Transcriptomic Profiling Identifies Determinant Regulation for Ammonium Tolerance in Wheat

Transcriptomic Profiling Identifies Determinant Regulation for Ammonium Tolerance in Wheat | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2025 V1 Latest version Share on Transcriptomic Profiling Identifies Determinant Regulation for Ammonium Tolerance in Wheat Authors : Adrien Blum 0000-0002-2849-049X [email protected] , Ivan Jáuregui , Yordan Muhovski 0000-0001-9253-2001 , and Hervé Vanderschuren 0000-0003-2102-9737 Authors Info & Affiliations https://doi.org/10.22541/au.174339725.54825356/v1 246 views 91 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Nitrogen is a crucial macronutrient for plant growth, supplied in agroecosystems primarily as nitrate and ammonium. However, inefficient nitrogen use causes significant environmental losses in wheat, where only 48% of applied nitrogen is converted into biomass, but stabilized ammonium-based fertilization offers a promising strategy to reduce losses and enhance sustainability. Ammonium nutrition can confer agronomic benefits, including improved grain quality and stress tolerance in wheat, but high ammonium concentrations can be toxic, impairing plant growth and triggering physiological and molecular defense responses. This study aimed to investigate ammonium tolerance within a diverse panel of hexaploid winter wheat ( Triticum aestivum L.) accessions from the Gediflux collection. Hydroponic screening revealed that while most wheat varieties preferred ammonium-nitrate, few genotypes exhibited superior biomass production under ammonium-rich conditions. RNA-seq analysis of two contrasting genotypes—one tolerant and one susceptible—uncovered distinct gene regulation patterns in the tolerant cultivar. Notably, at the leaf level, genes encoding chlorophyll-binding proteins and Rubisco were significantly upregulated, potentially enhancing photosynthetic capacity and biomass yield. Additionally, key genes involved in abscisic acid signaling, including PYL4 , PYL5 , and PYL6 , were upregulated, suggesting a crucial role in chloroplast protection and stress adaptation. To mitigate ammonium toxicity, the tolerant cultivar downregulated ammonium transport and storage-related genes, such as AMT2 , CAP1 , and TIP2;3 , indicating a controlled ammonium homeostasis mechanism that limits excessive uptake and vacuolar sequestration. In roots, auxin-responsive genes were predominantly upregulated in the tolerant cultivar, with ARF6 , ARF10 , ARF16 , and ARF17 potentially contributing to root system reorganization for improved ammonium tolerance. Ammonium nutrition significantly influenced transcriptional regulation across roots and leaves, with the tolerant genotype exhibiting a stronger root response and increased involvement in trichome morphogenesis, shoot development, hormonal signaling, and stress adaptation. K-means clustering identified differentially expressed transcription factors, including bZIP and WRKY-domain TFs associated with stress signaling, as well as nitrogen metabolism-related TFs such as ERF2 and HRE1 . These findings provide novel insights into the molecular mechanisms underlying ammonium tolerance in wheat, highlighting potential targets for breeding varieties with enhanced nitrogen use efficiency and stress resilience. Transcriptomic Profiling Identifies Determinant Regulation for Ammonium Tolerance in Wheat Blum, Adrien (1), Jáuregui, Ivan (1)(2), Muhovski, Yordan (3), and Vanderschuren, Hervé (1)(4), (1) Plant Genetics and Rhizosphere Processes, Department of Plant Sciences, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium (2) Dpto de Ciencias, Universidad Pública de Navarra, Campus Arrosadia, 31006 Pamplona, Spain (3) Biological Engineering Unit, Life Sciences Department, Walloon Agricultural Research Centre, Gembloux, Belgium (4) Tropical Crop Improvement Laboratory, Department of Biosystems, Katholieke Universiteit Leuven, Heverlee, Belgium Abstract Nitrogen is a crucial macronutrient for plant growth, supplied in agroecosystems primarily as nitrate and ammonium. However, inefficient nitrogen use causes significant environmental losses in wheat, where only 48% of applied nitrogen is converted into biomass, but stabilized ammonium-based fertilization offers a promising strategy to reduce losses and enhance sustainability. Ammonium nutrition can confer agronomic benefits, including improved grain quality and stress tolerance in wheat, but high ammonium concentrations can be toxic, impairing plant growth and triggering physiological and molecular defense responses. This study aimed to investigate ammonium tolerance within a diverse panel of hexaploid winter wheat ( Triticum aestivum L.) accessions from the Gediflux collection. Hydroponic screening revealed that while most wheat varieties preferred ammonium-nitrate, few genotypes exhibited superior biomass production under ammonium-rich conditions. RNA-seq analysis of two contrasting genotypes—one tolerant and one susceptible—uncovered distinct gene regulation patterns in the tolerant cultivar. Notably, at the leaf level, genes encoding chlorophyll-binding proteins and Rubisco were significantly upregulated, potentially enhancing photosynthetic capacity and biomass yield. Additionally, key genes involved in abscisic acid signaling, including PYL4 , PYL5 , and PYL6 , were upregulated, suggesting a crucial role in chloroplast protection and stress adaptation. To mitigate ammonium toxicity, the tolerant cultivar downregulated ammonium transport and storage-related genes, such as AMT2 , CAP1 , and TIP2;3 , indicating a controlled ammonium homeostasis mechanism that limits excessive uptake and vacuolar sequestration. In roots, auxin-responsive genes were predominantly upregulated in the tolerant cultivar, with ARF6 , ARF10 , ARF16 , and ARF17 potentially contributing to root system reorganization for improved ammonium tolerance. Ammonium nutrition significantly influenced transcriptional regulation across roots and leaves, with the tolerant genotype exhibiting a stronger root response and increased involvement in trichome morphogenesis, shoot development, hormonal signaling, and stress adaptation. K-means clustering identified differentially expressed transcription factors, including bZIP and WRKY-domain TFs associated with stress signaling, as well as nitrogen metabolism-related