Integration of GWAS and transcriptome approaches for the identification of nitrogen-, phosphorus-, and potassium-responsive genes in tomato

Abstract

Plant growth is inseparable from the presence of mineral nutrients such as nitrogen (N), phosphorus (P), and potassium (K), but the mechanism by which horticultural plants such as tomatoes respond to mineral elements is poorly understood. Here, we collected 28 phenotypic datasets, including 5 agronomic traits and 4 pigment accumulation traits, under full nutrition and nitrogen/phosphorus/potassium-deficiency conditions, most of which showed abundant variation. Phenotyping analysis suggested that the yellowing of leaves under low-nitrogen treatment was caused by an increase in the carotenoid content and a decrease in the chlorophyll b content. A genome-wide association study identified a total of 138 suggestive loci (including 23 significant loci) corresponding to 116 loci, including many reported and new candidate genes related to mineral element response and absorption. Transcriptome analysis of tomato seedlings under full nutrient and N/P/K-deficiency conditions revealed 1108 and 1507 common differentially expressed genes in above-ground and below-ground tissues, respectively, with 103 overlapping genes. Gene Ontology term enrichment analysis revealed that tomato plants resist low nutrient stress by increasing photosynthesis in the above-ground parts and ion transport capacity in the below-ground parts. Through the combined analysis of GWAS and RNA-Seq, we identified 28 mineral element response genes with high confidence, corresponding to 17 loci, which may be closely related to the response and utilization of N, P, and K in tomato. Two candidate genes, auxin-repressed protein (Solyc02g077880), which responds to carotenoid and chlorophyll b accumulation, and guanine nucleotide exchange factor-like protein (Solyc04g005560), which responds to low-phosphorus conditions, were further validated via haplotype analysis. This study provides new insights into the nitrogen, phosphorus, and potassium response mechanisms of tomato and offers valuable genetic resources for future improvements in tomato breeding.

Acknowledgments

This work was supported by the National Key Research and Development Plan (2022YFF10030002); Knowledge Innovation Program of Wuhan-Shuguang Project (2022020801020228); Fundamental Research Funds for the Central Universities (2662022YJ014, 2662023PY011); the Key Project of Hubei Hongshan Laboratory (2021hszd007); Young Scientist Fostering Funds for the National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops (11909920008); China Agricultural Research System (CARS-23-A13); and the Natural Science Foundation of Hubei Province (2022CFB153).

Author contributions

J.Y. planned and designed the research; M.Z., Q.X., and Z.X. performed the experiments; M.Z. and H.T analyzed the data; M.Z., C.Z., Y.Q., R.G., H.Z., J.W., and Z.X. conducted fieldwork; M.Z. and H.T wrote the manuscript; J.Y., Z.Y., J.Z., B.O., and T.W. revised the manuscript.

Data availability

All data are included in the paper and in the Supplementary Materials published online.

Conflict of interest statement

All authors declare that No conflict of interest exists.

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1] Kirkby E. Chapter 1 - Introduction, Definition and Classification of Nutrients. In: Marschner P,ed. Marschner’s Mineral Nutrition of Higher Plants. 3rd ed. Academic Press: San Diego, 2012,3-5

[2] Kanai S, Moghaieb RE, El-Shemy HA. et al. Potassium deficiency affects water status and photosynthetic rate of the vegetative sink in green house tomato prior to its effects on source activity. Plant Sci. 2011; 180:368-74

[3] Chatterjee A, Kundu S. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica. Sci Rep. 2015; 5:14975

[4] Duarte FCN, Cipriano MAP, Andrade SAL. et al. Foliar application of phosphite reduces grain weight of soybean facing postflower-ing phosphorus deficiency. J Plant Nutr Soil Sci. 2022; 186:30-7

[5] Zhang X, Davidson EA, Mauzerall DL. et al. Managing nitrogen for sustainable development. Nature. 2015; 528:51-9

