = (1 − F'v/F'm) × 100%; and to non-regulated dissipation (Ex) = (1 − qP) × F'v/F'm × 100%.\u003c/p\u003e\n\u003ch3\u003eTranscriptomic and Metabolomic Profiling\u003c/h3\u003e\n\u003cp\u003eThe seedlings subjected to the TN and UN treatments were sampled, immediately frozen in liquid nitrogen, and stored at − 80°C. For each treatment, three independent biological replicates were collected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTranscriptome Sequencing: Total RNA was extracted, assessed for quality, and used for library construction. Sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina, USA) to generate paired-end 150 bp reads. Raw RNA-seq reads were processed for quality control using fastp (v0.23.2) to remove adapter sequences and low-quality reads. Clean reads were aligned to the \u003cem\u003eMikania micrantha\u003c/em\u003e reference genome using HISAT2 (v2.2.1) with default parameters. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM) using StringTie (v2.2.1). Differential expression analysis between treatments was performed using DESeq2 (v1.38.3) with thresholds of |log\u003csub\u003e2\u003c/sub\u003e(fold change)|≥1 and adjusted P-value \u0026lt; 0.05. Alternative splicing events were identified and quantified using rMATS (v4.1.2). Transcription factors were predicted using iTAK software against the PlantTFDB database. Weighted gene co-expression network analysis (WGCNA) was conducted using the WGCNA R package to identify modules correlated with nitrogen treatments. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using clusterProfiler (v4.6.0) with a significance threshold of adjusted P-value \u0026lt; 0.05.2.)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMetabolomic Profiling: Metabolites were extracted from the same tissues and analyzed using a UPLC-MS/MS system (Waters, USA) coupled with a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, USA) in both positive and negative ionization modes. Metabolomic data processing and analysis were performed following the MetMiner pipeline as described by Wang et al., (2025). Briefly, raw LC-MS data were converted to mzXML format and processed using XCMS for peak detection, retention time correction, and alignment. Metabolite annotation was achieved by matching MS/MS spectra against an in-house plant-specific mass spectrometry database integrated in MetMiner. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied for multivariate statistical analysis. Differential metabolites were identified based on variable importance in projection (VIP) score \u0026gt; 1 from OPLS-DA and Student's t-test P-value \u0026lt; 0.05. Metabolite classification and KEGG enrichment analysis were conducted using the MDAtoolkits embedded in MetMiner.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll phenotypic and physiological data are presented as mean ± standard error (SE). One-way analysis of variance (ANOVA) followed by Tukey’s Honestly Significant Difference (HSD) post-hoc test (p \u0026lt; 0.05) was performed to assess significant differences among the four N treatments using SPSS software (version 26.0, IBM, USA). Figures were generated using GraphPad Prism 9.0.\u003c/p\u003e \u003cp\u003eFor multi-omics data, differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were identified based on thresholds of |log₂(fold change)| \u0026gt; 1 and a false discovery rate (FDR) \u0026lt; 0.05. Integrated analysis of transcriptomic and metabolomic data was conducted to elucidate the regulatory network responding to N deprivation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003ch2\u003ePhenotypic and Growth Responses to Nitrogen Deficiency\u003c/h2\u003e\u003cp\u003eNitrogen supply levels significantly modulated the growth performance and root system architecture of \u003cem\u003eM micrantha\u003c/em\u003e. In general, all phenotypic parameters exhibited a declining trend with decreasing nitrogen availability(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, PH, SD, SFW, RFW, SDW, and RDW were progressively reduced from TN to HN and UN treatments. Notably, the UN treatment resulted in a complete failure of root system development, precluding any root architecture analysis for this group. While branching nodes (BN) and SDW in the UN treatment did not differ significantly from the control (CK), they were lower than in TN and HN. NL remained statistically comparable among TN, HN, and CK, and LA showed no significant difference between TN and UN. For the root parameters measured in TN, HN, and CK, a consistent pattern of reduction with decreasing nitrogen was observed across most metrics, including TRL, RSA, RV, and RC. However, RAD and RT deviated from this trend, suggesting a differential sensitivity to nitrogen limitation in specific root architectural traits.