TFs such as ERF2 and HRE1 . These findings provide novel insights into the molecular mechanisms underlying ammonium tolerance in wheat, highlighting potential targets for breeding varieties with enhanced nitrogen use efficiency and stress resilience. Keywords Ammonium tolerance, bread wheat, transcriptomic profiling, transcription factors Introduction Within agroecosystems, inorganic nitrogen is supplied through fertilizers as nitrate (NO₃⁻) and ammonium (NH₄⁺), essential precursors for amino acid biosynthesis and protein formation in plants (Liu and von Wiren 2017). However, more than half of this nitrogen is lost to the environment, leading to significant ecological concerns (Coskun et al., 2017). This inefficiency is particularly evident in wheat, where only 48% of the applied nitrogen is converted into biomass (Ladha et al., 2016). To address these losses, NH₄⁺-based fertilizers combined with nitrification inhibitors have proven effective in reducing both nitrate leaching and greenhouse gas emissions from nitrogen fertilization. By slowing the microbial conversion of NH₄⁺ to NO₃⁻, nitrification inhibitors help retain nitrogen in a form less prone to leaching and denitrification (Beeckman et al., 2024). Additionally, optimizing nitrogen use efficiency involves identifying plant varieties with an enhanced capacity to assimilate NH₄⁺, further minimizing losses and improving sustainability. Sole NH₄⁺ nutrition presents potential benefits for wheat production, including improved grain quality and increased tolerance to salinity and cadmium stress (Ijato et al., 2020; Mousavi Shalmani et al., 2023). This preference varies with environmental conditions, making NH₄⁺ more advantageous in certain situations, such as cold weather or rapid growth phases (Cramer et al, 1993; Mousavi Shalmani et al., 2017). However, the use of sole NH₄⁺ nutrition remains debated due to concerns about toxicity at high concentrations and plant’s overall inclination toward NO₃⁻ (Bittsánszky et al., 2015). Generally, while NH₄⁺ is a crucial nitrogen source, its accumulation can be harmful, with toxicity thresholds varying depending on species and overall concentration (Britto & Kronzucker, 2002). Ammonium toxicity impairs root and shoot growth, induces leaf chlorosis, and disrupts ionic balance, pH stability, and oxidative stress responses (Esteban et al., 2016). The risk is particularly high when NH₄⁺ is the only nitrogen source, especially in soils with low nitrification rates or immediately after fertilization, where concentrations can reach up to 40 mM (Britto & Kronzucker, 2002). Under these conditions, excessive NH₄⁺ accumulation significantly increases the likelihood of toxicity and its detrimental effects on plant development. At the cellular level, plants absorb NH₄⁺ through high-affinity uptake transporters known as NH₄⁺ transporters (AMTs) and assimilate it into amino acids via the glutamate synthase (GS)/glutamine-2-oxoglutarate aminotransferase (GOGAT) cycle (Liu and von Wiren 2017). However, excessive NH₄⁺ accumulation can be toxic, prompting plants to activate various protective mechanisms (Xiao et al., 2023). To mitigate NH₄⁺ toxicity, plants regulate NH₄⁺ uptake by inhibiting AMTs (Straub et al., 2017), detoxify NH₄⁺-induced reactive oxygen species (ROS) through antioxidant enzymes such as APX, CAT, and SOD (Xie et al., 2015), and maintain ionic balance by increasing potassium levels to control NH₄⁺ influx and efflux (Szczerba et al., 2008). Additionally, NH₄⁺ efflux is restricted through the action of VTC1, a GDP-mannose pyrophosphorylase (GMPase) (Qin et al., 2008). Hormones also play a crucial role in NH₄⁺ detoxification. At high NH₄⁺ concentrations, abscisic acid (ABA) protects chloroplasts via the metalloprotease AMOS1/EGY1 (Li et al. 2012) and limits NH₄⁺ uptake by repressing AMTs through the CIPK23 receptor in Arabidopsis (Ganz et al., 2022). In rice, ABA facilitates NH₄⁺ detoxification by reducing ROS and free NH₄⁺ levels through OsSAPK9 and OsbZIP20 (Sun et al, 2020). Auxin, in turn, helps maintain gravitropism at elevated NH₄⁺ levels via the DnaJ-like protein GSA1/ARG1 (Zou et al., 2013) and promotes NH₄⁺ efflux in the root elongation zone through the transcription factor WRKY46 (Di et al., 2021). Furthermore, brassinosteroids (BRs) contribute to NH₄⁺ tolerance by activating the auxin pathway in Arabidopsis following BR treatment (Devi et al., 2022). Plant species vary widely in their tolerance to NH₄⁺, with some displaying a preference for it while others are highly sensitive. The response to NH₄⁺ nutrition exists on a continuum, largely depending on its concentration in the root medium. Certain species, like spinach, are extremely sensitive to NH₄⁺ (Lasa et al., 2022), whereas others, such as oil palm, are highly tolerant (De la Peña et al., 2023). The highest NH₄⁺ tolerance is typically found in plants adapted to low-nitrification environments, such as conifers in acidic soils (Britto et al., 2013), and rice in oxygen-limited paddy fields (Xiao et al., 2023. Additionally, intraspecific variation has been observed within species like Arabidopsis thaliana (Sarasketa et al., 2014), pea (Cruz et al., 2011), rice (Di et al., 2018), and the grass crop model Brachypodium distachyon (De la Peña et al., 2024), suggesting that similar variability could also exist within wheat. Wheat’s tolerance to NH₄⁺ remains largely unexplored, despite its importance in developing varieties suited to more environmentally friendly fertilization strategies. This study aimed to identify accessions with NH₄⁺ tolerance within a set of hexaploid winter wheat ( Triticum aestivum L.) accessions from the Gediflux collection. Hydroponic screening revealed that only a few varieties exhibited better leaf biomass in an NH₄⁺-rich medium compared to an NH₄NO₃ medium. RNA-seq analysis of two contrasting varieties for this trait led to the identification of genes, including transcription factors, that help clarify the molecular mechanisms underlying improved growth when NH₄⁺ is at high concentration. Material and method Wheat material collection, hydroponic experiment and ammonium tolerance trait assessment A collection of bread wheat ( Triticum aestivum L.) lines was established among the Gediflux collection (Reeves et al., 2004), based on various geographical origins across Europe, including genotypes obtained between 1945 and 