The effects of heading time on yield performance and HvGAMYB expression in spring barley subjected to drought

The effects of heading time on yield performance and HvGAMYB expression in spring barley subjected to drought | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effects of heading time on yield performance and HvGAMYB expression in spring barley subjected to drought Piotr Ogrodowicz, Anetta Kuczyńska, Paweł Krajewski, Michał Kempa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2246208/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2023 Read the published version in Journal of Applied Genetics → Version 1 posted 4 You are reading this latest preprint version Abstract In the lifetime of a plant, flowering is not only an essential part of the reproductive process but also a critical developmental stage that can be vulnerable to environmental stresses. To ensure survival during drought, plants accelerate the flowering process, and this response is known as „drought escape”. HvGAMYB – transcriptional factor associated, among others, with flowering process and anther development in barley has also an important role in developmental modification and yield performance in plants subjected to stressed conditions. However, there is limited information whether the mechanisms associated with the flowering acceleration may cause the anther or pollen disruption due to their influence on flower development via GAMYB overexpression. The aim of this study was to characterize differences in responses to drought among barley genotypes varied in terms of earliness. Two subgroups of barley plants differentiated in terms of phenology were analyzed, and a wide range of traits linked to plant morphology, physiology, and yield were investigated. The abiotic stress reactions of two barley plants subgroups varied in our study both in terms of yield, morphology, chlorophyll fluorescence kinetics and pollen viability. The results extend our knowledge about HvGAMYB expression in anthers of barley plants grown under drought conditions and indicate the role of this transcription factor in shaping the yield via influencing traits linked to spike morphology, especially in lateral stems. Hence, this gene could be regarded as an important factor in flowering process and, as a consequence, pollen and seed development in plants subjected to abiotic stress conditions. This study clearly show the important role of HvGAMYB in survival mechanism associated with so called “drought escape strategy”. Introduction Climate data show that the frequency of droughts has increased over the past century, and an increase in the impact of droughts on crop productivity has been predicted (IPCC 2021 ). Therefore, understanding the effects of droughts on crop development and productivity is important. During the stem elongation phase, appropriate irrigation conditions are essential for the development of fertile flowers at anthesis (Miralles and Slafer 2007). Water deprivation during this critical phase of development affects various aspects of plant metabolism and results in the impairment of many biochemical processes (Loggini et al. 1999 ; Farooq et al. 2009 ). The simplest way to withstand arid environmental conditions is to escape from drought (Passioura, 1996 ). Drought escape (DE) is an important strategy followed by plants to cope with unfavorable environmental conditions (Ogrodowicz et al. 2017 ). In addition, there are a few more complex mechanisms through which plants can respond to drought (e.g Ashraf 2010 ; Fang and Xiong 2015 ). Barley ( Hordeum vulgare L.) is a commonly used a model for studying molecular and physiological processes such as photosynthesis (Rothasperti et al. 2020) or plant development (Gomez and Wilson, 2012). Being a self-pollinating species with a diploid (2n) genome and a haploid complement of only seven chromosomes, barley is an excellent model organism for both basic and applied research. Physiological tolerance to drought requires that the plant maintain vigor to produce a minimum number of seeds or simply survive, while agronomic tolerance requires maintaining an economically acceptable yield (Schafleitner et al. 2007 ). In the life cycle of plants, pollen development is an important stage because only normally formed pollen contributes to proper fertilization and formation of seeds and fruits. Plant hormones, such as gibberellic acid (GA), are crucial regulators of reproductive development in plants. Maintaining the appropriate levels of hormones is critical for pollen development and drought tolerance (Yu et al. 2019 ). A moderate increase in the level of GA under drought conditions contributes to improved pollen fertility. The signaling pathways that promote the transition to flowering include the following: the photoperiod pathway, hormone biosynthesis and signaling (GA), the pathway that acts independently of light (autonomous pathway), and the pathway associated with low temperatures (vernalization pathway) (Moon et al. 2005 ; Ma et al. 2021 ). Previous studies have mapped the quantitative trait loci (QTL) associated with the heading date for all barley chromosomes (Pillen et al. 2003 ; Mikolajczak et al. 2016; Ogrodowicz et al. 2017 ). A series of events have been reported for anther development, ranging from the formation of the stamen primordium to the release of mature pollen during anther dehiscence (Zhang et al. 2011 ). GAMYB protein was initially isolated from aleurone cells in barley. However, the role of GAMYB is not limited to this tissue: this transcription factor play an essential role in flower development as well, particularly in stamen development. Reports have suggested that HvGAMYB is also involved in anther development and may activate the genes responsible for the initiation of the flowering phase (Murray et al. 2003 ; Aya et al. 2009 ). Unfavorable environmental conditions such as drought stress may adversely affect anther development. The effective quantum yield of photosystem II (PSII) is highly sensitive to environmental changes, and under unfavorable or stressful environmental conditions, the activity of PSII can be a valuable marker for physiological damage caused by stressors (Kalaji et al. 2017 ). Chlorophyll fluorescence measurements, such as induction kinetics studies in dark-adapted samples followed by OJIP (O–J–I–P-transient) analysis, are commonly used to study plant response to abiotic stress (Kalaji et al. 2016 ). Water stress, such as waterlogging (Bertholdsson et al. 2015 ) and especially water deficit (Wang et al. 2012a ), has also been frequently studied using chlorophyll fluorescence techniques. The aims of this study were to (i) investigate the effects of drought in phenologically differentiated barley plants and (ii) describe the role of HvGAMYB in anther and pollen development under stress conditions. Materials and methods Plant material and growth conditions In this study, six recombinant inbred lines (RILs) (hereafter referred to as LCam lines – LCam08, LCam12, LCam13, LCam37, LCam64, LCam71) and their parents (Lubuski and Cam/B1/CI08887//CI05761) were examined. The plant material was developed following Mikołajczak et al. ( 2016 ). Quench was used as the reference variety. Experimental setup All experiments were performed in growth chambers under fully controlled conditions (IPG PAS phytotrons). Five seeds from each of the accessions were sown in plastic pots (40 cm × 26 cm × 26 cm) filled with arable soil and peat (3:1, w/w), and the plants were cultivated under optimal conditions: temperature of 22°C/18°C day/night, humidity of 50–60%, and photoperiod of 16/8 h light/dark. Each treatment was conducted in triplicate. Barley external developmental stages scale was used (Gomez and Wilson 2012), in which the later stages of the Zadoks scale (Zadoks et al. 1974 ) were replaced by the last flag extension (LFE) stages to achieve a good system for developmental stages related to reproductive development. The samples were collected at the following stages: LFE1 (flag leaf fully emerged and uncoiling - time point 1) and LFE3 (flag leaf opening and awns clearly visible - time point 2). Application of abiotic stress The plants were irrigated until the flag leaf appeared (BBCH 39) and then subjected to two irrigation treatments: (i) well-watered treatment (abbreviated as C) in which the soil moisture was maintained at ~ 70% of field capacity (FC); and (ii) severe drought stress at 20% FC (abbreviated as D) following the methodology implemented by Kuczyńska et al. ( 2019 ). To maintain the targeted control and drought conditions, soil moisture in each pot was controlled gravimetrically by weighing and, if necessary, additionally volumetrically using the FOM/mts device (Ogrodowicz et al. 2017 ). Application of GA3 and Trinexapac-ethyl under drought conditions GA3 (10 mM, 100 mg/l) solution was sprayed directly onto the leaves at the tillering stage (BBCH 2.21–23). The bioactive GA3 solution (Sigma-Aldrich) was prepared by dissolving the powder in distilled water. For each plant, 1–2 ml of GA3 solution was used. For the control plants, distilled water was sprayed. The GA3 treatment option was applied as described by Boden et al. ( 2014 ) with some modifications (treatment abbreviation D + GA). Trinexapac-ethyl (TR) was used as the commercial product Moddus 250 EC (Syngenta, USA) and was applied at the tillering stage (BBCH 2.21–23). The TR treatment was applied according to the method of Grijalva-Contreras et al. ( 2012 ) with some modifications (treatment abbreviation D + TR). Phenotypic evaluation In this study, 11 developmental and yield-related traits were analyzed (Table 1 ). Table 1 List of phenotypic traits with description, abbreviations and measured units Trait (unit), (abbrev.) Trait description Total number of tillers, (Tn) Number of tillers with fertile and non-fertile (without grains) spikes Number of productive tillers, (PTn) Number of tillers with fertile spikes Length of main spike (cm), (LSm) Length of main spike from 10 randomly selected spikes in a pot (without awns) Number of spikelets per main spike, (NSSm) Number of spikelets in spike of main stem-average e for 10 main spikes in a pot Number of grains per main spike, (NGSm) Number of grains collected from one spike of main stem - average for 10 main spikes in a pot Weight of gains per main spike, (WGSm) Weight of grain collected from one spike of the main stem - average for 10 main spikes in a pot Length of lateral spike (cm), (LSl) Length of spike from lateral stem - average for 10 lateral spikes in a pot (without awns) Number of spikelets per lateral spike, (NSSl) Number of spikelets per spike of lateral stem - average for 10 lateral spikes in a pot Number of grains per lateral spike, (NGSl) Number of grains collected from spike of lateral stem - average for 10 lateral spikes in a pot Grain yield (g), (GY) Average weight of grains collected from one plant, calculated as average of measurements of grain weight for 10 plants. Thousand grain weight (g), (TGW) Average weight of 1000 grains, calculated as average of 1000 * average weight of one grain for 20 spikes in a pot Evaluation of anther morphology Anthers were measured using a stereomicroscope (Motic SMZ-161) following the protocol described by Browne et al. ( 2018 ) and were photographed at a fixed magnification using a digital camera system (Moticam CMOS BTU8). The photographs were studied using the Motic Advanced 3.2 software (Motic China Group Co., China). The length of the anthers (mm) was measured after