Acta agriculturae Slovenica, 120/4, 1–16, Ljubljana 2024 doi:10.14720/aas.2024.120.4.19357 Original research article / izvirni znanstveni članek Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Arash MOHAMADI 1, Hossein JAFARI 2, Omid SOFALIAN 1, 3, Ali ASGHARI 1, Farid SHEKARI 4, Seyed Mohammad mahdi MORTAZAVIAN 5, Fatemeh MOHAMADI AZAR 1 Received July 27, 2024; accepted October 27, 2024 Delo je prispelo 27. julij 2024, sprejeto 27. oktober 2024 1 Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Sciences, University of Mohaghegh Ardabili, Ardabil, Iran 2 Department of Plant Protection Research, Zanjan Agricultural and Natural Resources Research and Education Center, AREEO, Zanjan, Iran 3 corresponding author: sofalian@gmail.com 4 Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Sciences, University of Zanjan, Zanjan, Iran. 5 Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Sciences, University of Tehran, Tehran, Iran. Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Abstract: This study explored the relationship between QTLs associated with drought tolerance and functional SNPs using two populations: Vada × Susptrit (V × S) and Cebada Cappa × Susptrit (C. Cappa × S). Bioinformatics tools were employed to analyze significant SNPs within QTL regions, re- vealing markers on chromosomes 1H, 2H, 3H, 5H, and 7H for the V × S population, and on 4H and 6H for C. Cappa × S. In total, 24 proteins/enzymes related to drought tolerance were characterized in the V × S population, while 10 were identi- fied in C. Cappa × S. Notable proteins, including phytochrome B and SAPK7, were located on chromosome 4H. The identi- fied proteins are integral to the plant’s adaptive response to drought stress, mediating essential regulatory mechanisms that enhance resilience. Gene ontology analysis delineated four pri- mary cellular components—membrane, nucleus, chloroplast, and proteasome complex—linked to drought resistance path- ways. Additionally, five critical biological processes, including oxidation-reduction and protein phosphorylation, were identi- fied as pivotal in these adaptive responses. This comprehensive understanding underscores the potential application of these proteins in breeding strategies aimed at developing drought- tolerant barley cultivars. Overall, the study highlights potential functional SNP markers for validating QTLs related to drought tolerance in barley. Key words: barley, bioinformatics, gene ontology, QTL validation, single nucleotide polymorphism Funkcionalna analiza odpornosti na sušo (QTL) v dveh po- pulacijah ječmena z uporabo BLAST analize na povezanih SNP zaporedjih Izvleček: Raziskava preučuje razmerje med s toleranco na sušo povezanimi zaporedji (QTL) in funkcionalnimi SNP mesti v dveh populacijah ječmena: Vada × Susptrit (V × S) in Ceba- da Cappa × Susptrit (C. Cappa × S). Bioinformacijska orodja so bila uporabljena za analizo pomembnih SNP znotraj QTL območij za odkritje markerjev na kromosomih 1H, 2H, 3H, 5H in 7H za V × S populacijo in na kromosomih 4H in 6H za C. Cappa × S populacijo. Celokupno je bilo opisanih 24 proteinov/ encimov, povezanih s tolerance na sušo v V × S populaciji med tem, ko jih je bilo v populaciji C. Cappa × S deset. Pomembni proteini, ki vključujejo fitokrom B in serein treonin kinazo (SAPK7) so bili locirani na kromosomu 4H. Ti proteini so ses- tavni del rastlinskega prilagoditvenega odziva na sušni stres, ki je bistven za sprožitev mehanizmov uravnavanja procesov, ki povečuje odpornost. Analiza delovanja genov je odkrila štiri glavne celične oddelke, ki so povezani s procesi odpornosti na sušo in sicer celično membrano, celično jedro, kloroplast in proteasom. Dodatno je bilo v tem prilagoditvenem odzivu pre- poznanih pet ključnih bioloških procesov, ki so med drugimi obsegali oksidacijo, redukcijo in fosforilacijo proteinov. Takšno celostno razumevanje poudarja pomen potencilane uporabe teh proteinov v strategijah žlahtnenja z namenom vzgojiti na sušo toleratne sorte ječmena. Raziskava osvetljuje uporabo po- tencialnih funkcionalnih SNP markerjev za ovrednotenje QTL povezanih s toleranco na sušo pri ječmenu. Ključne besede: ječmen, bioinformatika, genska on- tologija, ovrednotenje QTLs, SNP (polimorfizem posameznih nukleotidov) Acta agriculturae Slovenica, 120/4 – 20242 A. MOHAMADI et al. 1 INTRODUCTION Drought stress is an unavoidable factor present in various environments, affecting plant biomass produc- tion, quality, and energy without regard for borders or providing clear warnings. Its pervasive nature hampers agricultural productivity by challenging plants’ ability to thrive under water-limiting conditions.