Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut

Abstract

Uncovering the genetic basis of photosynthetic trait variation under drought stress is essential for breeding climate-resilient walnut cultivars. To this end, we examined photosynthetic capacity in a diverse panel of 150 walnut families (1500 seedlings) from various agro-climatic zones in their habitats and grown in a common garden experiment. Photosynthetic traits were measured under well-watered (WW), water-stressed (WS) and recovery (WR) conditions. We performed genome-wide association studies (GWAS) using three genomic datasets: genotyping by sequencing data (~43 K SNPs) on both mother trees (MGBS) and progeny (PGBS) and the Axiom™ Juglans regia 700 K SNP array data (~295 K SNPs) on mother trees (MArray). We identified 578 unique genomic regions linked with at least one trait in a specific treatment, 874 predicted genes that fell within 20 kb of a significant or suggestive SNP in at least two of the three GWAS datasets (MArray, MGBS, and PGBS), and 67 genes that fell within 20 kb of a significant SNP in all three GWAS datasets. Functional annotation identified several candidate pathways and genes that play crucial roles in photosynthesis, amino acid and carbohydrate metabolism, and signal transduction. Further network analysis identified 15 hub genes under WW, WS and WR conditions including GAPB, PSAN, CRR1, NTRC, DGD1, CYP38, and PETC which are involved in the photosynthetic responses. These findings shed light on possible strategies for improving walnut productivity under drought stress.

References

[1] Vahdati K, Arab MM, Sarikhani S et al. Advances in Persian walnut (Juglans regia L.) breeding strategies . In: Al-Khayri JM, Jain SM, Johnson DV. (eds.), Advances in Plant Breeding Strategies: Nut and Beverage Crops: Volume 4.New York: Springer, 2019, 401-72.

[2] Vahdati K, Arab MM, Sarikhani S . Advances in biotechnology and propagation of nut trees in Iran. In: Egorov E, Ilina I, Zaporozhets N. (eds.), BIO Web of Conferences.Krasnodar, Russia: EDP Sciences, 2020; 25: 01003.

[3] Arab MM, Morrano A, Abdollahi-Arpanahi R et al. Combining phenotype, genotype, and environment to uncover genetic components underlying water use efficiency in Persian walnut. Comparative Study. 2020; 71: 1107-27.

[4] Vahdati K, Lofti N, Kholdebarin B et al. Screening for drought-tolerant genotypes of Persian walnuts (Juglans regia L) during seed germination . Hort Science. 2009; 44: 1815-9.

[5] Famula RA, Richards JH, Famula TR et al. Association genetics of carbon isotope discrimination and leaf morphology in a breeding population of Juglans regia L . Tree Genet Genomes. 2019; 15: 1-13.

[6] Karimi S, Karami H, Mokhtassi-Bidgoli A et al. Inducing drought tolerance in greenhouse grown Juglans regia by imposing controlled salt stress: the role of osmotic adjustment. Sci Hortic. 2018; 239: 181-92.

[7] Knipfer T, Reyecs C, Momayyezi M et al. A comparative study on physiological responses to drought in walnut genotypes (RX1, Vlach, VX211) commercially available as rootstocks. Trees. 2020; 34: 665-78.

[8] Liu B, Liang J, Tang G et al. Drought stress affects on growth, water use efficiency, gas exchange and chlorophyll fluorescence of Juglans rootstocks. Sci Hortic. 2019; 250: 230-5.

[9] Oakley CG, Savage L, Lotz S et al. Genetic basis of photosynthetic responses to cold in two locally adapted populations of Arabidopsis thaliana. J Exp Bot. 2018; 69: 699-709.

[10] Nikinmaa E, Holtta T, Hari P et al. Assimilate transport in phloem sets conditions for leaf gas exchange , Plant Cell Environ. 2013; 36: 655-69.

[11] Herritt M, Dhanapal AP, Purcell LC et al. Identification of genomic loci associated with 21chlorophyll fluorescence phenotypes by genome-wide association analysis in soybean. BMC Plant Biol. 2018; 18: 1-19.

[12] van Bezouw RF, Keurentjes JJ, Harbinson J et al. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. Plant J. 2019; 97: 112-33.

[13] Strasser RJ, Srivastava A, Tsimilli-Michael M . The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Yunus M, Pathre U, Mohanty P. (eds.), Probing Photosynthesis: Mechanism, Regulation & Adaptation.Boca Raton, Florida: CRC Press, 2000, 445-83.

[14] Mathur S, Mehta P, Jajoo A . Effects of dual stress (high salt and high temperature) on the photochemical efficiency of wheat leaves (Triticum aestivum). Physiol Mol Biol Plants. 2013; 19: 179-88.

[15] Martínez-García PJ, Crepeau MW, Puiu D et al. The walnut (Juglans regia) genome sequence reveals diversity in genes coding for the biosynthesis of non-structural polyphenols . Plant J. 2016; 87: 507-32.

[16] Marrano A, Martinez-Garcia PJ, Bianco L et al. A new genomic tool for walnut (Juglans regia L.): development and validation of the high-density axiom™ J. regia 700K SNP genotyping array. Plant Biotechnol J. 2019; 17: 1027-36.

