Phenotypic assessment
The germination indices including GP, RL, SL, germination index (GI), germination rate index (GRI), seedling of vigor index (SVI), mean germination time (MGT), seedling weight vigor index (SWVI), seedling length vigor index (SLVI), root length index (RLI), root dry weight index (RDWI), shoot length index (SLI), shoot dry weight index (SDWI), root/shoot ratio by length (R/SL), root/shoot ratio by dry weight (R/SDW), root/shoot ratio by length index (R/SLI) and root/shoot ratio by dry weight index (R/SDWI) were evaluated. Based on this, descriptive statistics for germination indices were calculated. Descriptive statistics including mean and range of germination indices were as central tendency and dispersion indices, respectively. These statistics were provided general information about the center and range of data. Also, Pearson’s skewness and kurtosis tests, as indices of vertical and horizontal symmetry, determined the frequency distribution of data (Supplementary Table 1). Based on the latter test, the phenotypic distribution of data in all germination indices was normal, and as a result, the germination indices had a quantitative nature and the data had a continuous distribution.
Linkage map construction
The SSR, ISSR, EST, TE, SCoT, CBDP, IRAP, RAPD, ISJ, iPBS, iPBS-iPBS combined, and combined ISSR-iPBS markers were distributed 217, 72, 56, 26, 27, 29, 7, 66, 73, 138, 3 and 5 polymorphic alleles, respectively. Chromosomes 1H (120 markers), 2H (90 markers), 3H (114 markers), 4H (110 markers), 5H (100 markers), 6H (82 markers) and 7H (103 markers) were covered 133.26, 120.01, 169.72, 161.32, 142.87, 144.94 and 172.94 cM of barley genome, respectively (Supplementary Fig. 1).
Closely linked QTLs controlling germination indices in control condition
A total of nine closely linked QTLs associated with GP, SL, GI, SLVI and R/SL were mapped on chromosomes 1H, 2H, 4H, 5H and 7H under control condition. qGP-C-1H, explaining 20.37% of phenotypic variation, was identified as a major QTL (Table 1).
Closely linked QTLs controlling germination indices in drought stress condition
A total of 28 closely linked QTLs related to RL, SL, GI, SWVI, SLVI, RDWI, SLI, SDWI, R/SL, R/SDW and R/SLI were located on chromosomes 1H, 2H, 3H, 4H, 5H, 6H and 7H under drought stress condition. The coefficients of determination for qRL-D-7H, qSL-D-5H, qGI-D-2H, qSLI-D-1H, qSLI-D-5Hb, qR/SL-D-7H, qR/SDW-D-3H and qR/SLI-D-6H were 20.48%, 20.40%, 31.46%, 21.27%, 20.38%, 24.02%, 21.37% and 2.01%, respectively. As a result, they were introduced as major QTLs (Table 2).
Closely linked QTLs controlling germination indices in salinity stress condition
A total of 34 closely linked QTLs affecting GP, RL, SL, GRI, SVI, MGT, SWVI, RLI, RDWI, SLI, R/SDW and R/SDWI were located on chromosomes 1H, 2H, 3H, 4H, 5H, 6H and 7H under salinity stress. qGP-S-2H, qRL-S-5Ha, qGRI-S-2H, qSWVI-S-2H and qR/SDW-S-4H explained 26.68%, 22.69%, 26.67%, 24.30% and 21.12% of phenotypic variations, respectively. Therefore, these QTLs were recognized as major QTLs (Table 3).
Candidate genes
In the present study, 14 major QTLs were identified under different environmental conditions including control (a major QTL), drought (eight major QTLs) and salinity (five major QTLs). Then, the candidate genes of the major QTLs were tracked. However, no significant genes were identified for two QTLs including qGI-D-2H and qSLI-D-5Hb. As a result, the candidate genes were identified for 12 major QTLs including qGP-C-1H, qSLI-D-1H, qSWVI-S-2H, qGRI-S-2H, qGP-S-2H, qR/SDW-D-3H, qR/SDW-S-4H, qSL-D-5H, qRL-S-5Ha, qR/SLI-D-6H, qRL-D-7H and qR/SL-D-7H. Based on this, a total of 501 candidate genes were associated with 12 major QTLs under control, drought and salinity conditions. Totally, 44, 49, 3, 111, 140, 9 and 145 candidate genes were located on chromosomes 1H to 7H, respectively. qR/SDW-S-4H and qSL-D-5H were related to 111 and 91 genes, respectively. Some candidate genes were common among major QTLs. Therefore, only 422 unique candidate genes were identified as final candidate genes and were used for further analysis. A circular plot showed the position of each major QTL on each chromosome (Fig. 1).