(Seleiman, 2021). Understanding the complex regulatory pathways guaran- tees in-depth consideration of a biological system. These challenges are closely related to informatics in biology, in other words “bioinformatics”. The field of multi-omics has witnessed unprecedented growth, converging multi- ple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-om- ics scientific publications within just two years (2022– 2023) since its first referenced mention in 2002, as in- dexed by the National Library of Medicine. (Moher et al., 2024). Bioinformatics actually manages the data collect- ed through various techniques -omics, including genom- ics, transcriptomics, proteomics and metabolomics. Sys- tems Bioinformatics is the framework in which systems approaches are applied to such data (oulas et al.,, 2017). Access to plant genome sequencing technology, the de- velopment of mapping populations, genetic diversity, and molecular markers with wide genomic coverage has led researchers to accelerate the identification of impor- tant QTLs (quantitative trait locus) and their responsible Figure 1: QTLs related to physiological traits under drought stress and normal irrigation in barley populations V × S (a) and C. Capa × S (b) and SNP markers associated with AFLP/SSR markers related to the QTLs. AFLP/SSR markers and the associated SNP markers are shown inside the rectangle. The letters S and N indicate drought and normal irrigation conditions, respectively. The numbers in the QTL area indicate the maximum LOD. Bold areas have significant LOD. CC1, CC2 and CC3 indicate chlorophyll content at 50 %, 30 %, and 20 % of field capacity, respectively. FC: field capacity; RWC: Relative leaf water content and SDM: Shoot dry mass and LT: Leaf temperature. Acta agriculturae Slovenica, 120/4 – 2024 3 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences genes (Kurakawa et al., 2007). Among recent advances in -omics technology, the emergence of high-efficiency methods for genomic sequencing and high-saturation genotyping using DNA markers such as single nucleo- tide polymorphism (SNP), has proven to be very effective (Kuromori et al., 2009). Including the bioinformatics im- plements, are Sequence Analysis and Similarity Searching Tools. In bioinformatics, sequence alignment is a method of arranging DNA, RNA, or protein sequences to identify “similar regions” that can reveal functional, structural, or evolutionary relationships between sequences (Stormo, 2000). In the aligning method, there are strong evidences that two similar sequences have the same nucleotids (or identical amino acids). Due to the large number of gaps, multiple alignments can occur between the two sequenc- es (Vassilev et al., 2005). Dynamic algorithms identify the optimal alignment. In the BLAST method, statisti- cal techniques are used to assess the probability of a spe- cific alignment between two sequences (Neumann et al., 2014). Alignments are widely used in bioinformatics to identify sequence similarity, prepare phylogenetic trees and homology models of protein structures (Dubey et al., 2010). The NCBI database (http://www.ncbi.nlm.nih. gov/BLAST/), is the most popular tool to search for align sequences (Altschul et al., 1990). Bioinformatics method has been used in many studies to identify or predict the proteins or enzymes involved in the response of differ- ent plants to drought stress (Faghani et al., 2015; Landi et al., 2017; Neumann et al., 2014; Shaar-Moshe et al., 2015; Wehner et al., 2015). Barley, scientifically known as Hordeum vulgare L., ranks fourth in cereals in terms of production. The ability to grow barley in harsh and low-yield environments is higher than other cereals, and this crop is best adapted to environmental stresses such as drought, salinity and cold (Jogaiah et al., 2013). The present study was performed to BLAST analysis on SNP sequences have significant correlation with QTL regions related to drought tolerance identified in our previous study (Mohammadi et al., 2018) as well as prediction of related genes and proteins/enzymes and their ontology study. 2 MATERIALS AND METHODS In our previous study (Mohammadi et al., 2018), drought tolerance chromosomal regions in seedling stage were identified and reported in two barley populations: ‘Vada’ ×’ Susptrit’ (V × S) and ‘Cebada Cappa’ × ‘Susptrit’ (C. Cappa × S). BLAST analysis was performed on SNP sequences have significant correlations with some AFLP/ SSR markers associated with QTLs related to physiologi- cal traits under drought conditions. SNP markers were developed in the University of Wagningen, Netherlands. The QTLs and their associated SNP markers are shown in Figure 1. In order to study of functional genomics, BLAST analysis was conducted against the NCBI non re- dundant (nr) nucleotide collection (www.ncbi.nlm.nih. gov). The UNIPROT database (www.uniprot.org) was used to predict proteins and their function. Gene ontolo- gy information was also extracted through the European Bioinformatics Institute website (EBI = www.ebi.ac.uk). In order to plot three types of ontologies, including cel- lular components, molecular functions, and biological processes, EXCEL software was used. Bioinformatics pipeline, is shown in Figure 2. 