[6] Islam MN, Hasanuzzaman AT, Zhang ZF. et al. High level of nitro-gen makes tomato plants releasing less volatiles and attracting more Bemisia tabaci (Hemiptera: Aleyrodidae). Front Plant Sci. 2017; 8:466

[7] Bénard C, Gautier H, Bourgaud F. et al. Effects of low nitro-gen supply on tomato (Solanum lycopersicum) fruit yield and quality with special emphasis on sugars, acids, ascor-bate, carotenoids, and phenolic compounds. J Agric Food Chem. 2009; 57:4112-23

[8] Liu Q, Wu K, Song W. et al. Improving crop nitrogen use efficiency toward sustainable green revolution. Annu Rev Plant Biol. 2022; 73: 523-51

[9] Raven JA, Lambers H, Smith SE. et al. Costs of acquiring phospho-rus by vascular land plants: patterns and implications for plant coexistence. New Phytol. 2018; 217:1420-7

[10] Wang Y, Wu WH. Regulation of potassium transport and signal-ing in plants. Curr Opin Plant Biol. 2017; 39:123-8

[11] Liu KH, Niu Y, Konishi M. et al. Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks. Nature. 2017; 545: 311-6

[12] Gaudinier A, Rodriguez-Medina J, Zhang L. et al. Transcriptional regulation of nitrogen-associated metabolism and growth. Nature. 2018; 563:259-64

[13] Gao Y, Xu Z, Zhang L. et al. MYB61 is regulated by GRF4 and promotes nitrogen utilization and biomass production in rice. Nat Commun. 2020; 11:5219

[14] Gao Z, Wang Y, Chen G. et al. The indica nitrate reductase gene OsNR2 allele enhances rice yield potential and nitrogen use efficiency. Nat Commun. 2019; 10:5207

[15] Liu Y, Wang H, Jiang Z. et al. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature. 2021; 590:600-5

[16] SunH, QianQ, WuK. et al. Heterotrimeric G proteins regulate nitrogen-use efficiency in rice. Nat Genet. 2014; 46:652-6

[17] Tang W, Ye J, Yao X. et al. Genome-wide associated study identi-fies NAC42-activated nitrate transporter conferring high nitro-gen use efficiency in rice. Nat Commun. 2019; 10:5279

[18] Wang Q, Nian J, Xie X. et al. Genetic variations in ARE1 mediate grain yield by modulating nitrogen utilization in rice. Nat Com-mun. 2018; 9:735

[19] Villaécija-Aguilar JA, Körösy C, Maisch L. et al. KAI2 promotes Arabidopsis root hair elongation at low external phosphate by controlling local accumulation of AUX1 and PIN2. Curr Biol. 2022; 32:228-236.e3

[20] Dai C, Dai X, Qu H. et al. The rice phosphate transporter OsPHT1;7 plays a dual role in phosphorus redistribution and anther development. Plant Physiol. 2022; 188:2272-88

[21] Jia H, Zhang S, Wang L. et al. OsPht1;8, a phosphate transporter, is involved in auxin and phosphate starvation response in rice. J Exp Bot. 2017; 68:5057-68

[22] Li Y, Zhang J, Zhang X. et al. Phosphate transporter OsPht1;8 in rice plays an important role in phosphorus redistribution from source to sink organs and allocation between embryo and endosperm of seeds. Plant Sci. 2015; 230:23-32

[23] Gao H, Xie W, Yang C. et al. NRAMP2, a trans-Golgi network-localized manganese transporter, is required for Arabidopsis root growth under manganese deficiency. New Phytol. 2018; 217: 179-93

[24] Li J, Long Y, Qi GN. et al. The Os-AKT1 channel is critical for K+ uptake in rice roots and is modulated by the rice CBL1-CIPK23 complex. Plant Cell. 2014; 26:3387-402

[25] Qin YJ, Wu WH, Wang Y. ZmHAK5 and ZmHAK1 function in K(+) uptake and distribution in maize under low K(+) conditions. J Integr Plant Biol. 2019; 61:691-705