they were removed from the primary flower of the largest spikelet. The width at their widest point was considered the width of the anthers (mm). Evaluation of pollen viability and morphology Pollen viability and fertility were evaluated using TTC (2,3,5-triphenyl tetrazolium chloride) and KI/I2 (potassium iodide/iodine) staining methods as described by Ma et al. ( 2019 ) and Wang et al. ( 2012b ), respectively, with minor modifications. Anthers from different plants were used for each replicate. Pollen was extracted according to the protocol of Impe et al. ( 2020 ). First, fresh pollen was collected from the studied plants on the same day at 09:00. Then, pollen from each line was divided into two samples: one sample was incubated in a 1.5-ml centrifuge tube containing 0.1% TTC at 37°C for 1 h, and the other was incubated in a 1.5-ml centrifuge tube containing 1% KI/I2 stain at room temperature for 5 min. The stained pollen was examined using a light microscope at a magnification of 400× (Motic BA410-E) and photographed using the Moticam digital camera. The images were analyzed using the Motic Advanced 3.2 software (Motic China Group Co., China). The pollen grains that had a round shape and were stained black with KI/I2 were classified as viable or alive, whereas those that were stained yellow or bright red were classified as sterile or dead. The pollen grains that stained red or pink with TTC were classified as viable (by the response to the presence of enzymatic activity), whereas those that appeared gray or colorless were considered sterile. A total of 2000 pollen grains were counted for each genotype in this study. Pollen viability for each genotype was then expressed as the percentage of the total number of live pollen grains to the total number of grains observed per field. Chlorophyll fluorescence measurements and OJIP test Chlorophyll and fluorescence were measured on both control and stressed plants. Data were collected at two time points— LFE1 and LFE3, always at the same time of the day (09:00). Measurements were taken using a FluorPen FP 100-MAX (Photon Systems Instruments, Drasov, Czech Republic). Fluorescence transients for chlorophyll-a were recorded from the center of the completely spent leaf (second from the top) after dark adaptation (30 min), which was performed using light with holding clamps. In each block, leaves from three plants of each line were measured. Nine replicates were performed for each cultivar and treatment (three leaves from three plants/treatment). The parameters used in this study to quantify the PSII behavior were as follows: the absorbed energy flux (ABS_RC), the trapped energy flux (TRo_RC), the electron transport flux (ETo_RC), the dissipated energy flux (DIo_RC), the maximum quantum yield of primary photochemistry (Fv_Fm), the probability/efficiency that a trapped exciton moves an electron in the electron transport chain beyond QA (Ψ_o), the quantum yield of the electron transport (Φ_Eo), the probability that the energy of an absorbed photon is dissipated as heat (Φ_Do), and the power index (PI_Abs). Genotyping In this study, data from the Illumina 50K iSelect SNP array for barley were used to examine genomic similarities between the examined genotypes. The array included 44,040 working markers (Bayer et al. 2017 ). Further details on the genotyping procedure have been presented elsewhere (Mikolajczak et al. 2022). From the full set of markers, a subset of 23747 markers polymorphic between the studied genotypes was selected. qRT-PCR analysis Anthers were collected at two developmental stages (LFE1 and LFE3). Four biological replicates were collected for each stage, comprising approximately 100 anthers from four individual ears. After dissection, the anthers were immediately frozen in liquid nitrogen and stored at − 80°C until RNA extraction was performed. The RNA was extracted using the RNeasy Mini Kit (QIAGEN, Germany) according to the manufacturer’s protocol with on-column DNase treatment (QIAGEN, Germany). Additionally, all isolated RNA samples were treated with TURBO DNase (Thermo Fisher Scientific, Lithuania) according to the manufacturer’s instructions to exclude trace contamination of samples with genomic DNA. The purity of all RNA samples was assessed via OD260/280 and OD260/230 absorbance ratios, whereas their structural integrity was evaluated using denaturing agarose gel electrophoresis. All RNA samples were adjusted to the same concentration (100 ng/µl). The quantitative real-time PCR (qRT-PCR) analysis performed in this study met the MIQE criteria (Bustin et al. 2009 ). Single-stranded cDNA was synthesized from 1 µg of total RNA using the iTaq Universal SYBR Green One-Step Kit according to the manufacturer’s instructions. To analyze the specific expression of each reference/target gene, qRT-PCR was performed using the CFX Connect Real-time PCR Detection System (Bio-Rad). Each 10 µl mixture for PCR contained 1 µl of a diluted RNA and 5 µM of each primer. To confirm the specificity of amplification and the absence of primer dimers, each run was completed with melting curve analysis (melting curve 63°C to 95°C, increasing by 0.5°C for 0.05 s). Moreover, the each, pooled qRT-PCR product underwent sequencing process (AMU, Poznań, Poland). Data were normalized using three stable reference genes and the stability of reference genes in the experimental setup was confirmed using a tool (Bio-Rad) that supports the geNorm algorithm. The qRT-PCR data for the genes and the endogenous controls were obtained from the means of three independent amplification reactions performed on four plants harvested at the same phenotypic stage (biological replicates). In each qRT-PCR run, extracts from the negative controls were applied. Gene expression data were analyzed using the Bio-Rad CFX Manager (Bio-Rad) software-CFX Maestro v2.0. Relative changes in the gene expression were calculated using the comparative 2 −ΔΔCt method and were normalized to the appropriate reference genes (Dawidziuk et al. 2014 ). HvGAMYB primers were designed using the Primer3 tool ( https://primer3.org/ ). The complete list of primers and probes used is presented in Supplementary File 1. Statistical analysis Kinship between genotypes was evaluated using Dice similarity coefficients computed from Illumina iSelect 50K SNP array data. The matrix of kinship estimates was used for hierarchical clustering of genotypes based on the average similarity algorithm, and for principal coordinate analysis. Analysis of variance for observed quantitative traits was performed in the model containing fixed effects of groups of genotypes (early, late; G), treatments (T) and of G × T interaction; significant effects were selected at p < 0.001 (approximate threshold resulting from application of the Bonferroni correction for multiple testing for all traits). In case of physiological parameters, due to non-normal distributions of observed variables, analysis of variance was performed on the data transformed by optimal Box-Cox transformation (Box, Cox, 1964 ). Grouping of experimental variants was done on the basis of Fisher's protected least significant difference method at p < 0.05. Biplots were obtained using the principal component method. Pearson correlation coefficients were tested for significance based on t distribution. All statistical computations and visualizations were made in Genstat 22 (VSN International 2022 ). Results Classification of the genotypes Based on phenology observation and a previous study (Ogrodowicz et al. 2017 ), the plant material was divided into two subgroups: early-heading (CamB, LCam37, LCam64, and LCam71) and late-heading (Lubuski, LCam08, LCam12, and LCam13) genotypes. The genetic similarity of the studied barley genotypes was analyzed using hierarchical clustering and principal coordinate analysis (PCoA) (Fig. 1 ). The genotypes of the plants clustered mainly according to their heading time: plants classified as early-heading genotypes clustered together. Plant phenology under stress conditions The mean values for all studied traits are presented in Supplementary File 2. The results of the analysis of variance (ANOVA) showed significant differences (at p < 0.001) between groups of genotypes for all phenological traits and a significant effect of the applied treatments only on heading (Supplementary File 3). The influence of the treatments in the heading stages (Fig. 2 B) was such that the drought conditions delayed the plant development for both early- and late-heading genotypes. Under D + GA conditions, acceleration of plant development was observed in both plant subgroups compared with the drought conditions. The D + TR conditions also resulted in the acceleration of development compared with the drought conditions, but the greatest differences in DAS were observed in the early-heading genotypes. Evaluation of yield-related traits The results of ANOVA revealed the significant effects of the applied treatments on all the investigated yield-related traits (Supplementary File 3). Significant differences between groups of genotypes were also observed, as well as effects of G × T interaction for the vast majority of the studied traits. The mean values of most of the studied traits showed significant reduction under drought conditions (except for Tn and PTn). Under drought conditions, a significant increase in Tn and PTn was observed in both early- and late-heading plants compared to C condition (Fig. 3 A). An increase in Tn was also observed under D + GA conditions, but only in the late-heading plants. In this conditions, the early-heading plants showed Tn mean values similar to those under control conditions. The results of ANOVA showed significant effects of treatments, subgroups, and of G × T interaction for both LSm and NSSm (Table S3). Comparison between two subgroups of genotypes showed that higher LSm mean values were observed under D + GA conditions in the early-heading genotypes. Under D + TR conditions, an increase in NSSm values was observed compared to the C condition in the late-heading genotypes (Fig. 3 A). Significant differences in NSSm were observed between two subgroups of plants under all types of conditions where stresses were applied (D, D + GA, and D + TR). Differences in NGSm values were observed between the early- and late-heading genotypes in the treatments with additional foliar application and under control conditions. For traits related to the lateral spike (LSl, NSSl, NGSl, and WGSl), drought conditions generally resulted in a significant decrease in mean values, with a few exceptions, such as the mean value of NSSl in the late-heading plants. Application of stress contributed to a significant decrease in GY mean values for both early- and late-heading plants. In general, early- and late-heading plants differed in terms of main spike fertility (Fig. 3 ). However, no significant differences were found in FSl between early- and late-heading plants. The results of ANOVA showed a significant effect for FSm in relation to treatments and T×G. Principal component analysis (PCA) was used to visualize the variability of yield-related traits in two variants: to show differences between treatments and between earliness groups. Plants grown under C conditions were distributed on the right side of the plot in close proximity to each other which shows low variability, especially with respect to tillering traits correlated with PC2 (Fig. 3 B). Figure 3 B also shows that the accessions had a long projection on the vectors associated with the