3 RESULTS AND DISCUSSION 3.1 BLAST ANALYSIS SNPS ASSOCIATED TO QTL REGIONS IDENTIFIED IN V × S POPULATION Summary of BLAST results SNP sequences with sig- nificant correlations with QTL regions identified in the V × S population are shown in Table 1. In this population, a total of 24 types of proteins / enzymes related to QTL regions were identified whose sequences of genes encod- ing them had very low FDR (near zero), which indicates Figure 2: Bioinformatics pipeline used in this work Acta agriculturae Slovenica, 120/4 – 20244 A. MOHAMADI et al. a very high similarity between SNP sequences and their alignments. These genes were identified on chromo- somes 1H, 2H, 3H, 5H and 7H. On chromosome 1H, proteins/enzymes glycosyltransferases, protein dehydra- tion-induced 19 homolog 5, dihydrolipoyl dehydroge- nase, hexosyltransferase, salt tolerant protein-GSK-like kinase, peroxidase, glycine-rich RNA-binding protein RZ1B, mitogen-activated protein kinase, pectinesterase, cinnamoyl-CoA reductase-like SNL6, ribosomal protein, were identified. Two proteins/enzymes include S-aden- osylmethionine decarboxylase proenzyme and ATP-de- pendent Clp protease proteolytic subunit, were predicted on chromosomes 2H and 3H, respectively. On chromo- some 5H: pectinesterase, protoporphyrinogen oxidase, beta-carotene hydroxylase, zinc finger A20 and AN1 do- main-containing stress-associated protein 1, chlorophyll a-b binding protein, probable anion transporter 5; and on chromosome 7H: proteasome subunit beta type, lac- toylglutathione lyase, xyloglucan endotransglucosylase/ hydrolase, phosphoglycerate kinase, D-3-phosphoglyc- erate dehydrogenase were identified. Pectin esterase and zinc finger proteins play crucial roles in plant responses to drought stress. Identifying these proteins can aid in developing new strategies for enhancing drought toler- ance in barley and other crops. Given their involvement in key processes like water regulation and plant metabo- lism, a deeper understanding of their functions could assist farmers in improving crop yields under challeng- ing environmental conditions, ultimately contributing to food security. Pectin esterase was detected on chromo- somes 1H and 5H and also zinc finger A20 and AN1 do- main-containing stress-associated protein 1 was identi- fied in two regions on chromosome 5H. Based on BLAST results, it was observed that most genes were found in barley (Hordeum vulgare subsp. vulgare Spenn.) (Table 1). A summary of the results of former studies on the effect of identified proteins/enzymes on drought toler- ance in different plants is given in Table 2. A review of the relationship between the identified proteins/enzymes and drought stress in different studies showed well, that all the proteins/enzymes identified in the present study, were directly involved in the drought stress response in previous studies conducted on different plants. The re- sults confirm the QTLs reported in our previous study (Nadarajah and Sidek, 2010). In most studies, increase or induction of identified proteins, enhanced drought stress tolerance (Table 2). Rollins (2012) studied effect of heat stress in barley, that two dihydrolipoyl dehydro- genase proteins named F2E5U7 and F2E2T3 have been reported, which one of them, F2E5U7, was also identified in present study. As a result of BLAST on SNP marker named “BOPA2_12_20641” located on chromosome 1H correlated with AFLP marker E33M54-263, a glycogen synthase kinase enzyme called Q8LK43, which is the same protein reported by Talami et al. (2007) (Table 2). Qin et al. applied drought stress in rice and Arabidopsis and studied BLAST on sequence of genes related to chlo- rophyll a/b binding proteins in barley genomic database, that 17 genes expressing chlorophyll a/b binding proteins were reported. In this study, we identified MLOC_44755, which corresponds to the protein F2CRC1, through BLAST analysis of SNP “BOPA1_1583-522” located on chromosome 5. The identification of this protein sug- gests its potential role in biological processes related to the SNP. Understanding the functional implications of MLOC_44755 can provide insights into its contribution to phenotypic traits or disease susceptibility. Further ex- ploration of this relationship may reveal important con- nections that enhance our understanding of the underly- ing genetic mechanisms involved (Table 1). 