[26] Yang T, Zhang S, Hu Y. et al. The role of a potassium transporter OsHAK5 in potassium acquisition and transport from roots to shoots in rice at low potassium supply levels. Plant Physiol. 2014; 166:945-59

[27] Ragel P, Ródenas R, García-Martín E. et al. The CBL-interacting protein kinase CIPK23 regulates HAK5-mediated high-affinity K+ uptake in Arabidopsis roots. Plant Physiol. 2015; 169:2863-73

[28] Jung JY, Shin R, Schachtman DP. Ethylene mediates response and tolerance to potassium deprivation in Arabidopsis. Plant Cell. 2009; 21:607-21

[29] Koyama ML, Levesley A, Koebner RM. et al. Quantitative trait loci for component physiological traits determining salt tolerance in rice. Plant Physiol. 2001; 125:406-22

[30] Lin HX, Zhu MZ, Yano M. et al. QTLs for Na+ and K+ uptake of the shoots and roots controlling rice salt tolerance. Theor Appl Genet. 2004; 108:253-60

[31] Pandit A, Rai V, Bal S. et al. Combining QTL mapping and tran-scriptome profiling of bulked RILs for identification of func-tional polymorphism for salt tolerance genes in rice (Oryza sativa L.). Mol Gen Genomics. 2010; 284:121-36

[32] Wang Y, He L, Li HD. et al. Potassium channel alpha-subunit AtKC1 negatively regulates AKT1-mediated K(+) uptake in Ara-bidopsis roots under low-K(+) stress. Cell Res. 2010; 20:826-37

[33] Ye J, Wang X, Wang W. et al. Genome-wide association study reveals the genetic architecture of 27 agronomic traits in tomato. Plant Physiol. 2021; 186:2078-92

[34] Yin B, Xu L, Li J. et al. Integrated transcriptome and GWAS analy-sis to identify candidate genes for Ustilago maydis resistance in maize. Agriculture. 2024; 14:958

[35] Desaint H, Héreil A, Belinchon-Moreno J. et al. Integration of QTL and transcriptome approaches for the identification of genes involved in tomato response to nitrogen deficiency. JExp Bot. 2024; 75:5880-96

[36] Hakla HR, Sharma S, Urfan M. et al. Genome-wide associ-ation study (GWAS) for identifying SNPs and genes related to phosphate-induced phenotypic traits in tomato (Solanum lycopersicum L.). Plan Theory. 2024; 13:457

[37] Liu X, Yang W, Wang J. et al. SlGID1a is a putative candidate gene for qtph1.1, a major-effect quantitative trait locus controlling tomato plant height. Front Genet. 2020; 11:881

[38] Wang Z, Hong Y, Zhu G. et al. Loss of salt tolerance during tomato domestication conferred by variation in a Na+/K+ transporter. EMBO J. 2020; 39:e103256

[39] Xiong D, Chen J, Yu T. et al. SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteris-tics. Sci Rep. 2015; 5:13389

[40] Lin T, Zhu G, Zhang J. et al. Genomic analyses provide insights into the history of tomato breeding. Nat Genet. 2014; 46:1220-6

[41] Ye J, Li W, Ai G. et al. Genome-wide association analysis identifies a natural variation in basic helix-loop-helix transcription factor regulating ascorbate biosynthesis via D-mannose/L-galactose pathway in tomato. PLoS Genet. 2019; 15:e1008149

[42] Sravankumar T, Akash NN, Kumar R. A ripening-induced SlGH3-2 gene regulates fruit ripening via adjusting auxin-ethylene lev-els in tomato (Solanum lycopersicum L.). Plant Mol Biol. 2018; 98: 455-69

[43] Fang S, Ji M, Zhu T. et al. Multi-omics analysis reveals the transcriptional regulatory network of maize roots in response to nitrogen availability. Agronomy. 2024; 14:1541

[44] Goto C, Ikegami A, Goh T. et al. Genetic interaction between Ara-bidopsis SUR2/CYP83B1 and GNOM indicates the importance of stabilizing local auxin accumulation in lateral root initiation. Plant Cell Physiol. 2023; 64:1178-88