\u003c/p\u003e\u003ch3\u003ePhysiological Responses: Photosynthesis and Nitrogen Accumulation\u003c/h3\u003e\u003cp\u003eConcentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl t), and carotenoids (Car) exhibited a progressive decline with decreasing nitrogen availability, with the UN treatment showing significantly lower values than CK (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, root activity (RA) and total nitrogen content in plants (TNP) followed the same monotonic decreasing trend across treatments, from TN through HN to UN. All measured parameters in the UN treatment were significantly suppressed compared to CK, indicating that complete nitrogen deprivation severely impairs both photosynthetic pigment biosynthesis and root physiological function.\u003c/p\u003e\u003cp\u003eGas exchange parameters, including net photosynthetic rate (Pn), transpiration rate (Tr), boundary layer conductance (gbw), stomatal limitation (LS), and water-use efficiency (WUE), consistently followed the pattern TN \u0026gt; HN \u0026gt; CK \u0026gt; UN, with all parameters under UN treatment significantly lower than CK (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). This hierarchical response demonstrates a linear positive relationship between nitrogen supply and photosynthetic capacity. Conversely, total conductance to water vapor (gtw) peaked under HN treatment, with no significant difference between TN and CK, while UN remained substantially depressed. Stomatal conductance (Gs) under UN was significantly reduced relative to TN and HN but did not differ from CK. The total conductance to CO₂ (gtc) was significantly impaired under UN compared to TN and HN, yet only HN exhibited significantly higher gtc than CK.\u003c/p\u003e\u003ch2\u003eChlorophyll Fluorescence and PSII Performance\u003c/h2\u003e\u003cp\u003eFluorescence parameters revealed pronounced photosystem II (PSII) dysfunction under nitrogen deprivation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Minimum fluorescence (Fo), maximum fluorescence (Fm), maximum fluorescence in light-adapted state (Fm′), variable fluorescence in light-adapted state (Fv′), and steady-state fluorescence (Fs) were all significantly depressed under UN relative to all other treatments. Maximum quantum efficiency of PSII (Fv/Fm) under UN was significantly lower than TN and HN but statistically indistinguishable from CK. Potential PSII activity (Fv/Fo), effective quantum yield (Fv′/Fm′), electron transport rate (ETR), and photochemical quenching (qP) under UN were significantly reduced only compared to TN, with no differences detected relative to HN or CK. These patterns collectively indicate that complete nitrogen deprivation imposes severe constraints on both stomatal and non-stomatal components of photosynthesis, while partial nitrogen supply (HN) enables partial compensation through enhanced conductance parameters.\u003c/p\u003e\u003ch2\u003eMetabolomic Reprogramming under Nitrogen Deficiency\u003c/h2\u003e\u003cp\u003eMetabolomic profiling revealed pronounced nitrogen deficiency-induced metabolic reprogramming in \u003cem\u003eM. micrantha\u003c/em\u003e. Principal component analysis demonstrated clear separation between TN and UN treatments, indicating extensive reconfiguration of the metabolic landscape under nitrogen deprivation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). A total of 119 significantly differential metabolites (DMs, P \u0026lt; 0.05) were identified, of which 43 were down-regulated under UN. Phenolic acids (23 metabolites, 19.30%) and flavonoids (14 metabolites, 11.70%) constituted the two largest DM classes, collectively accounting for nearly one-third of all differentially accumulated compounds (SFig. 1A). The top 10 up- and down-regulated DMs encompassed diverse chemical categories, including lipids, phenolic acids, flavonoids, amino acid derivatives, alkaloids, lignans, and coumarins (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eKEGG enrichment analysis identified five significantly enriched pathways (P \u0026lt; 0.005): galactose metabolism (most highly enriched), ABC transporters, caffeic acid derivative biosynthesis, glutathione metabolism, and