3.2 BLAST ANALYSIS OF CORRELATED SNP SE- QUENCES OR QTL REGIONS IDENTIFIED IN C.CAPA × S POPULATION Summary of BLAST analysis on SNP sequences have significant correlations with QTL regions identified in C.Cappa × S population are shown in Table 2. A total of 10 proteins/enzymes associated with QTL regions were identified, and their coding gene sequences exhibited a very low false discovery rate (near zero). This indicates a high level of similarity between the SNP sequences and the identified alignments. In other words, these findings suggest a strong and meaningful connection between the SNPs and the identified proteins. These genes were identified on chromosomes 4 and 6. On chromosome 4, proteins/enzymes phytochrome B, serine/threonine- protein kinase SAPK7, o-methyltransferase were identi- fied. Proteasome subunit alpha type, nuclear cap-binding protein subunit 2, 3-ketoacyl-CoA synthase, heat shock protein 16.9C, calcium-dependent protein kinase 4, beta- carotene hydroxylase and HGWP repeats were detected on chromosome 6H. 3.3 REVIEW OF PREVIOUS STUDIES ON IDEN- TIFIED PROTEINS / ENZYMES AND THEIR RELATIONSHIP WITH DROUGHT STRESS IN DIFFERENT PLANTS The results of this section in V × S and C. Capa × S populations are given in Tables 3 and 4, respectively. As can be seen, in both populations direct relationship of all Acta agriculturae Slovenica, 120/4 – 2024 5 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Table 1: Briefing of BLASTn analysis on SNP sequences having significant correlation with AFLP/SSR markers linked to QTLs re- lated to drought stress in V × S population. Note. SNP: Single Nucleotide Sequence; AFLP: Amplified Fragment Length Polymor- phism; SSR: Single Sequence Repeat; FDR: False Discovery Rate Chromosome number, AFLP marker SNPs correlated to associated AFLP marker Correlation rate between SNPs and AFLPs Accession Species FDR Query cover Gene Uniprot name Protein 1H,E33M54-263 SCRI_RS_170869 0.88435955 AK250861.1 Hordeum vulgare subsp. vulgare 2.00E-51 100 % pglcat4 Q7XHJ7 Glycosyltransferases SCRI_RS_230987 0.90629731 AP014957.1 Oryza sativa japonica 4.00E-15 82 % DI19-2 Q5JME8 Protein DEHYDRA- TION-INDUCED 19 homolog 5 BOPA1_409-1643 0.92889292 AK371521.1 Hordeum vulgare subsp. vulgare 3.00E-116 100 % N/A F2E5U7 Dihydrolipoyl dehydro- genase SCRI_RS_147042 0.928893 AK362608.1 Hordeum vulgare subsp.. vulgare 7.00E-50 100 % N/A F2DFE1 Hexosyltransferase BOPA2_12_20641 0.9043956 AF525086.1 Triticum aestivum (bread wheat)1.00E-40 89 % N/A Q8LK43 = AAM77397 salt tolerant protein- GSK-like kinase BOPA1_ABC12199- 1-1-25 0.9043956 XM_006654903.2 Oryza brachyantha 4.00E-28 100 % N/A J3M3R4 Peroxidase BOPA1_3710-852 0.88129947 AK250786.1 Arabidopsis thaliana (Mouse-ear cress) RZ1B O22703 Glycine-rich RNA- binding protein RZ1B BOPA2_12_30683 0.88129947 AK356908.1 Hordeum vulgare subsp.. vulgare 2.00E-51 100 % N/A F2CZ52 Mitogen-activated protein kinase SCRI_RS_189248 0.83214722 AK371220.1 Hordeum vulgare subsp.. vulgare 2E-51 100% N/A F2E4Z6 Pectinesterase SCRI_RS_17256 0.80792947 XM_020338974.1 Oryza sativa subsp. japonica (Rice) SNL6 Q0JKZ0 Cinnamoyl-CoA reductase-like SNL6 SCRI_RS_225107 0.71110956 AK359627.1 Hordeum vulgare subsp. vulgare 2.00E-51 100 % N/A F2D6W5 Ribosomal protein 2H,E38M54-176 SCRI_RS_193100 0.47013575 AP014960.1 Oryza sativa japonica 7.00E-12 98 % SAMDC Q0JC10 S-adenosylmethionine decarboxylase 3H,Bmag0136 BOPA2_12_20591 0.95060956 AK249478.1 Hordeum vulgare subsp. vulgare 2.00E-51 100 % clpP P48883 (CLPP_ HORVU) ATP-dependent Clp protease proteolytic subunit 5H,E35M55-181 BOPA2_12_11318 0.711 AK365601.1 Hordeum vulgare subsp. vulgare 2.00E-49 100% N/A F2DNY3 Pectinesterase SCRI_RS_126187 0.711 AK356154.1 Hordeum vulgare subsp.. vulgare 7.00E-50 100% N/A F2CX01 Protoporphyrinogen oxidase SCRI_RS_220645 0.8 AP014959.1 Oryza sativa japonica 8E-22 96% Os03g0125100 Q10SE7 Beta-carotene hydroxy- lase, putative, expressed SCRI_RS_158981 0.879 AP014965.1 Oryza sativa japonica 4.00E-34 98% SAP1 A3C039 Zinc finger A20 and AN1 domain-containing stress-associated protein 1 5H,E38M54-375 BOPA1_1583-522 0.815 AK354173.1 Hordeum vulgare subsp.. vulgare 9.00E-116 100% N/A F2CRC1 : MLOC_44755, MLOC_44755.2 Chlorophyll a-b binding protein, chloroplastic 5H,E42M48-282 SCRI_RS_199964 0.7082 AK059357.1 Oryza sativa japonica 1E-14 89% PHT4;5 Q0IZQ3 Probable anion trans- porter 5, chloroplastic SCRI_RS_212515 0.7082 AP014965.1 Oryza sativa subsp.. japonica 1.00E-28 90% SAP1 A3C039 Zinc finger A20 and AN1 domain-containing stress-associated protein 1 7H,P15M47-184 SCRI_RS_78255 0.767 AK365413.1 Hordeum vulgare subsp.. vulgare 2.00E-36 79% N/A F2DNE5 Proteasome subunit beta type SCRI_RS_170038 0.921 AP014964.1 Oryza sativa japonica 4.00E-21 86% GLYI-11 Q948T6 Lactoylglutathione lyase SCRI_RS_78255 0.767 AK365413.1 Hordeum vulgare subsp. vulgare 2.00E-36 79% N/A F2DNE5 Proteasome subunit beta type SCRI_RS_170038 