yield-related traits such as NGSm, WGSl, and WGSm. The use of additional stressors (GA and TR) resulted in a shift of genotypes in the biplot. In the D + GA treatment, the plants were largely dispersed, with the parental genotypes on the left side of the plot. The studied genotypes were not discriminated into subgroups corresponding with their developmental patterns (early- and late-heading plants) (Fig. 3 C). The results of this experiment show that all traits associated with spike characteristics and grain yield were significantly positively correlated with each other (Supplementary File 4). OJIP parameter analysis The effects of treatment on the majority of the studied parameters were significant at LFE3, whereas significant effects of treatment were found in two cases at LFE1 (Psi_o and Phi_Eo). A significant difference was observed for ABS_RC at LFE1 between two subgroups of plants in all types of stress treatments (Fig. 4 A). At LFE3, the ABS_RC differences were also observed under control conditions. No significant differences were observed for this trait under D conditions at LFE3. A similar pattern was observed for TRo_RC with one difference: no significant differences were observed under D + GA conditions at LFE3. ETo_RC was primarily affected under D and D + GA conditions at LFE3, where the differences in the mean values of this trait between the two subgroups were clearly visible. Dlo_RC was affected by all the applied treatments at both LFE1 and LFE3, and differences were observed between the subgroups (except under D + TR conditions at LFE3). The applied stress conditions affected Fv_Fm, and differences were detected between the plant subgroups (except under D + TR conditions at LFE3). A significant, rapid decrease in Psi_o was observed at the second measurement. A significant decrease in the performance index Phi_Eo was also observed at LFE3 under stress conditions. An increase in Phi_Do was observed at LFE1 under stress conditions for the late-heading plants, whereas a decrease in Phi_Do performance was observed for the early-heading plants. In the second Phi_Do measurement, a significant increase was observed for the early-heading plants. Pi_Abs was significantly reduced under stress conditions, especially for the early-heading plants at LFE3. Supplementary File 5 visualize the variability of OJIP parameters in the studied accessions, with respect to the applied treatments (Supplementary File 5A-B) and the division between the two subgroups of plants (Supplementary File 5C-D). On the first case, random distribution of the studied genotypes was observed, whereas in the second, the plant material is grouped in terms of earliness: in the first measurement, the early-heading genotypes were located on the left side of the plot, whereas the late-heading plants were on the right side (with some exceptions). This pattern is not visible in the second measurement of the fluorescence parameters. The correlation coefficients between studied OJIP parameters are showed in Supplementary File 6. Anther morphology evaluation The results of ANOVA showed significant effects of treatment, subgroups, and of T×G interaction on anther length and width (Supplementary File 3). Under control conditions, no significant differences in anther lengths were observed between the two subgroups of plants (Fig. 5 B). Both D + GA and D + TR conditions affected the anther length of the studied genotypes. Anther width differed between the two plant subgroups under control conditions, and the applied stress treatments had an impact on the reduction in the anther width of the early-heading plants. The reduction in this trait of this magnitude was not observed in the late-heading plants. Pollen viability evaluation The results of ANOVA showed significant effects of treatments on pollen viability using two different staining methods (Supplementary File 3, Fig. 5 A). Comparison of pollen viability between the two subgroups of plants using method 1 showed no significant differences under control conditions. Applied stressed conditions caused the reduction of pollen viability evaluated by method 1. The lowest mean values of this trait were observed for early-heading genotypes under D + GA conditions, whereas in the late-heading plants, a slight increase in pollen viability using method 1 was observed in these treatments compared with drought conditions. A rapid decrease in pollen viability estimated using method 2 was observed for the studied plants under stress conditions (a greater decrease in this trait was observed for the early-heading plants) (Fig. 5 C). HvGAMYB expression HvGAMYB expression was analyzed at two time points. The results of ANOVA showed significant effects of treatments at LFE1 ( p < 0.001) on the expression of the studied gene. Under drought conditions, a slight decrease in gene expression was observed for both plant subgroups at LFE3 compared with LFE1 (Fig. 6 ). Under D + GA conditions, the early-heading plants exhibited higher levels of gene expression at LFE1 and LFE3, but the decrease in HvGAMYB expression for the early-heading subgroup at LFE3 was still observed compared with LFE1, whereas for the late-heading plants, an increase in the studied gene expression level was noticed at LFE3 compared with LFE1. Under D + TR conditions, gene expression was lower in the early-heading plants than in the late-heading plants for both times of gene expression level measurements. At LFE3, HvGAMYB expression increased in the early-heading plants under this condition compared with the studied gene expression evaluation at LFE1. HvGAMYB expression at time point 2 was positively correlated with NGSl (r2 = 0.53; p = 0.004) and WGSl (r2 = 0.59; p = 0.002). The highest expression for Lubuski under "drought + TR" was accompanied by the highest value of WGSl. The relationship between NGSl, WGSl and HvGAMYB expression level at time point 2 is presented on Fig. 7 . Greater values of HvGAMYB expression level and NGSl were recorded for plants subjected to drought condition compared to plants grown under artificial development modification combined with drought. Discussion In this study, artificial growth stimulants (exogenous GA3 and the GA inhibitor - TR) were employed to highlight the effects of growth type habits on the reaction of plants to drought stress. The classification of the studied plants into two subgroups in terms of phenology allowed us to investigate the response of different plants to abiotic stress. In the present study, morphological and phenological observations were confirmed by genotyping data because plant division based on the genetic profiles overlapped with the type of growth habits. For the vast majority of the studied yield-related traits, significant effects were recorded for treatment, group, and T×G. According to previous studies (Wu et al. 2007 ; Kottmann et al. 2016 ), plants with different types of growth habits react in different ways to most of the abiotic stresses, which was confirmed in this investigation. For the early- and late-heading plants, the morphology and ability to adapt to conditions varied significantly and were the important reasons contributing to direct stress responses (Shavrukov et al. 2017 ). In the present study, under drought conditions, the plants developed more tillers compared with control conditions. Interestingly, the early-heading plants were characterized by higher mean values of productive tillers compared with the late-heading plants, which suggests that the development of tillers with fertile spikes was the main goal of the plant’s strategy to survive (to distribute the progeny) under unfavorable conditions. This finding is in line with a previous studies (Mosaad et al. 1995 ; Xie et al. 2016 ; Moeller and Rebetzke 2017 ). The acceleration of plant development (using external GA application) disturbed this strategy: no rapid tiller development was observed under drought conditions for the early-heading plants, which may be associated with the changes in plant growth. Under D conditions, the early-heading genotypes showed rapid development and shaped the tillers only in the secondary tillering process in the rewatering phase. These findings are in line with the nature of tiller development, which is considered a plastic process, being strongly dependent on environmental factors that may promote, or repress, lateral shoot development through a complex network of hormonal and regulatory signals (Kebrom et al. 2012 ). ANOVA showed that for a trait linked to spike fertility (FSm), significant effects were recorded for treatments and G×T interaction, which suggests that an appropriate seed development process may be associated with the right growth strategy under stressful conditions. This finding is in agreement with that of a previous study (Begum et al. 2022 ). The results of the PCA of the yield-related traits of the studied plants revealed that the genotypes clustered in close proximity to each other in terms of the applied treatment, but the locations of the studied plants were disrupted when drought conditions alone and drought conditions combined with foliar growth stimulations were applied. This finding shows that the studied plants exhibited different drought response strategies and, as a consequence, showed different yield performers. In this study, an increase in some yield-related traits may be associated with initiations of sophisticated tools by plants to adapt to unfavorable water conditions. Although the knowledge of plant defense against abiotic stresses like drought has been acquired thanks to many studies conducted recently (e.g. Rehaman et al. 2021 ; Zhang et al. 2022 ), the interplay between different signals to generate defense responses still remain elusive (Zhu et al. 2016), mainly due to their complex nature. In many studies, chlorophyll fluorescence has long been used as a convenient and sensitive indicator of plant stress responses (e.g. Goltev et al. 2005; Kalaji et al. 2016 ). Fluorescence increase or induction curves, usually called the OJIP test, have been also adapted for screening different varieties of crops subjected to drought stress (Yao et al. 2018 ), including barley (Daszkowska-Golec et al. 2019 ). Rosales-Serna et al. ( 2000 ) suggested that drought stress is a complicated stressor and that different aspects of plant growth and physiology should be taken into account for the evaluation of plants’ response to drought stress. Therefore, in the present study, the exploration of plants’ response to drought was complemented with both yield performance and physiological analyses. Under stress conditions, significant increases in the parameters linked to RC damage (e.g., ABS_RC) and the parameters associated with heat dissipation (DIo_RC and Φ_Do) were recorded. In the present study, significant differences were also observed for ABS_RC at LFE1 between two subgroups of plants in all types of treatments. It is worth noting that much lower ABS_RC values were recorded for the early heading plants during the first measurement, but over time (LFE3), the mean values observed for this trait were similar for both plant subgroups, which emphasizes the role of stress duration in plants’ response to unfavorable conditions. According to Jedmowski and Brüggemann ( 2015 ), inactivation of some RCs, as already mentioned, increases the ABS/RC under drought stress conditions. Another reason for the increase in ABS/RC is degradation of chlorophyll through early leaf senescence induced by drought stress (Boureima et al. 