[45] Yadav A. The link between hydrotropism and phototropism in Arabidopsis roots. JExp Bot. 2023; 74:4892-5

[46] Demirer GS, Gibson DJ, Yue X. et al. Phosphate deprivation-induced changes in tomato are mediated by an interac-tion between brassinosteroid signaling and zinc. New Phytol. 2023; 239:1368-83

[47] Zhao X, Liu Y, Liu X. et al. Comparative transcriptome profiling of two tomato genotypes in response to potassium-deficiency stress. Int J Mol Sci. 2018; 19:2402

[48] Feng J, Meyer CA, Wang Q. et al. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics. 2012; 28:2782-8

[49] Dong S, Tarkowska D, Sedaghatmehr M. et al. The HB40-JUB1 transcriptional regulatory network controls gibberellin home-ostasis in Arabidopsis. Mol Plant. 2022; 15:322-39

[50] Akbudak MA, Filiz E, Çetin D. Genome-wide identification and characterization of high-affinity nitrate transporter 2 (NRT2) gene family in tomato (Solanum lycopersicum) and their tran-scriptional responses to drought and salinity stresses. JPlant Physiol. 2022; 272:153684

[51] Albornoz F, Gebauer M, Ponce C. et al. LeNRT1.1 improves nitrate uptake in grafted tomato plants under high nitrogen demand. Int JMol Sci. 2018; 19:3921

[52] Zhao P, You Q, Lei M. A CRISPR/Cas9 deletion into the phosphate transporter SlPHO1;1 reveals its role in phosphate nutrition of tomato seedlings. Physiol Plant. 2019; 167:556-63

[53] González Besteiro MA, Ulm R. ATR and MKP1 play distinct roles in response to UV-B stress in Arabidopsis. Plant J. 2013; 73: 1034-43

[54] Torres-Galea P, Hirtreiter B, Bolle C. Two GRAS proteins, SCARECROW-LIKE21 and PHYTOCHROME a SIGNAL TRANS-DUCTION1, function cooperatively in phytochrome a signal TRANSDUCTION. Plant Physiol. 2013; 161:291-304

[55] Rudenko NN, Ignatova LK, Nadeeva-Zhurikova EM. et al. Advances in understanding the physiological role and locations of carbonic anhydrases in C3 plant cells. Protoplasma. 2021; 258: 249-62

[56] Chapman NH, Bonnet J, Grivet L. et al. High-resolution mapping of a fruit firmness-related quantitative trait locus in tomato reveals epistatic interactions associated with a complex com-binatorial locus. Plant Physiol. 2012; 159:1644-57

[57] Peralta IE, Knapp S, Spooner DM. New species of wild toma-toes (Solanum section Lycopersicon: Solanaceae) from northern Peru. Syst Bot. 2005; 30:424-34

[58] Poorter H, Nagel OW. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Aust J Plant Physiol. 2000; 27:595-607

[59] Khavari-nejad RA, Najafi F, Tofighi C. Diverse responses of tomato to N and P deficiency. Int J Agric Biol. 2009; 11:209-13

[60] Oleszkiewicz T, Sala-Cholewa K, Godel-Je˛drychowska K. et al. Nitrogen availability modulates carotene biosynthesis, chromo-plast biogenesis, and cell wall composition in carrot callus. Plant Cell Rep. 2025; 44:31

[61] Voitsekhovskaja OV, Tyutereva EV. Chlorophyll b in angiosperms: functions in photosynthesis, signaling and ontogenetic regulation. J Plant Physiol. 2015; 189:51-64

[62] Guo S, Zhao S, Sun H. et al. Resequencing of 414 cultivated and wild watermelon accessions identifies selection for fruit quality traits. Nat Genet. 2019; 51:1616-23

[63] Kim M, Nguyen TTP, Ahn JH. et al. Genome-wide association study identifies QTL for eight fruit traits in cultivated tomato (Solanum lycopersicum L.). Hortic Res. 2021; 8:203