0.921 AP014964.1 Oryza sativa japonica 4.00E-21 86% GLYI-11 Q948T6 Lactoylglutathione lyase SCRI_RS_190665 0.948 AK371075.1 Hordeum vulgare subsp.. vulgare 2.00E-51 100% N/A F2DXV8 Xyloglucan endotrans- glucosylase/hydrolase BOPA1_1213-1959 0.817 FN179374.1 Hordeum vulgare subsp. vulgare 3.00E-116 100% SSI C3W8L3 Starch synthase, chloro- plastic/amyloplastic 7H,E17M54-169 SCRI_RS_176094 0.757 AK354003.1 Hordeum vulgare subsp.vulgare 8.00E-49 100% N/A F2CQV1 Phosphoglycerate kinase 7H,E42M48-334 SCRI_RS_187512 0.751 AK371688.1 Hordeum vulgare subsp. vulgare 3.00E-48 99% N/A F2DG93 D-3-phosphoglycerate N/A: Indicates that the gene name is not available in the database. Acta agriculturae Slovenica, 120/4 – 20246 A. MOHAMADI et al. C hr om os om e nu m be r, A FL P/ SS R m ar ke rs SN Ps co rr el at ed to as so ci at ed A FL P/ SS R m ar ke rs C or re la tio n ra te b et w ee n SN Ps a nd A FL Ps /S SR s A cc es sio n Sp ec ie s FD R Q ue ry co ve r G en e U ni pr ot na m e Pr ot ei n 4H , E 35 M 55 -3 02 BO PA 2_ 12 _3 08 66 0. 62 31 26 46 7 D Q 20 11 44 .1 H or de um v ul ga re su bs p. v ul ga re 2E -5 1 10 0  % Ph yB Q 2I 7M 0 P hy to ch ro m e B BO PA 1_ A BC 07 63 1- 1- 1- 83 0. 66 75 39 A P0 14 96 0. 1 O ry za sa tiv a ja - po ni ca 6E -2 2 67  % SA PK 7 Q 7X Q P4 Se rin e/ th re on in e- pr ot ei n ki na se S A PK 7 BO PA 2_ 12 _3 09 93 0. 71 28 02 AY 17 74 04 .1 Se ca le ce re al e 2e -1 9 77  % N /A Q 84 XW 5 O -m et hy ltr an sf er as e 6H , E 33 M 54 -3 50 BO PA 1_ 33 79 -1 03 7 0. 65 55 09 89 6 A K 36 01 06 .1 H or de um v ul ga re su bs p. v ul ga re 3. 00 E- 11 6 10 0  % N /A F2 D 89 4 Pr ot ea so m e su bu ni t a lp ha ty pe BO PA 2_ 12 _2 11 14 0. 72 41 40 47 9 A K 35 55 32 .1 H or de um v ul ga re su bs p. v ul ga re 2. 00 E- 51 10 0  % N /A F2 C V 80 N uc le ar c ap -b in di ng pr ot ei n su bu ni t 2 SC RI _R S_ 80 32 7 0. 70 43 18 35 8 A K 36 23 51 .1 H or de um v ul ga re su bs p. v ul ga re 5. 00 E- 51 10 0  % N /A F2 D EN 4 3- ke to ac yl -C oA sy nt ha se SC RI _R S_ 10 24 26 0. 60 85 80 85 L1 44 44 .1 Tr iti cu m a es tiv um 2. 00 E- 43 10 0  % hs p1 6. 9C Q 41 56 1 H ea t s ho ck p ro te in 16 .9 C SC RI _R S_ 18 71 14 0. 70 17 62 41 9 A P0 14 95 8. 1 O ry za sa tiv a ja - po ni ca 7. 00 E- 31 99 % C PK 4 Q 6Z 2M 9 C al ci um -d ep en de nt pr ot ei n ki na se 6H , E 42 M 48 -3 80 SC RI _R S_ 16 11 17 0. 57 51 80 58 7 A P0 14 95 9. 1 O ry za sa tiv a ja - po ni ca 7e -1 2 89  % O s0 3g 01 25 10 0 Q 10 SE 7 Be ta -c ar ot en e hy dr ox y- la se , p ut at iv e, ex pr es se d BO PA 2_ 12 _3 09 56 0. 64 95 79 A P0 08 22 0. 2 O ry za sa tiv a ja - po ni ca 96  % O SJ N O a2 46 I1 0. 1 Q 6E P2 5 H G W P re pe at co nt ai ni ng pr ot ei n- lik e Ta bl e 2: B rie fin g of B LA ST n an al ys is on S N P se qu en ce s h av in g sig ni fic an t c or re la tio n w ith A FL P/ SS R m ar ke rs li nk ed to Q TL s r el at ed to d ro ug ht st re ss in b ar le y C . C ap pa ×S po pu la tio n. Acta agriculturae Slovenica, 120/4 – 2024 7 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Protein/enzyme Species studied Treated stress Gene/peotein expressed Expression type, effect Reference Glycosyltransferase Arabidopsis Drought UDP-Glycosyltransferase Down-regulation [Li et al., 2015] Rice Drought Gene LOC_Os01g68324.3 encod- ing a glicosyltransferase named Dolichyl-diphosphooligosaccharide Suppression [Landi et al., 2017] Protein DEHYDRATION-IN- DUCED 19 homolog 5 Arabidopsis Drought Gene DI19 Up-regulation, increasing drought tolerance [Liu et al., 2013] Wheat Drought Gene TiDI19-2 Induction, enhancing drought tolerance [Li & Chen, 2000] Dihydrolipoyl dehydrogenase (LPD) Sugarcane Drought Gene LPD Up-regulation in tolerant cultivars [da Silva et al., 2017] Barley High temperature Two LPD proteins: F2E5U7 and F2E2T3, that F2E5U7 identified in our study under drought stress Induction, enhancing tolerance to high temperature [Rollins, 2012] Hexosyltransferase Wheat Drought One hexosyltransferase gene Induction, enhancing drought tolerance [Ajigboye et al., 2016] Chickpea (Cicer arietinum) Drought 11 hexosyltransferase genes related to drought tolerance QTLs included 7 sucrose synthase genes (SuSy) and 4 sucrose phosphate synthase (SPS) Induction, enhancing drought tolerance [Nagesh Nayak, 2010] Salt-tolerant protein-GSK-like kinase Rice Drought and salinity Mutation in gene OsGSK1 that was caused to enhanced expression of special stress respond genes and increased to drought and salinity Up-regulation, increasing drought and salinity tolerance [Koh et al., 2007] Barley Dehydration and drought then rehydration One glicosyle synthase kinase named AAM77397 that is same to Q8LK43 identified in our study. Induction, enhancing drought tolerance [Talamé et al., 2007] Peroxidase Barley Drought Peroxidase Up-regulation