2010. The collection also distinguishes genotypes selected during or after the Green Revolution, comprising 110 genotypes (Figure S1). To assess their performance in NH₄⁺ tolerance trait, the genotypes were grown in a hydroponic system with either NH₄⁺ or NH₄NO₃ as the nitrogen source at a high level (10mM) of nitrogen. Fresh biomass was measured for each genotype, and biomass ratios were calculated from three independent experiments. Ratios greater than 1 indicate higher biomass production when grown with NH₄⁺, reflecting superior performance for the NH₄⁺ tolerance trait. Confidence intervals between ratios were computed using mratios package for R. Composition of the NH₄⁺ and NH₄NO₃ media were the following: KH 2 PO 3 (0.5 mM); MgSO 4 (1.00 mM), Fe EDTA (0.20 g.L -1 ), CaCl 2 (3.00 mM), KCl (2,50 mM) for the macronutrients; H 3 BO 3 (2.86 mg.L -1 ), MnCl 2 (1.81 mg.L -1 ), ZnSO 4 (0.22 mg.L -1 ), CuSO 4 (0.051 mg.L -1 ), Na 2 MoO 4 (0.09 mg.L -1 ) for the micronutrients. Nitrogen source were brought either by (NH 4 ) 2 SO 4 (10.00 mM) and CaSO 4 (10.00 mM) or NH₄NO₃ (10.00 mM). The genotypes were sown and pre-germinated in pots filled with sand, which was watered to its maximum holding capacity. The pots were placed in a temperate growth room at 24°C with 16 hours light and 8 hours dark for six days. On the seventh day, the plants were transferred to hydroponic systems. The plants were placed in two hydroponic tanks and aeration pump was added to each tank. The final solution pH was adjusted to 7. The hydroponic system was kept in a greenhouse for 13 days before harvesting. The temperature in the greenhouse was 22°C during the day and 16°C at night with light intensity, between 100 and 120 µmol.s -1 .m - ² from Lumigrow pro 325 W LED light system (Lumigrow, Emeryville, CA, USA). RNA extraction of wheat, library construction and sequencing Four biological replicates of leaves and roots of wheat cultivars Cellule, showing a good performance for NH₄⁺ tolerance trait, and Pajero, bad performance, growing either on NH₄⁺ or on NH₄NO₃ hydroponic medium resulted in 32 samples. The samples have been reduced to powder using liquid nitrogen, mortar and pestle, and then the extraction and purification of RNA of the samples were performed using the NucleoSpin RNA Plant (Macherey-Nagel, Düren, Germany), following the manufacturer’s instructions then stored at -80°C. RNA quality (RIN≥6.0, 28S/18S≥1.0) and quantity were checked using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, United States) and Thermo Scientific™ NanoDrop™ One Microvolume UV-Vis Spectrophotometers (Thermo Scientific, Waltham, MA, United States). RNA library construction and sequencing were performed by BGI Tech Solutions (Hong-Kong). Briefly, mRNA was isolated from total RNA using oligo dT beads, then fragmented, and converted to cDNA with N6 random primers. A second cDNA strand was synthesized with dUTP, marking it for selective degradation. The double-stranded cDNA were end-repaired, adenylated at the 3’ ends, and ligated with adaptors. Uracil-DNA Glycosylase removed the dUTP-marked strand, leaving a single strand that was PCR-amplified to create a sequencing-ready library. The amplified product was then denatured by heat, cyclized using a splint oligo and DNA ligase and converted into DNA nanoballs. The prepared DNA nanoballs were sequenced on the DNBSEQ (MGI Tech, Shenzhen, China) platform. Data filtering and mapping of reads The sequencing data was filtered using SOAPnuke v1.5.6 by removing reads with sequencing adapters, reads with more than 20% low-quality bases, and reads with over 5% unknown bases. The remaining clean reads were saved in FASTQ format. The clean reads were mapped to the Triticum aestivum cv. Chinese Spring IWGSC RefSeq v1.1 (Zhu et al., 2021) using HISAT2 v2.2.1 and Bowtie2 v2.4.4 was applied to align the clean reads to the gene set. Differentially expressed genes (DEGs), GO enrichment, cluster and network construction Expression level of gene was calculated by RSEM v1.2.28 to get read count, FPKM (Fragment per kilobase of transcript per million reads mapped) and TPM (Transcript per million reads mapped). Differential expression gene (DEG) analysis was performed using the DESeq2 with Q value (corrected Pvalue) ≤ 0.05. In this experiment, the gene expression comparison was performed separately between two treatments (NH₄⁺ vs. NH₄NO₃ ) and between two cultivars (Cellule vs. Pajero) at the leaves and roots level. Gene Ontology (GO) enrichment was performed using Gene Ontology database, then Phyper, a function of R v3.4.2 based on Hypergeometric test. The significant levels of terms and pathways were corrected by Q value (corrected Pvalue) ≤ 0.05. The graphical representation of the cellular NH₄⁺ transport and genes activation or repression in Figure 3 was created using BioRender (https://app.biorender.com/) . In this study, differentially expressed transcription factors (DE-TFs) were obtained using “transcription molecular activity” term of GO Molecular function, then a filter of TPM>0.05 was applied. GO biological process of the DE-TFs were obtained using Q value (corrected Pvalue) ≤0.05. All alluvial diagrams were made with RAWgraph (https://app.rawgraphs.io/). For the construction of clusters and interaction network, amino acid sequences of the DE-TFs were first collected through BioMart (http://plants.ensembl.org/biomart), then for each part (e.g. leaves and roots) interaction network were built using STRING v12.0 (https://string-db.org/) based on k-means clustering method, an unsupervised machine learning algorithm used for clustering data into distinct groups. Visualization of the different DE-TFs clusters and interaction were made under Cytoscape v3.10.3 (https://cytoscape.org/) by using the function “exportNetworkToCytoscape”. Results Phenotypic assessment of wheat genotypes in response to ammonium-rich environment The selected panel consists of 110 winter wheat genotypes from northeastern Europe, including local landraces and elite lines, to maximize genotypic variation (Figure S1). Trait performance is based on the shoot biomass ratio (fresh weight) between plants growing on NH₄⁺ and NH₄NO₃. Values <1 were considered as bad performers and 5 exhibited higher fresh biomass in NH₄⁺ medium compared to NH₄NO₃ medium. These genotypes, including Bledor, RUFUS, Folke, Starke II, and Cellule, were released