nucleotide metabolism (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Galactose metabolism exhibited a strongly coordinated down-regulation pattern (DA = -0.73), whereas nucleotide metabolism was the sole pathway showing net up-regulation (DA = + 0.27) in the TN vs. UN comparison. Correlation analysis of the eight DMs mapping to these pathways revealed a tightly interconnected module centred on 2,2-dimethylsuccinic acid, which displayed significant positive correlations with three phenolic acids (3,4-dimethoxycinnamic acid, 3-O-caffeoylshikimic acid, and 4-O-(4'-O-alpha-D-glucopyranosyl)caffeoylquinic acid), two terpenoids (zaluzanin C, verbascose), and the amino acid derivative N-acetyl-tryptophan (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003ch2\u003eTranscriptomic Reprogramming under Nitrogen Deficiency\u003c/h2\u003e\u003cp\u003eComparative transcriptomic analysis of TN and UN treatments revealed significant transcriptional reprogramming in M. micrantha seedlings in response to nitrogen deprivation. In total, 3,409 differentially expressed genes (DEGs) were identified in the TN vs. UN comparison (SFig. 1B). Gene set enrichment analysis (GSEA) demonstrated that under nitrogen sufficiency (TN), M. micrantha actively engages in defense-related processes, with top-ranked GO terms including \"defense response to fungus,\" \"response to chitin,\" and \"response to organonitrogen compound\" (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Conversely, only \"organelle fission\" was induced under UN, suggesting cellular remodeling as a specific starvation response.\u003c/p\u003e\u003cp\u003eKEGG enrichment analysis revealed profound metabolic reconfiguration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The significant upregulation of \"Photosynthesis\" and \"Photosynthesis proteins\" pathways under TN (i.e., repressed under UN) is a hallmark response to nitrogen limitation, restricting the synthesis of chlorophyll and photosynthetic enzymes. The upregulation of \"alpha-Linolenic acid metabolism\" under TN suggests that nitrogen-starved plants may down-regulate jasmonate precursor biosynthesis to conserve resources. In stark contrast, pathways for \"Flavonoid biosynthesis\" and \"Selenocompound metabolism\" were up-regulated under UN, representing a strategic investment in constitutive chemical defense when resources for rapid growth are unavailable.\u003c/p\u003e\u003cp\u003eAt the post-transcriptional level, nitrogen deficiency induced substantial alternative splicing, with 646 mutually exclusive exons (MEX) and 1,881 skipped exon (SE) events identified. This was accompanied by the differential expression of 1,845 transcription factors (TFs), predominantly from the bHLH, NAC, ERF, MYB-related, WRKY, and C2H2 families (SFig. 1C), pointing to their central role in orchestrating the nitrogen-responsive transcriptional network. In parallel, 840 novel transcripts were identified, further expanding the repertoire of nitrogen-responsive transcriptional units.\u003c/p\u003e\u003ch2\u003eMulti-Omics Integration Identifies Regulatory Hubs\u003c/h2\u003e\u003cp\u003eIntegrative multi-omics analysis revealed two key regulatory modules linking nitrogen deficiency-responsive genes, metabolites, and phenotypic traits in M. micrantha (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The transcription factor gene HHO3-like (R6Q59_002673) exhibited strong negative correlations with the phenolic acid metabolite 3,4-dimethoxycinnamic acid, and positive correlations with three growth parameters (PH, RDW, SD) and eight physiological traits, including Car, Chl a, Chl t, Fo, Pn, gbw, RA, and TNP. Under nitrogen deficiency (UN), HHO3-like expression was significantly down-regulated (log\u003csub\u003e2\u003c/sub\u003eFC = 2.80), coinciding with elevated 3,4-dimethoxycinnamic acid accumulation and concomitant reductions in all positively correlated traits.\u003c/p\u003e\u003cp\u003eSimilarly, the probable zinc transporter 10 gene (R6Q59_005338) showed strong negative association with the raffinose family oligosaccharide verbascose and positive association with SDW. Under UN treatment, this transporter was markedly suppressed (log2FC = 15.99), leading to verbascose accumulation and reduced SDW. These coordinated responses demonstrate that nitrogen deprivation simultaneously represses key transcriptional regulators and facilitates accumulation of specific secondary metabolites that collectively inhibit growth, photosynthetic capacity, pigment biosynthesis, root function, and nitrogen acquisition.