2012 ) or regrouping of antennae from inactive PSII RCs to active (Kalaji et al. 2016 ). Changes in ABS_TR recorded in the present study may suggest that the early-heading genotypes react differently to the initial phase of drought stress, but after a while, damage to RCs occurs in this type of plant. The increases in the parameters linked to heat dissipation (DIo_RC and Φ_Do) were observed in the studied plants, especially for the early-heading plants in the second time of measurement, which, on the one hand, contributed to RC damage and, on the other hand, an increase in heat dissipation was recognized as an effective way for a plant to protect its thylakoid membranes from oxidative damage (Demmig-Adams et al. 2006 ). In the D + TR treatment, where plant development was inhibited, increases in Φ_Do and Dlo_RC recorded for the early-heading plants were much lower than those noticed for the rest of the stress conditions, which emphasizes the role of earliness in the effective distribution of heat in mitigating the devastating influence of drought on chlorophyll parameters. The decrease in quantum efficiency (Ψ_o, PI_Abs) and the increase in heat dissipation (indicated by DIo_RC and Φ_Do) were observed in the present study. These findings are in line with the studies of Zhu et al. ( 2021 ) and Sousaraei et al. ( 2021 ), who found a rapid increase in parameters DIo_RC, Φ_Do, and TRo_RC and a decrease in PI_Abs, Fv_Fm, and Eto_RC mean values. Developmental defects in the tapetum and a lack of starch accumulation are caused by water-deficit stress in pollen grains (Nguyen et al. 2009 ; Ji et al. 2010 ), which was confirmed in our investigation as viability monitoring by method 1 decreases significantly under stressed conditions. Stress-tolerant wheat cultivars can maintain starch accumulation and sink strength during the young microspore stage under water stress conditions (Ji et al. 2010 ), which was not confirmed in our study, as there were no differences in pollen viability evaluated by method 1 (JKJ method) between the early- and late-heading plants under D conditions. The artificial acceleration of the growth of the early-heading plants contributed to the impairment of pollen viability, exacerbating the negative impact of drought on pollen development. In many plant tissues (barley aleurone, wheat internodes, and anthers), GAMYB expression has been shown to be directly upregulated by the gibberellin GA3 (Gubler et al. 1995 ). In the present study, HvGAMYB expression was confirmed in the anther tissues of plants subjected to different water conditions. It is interesting to note that depending on the applied growth stimulators, HvGAMYB expression was different for the early- and late-heading plants. Transgenic barley lines with an excess of fourfold levels of endogenous GAMYB protein in their anthers were reported to be male sterile (Murray et al. 2003 ; Duca et al. 2008 ). Also, a progressive decrease in anther size was associated with the increase in GAMYB levels, particularly a decrease in anther length (Murray et al. 2003 ). These findings are in line with results obtained in the present study, where for the early-heading plants with a higher HvGAMYB expression, lower mean values of anther width were recorded. In the present study, HvGAMYB is expressed at a relatively high level under D conditions, which can be linked to the growth process, especially in the early-heading genotypes. Contrary to the expectation, exogenous Ga application did not positively influence HvGAMYB expression, which highlights the complex nature of plant development under stress conditions. On the other hand, the early-heading plants still show a higher level of HvGAMYB expression under D + GA conditions, which confirms the association of the studied gene with early flowering. The use of the GA inhibitor TR under D + TR conditions contributed to the decrease in the HvGAMYB expression level in the early-heading plants, but did not change the HvGAMYB level in the anther tissues of the late-heading genotypes. This phenomenon confirmed the role of Ga in HvGAMYB level regulation under drought conditions and stressed the role of transcription factors like HvGAMYB in the flowering process and anther development under unfavorable environmental conditions. The results of this study show that the HvGAMYB expression level evaluated at time point 2 is correlated positively with traits associated with lateral spike morphology (NGSl and WGSl), which indicate that this gene has an important role in yield performance of plants grown under unfavorable environmental conditions. This finding is in opposite with previous studies (Matsui et al. 2000 ; Murray et al. 2003 ), where plants with strong HvGAMYB over-expression turned out to be male sterile. This contrasting revelations highlight the complex nature of pollen development where GA signal transduction pathway may be modify by wide range of internal and external factors. Declarations Declarations Ethics approval Not applicable. Consent for publication Not applicable. Consent to participate Not applicable. Conflict of interest The authors declare no competing interests. Funding The research was supported by National Science Centre, Poland, project SONATA 12 no. 2016/23/D/NZ9/00042 Author Contribution P.O. conceptualization, methodology, investigation, writing – original draft preparation, funding acquisition, project administration A.K. supervision, methodology, investigation, writing-review and editing P.K. data curation, formal analysis, software, validation, data visualization, writing-review and editing M.K. investigation, writing-review. All authors have read and agreed to the published version of the manuscript. References Ashraf M (2010) Inducing drought tolerance in plants: recent advances. 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Int J Mol Sci 22:10682. https://doi.org/10.3390/ijms221910682 Unsectioned Paragraphs Supplementary Information Supplementary File 1 DNA sequences of the gene specific primers Supplementary File 2 Mean values for studied traits Supplementary File 3 Results of analysis of variance for observed traits Supplementary File 4 Correlations between the studied traits of genotypes differentiated in terms of phenology Supplementary File 5 Biplot visualisation of variability of OJIP parameters Supplementary File 6 Correlations between the studied OJIP parameters of genotypes differentiated in terms of phenology Supplementary Files CoverletterJAG.docx SupplementaryFile1.docx SupplementaryFile2.xlsx SupplementaryFile3.docx SupplementaryFile4.docx SupplementaryFile5.docx SupplementaryFile6.docx Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2023 Read the published version in Journal of Applied Genetics → Version 1 posted Reviewers agreed at journal 24 Nov, 2022 Reviewers invited by journal 24 Nov, 2022 Editor assigned by journal 15 Nov, 2022 First submitted to journal 06 Nov, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:53:18","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":21795,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile6.docx","url":"https://assets-eu.researchsquare.com/files/rs-2246208/v1/7e96b92e56638969be65346d.docx"}],"financialInterests":"","formattedTitle":"The effects of heading time on yield performance and HvGAMYB expression in spring barley subjected to drought","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate data show that the frequency of droughts has increased over the past century, and an increase in the impact of droughts on crop productivity has been predicted (IPCC \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, understanding the effects of droughts on crop development and productivity is important.\u003c/p\u003e \u003cp\u003eDuring the stem elongation phase, appropriate irrigation conditions are essential for the development of fertile flowers at anthesis (Miralles and Slafer 2007). Water deprivation during this critical phase of development affects various aspects of plant metabolism and results in the impairment of many biochemical processes (Loggini et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Farooq et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The simplest way to withstand arid environmental conditions is to escape from drought (Passioura, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Drought escape (DE) is an important strategy followed by plants to cope with unfavorable environmental conditions (Ogrodowicz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, there are a few more complex mechanisms through which plants can respond to drought (e.g Ashraf \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fang and Xiong \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) is a commonly used a model for studying molecular and physiological processes such as photosynthesis (Rothasperti et al. 2020) or plant development (Gomez and Wilson, 2012). Being a self-pollinating species with a diploid (2n) genome and a haploid complement of only seven chromosomes, barley is an excellent model organism for both basic and applied research.\u003c/p\u003e \u003cp\u003ePhysiological tolerance to drought requires that the plant maintain vigor to produce a minimum number of seeds or simply survive, while agronomic tolerance requires maintaining an economically acceptable yield (Schafleitner et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the life cycle of plants, pollen development is an important stage because only normally formed pollen contributes to proper fertilization and formation of seeds and fruits. Plant hormones, such as gibberellic acid (GA), are crucial regulators of reproductive development in plants. Maintaining the appropriate levels of hormones is critical for pollen development and drought tolerance (Yu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A moderate increase in the level of GA under drought conditions contributes to improved pollen fertility.\u003c/p\u003e \u003cp\u003eThe signaling pathways that promote the transition to flowering include the following: the photoperiod pathway, hormone biosynthesis and signaling (GA), the pathway that acts independently of light (autonomous pathway), and the pathway associated with low temperatures (vernalization pathway) (Moon et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have mapped the quantitative trait loci (QTL) associated with the heading date for all barley chromosomes (Pillen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mikolajczak et al. 2016; Ogrodowicz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A series of events have been reported for anther development, ranging from the formation of the stamen primordium to the release of mature pollen during anther dehiscence (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). \u003cem\u003eGAMYB\u003c/em\u003e protein was initially isolated from aleurone cells in barley. However, the role of \u003cem\u003eGAMYB\u003c/em\u003e is not limited to this tissue: this transcription factor play an essential role in flower development as well, particularly in stamen development. Reports have suggested that \u003cem\u003eHvGAMYB\u003c/em\u003e is also involved in anther development and may activate the genes responsible for the initiation of the flowering phase (Murray et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Aya et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Unfavorable environmental conditions such as drought stress may adversely affect anther development.