in tolerante cultivares [Hellal et al., 2018] Wheat Osmotic stress Peroxidase (TaPrx) Up-regulation in tolerante cultivares [Csiszár et al., 2012] Glycine-rich RNA-binding protein RZ1B (GRP) Transgenic tobacco Salinity GRP gene belong to Limonium bicolor Up-regulation in tolerante cultivares [Wang et al., 2014] Transgenic rice Drought Two GRP gene belong to Arabidop- sis included AtGRP2 and AtGRP7 Enhancing drought tolerance [Yang et al., 2012] Mitogen-activated protein kinase Arabidopsis Drought Two genes AtMPK4 and AtMPK6 Up-regulation, increasing drought tolerance [Nadarajah and Sidek, 2010.] Rice Drought Two genes OsMAPK2 and OsMAPK5 Up-regulation, increasing drought tolerance [Rohila & Yang, 2007] Pectinesterase Barley Drought Gene PME49 Up-regulation, increasing drought tolerance [Wendelboe-Nel- son et al., 2012] Rice Drought and salinity Three root genes that encoded pectinesterase Up-regulation, increasing drought and salinity tolerance [Koh et al., 2007] Cinnamoyl-CoA reductase-like SNL6 (CCR) Transgenic tobacco species Nicotiana benthamiana Drought Three CCR genes belong to sorghum including to SbCCR1, SbCCR2-1 and SbCCR2-2 Up-regulation, increasing drought tolerance [Li et al., 2016] Tea Drought One CCR gene Up-regulation, increasing drought tolerance [Gupta et al., 2012] Ribosomal protein Maize Drought Ribosomal protein S18 Up-regulation, increasing drought tolerance [Benešová et al., 2012] Bermuda grass Drought Two ribosomal protein S1 and L12 Down-regulation in sensitive cultivares [Zhao et al., 2011] S-adenosylmethionine decarboxyl- ase proenzyme (SAMDC) Transgenic Arabidopsis lines Drought One SAMDC gene from Capsicum annuum Enhanced tolerance in transgenic plants than wiled types [Wilkins et al., 2010] Wheat Drought, salinity and external ABA treatment TaSAMDC gene Induction and increasing tolerance [Li & Chen, 2000] Table 3: Results of studies on proteins/enzymes identified in the population V × S under drought stress and other stresses. Acta agriculturae Slovenica, 120/4 – 20248 A. MOHAMADI et al. identified proteins/enzymes with drought stress has been proven in previous studies. As can be seen, all the proteins/enzymes identified in both populations were directly involved in the response to drought stress in several studies on different plants, which indicates the confirmation of the validity of the QTLs found in our previous study (Mohammadi et al., 2018). In order to identify genes related to drought stress in rice, Gorantla et al. (2007), prepared ESTs from the leaf tissue cDNA library of a rice cultivar under drought treatment, and one of the genes expressed in response to drought stress, was heat shock protein C16.9, which is consistent with the present research, and also Szűcs et al. (2006) reported 6 QTLs related to photoperiod in barley, and one of genes, HvPhyB, located on chromosome 4, i.e. ATP-dependent Clp protease proteo- lytic subunit Wheat Drought Two ATP-dependent chlroplastic protease proteolytic subunits Up-regulation, increasing drought tolerance [Cheng et al., 2016] Parthenium hysterophorus Drought and salinity ATP-dependent chlroplastic protease proteolytic subunit Induction and increasing tolerance [Ahmad et al., 2017] Beta-carotene hydroxylase, putative, expressed Mutant rice Drought Mutation in beta-carotene hydroxy- lase gene (BCH) Decreased tolerance in mutant than wiled type [Du et al., 2010] Transgenic tobacco Drought Overexpression of beta-carotene hydroxylase gene from arabidopsis (chyB) Increasing beta-carotene and drought tolerance [Zhao et al., 2014] Probable anion transporter 5, chloroplastic Populus trichocarpa Drought Two genes PtPHT1.2 and PtPHO9 Up-regulation [Zhang et al., 2016] Apple Drought Genes MdPHT3;6, MdPHT3;7, MdPHT4;5, MdPHT1;12 and MdPHT4;7 Up-regulation in stressed plants than controls and enhanced drought tolerance [Sun et al., 2017] Zinc finger A20 and AN1 domain- containing stress-associated protein 1 Rice Drought Overexpression of OsiSAP1 gene Enhanced drought tolerance [Dansanaet al., 2014] Alfalfa Drought MtSAP1 gene Enhanced drought tolerance [Gimeno-Gilles et al., 2011] Protoporphyrinogen oxidase Transgenic rice Drought Overexpression of two Protopor- phyrinogen oxidase genes (PPO) from Arabidopsis thaliana and Myxocococcus xanthus Enhanced drought tolerance [Thu-Ha et al., 2011] Chlorophyll a-b binding protein, chloroplastic Rice and Arabidopsis Drought, dark, high tempera- ture and salinity Based on BLAST analysis of se- quences of light-harvesting complex (LHC) genes in barley genomic database, 17 LHC genes encoding Chlorophyll a-b binding proteins (HvLHC) were identified that one of them (MLOC_44755) is same to F2CRC1 characterized in our study. Induction [Qin et al., 2017] Morus indica L. Drought Two chloroplastic chlorophyll a-b