[64] YanP, DuQ, ChenH. et al. Biofortification of iron content by regulating a NAC transcription factor in maize. Science. 2023; 382: 1159-65

[65] Yu J, Zhu C, Xuan W. et al. Genome-wide association studies identify OsWRKY53 as a key regulator of salt tolerance in rice. Nat Commun. 2023; 14:3550

[66] Yu Y, Guan J, Xu Y. et al. Population-scale peach genome analyses unravel selection patterns and biochemical basis underlying fruit flavor. Nat Commun. 2021; 12:3604

[67] Rutschow H, Ytterberg AJ, Friso G. et al. Quantitative proteomics of a chloroplast SRP54 sorting mutant and its genetic interac-tions with CLPC1 in Arabidopsis. Plant Physiol. 2008; 148:156-75

[68] Prodhan MA, Pariasca-Tanaka J, Ueda Y. et al. Comparative transcriptome analysis reveals a rapid response to phospho-rus deficiency in a phosphorus-efficient rice genotype. Sci Rep. 2022; 12:9460

[69] Fu Y, Yi H, Bao J. et al. LeNRT2.3 functions in nitrate acquisi-tion and long-distance transport in tomato. FEBS Lett. 2015; 589: 1072-9

[70] Parapunova V, Busscher M, Busscher-Lange J. et al. Identification, cloning and characterization of the tomato TCP transcription factor family. BMC Plant Biol. 2014; 14:157

[71] Wu M, Xu X, Hu X. et al. SlMYB72 regulates the metabolism of chlorophylls, carotenoids, and flavonoids in tomato fruit. Plant Physiol. 2020; 183:854-68

[72] Huang W, Xian Z, Kang X. et al. Genome-wide identification, phy-logeny and expression analysis of GRAS gene family in tomato. BMC Plant Biol. 2015; 15:209

[73] Liu M, Gomes BL, Mila I. et al. Comprehensive profiling of ethy-lene response factor expression identifies ripening-associated ERF genes and their link to key regulators of fruit ripening in tomato. Plant Physiol. 2016; 170:1732-44

[74] Yoshida S, Forno DA, Cock J. Chapter 1 - Sampling and sample preparation. In: Laboratory Manual for Physiological Studies of Rice. 3rd ed. International Rice Research Institute, 1976, 7-11.

[75] Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009; 25:1754-60

[76] McKenna A, Hanna M, Banks E. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20:1297-303

[77] Purcell S, Neale B, Todd-Brown K. et al. PLINK: a tool set for whole-genome association and population-based linkage anal-yses. Am J Hum Genet. 2007; 81:559-75

[78] Zhang Z, Ersoz E, Lai CQ. et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010; 42:355-60

[79] Browning BL, Tian X, Zhou Y. et al. Fast two-stage phasing of large-scale sequence data. Am J Hum Genet. 2021; 108:1880-90

[80] Dong SS, He WM, Ji JJ. et al. LDBlockShow: a fast and con-venient tool for visualizing linkage disequilibrium and haplo-type blocks based on variant call format files. Brief Bioinform. 2021;22:bbaa227

[81] Chen S, Zhou Y, Chen Y. et al. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884-90

[82] Anders S, Pyl PT, Huber W. HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics. 2015; 31: 166-9

[83] Kumar L, Futschik ME. Mfuzz: a software package for soft clus-tering of microarray data. Bioinformation. 2007; 2:5-7

[84] Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9:559

[85] Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17

[86] Shannon P, Markiel A, Ozier O. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13:2498-504

[87] Yin X, He T, Yi K. et al. Comprehensive evaluation of candidate reference genes for quantitative real-time PCR-based analysis in Caucasian clover. Sci Rep. 2021; 11:3269

[88] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841-2

[89] Sato S, Tabata S, Hirakawa H. et al. The tomato genome sequence provides insights into fleshy fruit evolution. Nature. 2012; 485: 635-41

[90] Zhang R, Jia G, Diao X. geneHapR: an R package for gene haplo-typic statistics and visualization. BMC Bioinformatics. 2023; 24:199