\u003c/p\u003e \u003cp\u003eThe effective quantum yield of photosystem II (PSII) is highly sensitive to environmental changes, and under unfavorable or stressful environmental conditions, the activity of PSII can be a valuable marker for physiological damage caused by stressors (Kalaji et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Chlorophyll fluorescence measurements, such as induction kinetics studies in dark-adapted samples followed by OJIP (O\u0026ndash;J\u0026ndash;I\u0026ndash;P-transient) analysis, are commonly used to study plant response to abiotic stress (Kalaji et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Water stress, such as waterlogging (Bertholdsson et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and especially water deficit (Wang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e), has also been frequently studied using chlorophyll fluorescence techniques.\u003c/p\u003e \u003cp\u003eThe aims of this study were to (i) investigate the effects of drought in phenologically differentiated barley plants and (ii) describe the role of \u003cem\u003eHvGAMYB\u003c/em\u003e in anther and pollen development under stress conditions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and growth conditions\u003c/h2\u003e \u003cp\u003eIn this study, six recombinant inbred lines (RILs) (hereafter referred to as LCam lines \u0026ndash; LCam08, LCam12, LCam13, LCam37, LCam64, LCam71) and their parents (Lubuski and Cam/B1/CI08887//CI05761) were examined. The plant material was developed following Mikołajczak et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Quench was used as the reference variety.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExperimental setup\u003c/h2\u003e \u003cp\u003eAll experiments were performed in growth chambers under fully controlled conditions (IPG PAS phytotrons). Five seeds from each of the accessions were sown in plastic pots (40 cm \u0026times; 26 cm \u0026times; 26 cm) filled with arable soil and peat (3:1, w/w), and the plants were cultivated under optimal conditions: temperature of 22\u0026deg;C/18\u0026deg;C day/night, humidity of 50\u0026ndash;60%, and photoperiod of 16/8 h light/dark. Each treatment was conducted in triplicate. Barley external developmental stages scale was used (Gomez and Wilson 2012), in which the later stages of the Zadoks scale (Zadoks et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) were replaced by the last flag extension (LFE) stages to achieve a good system for developmental stages related to reproductive development. The samples were collected at the following stages: LFE1 (flag leaf fully emerged and uncoiling - time point 1) and LFE3 (flag leaf opening and awns clearly visible - time point 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eApplication of abiotic stress\u003c/h2\u003e \u003cp\u003eThe plants were irrigated until the flag leaf appeared (BBCH 39) and then subjected to two irrigation treatments: (i) well-watered treatment (abbreviated as C) in which the soil moisture was maintained at ~\u0026thinsp;70% of field capacity (FC); and (ii) severe drought stress at 20% FC (abbreviated as D) following the methodology implemented by Kuczyńska et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To maintain the targeted control and drought conditions, soil moisture in each pot was controlled gravimetrically by weighing and, if necessary, additionally volumetrically using the FOM/mts device (Ogrodowicz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eApplication of GA3 and Trinexapac-ethyl under drought conditions\u003c/h2\u003e \u003cp\u003eGA3 (10 mM, 100 mg/l) solution was sprayed directly onto the leaves at the tillering stage (BBCH 2.21\u0026ndash;23). The bioactive GA3 solution (Sigma-Aldrich) was prepared by dissolving the powder in distilled water. For each plant, 1\u0026ndash;2 ml of GA3 solution was used. For the control plants, distilled water was sprayed. The GA3 treatment option was applied as described by Boden et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) with some modifications (treatment abbreviation D\u0026thinsp;+\u0026thinsp;GA). Trinexapac-ethyl (TR) was used as the commercial product Moddus 250 EC (Syngenta, USA) and was applied at the tillering stage (BBCH 2.21\u0026ndash;23). The TR treatment was applied according to the method of Grijalva-Contreras et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with some modifications (treatment abbreviation D\u0026thinsp;+\u0026thinsp;TR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e \u003cp\u003eIn this study, 11 developmental and yield-related traits were analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of phenotypic traits with description, abbreviations and measured units\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait (unit), (abbrev.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of tillers, (Tn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of tillers with fertile and non-fertile (without grains) spikes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of productive tillers, (PTn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of tillers with fertile spikes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of main spike (cm), (LSm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of main spike from 10 randomly selected spikes in a pot (without awns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of spikelets per main spike, (NSSm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of spikelets in spike of main stem-average e for 10 main spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of grains per main spike, (NGSm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of grains collected from one spike of main stem - average for 10 main spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight of gains per main spike, (WGSm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight of grain collected from one spike of the main stem - average for 10 main spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of lateral spike (cm), (LSl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of spike from lateral stem - average for 10 lateral spikes in a pot (without awns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of spikelets per lateral spike, (NSSl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of spikelets per spike of lateral stem - average for 10 lateral spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of grains per lateral spike, (NGSl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of grains collected from spike of lateral stem - average for 10 lateral spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield (g), (GY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage weight of grains collected from one plant, calculated as average of measurements of grain weight for 10 plants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThousand grain weight (g), (TGW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage weight of 1000 grains, calculated as average of 1000 * average weight of one grain for 20 spikes in a pot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of anther morphology\u003c/h2\u003e \u003cp\u003eAnthers were measured using a stereomicroscope (Motic SMZ-161) following the protocol described by Browne et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and were photographed at a fixed magnification using a digital camera system (Moticam CMOS BTU8). The photographs were studied using the Motic Advanced 3.2 software (Motic China Group Co., China). The length of the anthers (mm) was measured after they were removed from the primary flower of the largest spikelet. The width at their widest point was considered the width of the anthers (mm).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of pollen viability and morphology\u003c/h2\u003e \u003cp\u003ePollen viability and fertility were evaluated using TTC (2,3,5-triphenyl tetrazolium chloride) and KI/I2 (potassium iodide/iodine) staining methods as described by Ma et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e), respectively, with minor modifications. Anthers from different plants were used for each replicate. Pollen was extracted according to the protocol of Impe et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). First, fresh pollen was collected from the studied plants on the same day at 09:00. Then, pollen from each line was divided into two samples: one sample was incubated in a 1.5-ml centrifuge tube containing 0.1% TTC at 37\u0026deg;C for 1 h, and the other was incubated in a 1.5-ml centrifuge tube containing 1% KI/I2 stain at room temperature for 5 min. The stained pollen was examined using a light microscope at a magnification of 400\u0026times; (Motic BA410-E) and photographed using the Moticam digital camera. The images were analyzed using the Motic Advanced 3.2 software (Motic China Group Co., China). The pollen grains that had a round shape and were stained black with KI/I2 were classified as viable or alive, whereas those that were stained yellow or bright red were classified as sterile or dead. The pollen grains that stained red or pink with TTC were classified as viable (by the response to the presence of enzymatic activity), whereas those that appeared gray or colorless were considered sterile. A total of 2000 pollen grains were counted for each genotype in this study. Pollen viability for each genotype was then expressed as the percentage of the total number of live pollen grains to the total number of grains observed per field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eChlorophyll fluorescence measurements and OJIP test\u003c/h2\u003e \u003cp\u003eChlorophyll and fluorescence were measured on both control and stressed plants. Data were collected at two time points\u0026mdash; LFE1 and LFE3, always at the same time of the day (09:00). Measurements were taken using a FluorPen FP 100-MAX (Photon Systems Instruments, Drasov, Czech Republic). Fluorescence transients for chlorophyll-a were recorded from the center of the completely spent leaf (second from the top) after dark adaptation (30 min), which was performed using light with holding clamps. In each block, leaves from three plants of each line were measured. Nine replicates were performed for each cultivar and treatment (three leaves from three plants/treatment). The parameters used in this study to quantify the PSII behavior were as follows: the absorbed energy flux (ABS_RC), the trapped energy flux (TRo_RC), the electron transport flux (ETo_RC), the dissipated energy flux (DIo_RC), the maximum quantum yield of primary photochemistry (Fv_Fm), the probability/efficiency that a trapped exciton moves an electron in the electron transport chain beyond QA (Ψ_o), the quantum yield of the electron transport (Φ_Eo), the probability that the energy of an absorbed photon is dissipated as heat (Φ_Do), and the power index (PI_Abs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eIn this study, data from the Illumina 50K iSelect SNP array for barley were used to examine genomic similarities between the examined genotypes. The array included 44,040 working markers (Bayer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further details on the genotyping procedure have been presented elsewhere (Mikolajczak et al. 2022). From the full set of markers, a subset of 23747 markers polymorphic between the studied genotypes was selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR analysis\u003c/h2\u003e \u003cp\u003eAnthers were collected at two