between 1950 and 2010 (Table S1). Differential transcriptomic response between contrasting phenotypes for ammonium tolerance trait The analysis of transcriptional responses examined two genotypes with contrasting phenotypes under high-dose NH₄⁺ nutrition compared to NH₄NO₃: Cellule, a high-performing genotype on NH₄⁺, and Pajero, a low-performing genotype. The average alignment ratio of the samples to the reference genome was 90.34%, and the average alignment of the gene set was 79.31% with a total of 91,274 genes were detected. For each genotype and each plant part, leaves and roots, between 2,800 and 3,600 genes were differentially expressed (DEGs) between NH₄⁺ and NH₄NO₃ conditions, except in the roots of Pajero, where only slightly over 1,000 genes were upregulated (Figure 2a). Ammonium nutrition triggered the differential expression of 6.20% of total genes in leaves and 5.64% in roots for Cellule, while in Pajero, 4.07% of total genes were differentially expressed in leaves and 5.58% in roots. In both cultivars, the NH₄⁺ signal induces DEGs with motifs associated with perception (e.g., LRR, jacalin-like lectin) and transduction (e.g. kinase, NB-ARC) (Figure S2a and Figure S2b). As shown in Figure 2b, in leaves, 1.29% of upregulated genes (89 out of 6,893) and 15.74% of downregulated genes (911 out of 5,785) were common between both genotypes. In roots, 39.42% of upregulated genes (1,684 out of 4,271) and 35.40% of downregulated genes (1,587 out of 4,483) were shared between the two genotypes. Interestingly, 372 out of 2,710 pfam-enriched up-regulated genes in Cellule and down-regulated in Pajero were directly related to photosynthesis, including 103 genes encoding chlorophyll-binding proteins and Rubisco (Figure S2a). Among them, 78 out of 135 were chlorophyll A-B binding protein genes, while all 25 ribulose bisphosphate carboxylase small chain genes and 24 ribulose-1,5-bisphosphate carboxylase small subunit genes were upregulated. In Cellule, NH₄⁺ triggered the expression of stress response markers in roots, including wound-induced genes, chitinase-recognition genes, and chitinase (Figure S2b). Additionally, ENOD93 , a gene known to be associated with nitrogen fixation in legumes but conserved across the plant kingdom, was upregulated in Cellule roots (Figure S2b). In contrast, NH₄⁺ activated detoxification response markers in Pajero, including peroxidases, dirigent-like proteins, cytochrome P450, and glutathione S-transferases suggesting an increased requirement to manage NH₄⁺ toxicity (Figure S2f). Identification of Differentially Expressed Genes Involved in the Ammonium and Hormonal Pathways Contributing to Ammonium tolerance A schematic representation of the tolerance observed in Cellule is shown in Figure 3 and a detailed heat-map in Figure S4 and S5 for both genotypes. In Cellule, NH₄⁺ transporters AMT2 and AMT1;4 were significantly downregulated in both leaves and roots, whereas in Pajero, only a single AMT gene was downregulated in leaves. In Cellule, CIPK15 (CBL-interacting protein kinase 15) was overexpressed in roots, while in Pajero, CIPK23 was upregulated in leaves. Notably, CIPK15 is known to suppress AMT1 genes in Arabidopsis to mitigate NH₄⁺ toxicity (Chen et al., 2020). Increasing potassium levels is believed to regulate NH₄⁺ influx and efflux by modulating shared transport mechanisms and maintaining ion homeostasis (Szczerba et al., 2008). The main difference in behavior between Cellule and Pajero was linked to the number of upregulated and downregulated DEGs. While Cellule tends to reduce its potassium transport capacity—evidenced by the majority of DEGs being downregulated in both leaves and roots—Pajero exhibits a more balanced pattern, with an equal number of upregulated and downregulated DEGs. In Cellule roots, CAP1 and TIP2;3 were significantly downregulated in roots. CAP1 , a tonoplast membrane malectin receptor, activates aquaporins TIP2;1 and TIP2;3 , which facilitate NH₄⁺ storage in vacuoles (Bai et al., 2014). Within the GS/GOGAT cycle, DEGs associated with nitrate reductase (NR) were downregulated in both roots and leaves of Cellule, whereas were upregulated in the leaves and downregulated in the roots of Pajero. Two DEGs associated with glutamine synthetase (GS) are upregulated in Cellule, while only one is upregulated in Pajero. In contrast, two DEGs associated with glutamate dehydrogenase (GDH) are upregulated in the leaves of Pajero, while only one is upregulated in Cellule. The USUALLY MULTIPLE ACIDS MOVE IN AND OUT TRANSPORTERS (UMAMITs) family, specifically UMAMIT14 and UMAMIT19 and UMAMIT3 3, has been previously identified as part of the NH₄⁺ -specific transcriptome in Arabidopsis (Patterson et al., 2010). In our dataset, only DEGs for UMAMIT19 and UMAMIT3 3 orthologs were detected in both leaves and roots. In both cultivars, UMAMITs followed the pattern: downregulated in leaves but upregulated in roots. In contrast, TraesCS6A02G330100 , which encodes UMAMIT19 in leaves, was overexpressed in Cellule but downregulated in Pajero. Auxin-pathway response DEGs exhibited contrasting expression patterns between the two genotypes, with Cellule showing a stronger auxin-driven activation, as 22 unique DEGs in leaves and 16 in roots were and 16 in leaves and 11 in roots were downregulated. In Cellule, auxin-related genes such as ARF6 (Auxin Response Factor 6) , ARF17, IAA18 (indole-3-acetic acid inducible 18) , IAA15, IAA26 (PAP1), GH3.2 (Auxin-responsive GH3 family protein) , GH3.3 and GH3.4 were upregulated in leaves, while ARF10, ARF16 ARF17 , ETT (AUXIN RESPONSE TRANSCRIPTION FACTOR 3) , GH3.2, GH3.4 and GH3.3 in roots. Downregulated genes were GH3.2 , GH3.3 and GH3.4 in leaves and, GH3.2 , GH3.3, GH3.4, and GH3.5 ( WES1 ) in roots. Additionally, Small Auxin Up-Regulated RNA ( SAUR) genes, which are auxin-responsive but whose functions remain unclear (Zhang et al., 2021), were predominantly upregulated in Cellule, with SAUR76 and SAUR34 upregulated in leaves, and SAUR71 upregulated in roots, whereas Pajero showed downregulation of SAUR genes. In the abscisic acid (ABA)-activated signaling pathway, Cellule exhibited the strongest ABA-pathway response with 15 unique upregulated DEGs in both leaves and roots while 5 in leaves and 15 in roots downregulated DEGs. The upregulated DEGS were PYL4 (pyrabactin resistance 1-like 4), PYL5, PY6, EHB1 (ENHANCED