[17] Arab MM, Marrano A, Abdollahi-Arpanahi R et al. Genome-wide patterns of population structure and association mapping of nut-related traits in Persian walnut populations from Iran using the axiom J regia 700K SNP array. Sci Rep. 2019; 9: 1-14.

[18] Bernard A, Marrano A, Donkpegan A et al. Association and linkage mapping to unravel genetic architecture of phenological traits and lateral bearing in Persian walnut (Juglans regia L.) . BMC Genomics. 2020; 21: 1-25.

[19] Bükücü SB, Sutyemez M, Kefayati S et al. Major QTL with pleiotropic effects controlling time of leaf budburst and flowering-related traits in walnut (Juglans regia L) . Sci Rep. 2020; 10: 1-10.

[20] Sideli GM, Marrano A, Montanari S et al. Quantitative phenotyping of shell suture strength in walnut (Juglans regia L) enhances precision for detection of QTL and genome-wide association mapping . PLoS One. 2020; 15: e0231144.

[21] Marrano A, Sideli GM, Leslie CA et al. Deciphering of the genetic control of phenology, yield, and pellicle color in Persian walnut (Juglans regia L.) . Front Plant Sci. 2019; 10: 1140.

[22] Marrano A, Britton M, Zaini PA et al. High-quality chromosome-scale assembly of the walnut (Juglans regia L.) reference genome . Gigascience. 2020; 9: giaa050.

[23] Zhu T, Wang L, You FM et al. Sequencing a Juglans regia× J microcarpa hybrid yields high-quality genome assemblies of parental species. Hortic Res. 2019; 6: 1-16.

[24] Elbasyoni IS, Lorenz AJ, Guttieri M et al. A comparison between genotyping-by-sequencing and array-based scoring of SNPs for genomic prediction accuracy in winter wheat. Plant Sci. 2018; 270: 123-30.

[25] Negro SS, Millet EJ, Madur D et al. Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies. BMC Plant Biol. 2019; 19: 1-22.

[26] Li H, Thrash A, Tang JD et al. Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis. Plant J. 2019; 98: 853-63.

[27] Thrash A, Tang JD, DeOrnellis M et al. PAST: the pathway association studies tool to infer biological meaning from GWAS datasets. Plants (Basel). 2020; 9: 58.

[28] Esmaeili-Fard SM, Gholizadeh M, Hafezian SH et al. Genes and pathways affecting sheep productivity traits: genetic parameters, genome-wide association mapping, and pathway enrichment analysis. Front Genet. 2021; 12.

[29] Strasser RJ, Tsimilli-Michael M, Srivastava A . Analysis of the chlorophyll a fluorescence transient in Chlorophyll a fluorescence. In: Papageorgiou GC, Govindjee S. (eds.), Chlorophyll a Fluorescence: A Signature of Photosynthesis.Dordrecht: Springer Dordrecht, 2004, 321-62.

[30] Zhang J, Jia W, Yang J et al. Role of ABA in integrating plant responses to drought and salt stresses. Field Crop Res. 2006; 97: 111-9.

[31] Rosati A, Metcalf S, Buchner R et al. Tree water status and gas exchange in walnut under drought, high temperature and vapour pressure deficit. J Hortic Sci Biotechnol. 2006; 81: 415-20.

[32] Kalaji HM, Jajoo A, Oukarroum A et al. Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions. Acta Physiol Plant. 2016; 38: 102.

[33] Lee U, Rioflorido I, Hong SW et al. The Arabidopsis ClpB/Hsp100 family of proteins: chaperones for stress and chloroplast development. Plant J. 2007; 49: 115-27.

[34] Müller M, Munné-Bosch S . Ethylene response factors: a key regulatory hub in hormone and stress signaling. Plant Physiol. 2015; 169: 32-41.

[35] Järvi S, Gollan PJ, Aro EM . Understanding the roles of the thylakoid lumen in photosynthesis regulation. Front Plant Sci. 2013; 4: 434.

[36] Chen X, Ding Y, Yang Y et al. Protein kinases in plant responses to drought, salt, and cold stress. J Integr Plant Biol. 2021; 63: 53-78.

[37] Doukhanina EV, Chen S, van der Zalm E et al. Identification and functional characterization of the BAG protein family in Arabidopsis thaliana. J Biol Chem. 2006; 281: 18793-801.

[38] Jiang SC, Mei C, Liang S et al. Crucial roles of the pentatricopeptide repeat protein SOAR1 in Arabidopsis response to drought, salt and cold stresses. Plant Mol Biol. 2015; 88: 369-85.

[39] Du ZY, Chen MX, Chen QF et al. Overexpression of Arabidopsis acyl-CoA-binding protein ACBP2 enhances drought tolerance. Plant Cell Environ. 2013; 36: 300-14.

[40] Hou X, Tong H, Selby J et al. Involvement of a cell wall-associated kinase, WAKL4, in Arabidopsis mineral responses. Plant Physiol. 2005; 139: 1704-16.