developmental stages (LFE1 and LFE3). Four biological replicates were collected for each stage, comprising approximately 100 anthers from four individual ears. After dissection, the anthers were immediately frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction was performed. The RNA was extracted using the RNeasy Mini Kit (QIAGEN, Germany) according to the manufacturer\u0026rsquo;s protocol with on-column DNase treatment (QIAGEN, Germany). Additionally, all isolated RNA samples were treated with TURBO DNase (Thermo Fisher Scientific, Lithuania) according to the manufacturer\u0026rsquo;s instructions to exclude trace contamination of samples with genomic DNA. The purity of all RNA samples was assessed via OD260/280 and OD260/230 absorbance ratios, whereas their structural integrity was evaluated using denaturing agarose gel electrophoresis. All RNA samples were adjusted to the same concentration (100 ng/\u0026micro;l). The quantitative real-time PCR (qRT-PCR) analysis performed in this study met the MIQE criteria (Bustin et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Single-stranded cDNA was synthesized from 1 \u0026micro;g of total RNA using the iTaq Universal SYBR Green One-Step Kit according to the manufacturer\u0026rsquo;s instructions. To analyze the specific expression of each reference/target gene, qRT-PCR was performed using the CFX Connect Real-time PCR Detection System (Bio-Rad). Each 10 \u0026micro;l mixture for PCR contained 1 \u0026micro;l of a diluted RNA and 5 \u0026micro;M of each primer. To confirm the specificity of amplification and the absence of primer dimers, each run was completed with melting curve analysis (melting curve 63\u0026deg;C to 95\u0026deg;C, increasing by 0.5\u0026deg;C for 0.05 s). Moreover, the each, pooled qRT-PCR product underwent sequencing process (AMU, Poznań, Poland). Data were normalized using three stable reference genes and the stability of reference genes in the experimental setup was confirmed using a tool (Bio-Rad) that supports the geNorm algorithm. The qRT-PCR data for the genes and the endogenous controls were obtained from the means of three independent amplification reactions performed on four plants harvested at the same phenotypic stage (biological replicates). In each qRT-PCR run, extracts from the negative controls were applied. Gene expression data were analyzed using the Bio-Rad CFX Manager (Bio-Rad) software-CFX Maestro v2.0. Relative changes in the gene expression were calculated using the comparative 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method and were normalized to the appropriate reference genes (Dawidziuk et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cem\u003eHvGAMYB\u003c/em\u003e primers were designed using the Primer3 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://primer3.org/\u003c/span\u003e\u003cspan address=\"https://primer3.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The complete list of primers and probes used is presented in Supplementary File 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eKinship between genotypes was evaluated using Dice similarity coefficients computed from Illumina iSelect 50K SNP array data. The matrix of kinship estimates was used for hierarchical clustering of genotypes based on the average similarity algorithm, and for principal coordinate analysis. Analysis of variance for observed quantitative traits was performed in the model containing fixed effects of groups of genotypes (early, late; G), treatments (T) and of G \u0026times; T interaction; significant effects were selected at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (approximate threshold resulting from application of the Bonferroni correction for multiple testing for all traits). In case of physiological parameters, due to non-normal distributions of observed variables, analysis of variance was performed on the data transformed by optimal Box-Cox transformation (Box, Cox, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). Grouping of experimental variants was done on the basis of Fisher's protected least significant difference method at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Biplots were obtained using the principal component method. Pearson correlation coefficients were tested for significance based on \u003cem\u003et\u003c/em\u003e distribution. All statistical computations and visualizations were made in Genstat 22 (VSN International \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClassification of the genotypes\u003c/h2\u003e \u003cp\u003eBased on phenology observation and a previous study (Ogrodowicz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the plant material was divided into two subgroups: early-heading (CamB, LCam37, LCam64, and LCam71) and late-heading (Lubuski, LCam08, LCam12, and LCam13) genotypes. The genetic similarity of the studied barley genotypes was analyzed using hierarchical clustering and principal coordinate analysis (PCoA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The genotypes of the plants clustered mainly according to their heading time: plants classified as early-heading genotypes clustered together.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePlant phenology under stress conditions\u003c/h2\u003e \u003cp\u003eThe mean values for all studied traits are presented in Supplementary File 2. The results of the analysis of variance (ANOVA) showed significant differences (at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between groups of genotypes for all phenological traits and a significant effect of the applied treatments only on heading (Supplementary File 3). The influence of the treatments in the heading stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) was such that the drought conditions delayed the plant development for both early- and late-heading genotypes. Under D\u0026thinsp;+\u0026thinsp;GA conditions, acceleration of plant development was observed in both plant subgroups compared with the drought conditions. The D\u0026thinsp;+\u0026thinsp;TR conditions also resulted in the acceleration of development compared with the drought conditions, but the greatest differences in DAS were observed in the early-heading genotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of yield-related traits\u003c/h2\u003e \u003cp\u003eThe results of ANOVA revealed the significant effects of the applied treatments on all the investigated yield-related traits (Supplementary File 3). Significant differences between groups of genotypes were also observed, as well as effects of G \u0026times; T interaction for the vast majority of the studied traits. The mean values of most of the studied traits showed significant reduction under drought conditions (except for Tn and PTn).\u003c/p\u003e \u003cp\u003eUnder drought conditions, a significant increase in Tn and PTn was observed in both early- and late-heading plants compared to C condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn increase in Tn was also observed under D\u0026thinsp;+\u0026thinsp;GA conditions, but only in the late-heading plants. In this conditions, the early-heading plants showed Tn mean values similar to those under control conditions. The results of ANOVA showed significant effects of treatments, subgroups, and of G \u0026times; T interaction for both LSm and NSSm (Table S3). Comparison between two subgroups of genotypes showed that higher LSm mean values were observed under D\u0026thinsp;+\u0026thinsp;GA conditions in the early-heading genotypes. Under D\u0026thinsp;+\u0026thinsp;TR conditions, an increase in NSSm values was observed compared to the C condition in the late-heading genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Significant differences in NSSm were observed between two subgroups of plants under all types of conditions where stresses were applied (D, D\u0026thinsp;+\u0026thinsp;GA, and D\u0026thinsp;+\u0026thinsp;TR). Differences in NGSm values were observed between the early- and late-heading genotypes in the treatments with additional foliar application and under control conditions. For traits related to the lateral spike (LSl, NSSl, NGSl, and WGSl), drought conditions generally resulted in a significant decrease in mean values, with a few exceptions, such as the mean value of NSSl in the late-heading plants. Application of stress contributed to a significant decrease in GY mean values for both early- and late-heading plants. In general, early- and late-heading plants differed in terms of main spike fertility (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, no significant differences were found in FSl between early- and late-heading plants. The results of ANOVA showed a significant effect for FSm in relation to treatments and T\u0026times;G.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was used to visualize the variability of yield-related traits in two variants: to show differences between treatments and between earliness groups. Plants grown under C conditions were distributed on the right side of the plot in close proximity to each other which shows low variability, especially with respect to tillering traits correlated with PC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB also shows that the accessions had a long projection on the vectors associated with the yield-related traits such as NGSm, WGSl, and WGSm. The use of additional stressors (GA and TR) resulted in a shift of genotypes in the biplot. In the D\u0026thinsp;+\u0026thinsp;GA treatment, the plants were largely dispersed, with the parental genotypes on the left side of the plot. The studied genotypes were not discriminated into subgroups corresponding with their developmental patterns (early- and late-heading plants) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe results of this experiment show that all traits associated with spike characteristics and grain yield were significantly positively correlated with each other (Supplementary File 4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOJIP parameter analysis\u003c/h2\u003e \u003cp\u003eThe effects of treatment on the majority of the studied parameters were significant at LFE3, whereas significant effects of treatment were found in two cases at LFE1 (Psi_o and Phi_Eo). A significant difference was observed for ABS_RC at LFE1 between two subgroups of plants in all types of stress treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt LFE3, the ABS_RC differences were also observed under control conditions. No significant differences were observed for this trait under D conditions at LFE3. A similar pattern was observed for TRo_RC with one difference: no significant differences were observed under D\u0026thinsp;+\u0026thinsp;GA conditions at LFE3. ETo_RC was primarily affected under D and D\u0026thinsp;+\u0026thinsp;GA conditions at LFE3, where the differences in the mean values of this trait between the two subgroups were clearly visible. Dlo_RC was affected by all the applied treatments at both LFE1 and LFE3, and differences were observed between the subgroups (except under D\u0026thinsp;+\u0026thinsp;TR conditions at LFE3). The applied stress conditions affected Fv_Fm, and differences were detected between the plant subgroups (except under D\u0026thinsp;+\u0026thinsp;TR conditions at LFE3). A significant, rapid decrease in Psi_o was observed at the second measurement. A significant decrease in the performance index Phi_Eo was also observed at LFE3 under stress conditions. An increase in Phi_Do was observed at LFE1 under stress conditions for the late-heading plants, whereas a decrease in Phi_Do performance was observed for the early-heading plants. In the second Phi_Do measurement, a significant increase was observed for the early-heading plants. Pi_Abs was significantly reduced under stress conditions, especially for the early-heading plants at LFE3.