BENDING 1), C2 (CaLB domain family protein), BETA-OHASE 1 (beta-hydroxylase 1), BETA-OHASE 2 , HHP1 (heptahelical transmembrane protein1), AO1 (aldehyde oxidase 1), AAO2 , AAO3, and AO4 in leaves and AREB3 (ABA-responsive element binding protein 3), EEL (ATBZIP12), SDR2 (NAD(P)-binding Rossmann-fold superfamily protein), NCED2 (nine-cis-epoxycarotenoid dioxygenase 2), NCED3 , NCED5 , NCED9 , AO1, AAO2 , AAO3, AO4 , and KOB1 (ABA INSENSITIVE 8) in roots. The downregulated DEGS were only BETA-OHASE 1 , BETA-OHASE 2 , SDR2 , AO1 , AO4 , and AAO3 in roots. In the brassinosteroid-mediated signaling pathway, only a few DEGs were identified. In leaves, Cellule exhibited upregulation of BR6OX1 (brassinosteroid-6-oxidase 1), BR6OX2 , BSK3 (brassinosteroid-signaling kinase 3) BSK4 and, and in roots, BR6OX1 , BR6OX2 , BSK4 , BSK3 and ASP1 (aspartate aminotransferase 1). Downregulated DEGs were in leaves BR6OX1 and BR6OX2 in leaves and BR6OX1 , BR6OX2 , BSK1, KAO2 (ent-kaurenoic acid hydroxylase 2), CYP88A3 (ENT-KAURENOIC ACID OXYDASE 1) and CYP708A1 . Transcription Factors Driving Ammonium Tolerance Across Contrasting Genotypes A total of 168 differentially expressed transcription factors (DE-TFs) with a Log₂ fold change > 1 were observed in the leaves of Cellule, compared to 126 in the roots. In Pajero, 151 DE-TFs were detected in the leaves and 134 in the roots. Overall, Cellule exhibited a higher number of DE-TFs than Pajero, with a notably greater proportion of DE-TFs showing a Log₂ fold change between 2 and 5 (Figure 4a). Among these DE-TFs, 104 in the leaves and 97 in the roots of Cellule, as well as 63 in the leaves and 94 in the roots of Pajero, were assigned GO P terms and are presented in Figure 4b. In the leaves, transcriptional activity regulation is a common process between the two genotypes and represents the majority of the identified terms (66.34% in Cellule and 85.57% in Pajero). A similar trend is observed in the roots, accounting for 75.25% in Cellule and 80.85% in Pajero. Some DE-TFs are also associated with salicylic acid and adventitious root development in both genotypes. In Cellule, the leaf response is characterized by DE-TFs related to leaf development, such as trichome morphogenesis, the regulation of shoot system development, and phosphorous regulation, including phosphate ion homeostasis and positive regulation of cellular response to phosphate starvation and hormones (jasmonic acid and gibberellins). Additionally, 10 DE-TFs are associated with the cellular response to heat, including orthologs of heat shock transcription factors found in Arabidopsis. In Pajero, DE-TFs are notably associated with regulation of secondary shoot formation and nuclear envelope organization. In the roots, Cellule is distinguished by DE-TFs related to root development, particularly anatomical structure development, while Pajero shows associations with phosphorous regulation, such as positive regulation of cellular response to phosphate starvation and cell cycle regulation. Clusters of DE-TFs were established based on genes previously enriched using the k-means clustering method. A total of nine clusters were identified in leaves and seven in roots (Table 1). In both leaves and roots, cluster 1 contains the highest number of genes, with the most diverse protein descriptions. Remarkably, 13 DE-TFs are shared between these two cluster 1 groups, suggesting a common regulation mechanism. The association network between the DE-TFs of cluster 1 is represented in Figure 4c in leaves and in roots. The confidence score of the predicted associations between DE-TFs ranges from 0.40 to 0.96 in leaves and from 0.41 to 0.96 in roots. Among TFs involved in plant biotic and abiotic stress responses, we identified bZIP-motif TFs and form the highest number of associations (8–10 connections), including the bZIP49 ortholog ( TraesCS2B02G167900 ), which interacts with bZIP25 ( TraesCS5A02G057500 ) and bZO2H1 ( TraesCS5D02G068800 ) with a confidence score of 0.944 for both interactions. Additionally, we identified WRKY-motif TFs, including wheat orthologs of WRKY50 ( TraesCS3B02G240200, TraesCS3D02G212700 ), WRKY55 ( TraesCS1B02G243100, TraesCS6A02G080500, TraesCS6B02G108000 ), and WRKY46 ( TraesCS2B02G187500, TraesCS3D02G238300, TraesCS7B02G012600 ). Particularly, Arabidopsis WRKY46 had been previously reported as a key regulator of NH₄⁺ tolerance (Di et al., 2021). Additional TFs of the cluster 1 linked to nitrogen metabolism are shown in Table 2. Discussion Transcriptional Adaptation in Response to an Ammonium-Rich Nutritional Environment In general, wheat shoot and root biomass are significantly higher under NH₄NO₃ treatment compared to pure NH₄⁺ (Chen et al., 2024). Based on shoot biomass, our results indicate that the vast majority of wheat genotypes in the collection exhibit a preference for NH₄NO₃. However, five genotypes consistently preferred NH₄⁺ across three independent experiments. Similar to findings in Arabidopsis (Sarasketa et al., 2014) and Brachypodium (De la Peña et al., 2024), our study highlights intraspecific variability in NH₄⁺ tolerance within wheat, demonstrating diverse adaptive responses to nitrogen sources. Within two contrasting wheat genotypes for NH₄⁺ tolerance, Cellule (tolerant) and Pajero (susceptible), we observed that NH₄⁺ signaling triggered DEGs motifs associated with perception (e.g., LRR, jacalin-like lectin) and transduction (e.g., kinase, NB-ARC) in both genotypes. These motifs, typically found in membrane receptors, may play a role in membrane and cell wall reorganization, as well as exocytosis, as suggested by GO cellular enrichment analysis (Figure S3a and Figure S3b). Additionally, as illustrated in the Venn diagram (Figure 2b), Cellule exhibited a more specific functional response in leaves than in roots. In leaves of Cellule, the upregulation of pfam-enriched and GO cellular-enriched genes was directly related to photosynthesis, suggesting that NH₄⁺ nutrition enhances Cellule’s photosynthetic capacity and potentially contributes to its higher biomass yield. In roots, GO cellular enrichment analysis revealed that NH₄⁺ triggered the expression of stress response markers in Cellule, whereas Pajero exhibited a stronger detoxification response. Interestingly, ENOD93 was upregulated in Cellule: studies in rice have identified ENOD93 as an N-responsive gene, and its overexpression