binding protein Up-regulation and increasing drought tolerance [Guha et al., 2013] Starch synthase, chloroplastic / amyloplastic Rice Drought LOC_Os07g22930 (AT1G32900) as a starch synthase gene Up-regulation and increasing drought tolerance [Basu & Roychoud- hury, 2014] Sorghum Drought Starch synthase enzyme Down-regulation and decreasing drought tolerance [Yi et al., 2014] Proteasome subunit beta type Common bean Drought Proteasome subunit beta type-3-A Down-regulation [Zadražnika et al., 2013] Barley Drought Proteasome subunit beta type Up-regulation [Wehner et al., 2015] Lactoylglutathione lyase Eucalyptus drought Lactoylglutathione lyase gene Down-regulation [Ghosh & Dharanis- hanthi, 2017] Humulus lupulus L. Drought Lactoylglutathione lyase protein related to ROS pathwey [Kolenc et al., 2016] Xyloglucan endotransglucosylase/ hydrolase Transgenic tomato Drought CaXEH3 gene from Capsicum annum L. Overexpression [Choi et al., 2011] Medicago truncatula Drought 21 XEH genes Induction [Yi et al., 2014] Phosphoglycerate kinase Soybean Drought and high temperature Phosphoglycerate kinase protein Up-regulation [Das et al., 2016] Barley Drought Phosphoglycerate kinase protein Down-regulation [Pieczynski et al., 2012] D-3-phosphoglycerate dehydro- genase Arabidopsis H2O2 and ABA treatment D-3-phosphoglycerate dehydroge- nase protein Induction [Cramer et al., 2013] Grapevine (Vitis vinifera L.) Drought D-3-phosphoglycerate dehydroge- nase protein Down-regulation [Alqurashi et al., 2017] Acta agriculturae Slovenica, 120/4 – 2024 9 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Protein/enzyme Species studied Treated stress Gene/protein expressed Expression type, effect Reference Phytochrome B Rice PhyB mutant Drought PhyB mutant protein Increased drought tolerance [Liu et al., 2012] Barley Drought The HvPhyB gene located on chromosome 4H, i.e. accession “DQ201144”, which was also iden- tified in the present study. Increased drought tolerance [Talamé et al., 2007] Serine/threonine-pro- tein kinase SAPK7 Rice Dehydration SAPK5 gene Increased expression [Basu & Roychoudhury, 2014] Groundnut Drought Serine/threonine-protein kinase HT1 Induced expression [Ding et al., 2014] O-methyltransferase Sugarcane Drought O-methyltransferase 2 Increased expression and drought tolerance [da Silva et al., 2017] Ocimum basilicum Drought Chavicol O-methyltransferase and eugenol O-methyltransferase genes Increased expression [Abdollahi Mandoula- kanieet al., 2017] Tea Drought Caffeic acid 3-O-methyltransferaseDecreased expression [Wang et al., 2017] Proteasome subunit alpha type Alfalfa Drought and salt Two proteins related to the proteasomal subunit, including the alpha-7 and beta-2-B subunits Decreased expression [Ma et al., 2016] Soybean Drought Proteasome subunit alpha type Increased expression [Pour Mohammadi et al., 2012] Common bean Drought Proteasome subunit alpha type Decreased expression [Zadražnika et al., 2013] Wheat Drought Proteasome subunit alpha type Decreased expression [Jiang et al., 2012] Nuclear cap-binding protein subunit 2 Mutant barley in HvCBP20 gene Drought HvCBP20 gene Increased drought tolerance [Daszkowska-Golec et al., 2017] Potato CBP20 and CBP80 mutants Drought CBP20 and CBP80 Increased drought tolerance [Pieczynski et al., 2012] 3-ketoacyl-CoA synthase Cotton Drought 3-ketoacyl-CoA synthase gene Induction [ Wang et al., 2010] Two varieties of silver fir (Abies alba Mill.) Drought Two 3-ketosyl-CoA synthase genesThe expression of these two genes increased under drought conditions in one cultivar and decreased in the other cultivar [Behringer et al., 2015] Heat shock protein 16.9C Rice Drought Heat shock protein 16.9C Induction [Gou et al., 2017] Wheat Drought and high temperature HSP proteins Induction [Guha et al., 2013] Calcium-dependent protein kinase Barley Drought HvCPK2a Increased expression [Ciésla et al., 2016] Rice Drought OsCPK4 Overexpressed [Campo et al., 2014] HGWP repeat contain- ing protein-like Maize, wheat and barley Drought Six genes encoding HGWP repeat containing protein-like Increased tolerance [Swamy et al., 2011] Two sensitive and drought-resistant varieties of barley Drought HGWP repeat containing protein- like Increased expression in the sensitive variety [Wendelboe-Nelson, 2012] Table 4: Results of studies on protein/enzymes identified in the population C. Cappa × S under drought stress and other stresses. Acta agriculturae Slovenica, 120/4 – 202410 A. MOHAMADI et al. accession “DQ201144”, which has been identified in the present study in the C. Cappa × S population on chromo- some 4H (Tables 3 and 4). 