\u003c/p\u003e \u003cp\u003eSupplementary File 5 visualize the variability of OJIP parameters in the studied accessions, with respect to the applied treatments (Supplementary File 5A-B) and the division between the two subgroups of plants (Supplementary File 5C-D). On the first case, random distribution of the studied genotypes was observed, whereas in the second, the plant material is grouped in terms of earliness: in the first measurement, the early-heading genotypes were located on the left side of the plot, whereas the late-heading plants were on the right side (with some exceptions). This pattern is not visible in the second measurement of the fluorescence parameters. The correlation coefficients between studied OJIP parameters are showed in Supplementary File 6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAnther morphology evaluation\u003c/h2\u003e \u003cp\u003eThe results of ANOVA showed significant effects of treatment, subgroups, and of T\u0026times;G interaction on anther length and width (Supplementary File 3). Under control conditions, no significant differences in anther lengths were observed between the two subgroups of plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Both D\u0026thinsp;+\u0026thinsp;GA and D\u0026thinsp;+\u0026thinsp;TR conditions affected the anther length of the studied genotypes. Anther width differed between the two plant subgroups under control conditions, and the applied stress treatments had an impact on the reduction in the anther width of the early-heading plants. The reduction in this trait of this magnitude was not observed in the late-heading plants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePollen viability evaluation\u003c/h2\u003e \u003cp\u003eThe results of ANOVA showed significant effects of treatments on pollen viability using two different staining methods (Supplementary File 3, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Comparison of pollen viability between the two subgroups of plants using method 1 showed no significant differences under control conditions. Applied stressed conditions caused the reduction of pollen viability evaluated by method 1. The lowest mean values of this trait were observed for early-heading genotypes under D\u0026thinsp;+\u0026thinsp;GA conditions, whereas in the late-heading plants, a slight increase in pollen viability using method 1 was observed in these treatments compared with drought conditions. A rapid decrease in pollen viability estimated using method 2 was observed for the studied plants under stress conditions (a greater decrease in this trait was observed for the early-heading plants) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHvGAMYB expression\u003c/h2\u003e \u003cp\u003e \u003cem\u003eHvGAMYB\u003c/em\u003e expression was analyzed at two time points. The results of ANOVA showed significant effects of treatments at LFE1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on the expression of the studied gene. Under drought conditions, a slight decrease in gene expression was observed for both plant subgroups at LFE3 compared with LFE1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder D\u0026thinsp;+\u0026thinsp;GA conditions, the early-heading plants exhibited higher levels of gene expression at LFE1 and LFE3, but the decrease in \u003cem\u003eHvGAMYB\u003c/em\u003e expression for the early-heading subgroup at LFE3 was still observed compared with LFE1, whereas for the late-heading plants, an increase in the studied gene expression level was noticed at LFE3 compared with LFE1. Under D\u0026thinsp;+\u0026thinsp;TR conditions, gene expression was lower in the early-heading plants than in the late-heading plants for both times of gene expression level measurements. At LFE3, \u003cem\u003eHvGAMYB\u003c/em\u003e expression increased in the early-heading plants under this condition compared with the studied gene expression evaluation at LFE1.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHvGAMYB\u003c/em\u003e expression at time point 2 was positively correlated with NGSl (r2\u0026thinsp;=\u0026thinsp;0.53; p\u0026thinsp;=\u0026thinsp;0.004) and WGSl (r2\u0026thinsp;=\u0026thinsp;0.59; p\u0026thinsp;=\u0026thinsp;0.002). The highest expression for Lubuski under \"drought\u0026thinsp;+\u0026thinsp;TR\" was accompanied by the highest value of WGSl. The relationship between NGSl, WGSl and \u003cem\u003eHvGAMYB\u003c/em\u003e expression level at time point 2 is presented on Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Greater values of \u003cem\u003eHvGAMYB\u003c/em\u003e expression level and NGSl were recorded for plants subjected to drought condition compared to plants grown under artificial development modification combined with drought.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, artificial growth stimulants (exogenous GA3 and the GA inhibitor - TR) were employed to highlight the effects of growth type habits on the reaction of plants to drought stress. The classification of the studied plants into two subgroups in terms of phenology allowed us to investigate the response of different plants to abiotic stress. In the present study, morphological and phenological observations were confirmed by genotyping data because plant division based on the genetic profiles overlapped with the type of growth habits. For the vast majority of the studied yield-related traits, significant effects were recorded for treatment, group, and T\u0026times;G. According to previous studies (Wu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kottmann et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), plants with different types of growth habits react in different ways to most of the abiotic stresses, which was confirmed in this investigation. For the early- and late-heading plants, the morphology and ability to adapt to conditions varied significantly and were the important reasons contributing to direct stress responses (Shavrukov et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present study, under drought conditions, the plants developed more tillers compared with control conditions. Interestingly, the early-heading plants were characterized by higher mean values of productive tillers compared with the late-heading plants, which suggests that the development of tillers with fertile spikes was the main goal of the plant\u0026rsquo;s strategy to survive (to distribute the progeny) under unfavorable conditions. This finding is in line with a previous studies (Mosaad et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moeller and Rebetzke \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The acceleration of plant development (using external GA application) disturbed this strategy: no rapid tiller development was observed under drought conditions for the early-heading plants, which may be associated with the changes in plant growth. Under D conditions, the early-heading genotypes showed rapid development and shaped the tillers only in the secondary tillering process in the rewatering phase. These findings are in line with the nature of tiller development, which is considered a plastic process, being strongly dependent on environmental factors that may promote, or repress, lateral shoot development through a complex network of hormonal and regulatory signals (Kebrom et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eANOVA showed that for a trait linked to spike fertility (FSm), significant effects were recorded for treatments and G\u0026times;T interaction, which suggests that an appropriate seed development process may be associated with the right growth strategy under stressful conditions. This finding is in agreement with that of a previous study (Begum et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results of the PCA of the yield-related traits of the studied plants revealed that the genotypes clustered in close proximity to each other in terms of the applied treatment, but the locations of the studied plants were disrupted when drought conditions alone and drought conditions combined with foliar growth stimulations were applied. This finding shows that the studied plants exhibited different drought response strategies and, as a consequence, showed different yield performers. In this study, an increase in some yield-related traits may be associated with initiations of sophisticated tools by plants to adapt to unfavorable water conditions. Although the knowledge of plant defense against abiotic stresses like drought has been acquired thanks to many studies conducted recently (e.g. Rehaman et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the interplay between different signals to generate defense responses still remain elusive (Zhu et al. 2016), mainly due to their complex nature.\u003c/p\u003e \u003cp\u003eIn many studies, chlorophyll fluorescence has long been used as a convenient and sensitive indicator of plant stress responses (e.g. Goltev et al. 2005; Kalaji et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Fluorescence increase or induction curves, usually called the OJIP test, have been also adapted for screening different varieties of crops subjected to drought stress (Yao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), including barley (Daszkowska-Golec et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rosales-Serna et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) suggested that drought stress is a complicated stressor and that different aspects of plant growth and physiology should be taken into account for the evaluation of plants\u0026rsquo; response to drought stress. Therefore, in the present study, the exploration of plants\u0026rsquo; response to drought was complemented with both yield performance and physiological analyses. Under stress conditions, significant increases in the parameters linked to RC damage (e.g., ABS_RC) and the parameters associated with heat dissipation (DIo_RC and Φ_Do) were recorded. In the present study, significant differences were also observed for ABS_RC at LFE1 between two subgroups of plants in all types of treatments. It is worth noting that much lower ABS_RC values were recorded for the early heading plants during the first measurement, but over time (LFE3), the mean values observed for this trait were similar for both plant subgroups, which emphasizes the role of stress duration in plants\u0026rsquo; response to unfavorable conditions. According to Jedmowski and Br\u0026uuml;ggemann (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), inactivation of some RCs, as already mentioned, increases the ABS/RC under drought stress conditions. Another reason for the increase in ABS/RC is degradation of chlorophyll through early leaf senescence induced by drought stress (Boureima et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or regrouping of antennae from inactive PSII RCs to active (Kalaji et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Changes in ABS_TR recorded in the present study may suggest that the early-heading genotypes react differently to the initial phase of drought stress, but after a while, damage to RCs occurs in this type of plant. The increases in the parameters linked to heat dissipation (DIo_RC and Φ_Do) were observed in the studied plants, especially for the early-heading plants in the second time of measurement, which, on the one hand, contributed to RC damage and, on the other hand, an increase in heat dissipation was recognized as an effective way for a plant to protect its thylakoid membranes from oxidative damage (Demmig-Adams et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the D\u0026thinsp;+\u0026thinsp;TR treatment, where plant development was inhibited, increases in Φ_Do and Dlo_RC recorded for the early-heading plants were much lower than those noticed for the rest of the stress conditions, which emphasizes the role of earliness in the effective distribution of heat in mitigating the devastating influence of drought on chlorophyll parameters. The decrease in quantum efficiency (Ψ_o, PI_Abs) and the increase in heat dissipation (indicated by DIo_RC and Φ_Do) were observed in the present study. These findings are in line with the studies of Zhu et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Sousaraei et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found a rapid increase in parameters DIo_RC, Φ_Do, and TRo_RC and a decrease in PI_Abs, Fv_Fm, and Eto_RC mean values.