in transgenic rice lines has been shown to enhance nitrogen use efficiency (Bi et al., 2009). In the NH₄⁺ assimilation pathway, the downregulation of AMT2 in both leaves and roots of Cellule suggests a moderated regulation of NH₄⁺ uptake. AMT2 plays a crucial role in facilitating NH₄⁺ movement between the apoplast and symplast across plant cells (Sohlenkamp et al., 2002). This downregulation may contribute to NH₄⁺ tolerance in Cellule by limiting excessive extracellular NH₄⁺ uptake and restricting its internal transport, thereby reducing potential toxicity. Additionally, the downregulation of CAP1 and TIP2;3 in Cellule roots indicates a reduced tendency for NH₄⁺ storage in vacuoles, further suggesting a controlled NH₄⁺ homeostasis strategy. In contrast, Pajero exhibits a distinct response, as indicated by the upregulation of multiple potassium transport-related DEGs in both leaves and roots. This suggests a more active potassium transport system, which likely serves as a protective mechanism to counteract NH₄⁺ toxicity by maintaining ionic balance and reducing NH₄⁺ stress (Szczerba et al., 2008). Excess NH₄⁺ inhibits plant growth; however, glutamine synthetase (GS) plays a crucial role in mitigating its toxicity by assimilating NH₄⁺ into organic compounds (Leng et al., 2024). In Cellule roots, we identified two upregulated DEGs encoding GS, which may contribute to enhanced NH₄⁺ detoxification and improved tolerance in this genotype. Interestingly, the large majority of DEGs related to auxin-responsive were found in Cellule leaves and roots, and most of them were upregulated. Among these, members of the Auxin Response Factor (ARF) family were identified, ARFs are key regulators of auxin signaling, modulating the expression of early auxin-responsive genes such as Aux / IAA , GH3 , and SAUR , and thereby regulating downstream signaling pathways (Abel and Theologis, 1996). In leaves, wheat ortholog of ARF6 was found, previous studies showed that OsARF6 regulates flag-leaf angles (Chen et al., 2018) and influences tiller number (Liu et al., 2012). In roots, ARF10 , ARF16 , and ARF17 were identified; other research indicated that AtARF10 controls root cap cell formation (Wang et al., 2005), OsARF16 inhibits primary root length and lateral root formation (Shen et al., 2013), and AtARF17 plays a crucial role in adventitious root formation (Gutierrez et al., 2009). These findings suggest that the reorganization of the root system may be critical for NH₄⁺ tolerance. Among the ABA-responsive DEGs, Cellule upregulates members of the pyrabactin resistance 1-like ( PYL ) family in leaves, including PYL4 , PYL5 , and PYL6 , which are ABA perception receptors (Dupeux et al., 2011). These receptors are expressed in response to various stresses, including drought, heat, and salt stress. Notably, OsPYL6 exhibited the highest expression levels across all tissues and under different abiotic stresses (Yadav et al., 2020). ABA signaling plays a crucial role in regulating the expression of NH₄⁺-responsive genes that help maintain chloroplast integrity in the presence of high NH₄⁺levels (Li et al., 2012), and enhanced perception in Cellule may facilitate this process. Transcription Factors Driving Ammonium Tolerance Across Contrasting Genotypes The transcriptional response of plants to environments where NH₄⁺ is the sole nitrogen source has been previously studied in Arabidopsis and described as distinct from the NO₃ - response (Patterson et al., 2010). However, the transcriptional response to high NH₄⁺ concentrations remains unexplored, particularly in wheat. Identifying TFs involved in mitigating NH₄⁺ toxicity at elevated concentrations is therefore crucial. Our study focused on the identification and characterization of TFs in two wheat genotypes with contrasting tolerance to NH₄⁺. To isolate these TFs, we selected those whose expression significantly differed between NH₄⁺ and NH₄NO₃ conditions in both genotypes. Cellule exhibited a higher number of differentially expressed TFs than Pajero, particularly in leaves, with a log₂ fold change between 2 and 5. Under NH₄⁺ nutrition, transcriptional regulation emerged as the most TF-enriched biological process in both genotypes, in both root and leaf tissues. In addition, NH₄⁺ signal induced TFs associated with morphological reorganization of the shoot system in both genotypes, while Cellule exhibited a stronger root response. However, the nature of this morphological adaptation differed: Cellule displayed a greater involvement of TFs related to trichome morphogenesis, regulation of shoot system development, and a higher dependence on jasmonic acid and gibberellin signaling. Additionally, Cellule showed a strong induction of TFs related to cellular response to heat in leaves, suggesting a heightened ability to mobilize a transcriptomic response to stress. Notably, TFs regulating phosphorus homeostasis also appeared to contribute to Cellule’s response to NH₄⁺ signal. Furthermore, a k-means clustering analysis based on TF interactions identified foliar cluster 1 and root cluster 1 with 13 common DE-TFs in both leaves and roots. At the core of these interactions were three bZIP-domain TFs: the orthologs bZIP49 , bZIP25 , and BZO2H1 . In plants, bZIP TFs are well-documented for their roles in both biotic and abiotic stress responses, including pathogen defense and stress signaling. However, like many TF families, their functions often overlap, adding complexity to their analysis (Jakoby et al., 2002). Patterson et al. (2010) previously identified WRKY-domain TFs in the NH₄⁺-responsive transcriptome of Arabidopsis, including WRKY33 , WRKY40 , WRKY53 , and WRKY70 . These TFs, already known for their roles in pathogen defense regulation, were also upregulated by NH₄⁺, serving as markers of this nitrogen nutrition pathway. In our study, within cluster 1, we identified the differential expression of the wheat orthologs of WRKY50 , WRKY55 , and WRKY46 , which may represent specific markers of the transcriptional response to high NH₄⁺ concentrations. Particularly, orthologs of Arabidopsis WRKY46 , were detected in the leaf cluster 1 and had been previously reported as a key regulator of NH₄⁺ tolerance (Di et al., 2021). This gene binds to the promoters of NUDX9 and IAA-conjugating genes, repressing their transcription and ultimately inhibiting NH₄⁺ efflux in the elongation