3.4 GENE ONTOLOGY 3.4.1 Ontology results in V × S population The results of gene ontology in population V × S are shown in Figures 3 and 4. The frequencies of three GO description, including cellular components, biologi- cal processes, and molecular functions, were 25 %, 32 %, and 43 %, respectively (Figure 3). Based on the results of complete gene ontology, four types of cell components including membrane, integral component of membrane, nucleus and chloroplast had the highest frequencies: 4.9  %, 12.4  %, 12.4  % and 9.1  % respectively (Fig. 4a). Two types of biological processes, including oxidation- reduction process and protein phosphorylation, had the highest frequencies: 11.5 % and 5.1 %, respectively (Fig- ure 4b). The study of the frequency distribution of mo- lecular functions showed that ATP binding, oxidoreduc- tase activity, transferase activity, DNA binding, and metal ion binding, were most frequent: 9.0 %, 7.1 %, 5.69 %, 5.2 % and 4.74 % respectively (Figure 4c). Wilkins et al. (2010) studied drought stress-induced transcripts in Arabidopsis, and based on ontology analy- sis, reported that transferase activity as a molecular func- tion; chloroplast and membrane as cellular components had the highest frequency. Kokas et al. (2016) investigat- ed the transcripts of wild barley in response to drought stress and as a result of the ontology study, they reported molecular functions such as binding, catalytic activity and binding to nucleic acid (Kokas et al., 2016). Similar results have been reported in other studies on different plants in drought conditions (Bedada et al., 2014; Zeng et al., 2016; Gou et al., 2017). All these studies confirm the results of the present research. 3.4.2 Ontology results in C. Capa × S population The results of gene ontology in C. Capa × S popula- tion are shown in Figures 5 and 6. The frequency of GO descriptions including molecular functions, biological processes, and cellular components were 40.2 %, 30.4 %, and 29.4 %, respectively (Figure 5). Based on the analy- Figure . Frequency distribution of GO description types (number, percentage) in population V×S GO terms of Cellular Components (a) Acta agriculturae Slovenica, 120/4 – 2024 11 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences Figure 4: Frequencies of three types of GO descriptions characterized in V × S population; a: Cell Components, b: Biological Processes and c: Molecular Functions Acta agriculturae Slovenica, 120/4 – 202412 A. MOHAMADI et al. sis results, the highest frequency of cellular components was associated with the nucleus, comprising 26.7  % of the total. Additionally, the membrane and cytoplasm ac- counted for 20  %, while the proteasome core complex and integral membrane components each represented 10 %. These findings highlight the predominant roles of these cellular structures in the context of the study, em- phasizing their significance in cellular functions and pro- cesses (Figure 6a). Also 9 types of molecular functions including ATP binding and transferase activity (with a frequency of 9.8 %); thereonine-type endopeptidase ac- tivity and protein serine/thereonine kinase activity (with a frequency of 7.3 %); RNA binding, transferase activity, transferring acyl groups, kinase activity, protein kinase activity and methyltransferase activity (with a frequency of 4.9 %) had the highest frequency (Figure 6b). Among the biological processes, the highest frequencies were related to five types of biological processes, including protein phosphorylation (12.9 %), fatty acid biosynthetic process (6.5 %), proteolysis (6.5 %), intracellular signal transduction (6.5  %) and oxidation-reduction process (6.5 %) (Figure 6c). In Saavedra experience the highest frequencies were related to oxidation-reduction process (tree time of occurrence) (Saavedra et al., 2017). 4 CONCLUSIONS The results of this study, as well as previous similar Figure 5: Frequency distribution of GO description types (number, percentage) in population C. Cappa × S Figure 6: Frequencies of three types of GO descriptions char- acterized in C. Cappa × S population; a: Cell Components, B: Biological Processes and c: Molecular Functions Acta agriculturae Slovenica, 120/4 – 2024 13 Functional analysis of drought tolerance QTLs in two barley populations using BLAST on associated SNP sequences studies, showed that BLAST analysis on SNP sequences is a very effective tool for validating the identified QTLs. Based on the results of the present study, all proteins/en- zymes identified in two populations are directly involved in the response to drought stress in different plants. Both up-regulation (increased expression) and down-regula- tion (decreased expression) have been reported for the identified proteins/enzymes in response to drought stress in different plants. The gene ontology results showed that the identified genes are significantly involved in drought stress. 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