\u003c/p\u003e \u003cp\u003eDevelopmental defects in the tapetum and a lack of starch accumulation are caused by water-deficit stress in pollen grains (Nguyen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ji et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which was confirmed in our investigation as viability monitoring by method 1 decreases significantly under stressed conditions. Stress-tolerant wheat cultivars can maintain starch accumulation and sink strength during the young microspore stage under water stress conditions (Ji et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which was not confirmed in our study, as there were no differences in pollen viability evaluated by method 1 (JKJ method) between the early- and late-heading plants under D conditions. The artificial acceleration of the growth of the early-heading plants contributed to the impairment of pollen viability, exacerbating the negative impact of drought on pollen development.\u003c/p\u003e \u003cp\u003eIn many plant tissues (barley aleurone, wheat internodes, and anthers), \u003cem\u003eGAMYB\u003c/em\u003e expression has been shown to be directly upregulated by the gibberellin GA3 (Gubler et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In the present study, \u003cem\u003eHvGAMYB\u003c/em\u003e expression was confirmed in the anther tissues of plants subjected to different water conditions. It is interesting to note that depending on the applied growth stimulators, \u003cem\u003eHvGAMYB\u003c/em\u003e expression was different for the early- and late-heading plants. Transgenic barley lines with an excess of fourfold levels of endogenous \u003cem\u003eGAMYB\u003c/em\u003e protein in their anthers were reported to be male sterile (Murray et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Duca et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Also, a progressive decrease in anther size was associated with the increase in \u003cem\u003eGAMYB\u003c/em\u003e levels, particularly a decrease in anther length (Murray et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These findings are in line with results obtained in the present study, where for the early-heading plants with a higher \u003cem\u003eHvGAMYB\u003c/em\u003e expression, lower mean values of anther width were recorded. In the present study, \u003cem\u003eHvGAMYB\u003c/em\u003e is expressed at a relatively high level under D conditions, which can be linked to the growth process, especially in the early-heading genotypes. Contrary to the expectation, exogenous Ga application did not positively influence \u003cem\u003eHvGAMYB\u003c/em\u003e expression, which highlights the complex nature of plant development under stress conditions. On the other hand, the early-heading plants still show a higher level of \u003cem\u003eHvGAMYB\u003c/em\u003e expression under D\u0026thinsp;+\u0026thinsp;GA conditions, which confirms the association of the studied gene with early flowering. The use of the GA inhibitor TR under D\u0026thinsp;+\u0026thinsp;TR conditions contributed to the decrease in the \u003cem\u003eHvGAMYB\u003c/em\u003e expression level in the early-heading plants, but did not change the \u003cem\u003eHvGAMYB\u003c/em\u003e level in the anther tissues of the late-heading genotypes. This phenomenon confirmed the role of Ga in \u003cem\u003eHvGAMYB\u003c/em\u003e level regulation under drought conditions and stressed the role of transcription factors like \u003cem\u003eHvGAMYB\u003c/em\u003e in the flowering process and anther development under unfavorable environmental conditions. The results of this study show that the \u003cem\u003eHvGAMYB\u003c/em\u003e expression level evaluated at time point 2 is correlated positively with traits associated with lateral spike morphology (NGSl and WGSl), which indicate that this gene has an important role in yield performance of plants grown under unfavorable environmental conditions. This finding is in opposite with previous studies (Matsui et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Murray et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), where plants with strong \u003cem\u003eHvGAMYB\u003c/em\u003e over-expression turned out to be male sterile. This contrasting revelations highlight the complex nature of pollen development where GA signal transduction pathway may be modify by wide range of internal and external factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe research was supported by National Science Centre, Poland, project SONATA 12 no. 2016/23/D/NZ9/00042\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e \u003cp\u003eP.O. conceptualization, methodology, investigation, writing \u0026ndash; original draft preparation, funding acquisition, project administration A.K. supervision, methodology, investigation, writing-review and editing P.K. data curation, formal analysis, software, validation, data visualization, writing-review and editing M.K. investigation, writing-review. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAshraf M (2010) Inducing drought tolerance in plants: recent advances. Biotechnol Adv 28(1):169\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biotechadv.2009.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.biotechadv.2009.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAya K, Ueguchi-Tanaka M, Kondo M, Hamada K, Yano K, Nishimura M, Matsuoka M (2009) Gibberellin modulates anther development in rice via the transcriptional regulation of GAMYB. 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Int J Mol Sci 22:10682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms221910682\u003c/span\u003e\u003cspan address=\"10.3390/ijms221910682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Unsectioned Paragraphs","content":"\u003cp\u003e\u003cb\u003eSupplementary Information\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNA sequences of the gene specific primers\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMean values for studied traits\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 3\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResults of analysis of variance for observed traits\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 4\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCorrelations between the studied traits of genotypes differentiated in terms of phenology\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 5\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBiplot visualisation of variability of OJIP parameters\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary File 6\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCorrelations between the studied OJIP parameters of genotypes differentiated in terms of phenology\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2246208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2246208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the lifetime of a plant, flowering is not only an essential part of the reproductive process but also a critical developmental stage that can be vulnerable to environmental stresses. To ensure survival during drought, plants accelerate the flowering process, and this response is known as \u0026bdquo;drought escape\u0026rdquo;. \u003cem\u003eHvGAMYB\u003c/em\u003e \u0026ndash; transcriptional factor associated, among others, with flowering process and anther development in barley has also an important role in developmental modification and yield performance in plants subjected to stressed conditions. However, there is limited information whether the mechanisms associated with the flowering acceleration may cause the anther or pollen disruption due to their influence on flower development \u003cem\u003evia GAMYB\u003c/em\u003e overexpression.\u003c/p\u003e \u003cp\u003eThe aim of this study was to characterize differences in responses to drought among barley genotypes varied in terms of earliness. Two subgroups of barley plants differentiated in terms of phenology were analyzed, and a wide range of traits linked to plant morphology, physiology, and yield were investigated.\u003c/p\u003e \u003cp\u003eThe abiotic stress reactions of two barley plants subgroups varied in our study both in terms of yield, morphology, chlorophyll fluorescence kinetics and pollen viability. The results extend our knowledge about \u003cem\u003eHvGAMYB\u003c/em\u003e expression in anthers of barley plants grown under drought conditions and indicate the role of this transcription factor in shaping the yield \u003cem\u003evia\u003c/em\u003e influencing traits linked to spike morphology, especially in lateral stems. Hence, this gene could be regarded as an important factor in flowering process and, as a consequence, pollen and seed development in plants subjected to abiotic stress conditions. This study clearly show the important role of \u003cem\u003eHvGAMYB\u003c/em\u003e in survival mechanism associated with so called \u0026ldquo;drought escape strategy\u0026rdquo;.\u003c/p\u003e","manuscriptTitle":"The effects of heading time on yield performance and HvGAMYB expression in spring barley subjected to drought","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-26 17:53:13","doi":"10.21203/rs.3.rs-2246208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-11-24T08:55:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-11-24T07:29:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-11-15T18:08:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2022-11-07T04:31:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4d8db6a8-340b-451e-9f17-8f6cd59a7c15","owner":[],"postedDate":"November 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:40:57+00:00","versionOfRecord":{"articleIdentity":"rs-2246208","link":"https://doi.org/10.1007/s13353-023-00755-x","journal":{"identity":"journal-of-applied-genetics","isVorOnly":false,"title":"Journal of Applied Genetics"},"publishedOn":"2023-03-10 19:31:21","publishedOnDateReadable":"March 10th, 2023"},"versionCreatedAt":"2022-11-26 17:53:13","video":"","vorDoi":"10.1007/s13353-023-00755-x","vorDoiUrl":"https://doi.org/10.1007/s13353-023-00755-x","workflowStages":[]},"version":"v1","identity":"rs-2246208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2246208","identity":"rs-2246208","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}