A circular plot represents the names and positions of QTLs on the seven barley chromosomes.
Gene Ontology (GO) analysis
GO analysis was performed for all candidate genes of 12 major QTLs. GO analysis was categorized into three classes including biological process, molecular function, and cellular components. The most important biological processes involved included phosphorelay signal transduction system, intracellular signal transduction, signal transduction, signaling, cell communication, response to stimulus, and cellular response to stimulus. The most important molecular functions are protein histidine kinase binding, histidine phosphotransfer kinase activity, protein kinase binding, kinase binding and enzyme binding. Two main cellular components involved were intracellular protein-containing complex and transferase complex (Fig. 2).
Gene ontology analysis of candidate genes related to 12 major QTLs.
Protein–Protein Interaction (PPI) network
The PPI network of candidate genes was investigated. The information about the protein names of this species in string was not a lot, therefore, a small network was created (Fig. 3). Totally, there are ten genes in this network which related to each other. One of them was HORVU.MOREX.r3.4HG0333690 which is regulatory protein NPR1. NPR1 and WRKY are identified as the master regulators of systemic acquired resistance19. Another gene was HORVU.MOREX.r3.7HG0656250 that is involved in ubiquitin-dependent protein catabolic process. The ubiquitin–proteasome system is a key role in regulating protein stability and turnover in plants, especially during adverse environmental conditions like drought, salinity, cold, and nutrient deprivation20. Most of these genes were involved in the response to abiotic stress through various pathways and processes such as regulation of DNA-templated transcription, nucleotide-excision repair, etc.
Protein–protein interaction networks of ten genes.
TFs
In total, 16 TFs including B3, basic Helix-Loop-Helix (bHLH), basic leucine zipper (bZIP), CO-like, E2F/DP, FAR-RED IMPAIRED RESPONSE1 (FAR1), Golden 2-Like (G2-like), GATA, LATERAL ORGAN BOUNDARIES DOMAIN (LBD), myeloblastosis (MYB), MYB_related, NF-YA, Nin-like, Trihelix, WRKY and YABBY were identified. These TFs were encoding one to four genes (Fig. 4A). Furthermore, the tyrosine kinase-like (TKL) kinases are a group of serine-threonine protein kinases with sequence similarity to tyrosine kinases. The present study, HORVU.MOREX.r3.4HG0332630 gene was related to TKL kinases (Fig. 4B).
The family of transcription factors (spider chart: A) and protein kinases (circular barplot: B) associated with candidate genes.
Protein kinases
A total, ten genes were related to receptor-like kinase (RLKs) family in this study (Table 4).
miRNAs and their target genes
The miRNAs are small non-coding RNAs that play a crucial role in post-transcriptional gene regulation. They bind to complementary sequences in target mRNA transcripts, leading to their degradation or translational repression. The psRNATarget server is a tool for detecting target genes of miRNAs. It uses a scoring schema to analyze complementary matching between miRNA and mRNA sequences, enhancing the discovery of miRNA-mRNA interactions. This server predicted 60 miRNAs that regulate the 176 target genes among all identified candidate genes (Fig. 5). Most of miRNAs were targeted more than one gene. Moreover, the top interactive miRNAs including hvu-miR5053, hvu-miR6192 and hvu-miR6214 were associated with the most target genes. Most of miRNAs which targeted candidate genes were related to tolerance-stress.
miRNA-target gene interaction network. Blue nodes represent target genes and yellow nodes showed miRNAs.
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- Source: https://www.nature.com/articles/s41598-024-66452-9