\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNitrogen Dependency as an Ecological Constraint on Invasiveness\u003c/h2\u003e \u003cp\u003eThe profound suppression of \u003cem\u003eM. micrantha\u003c/em\u003e growth and the complete inhibition of root development under nitrogen-free conditions underscore this species' high dependency on exogenous nitrogen, particularly for its rapid invasive growth. The optimal phenotypic responses observed under TN are consistent with recent findings demonstrating that increasing NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentrations significantly promote \u003cem\u003eM. micrantha\u003c/em\u003e growth, with optimal responses observed at 5 mM NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, concomitant with up-regulation of nitrate transporter genes and nitrogen metabolism enzymes. The inability of the UN treatment to initiate roots highlights a critical nitrogen threshold for fundamental developmental processes. This may be explained by systemic signaling pathways where nitrogen satiety or deficiency modulates root architecture. Recent work in \u003cem\u003eArabidopsis\u003c/em\u003e has elucidated that high nitrogen conditions induce peptides like LOHN1 that suppress lateral root development, whereas under severe nitrogen deprivation, the lack of nitrogen itself may fail to trigger necessary signaling cascades involving NLP7 transcription factors that coordinate root growth by integrating cytokinin and reactive oxygen species signals (Ito et al., 2025).\u003c/p\u003e \u003cp\u003eThe pronounced photosynthetic depression under UN treatment reflects a fundamental constraint in \u003cem\u003eM. micrantha\u003c/em\u003e's nitrogen acquisition strategy. Recent evidence demonstrates that this invader exhibits strong preference for nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) over ammonium, with nitrate transporter and assimilation genes constitutively upregulated under nitrogen-sufficient conditions. Complete nitrogen deprivation therefore disrupts not only substrate availability for Rubisco and photochemical machinery but also the expression of nitrate-specific uptake and assimilation pathways upon which \u003cem\u003eM. micrantha\u003c/em\u003e has evolved dependency. The observation that UN-treated plants performed significantly worse than water-only controls (CK) is particularly striking. This suggests that abrupt, complete withdrawal of nitrogen after initial exposure triggers a more severe stress response than chronic complete nutrient deprivation, potentially involving regulatory imbalances in nitrogen starvation signaling pathways (Qi et al., 2025).\u003c/p\u003e \u003cp\u003eThe partial compensation observed under HN treatment, particularly the elevated gtw and gtc, aligns with findings in low-nitrogen-tolerant genotypes that maintain photosynthetic function through optimized nitrogen allocation to electron transport and carboxylation components (Qi et al., 2025). However, \u003cem\u003eM. micrantha\u003c/em\u003e's failure to sustain Pn and WUE under HN relative to TN indicates that this species, despite its invasive success, requires relatively high nitrogen inputs to achieve its characteristically superior photosynthetic performance. This nitrogen-dependent physiological trade-off may constrain its invasive spread into severely nitrogen-impoverished habitats, consistent with the \"nitrogen amplification-preemption\" strategy observed in native competitors that suppress \u003cem\u003eM. micrantha\u003c/em\u003e through nitrogen cycling optimization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic Reprogramming: Carbon Reallocation from Growth to Defense\u003c/h2\u003e \u003cp\u003eThe coordinated up-regulation of galactose metabolism under UN observed in \u003cem\u003eM. micrantha\u003c/em\u003e seedlings suggests enhanced mobilization of cell wall polysaccharides and soluble sugars. Galactose metabolism is intimately linked to the turnover of cell wall galactans and raffinose family oligosaccharides (RFOs), which serve as carbon reserves and osmoprotectants under stress conditions (Van den Ende, 2013). The accumulation of verbascose\u0026mdash;a major RFO\u0026mdash;within the positively correlated metabolite network further supports active RFO biosynthesis or retention under nitrogen limitation, likely to maintain carbon balance and protect against oxidative stress. Similar metabolic adjustments have been reported in Panax ginseng under nitrogen deficiency, where increased accumulation of flavonoids, phenolic acids, and lipids was observed alongside down-regulation of terpenoid biosynthesis .