zone. Furthermore, several DE-TFs in cluster 1 are directly linked to the study by Gaudinier et al. (2018), which identified transcription factors (TFs) in Arabidopsis that regulate nitrogen metabolism. Within this cluster, we identified 12 DE-TFs in leaves and 12 in roots. Most of the selected TFs were found to target a single promoter gene, with the notable exception of ERF2. This TF, detected in wheat leaves, targeted seven promoters, including GLN1;2 (glutamine synthetase 1;2), GLT1 (NADH-dependent glutamate synthase 1), and GLN1;3 (glutamine synthetase 1;3). Funding This study is part of the GAIN project (Weed Management through Innovative Nitrogen Nutrition) and was funded by the Walloon Region of Belgium, through grants for agronomic research, innovation, and scientific and technical research aimed at agricultural applications (grant n° D31-1378/S1-GAIN). CRediT authorship contribution statement AB and HV conceived the original idea. AB wrote the manuscript. AB, IJ and YM carried out the experiments. AB and IJ carried out data processing. FH and HV fundings. Declaration of competing interest The authors declare that they have no conflicts of interest or personal relationships that could be perceived as influencing the research presented in this paper. Acknowledgements The authors are grateful to Sok-Lay Him, and Jimmy Bin at the Plant Genetics and Rhizophere Procresses lab, and Tiantian Wang at the Beijing Genomic Institute for their contributions and support throughout this study. Data availability The datasets supporting this article are available in the SRA data number PRJNA1238568. Figure 1: Identification of wheat cultivars within the Gediflux panel for ammonium tolerance trait. A, Trait performance is based on the shoot biomass (fresh weight) ratio between plants growing on NH₄⁺ and NH₄NO₃. Values <1 were considered as bad performers and experiments represented by either a green square, a blue triangle or a red round. Cultivar number (cv. Number) and associated genotype name are detailed in Table S1. B, Photo of hydroponic systems. Figure 2: Differentially expressed genes (DEGs) between two contrasted wheat cultivars for ammonium tolerance trait (Cellule, good performer; and Pajero, bad performer). A, Number of genes differentially expressed between NH₄⁺ and NH₄NO₃ in each wheat cultivar in leaves and roots; red bar: up-regulated and blue bar: down-regulated. B, Venn diagrams depicting the overlap between up- and down-regulated genes in the two contrasted cultivars, in leaves and roots. Figure 3: Schematic representation of ammonium transport at the cellular level in roots and leaf compartments of the high-performing variety (Cellule). The diagram highlights the activation and repression of genes associated with the ammonium pathway, including the GS/GOGAT cycle, as well as genes from related hormonal pathways (ABA, and Aux). Figure 4: Differentially expressed transcription factors (DE-TFs) between two contrasting wheat cultivars for ammonium tolerance. A, Number of DE-TFs categorized by expression levels: dark green bar – Cellule leaves, light green bar – Pajero leaves, brown bar – Cellule roots, orange bar – Pajero roots. B, Alluvial diagrams showing Gene Ontology (GO) biological process enrichments for all DE-TFs with Q-value ≤0.05 in leaves (left) and roots (right); the height of the bars is proportional to the number of genes. C, DE-TFs interactions in cluster 1 of leaves (left) and cluster 1 of roots (right), based on STRING analysis. The edge color indicates the confident score of the predicted association between DE-TFs from the STRING analysis, ranging from blue for a weak confidence to red for a strong confidence. Table 1: Clustering of DE-TFs using the k-means method. For each part (leaves and roots), the number of genes per cluster and the corresponding protein descriptions are provided. Leaves 1 44 AP2/ERF domain-containing protein. BHLH domain-containing protein. BZIP domain-containing protein. C2H2-type domain-containing protein. Ethylene response factor. HDAC_interact domain-containing protein. HSF_DOMAIN domain-containing protein. HTH cro/C1-type domain-containing protein. NAD(P)-bd_dom domain-containing protein. Q-type C2H2 zinc finger protein. RNA polymerase sigma factor; Sigma factors are initiation factors that promote the attachment of plastid-encoded RNA polymerase (PEP) to specific initiation sites and are then released. Transcription initiation factor IIF subunit alpha; TFIIF is a general transcription initiation factor that binds to RNA polymerase II and helps to recruit it to the initiation complex in collaboration with TFIIB. It promotes transcription elongation. Belongs to the TFIIF alpha subunit family. Uncharacterized protein. WRKY domain-containing protein. WRKY transcription factor WRKY1A. WRKY49 transcriptional factor. 2 7 WRKY domain-containing protein. 3 4 CBFD_NFYB_HMF domain-containing protein. 4 3 WRKY domain-containing protein. 5 3 WRKY domain-containing protein. 6 3 ASCH domain-containing protein. Uncharacterized protein. 7 2 Heat shock transcription factor. Homeobox domain-containing protein. 8 2 BHLH27. BZIP domain-containing protein. 9 2 Two-component response regulator; Transcriptional activator that binds specific DNA sequence. Roots 1 42 AP2/ERF domain-containing protein. ASCH domain-containing protein. BHLH domain-containing protein. BHLH27. BTB domain-containing protein. BZIP domain-containing protein. C2H2-type domain-containing protein. Ethylene response factor. Heat shock factor C2a. HSF_DOMAIN domain-containing protein. MIKC-type MADS-box transcription factor WM13. PWWP domain-containing protein. Q-type C2H2 zinc finger protein. TAZ-type domain-containing protein. Transcription initiation factor IIF subunit alpha; TFIIF is a general transcription initiation factor that binds to RNA polymerase II and helps to recruit it to the initiation complex in collaboration with TFIIB. It promotes transcription elongation. Belongs to the TFIIF alpha subunit family. Uncharacterized protein. Uncharacterized protein; Belongs to the PI3/PI4-kinase family. 2 14 Uncharacterized protein. WRKY domain-containing protein. 3 5 BZIP domain-containing protein. WRKY domain-containing protein. 4 3 Homeobox domain-containing protein. Uncharacterized protein. 5 3 AP2/ERF domain-containing protein. BHLH domain-containing protein. 