[41] Ma L, Li G . FAR1-related sequence (FRS) and FRS-related factor (FRF) family proteins in Arabidopsis growth and development. Front Plant Sci. 2018; 9: 692.

[42] Flexas J, Barón M, Bota J et al. Photosynthesis limitations during water stress acclimation and recovery in the drought-adapted Vitis hybrid Richter-110 (V. berlandieri× V rupestris). J Exp Bot. 2009; 60: 2361-77.

[43] Apel K, Hirt H . Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu Rev Plant Biol. 2004; 55: 373-99.

[44] Georgii E, Jin M, Zhao J et al. Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis. BMC Plant Biol. 2017; 17: 1-23.

[45] Hosseini SZ, Ismaili A, Nazarian-Firouzabadi F et al. Dissecting the molecular responses of lentil to individual and combined drought and heat stresses by comparative transcriptomic analysis. Genomics. 2021; 113: 693-705.

[46] Shi H, Ye T, Chen F et al. Manipulation of arginase expression modulates abiotic stress tolerance in Arabidopsis: effect on arginine metabolism and ROS accumulation. J Exp Bot. 2013; 64: 1367-79.

[47] Pedroso FK, Prudente DA, Bueno ACR et al. Drought tolerance in citrus trees is enhanced by rootstock-dependent changes in root growth and carbohydrate availability. Environ Exp Bot. 2014; 101: 26-35.

[48] Woldesemayat AA, Ntwasa M . Pathways and network based analysis of candidate genes to reveal cross-talk and specificity in the sorghum (Sorghum bicolor (L) Moench) responses to drought and it’s co-occurring stresses. Environ Exp Bot. 2018; 9: 557.

[49] Li X, Wei W, Li F et al. The plastidial glyceraldehyde-3-phosphate dehydrogenase is critical for abiotic stress response in wheat. Int J Mol Sci. 2019; 20: 1104.

[50] Shimizu H, Shikanai T . Dihydrodipicolinate reductase-like protein, CRR1, is essential for chloroplast NAD (P) H dehydrogenase in Arabidopsis. Plant J. 2007; 52: 539-47.

[51] Naranjo B, Mignée C, Krieger-Liszkay A et al. The chloroplast NADPH thioredoxin reductase C, NTRC, controls non-photochemical quenching of light energy and photosynthetic electron transport in Arabidopsis. Plant Cell Environ. 2016; 39: 804-22.

[52] Yu H-D, Yang XF, Chen ST et al. Downregulation of chloroplast RPS1 negatively modulates nuclear heat-responsive expression of HsfA2 and its target genes in Arabidopsis. PLoS Genet. 2012; 8: e1002669.

[53] Munekage Y, Takeda S, Endo T et al. Cytochrome b6f mutation specifically affects thermal dissipation of absorbed light energy in Arabidopsis. Plant J. 2001; 28: 351-9.

[54] Hijmans RJ, Cameron SE, Parra JL et al. Very high resolution interpolated climate surfaces for global land areas. Climatology. 2005; 25: 1965-78.

[55] Farquhar GD, Sharkey TD . Stomatal conductance and photosynthesis. Annu Rev Plant Physiol. 1982; 33: 317-45.

[56] Wójcik-Jagła M, Rapacz M, Tyrka M et al. Comparative QTL analysis of early short-time drought tolerance in polish fodder and malting spring barleys. Theor Appl Genet. 2013; 126: 3021-34.

[57] Wickham HJC . ggplot2-Elegant Graphics for Data Analysis. In: Gentleman R, Hornik K, Parmigiani G. (eds.), Use R!.Switzerland: Springer International Publishing, 2016, 89-107.

[58] Kassambara A. Factor Analysis of Mixed Data In: Kassambara A. (ed.), Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning (Multivariate Analysis).South Carolina: CreateSpace Independent Publishing Platform, 2017, 149-64.

[59] Poland JA, Brown PJ, Sorrells ME et al. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. 2012; 7: e32253.

[60] Glaubitz JC, Casstevens TM, Lu F et al. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One. 2014; 9: e90346.

[61] Li H, Durbin R . Fast and accurate long-read alignment with burrows-wheeler transform. Bioinformatics. 2010; 26: 589-95.

[62] Browning BL, Zhou Y, Browning SR . A one-penny imputed genome from next-generation reference panels. Am J Hum Genet. 2018; 103: 338-48.

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

[64] Lipka AE, Tian F, Wang Q et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012; 28: 2397-9.

[65] Liu X, Huang M, Fan B et al. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 2016; 12: e1005767.

[66] Segura V, Vilhjálmsson BJ, Platt A et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. 2012; 44: 825-30.

[67] Yu J, Pressoir G, Briggs WH et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006; 38: 203-8.

[68] Götz S, Garcia-Gomez JM, Terol J et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008; 36: 3420-35.

[69] The Gene Ontology Consortium . The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019; 47: D330-D338.

[70] Kanehisa M, Goto S, Kawashima S et al. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004; 32: 277D-280.

[71] Mering CV, Huynen M, Jaeggi D et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003; 31:258-61.

[72] 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.

[73] Chin C-H, Chen SH, Wu HH et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014; 8: 1-7.