\u003c/p\u003e \u003cp\u003eThe enrichment of caffeic acid derivative biosynthesis and glutathione metabolism pathways reflects enhanced phenylpropanoid flux and antioxidant capacity, respectively. Nitrogen limitation typically redirects carbon from nitrogen-rich primary metabolites toward carbon-rich secondary metabolites, particularly phenolic compounds, as demonstrated in Citrus sinensis where long-term nitrogen deficiency up-regulates the phenylpropanoid pathway and accumulates phenolic acids (Peng et al., 2023). The strong positive correlations between 2,2-dimethylsuccinic acid and multiple phenolic acids, together with the terpenoid zaluzanin C and the amino acid derivative N-acetyl-tryptophan, suggest coordinated carbon reallocation toward stress-protective secondary metabolites under nitrogen deficiency.\u003c/p\u003e \u003cp\u003eIn contrast, the down-regulation of nucleotide metabolism under UN reflects reduced demand for nitrogen-rich nucleotide biosynthesis under nitrogen-limited conditions, consistent with the general shift from N-rich to C-rich metabolic pools observed in nitrogen-starved plants (Kov\u0026aacute;čik \u0026amp; Klejdus, 2014; Peng et al., 2023). Recent multi-omics analyses in alpine plants have similarly demonstrated that nitrogen deficiency induces suppression of antioxidant enzymes concurrent with elevated secondary metabolites, with transcriptomic perturbations in nitrogen metabolism and photosynthetic pathways. Collectively, these findings reveal that \u003cem\u003eM. micrantha\u003c/em\u003e mounts a multi-faceted metabolic acclimation to nitrogen deficiency: enhancing carbon mobilization via galactose metabolism, boosting phenylpropanoid-mediated stress protection, strengthening antioxidant capacity through glutathione metabolism, and activating transporter-mediated nutrient salvage, while suppressing energetically costly nucleotide biosynthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional Regulation of Growth-Defense Trade-offs\u003c/h2\u003e \u003cp\u003eThe GSEA results demonstrating that under nitrogen sufficiency, \u003cem\u003eM. micrantha\u003c/em\u003e actively engages in defense-related processes is intriguing, as it suggests that nitrogen availability enables the expression of biotic stress pathways, potentially reflecting a resource-dependent priming of defense mechanisms. Similar observations have been reported in \u003cem\u003eArabidopsis\u003c/em\u003e, where nitrogen status modulates the expression of pathogen-responsive genes, with nitrogen-rich conditions often supporting more robust defense responses (Dietrich et al., 2004; Mur et al., 2017). This challenges the traditional growth-defense trade-off model and suggests a more nuanced relationship where resource availability determines the capacity to mount effective defense responses.\u003c/p\u003e \u003cp\u003eThe identification of HHO3-like as a central negative regulator of 3,4-dimethoxycinnamic acid under nitrogen sufficiency provides mechanistic insight into how nitrogen status modulates phenolic metabolism and growth performance. HHO3 (Homology to HAP2,3,4) belongs to the CDF (Dof-type zinc finger) transcription factor family, members of which integrate nutrient signals with developmental programs in plants (Hussain et al., 2022). The positive correlation between HHO3-like expression and multiple physiological parameters, particularly photosynthetic pigments and efficiency, suggests this transcription factor orchestrates a coordinated growth-promoting program under adequate nitrogen supply. Its down-regulation under nitrogen deficiency would release repression on phenolic acid biosynthesis, consistent with the observed accumulation of 3,4-dimethoxycinnamic acid and related phenylpropanoids. Excessive accumulation of specific phenolic acids can exert negative feedback on growth by competing with lignin biosynthesis for phenylpropanoid precursors or by direct allelopathic effects (Dong \u0026amp; Lin, 2021).