6 3 CBFD_NFYB_HMF domain-containing protein. Uncharacterized protein. 7 2 Homeobox domain-containing protein. Table 2: Transcription factors of the cluster 1 associated to nitrogen metabolism TraesCS7A02G160700 TraesCS7A02G160700 STZ (salt tolerance zinc finger) AFB2 (auxin signaling F-box 2) Gaudinier et al. 2018 TraesCS5A02G314600 TraesCS5B02G315500 TraesCS5D02G320800 TraesCS5B02G315500 HRE1 (Integrase-type DNA-binding superfamily protein) NIR1 (nitrite reductase 1) TraesCS2A02G417300 TraesCS2B02G436300 TraesCS2D02G414600 ERF2 (ethylene-responsive element binding factor 2) GLN1;2 (glutamine synthetase 1;2), GLT1 (NADH-dependent glutamate synthase 1), and GLN1;3 (glutamine synthetase 1;3), GLT1 (NADH-dependent glutamate synthase 1), MDH (malate dehydrogenase), PGI1 (phosphoglucose isomerase 1), ANR1 (AGAMOUS-like 44), DREB26 (Integrase-type DNA-binding superfamily protein) TraesCS2A02G417300 RAP2.12 (uncharacterized protein) GDH2 (glutamate dehydrogenase 2), MDH (malate dehydrogenase), ALAAT2 (alanine aminotransferase 2) TraesCS4B02G079000 TraesCS5A02G070600 TraesCS5B02G076400 TraesCS5D02G082700 TraesCS5A02G070600 TraesCS5B02G076400 TraesCS5D02G082700 AZF2 (zinc-finger protein) ALAAT2 (alanine aminotransferase 2) CNX2 (cofactor of nitrate reductase and xanthine dehydrogenase 2) TraesCS7A02G160700 TraesCS7A02G160700 AZF3 (zinc-finger protein) ALAAT2 (alanine aminotransferase 2) TraesCS6A02G080500 TraesCS6B02G108000 TraesCS1B02G243100 WRKY 55 (DNA-binding protein 55) XERICO (RING/U-box superfamily protein) TraesCS3B02G476000 TraesCS3B02G476000 SCRM2 (basic helix-loop-helix (bHLH) DNA-binding superfamily protein) SLR (indole-3-acetic acid inducible 14) Figure S1 : Geographical origins of bread wheat genotypes used from the Gediflux panel, distribution of genotypes by decade since 1940, and classification based on the Green Revolution. Figure S2: Enrichment bubble chart for Pfam annotation enrichment for DEGs in Cellule and Pajero genotypes at the leaf and root levels. A, Up- and down- regulated for both genotypes in leaves. B, Up- and down- regulated for both genotypes in roots. C, Up-regulated for Cellule or down-regulated for Pajero in leaves. D, Up-regulated for Cellule or down-regulated for Pajero in roots. E, Up-regulated for Pajero or down-regulated for Cellule in leaves. F, Up-regulated for Pajero or down-regulated for Cellule in roots. Annotations with Q-value ≤ 0.05 are considered significantly enriched. Figure S3 : Enrichment bubble chart for Gene Ontology (GO) Cellular enrichment for DEGs in Cellule and Pajero genotypes at the leaf and root levels. A, Up- and down-regulated for both genotypes in leaves. B, Up- and down-regulated for both genotypes in roots. C, Up-regulated for Cellule or down-regulated for Pajero in leaves. D, Up-regulated for Cellule or down-regulated for Pajero in roots. E, Up-regulated for Pajero or down-regulated for Cellule in leaves. F, Up-regulated for Pajero or down-regulated for Cellule in roots. Annotations with Q-value ≤ 0.05 are considered significantly enriched. Figure S4 : Heatmap representing DEGs associated with the ammonium pathway in Cellule and Pajero genotypes, at the leaf and root levels. A, Ammonium transporters and regulators, and vacuolar storage. B, Potassium transporters. C, GS/GOGAT cycle enzymes. D, UMAMIT19 and UMAMIT33 transporters. Figure S5 : Heatmap representing DEGs associated with hormonal pathways in Cellule and Pajero genotypes, at the leaf and root levels. A, Auxin signaling pathway. B, Abscisic acid (ABA) signaling pathway. C, Brassinosteroid (BR) signaling pathway. Table S1: List of bread wheat genotypes used from the Gediflux pane, their corresponding number, and year of release. 110 Bledor 1960 109 RUFUS 1950 108 Folke 1960 107 Starke II 1970 106 Cellule 2010 105 Midas 2000 104 Toronto 1950 103 CORINTHIAN 1940 102 Obelisk 1970 101 Tremie 2010 100 Caribo 1940 99 Recital 2000 98 Stava 1970 97 Beaver 1950 96 Premio 2010 95 Julius 2000 94 Rosario 2000 93 Champlein 1940 92 Valdor 2010 91 Walde 1970 90 Celesta 1940 89 Graham 2010 88 Professeur Marchal known as Prof Marshal 1960 87 Holme 1990 86 Ritmo 1970 85 Fidel 1960 84 Sideral 2000 83 Albatross 1950 82 Bergamo 2010 81 Stella 1990 80 Estica 1940 79 Capitole 1960 78 Arminda 1950 77 Biscay 1940 76 Eroica 1940 75 Expert 1990 74 Calumet 2010 73 Criewener 192 1940 72 Brons 2010 71 Zemon 1970 70 TARAS 1950 69 Cadenza 1960 68 Virtus 1950 67 Talent 1970 66 Paragon 2000 65 MIKON 1940 64 Manager 2000 63 Renan 1970 62 Florian 1940 61 Urban 1950 60 RECORD 1950 59 MIRAS 1950 58 Warrior 1970 57 Pontus 1950 56 Elite Lepeuple 1960 55 Soissons 1980 54 Almus 1940 53 Etoile de Choisy 1960 52 Attlass 2010 51 Edgar 2010 50 Aztec 1940 49 Moulin 1950 48 Granada 1940 47 Claire 1940 46 Paindor 1990 45 Galactic 2000 44 Reflection 2010 43 Kanzler 1940 42 Ceylon 2010 41 Danubius 1940 40 Courtot 2000 39 Tobak 2010 38 Dekan 2000 37 Apache 2000 36 Apollo 2000 35 Hardi 1960 34 Parador 2000 33 Norda 32 Centenaire 2010 31 Prima 1950 30 MINA 1950 29 Kranich 1960 28 MARCO 1940 27 Haven 1960 26 Minister 1950 25 Arina 2000 24 Sterling 1990 23 Festival 1980 22 Barok 2000 21 Tommi 2000 20 Vilmorin 53 1940 19 Maris Huntsman 1960 18 Pajero 2000 17 Boisseau 2010 16 Kadolzer 1940 15 Beaufort 1950 14 Leda 1960 13 Orestis 1970 12 Antonius 2000 11 Capitaine 1960 10 Pernel 1970 9 CYRANO 1940 8 Cama 1960 7 Cappelle Desprez 1950 6 Alba 1940 5 Odeon 1970 4 KWS Smart 3 Gamin 1960 2 Thesee 1980 1 LG Apollo References 1. 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Keywords ammonium tolerance bread wheat signaling transcription factors transcriptome transcriptomic profiling Authors Affiliations Adrien Blum 0000-0002-2849-049X [email protected] Universite de Liege Gembloux Agro-Bio Tech View all articles by this author Ivan Jáuregui Universite de Liege Gembloux Agro-Bio Tech View all articles by this author Yordan Muhovski 0000-0001-9253-2001 Centre wallon de Recherches agronomiques View all articles by this author Hervé Vanderschuren 0000-0003-2102-9737 Universite de Liege Gembloux Agro-Bio Tech View all articles by this author Metrics & Citations Metrics Article Usage 246 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adrien Blum, Ivan Jáuregui, Yordan Muhovski, et al. Transcriptomic Profiling Identifies Determinant Regulation for Ammonium Tolerance in Wheat. Authorea . 31 March 2025. 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