\u003c/p\u003e \u003cp\u003eThe involvement of a zinc transporter gene (probable zinc transporter 10) in the nitrogen response network highlights the intricate coupling between nitrogen and micronutrient metabolism. Zinc transporters are essential for maintaining zinc homeostasis, which critically influences protein synthesis, enzyme activities, and hormone signaling (Marschner, 2012). The strong negative correlation between this transporter and verbascose suggests a previously unrecognized link between zinc status and carbohydrate partitioning under nitrogen stress. The down-regulation of the zinc transporter under nitrogen deficiency could reflect reduced demand for zinc-containing proteins when overall protein synthesis is constrained while simultaneously promoting verbascose accumulation through unknown regulatory mechanisms. Recent studies on nitrogen supply optimization in rice have similarly demonstrated that nitrogen deficiency suppresses pigment biosynthesis and photosynthetic activity while elevating oxidative stress markers, reinforcing the conserved nature of these regulatory responses across species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEcological and Management Implications\u003c/h2\u003e \u003cp\u003eThe convergence of the two regulatory modules identified in this study\u0026mdash;transcriptional control of phenolic metabolism and micronutrient-transport-mediated carbohydrate partitioning\u0026mdash;reveals a multilayered adaptive strategy in \u003cem\u003eM. micrantha\u003c/em\u003e under nitrogen limitation. Rather than simply reducing growth, nitrogen deficiency actively reprograms gene expression to favor accumulation of specific secondary metabolites that may serve protective functions but simultaneously impose growth costs. This trade-off mirrors patterns observed in other species where nutrient stress triggers metabolic reconfiguration toward defense compound synthesis at the expense of primary metabolism (Sharma et al., 2019; Dong \u0026amp; Lin, 2021).\u003c/p\u003e \u003cp\u003eThese findings have important implications for understanding and potentially managing \u003cem\u003eM. micrantha\u003c/em\u003e invasion. The strong association between nitrogen availability and invasive success suggests that nitrogen-poor habitats may serve as refugia from invasion, and that management strategies aimed at reducing nitrogen bioavailability could suppress invasion potential. This aligns with recent work (Zhong et al.,2025) demonstrating that native competitors can suppress \u003cem\u003eM. micrantha\u003c/em\u003e through a \"nitrogen fixation-nitrogen preemption\" mechanism, effectively outcompeting the invader for available nitrogen. Furthermore, the identification of HHO3-like and zinc transporter 10 as regulatory hubs provides potential molecular targets for developing control strategies, whether through genetic approaches or through manipulation of soil nitrogen conditions to favor the expression of growth-suppressing secondary metabolite pathways.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that nitrogen deficiency profoundly constrains the invasive capacity of \u003cem\u003eM. micrantha\u003c/em\u003e through coordinated suppression of growth, photosynthesis, and nitrogen acquisition, accompanied by extensive metabolic and transcriptional reprogramming. Complete nitrogen deprivation arrests root development and severely impairs photosynthetic performance, revealing this species' high dependency on nitrogen availability. Multi-omics integration identifies two central regulatory modules: HHO3-like transcription factor and probable zinc transporter 10, both positively correlated with growth and physiological performance while negatively regulating accumulation of growth-inhibitory secondary metabolites\u0026mdash;3,4-dimethoxycinnamic acid and verbascose, respectively. Their downregulation under nitrogen deficiency drives accumulation of these metabolites, mechanistically linking nitrogen status to growth suppression via metabolic competition or allelopathic effects. These findings establish that \u003cem\u003eM. micrantha\u003c/em\u003e employs a high-nitrogen-demand strategy, with nitrogen limitation triggering resource reallocation from growth to stress metabolism, thereby constraining its invasive potential in nitrogen-poor habitats. The identification of these regulatory hubs provides potential molecular targets for managing invasion success through nitrogen manipulation.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: For research articles with seven authors, Jiyue Wang, Qinghong Duan, Zengling Liu, Hongxia Sheng, Xiuhuan Meng, Wenjia Yang and Hongkun Huang. methodology, Jiyue Wang, Qinghong Duan, Zengling Liu, Hongxia Sheng; validation, formal analysis, Jiyue Wang, Qinghong Duan and Zengling Liu; investigation, Jiyue Wang, Qinghong Duan, and Xiuhuan Meng; data curation, Jiyue Wang and Qinghong Duan; writing-original draft preparation, Jiyue Wang and Qinghong Duan; project administration, Hongkun Huangand; funding acquisition,Wenjia Yang. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Program of China (2023YFC2605204), the Program of Excellent Innovation Talents in Guizhou Province (GCC[2023]071), the Program for Natural Science Research in Guizhou Education Department (QJJ-[2023]-024), Guizhou Key Laboratory of Agricultural Biosecurity[QKHZSYS(2025)024]and the Guiyang Univeristy Multidisciplinary Team Construction Projects in 2025 (Gyxk202506).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed during this study are included in this published article.Raw sequencing datasets for RNA-Seq have been deposited in the NCBI repository under accession number PRJNA1433878.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanerjee, A. 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BMC plant biology, 25(1), 941. https://doi.org/10.1186/s12870-025-06953-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"M. micrantha, nitrogen deficiency, multi-omics, plant invasion","lastPublishedDoi":"10.21203/rs.3.rs-9155517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9155517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eNitrogen availability is a critical determinant of plant invasion success, yet how invasive species respond to nitrogen deficiency remains poorly understood. This study aimed to investigate the physiological and molecular responses of the globally invasive vine \u003cem\u003eMikania micrantha\u003c/em\u003e to nitrogen deficiency.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIntegrative transcriptomic and metabolomic analyses were performed on \u003cem\u003eM. micrantha\u003c/em\u003e under nitrogen deprivation. Physiological parameters including growth, root development, photosynthetic capacity, and nitrogen accumulation were assessed. Differentially expressed genes and accumulated metabolites were identified, and multi-omics network analysis was conducted to uncover central regulatory hubs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNitrogen deprivation severely suppressed growth, root development, photosynthetic capacity, and nitrogen accumulation in \u003cem\u003eM. micrantha\u003c/em\u003e. A total of 119 differentially accumulated metabolites and 3,409 differentially expressed genes were identified. Galactose metabolism and phenolic acid biosynthesis pathways were up-regulated, while photosynthesis and nucleotide metabolism were repressed. Multi-omics network analysis revealed two central regulators: the transcription factor HHO3-like negatively controlled 3,4-dimethoxycinnamic acid accumulation and positively regulated growth and photosynthetic traits; the probable zinc transporter 10 negatively associated with verbascose accumulation and positively influenced shoot biomass. Both genes were down-regulated under nitrogen deficiency, driving accumulation of growth-inhibitory secondary metabolites.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eM. micrantha\u003c/em\u003e exhibits a high-nitrogen-demand strategy. Nitrogen deficiency triggers a resource allocation shift from growth to stress metabolism via specific regulatory hubs, thereby constraining its invasive potential in nitrogen-poor environments.\u003c/p\u003e","manuscriptTitle":"Multi-omics identifies regulatory hubs coordinating growth-metabolism trade-offs under nitrogen deficiency in an invasive Mikania micrantha","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 11:32:05","doi":"10.21203/rs.3.rs-9155517/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-14T11:15:41+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T03:48:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-04-06T01:29:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T01:27:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-04-02T22:01:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6a69e66c-9f5a-4878-92ac-dd72200aa964","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-14T11:15:41+00:00","index":0,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T11:32:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 11:32:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9155517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9155517","identity":"rs-9155517","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}