Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat and F1 hybrids at different growth stages

doi:10.21203/rs.3.rs-3226159/v1

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Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat and F1 hybrids at different growth stages

https://doi.org/10.21203/rs.3.rs-3226159/v1

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Being a major source of protein and calories in the human diet, bread wheat is cultivated worldwide, and its yield is showing dwindling notion due to various environmental challenges. Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. Interestingly the genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. In this study, we assessed the differential expression profiles of yield-related negative regulators in elite cultivars of wheat and their F1 hybrids and identified various cis-regulatory signatures that might influence these expression patterns. Furthermore, the differential and preferential expression patterns of the homeologs showed dynamic expression profiles of A, B, and D subgenomes at six different growth stages, portraying the different homeolog-dominance at the same stage in the different cultivars and their F₁ hybrids. Based on the expression profiling, hybrid progenies of elite cultivars were developed that showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents and the F₂ segregating populations demonstrated varying yields from high-yielding parents. These results suggest the significance of dynamic transcriptional expression patterns and the regulatory signatures of major genes that can substantially contribute and provide insights for the selective introduction of better cultivars into hybrid breeding programs which can be harnessed for the improvement of future wheat.

Biological sciences/Biotechnology

Biological sciences/Computational biology and bioinformatics

Biological sciences/Genetics

Biological sciences/Molecular biology

Hexaploid bread wheat (Triticum aestivum spp. aestivum; AABBDD) is a major staple crop and nutritional source in terms of carbohydrates and proteins around the globe. In wheat, genetic vigor has apexed due to a series of polyploidization events through inter- and intraspecific hybridizations that conferred adaptive plasticity (Awan et al. 2022d). Moreover, these events have resulted in gene duplication and neofunctionalization of major agronomic genes. Owing to its adaptive plasticity, wheat has been domesticated and cultivated in various climatic zones ^1,2. However, grain yield production of wheat struggles to cope with the demand of the continuously soaring world population due to irrefutable challenges such as shrinkage of arable land, extreme climates, unalarmed pests, and disease infestations ³. Conventional breeding practices coupled with a molecular understanding such as genotyping of agro-economical traits of wheat can play a contributing factor in improving yield and combating foreseen global threat of food insecurity ⁴. To date, various strategies have been reported to improve grain yield such as the development of hybrids by crossing phenotypically elite cultivars, target-induced local lesions in the genome (TILLING), and CRISPR-Cas9-mediated genome editing to induce targeted mutations ^5–8. However, for the effective application of these pristine breeding tools, a vivid knowledge of potential germplasm, provided genotyping, genetic architecture, and the dynamic transcriptional expression profiles of candidate genes remain essential.

Hybrid breeding presents impeccable significance to enhance yield in crops. Wheat hybrids have been reported to be cultivated in less than 1% of the total wheat-growing area ⁸. Over the years, researchers have been investing their resources in the improvement and development of hybrid breeding programs to overcome the shortcomings of conventional seed production which is tedious, expensive, and low yielding. To achieve maximum heterosis, the identification and allocation of potential elite germplasm into the hybrid breeding clusters seems an inevitable cornerstone and promises an increased success rate. For the selection and prioritization of elite germplasms or cultivars, wheat genomics, and genomic prediction have been playing a pivotal role in understanding the phenotypic performance and identifying genetic variations to annotate phenotypes with genotypes ⁹. Thus, the introduction of resilient and high-yielding cultivars possessing distinct genetic diversity into hybrid breeding programs promises a sustainable solution for the development of improved grain-yield hybrids.

Interestingly, owing to genotyping-based trait association, various genes have been characterized in wheat that act as negative regulators for yield and have conserved homeologs in all three subgenomes A, B, and D, termed syntenic triads ². These genes can serve as potential candidates to explore the expression patterns that are being influenced by the cis/trans-regulatory elements at the genomic level. Out of these genes, the TaGW2 gene (an orthologue of rice) has been reported to be involved in negatively regulating grain width and thousand-grain weight (TGW) ¹⁰. The GW2 gene encodes a RING-type protein that acts E3 ubiquitin ligase to target proteins for cellular degradation ¹¹. Frame-shift mutation in the TaGW2 gene resulted in enhanced grain width and TGW ^6,7. Similarly, the TaCKX gene family controls the level of cytokinin by encoding the cytokinin oxidase/dehydrogenase (CKX) enzyme ¹². The CKX enzyme degrades the plant growth hormone cytokinin which plays a key role during seed and fruit development ¹³. The accumulation of cytokinin increases cell division in the inflorescence meristem region, resultantly, in enhancing the grain yield. The Tackx2-1 mutant showed an increase in several grains per spike in wheat, consolidating its function as a negative regulator for the number of grains ¹⁴. Apart from grain weight and grain number, tillering (shoot branching) is also a significant trait that is crucial for yield improvement in wheat. The TaD27 gene encodes an enzyme involved in the biosynthesis of strigolactones (SLs) that function as inhibitors for axillary buds’ outgrowth ¹⁵. The d27 mutant plants showed defective SLs synthesis and increased axillary buds’ outgrowth ^16,17. Similarly, in wheat, the TaD27-RNAi plants demonstrated an increase in the number of tillers contrary to TaD27-B-overexpressing (TaD27-B-OE) plants ¹⁸. These negative regulators can play a pivotal role in analyzing the expression patterns that can further enhance our understanding of these molecular actors controlling important agronomical traits.

In this study, distinct elite cultivars of wheat were opted based on high thousand-grain weight (TD-1) and high-grain number (GA-2002 and Auqaab-2000) ¹⁹. For functional analysis, the potential negative regulators (TaGW2, TaCKX, and TaD27) of grain yield in wheat have been assessed at the transcriptional level at six different growth stages of the elite cultivars and their F1 hybrids at flowering and maturing stages. A conclusive emphasis is made to decipher their preferential expression by different subgenomes (A, B, and D) to unravel the expression patterns that might be influencing plant growth and to predict the involvement of microRNAs and other cis-regulatory elements that can impact their expression through bioinformatics tools. Furthermore, to curate promising breeding germplasm for a hybrid breeding program, heterosis was observed for both trade-off traits such as grain number and grain weight by conducting reciprocal crosses, and the data were evaluated till F2 generation.

Differential expression analysis of yield negative regulators in wheat parents and F1 hybrids

Owing to their polyploid genetic architecture, bread wheat cultivars show significant quantitative variations that are being influenced by various sets of homeologs in the A, B, and D subgenomes. The interaction of these homeologs determines the expression pattern of a gene that can either confer buffering effect due to the functional redundancy of homeologs or a dominance effect due to genetic variation in the homeolog sequence, eventually leading to phenotypic diversity. Therefore, identification of the dynamic transcriptional expression patterns of various genes in different cultivars and their hybrids can decipher their interactions that might influence the growth of a plant. Four syntenic triads, namely TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 (accessions are mentioned in Supplementary Data 2 Table 1) in polyploid wheat, were assessed for differential expression analysis at six different growth stages such as one shoot, tillering, jointing, booting, flowering, and maturing (Fig. 1A), in three elite cultivars i.e., TD-1 (TD), Auqaab-2000 (AUQ), and GA-2002 (GAp), and at flowering and maturing stages in their F1 hybrids, namely TG (TD-1 x GA-2002), TA (TD-1 x Auqaab-2000), GT (GA-2002 x TD-1), GA (GA-2002 x Auqaab-2000), AT (Auqaab-2000 x TD-1), and AG (Auqaab-2000 x GA-2002), by retrieving whole tissue RNA from fresh leaves at each stage (Fig. 1B, 1C). The data indicated that the expression patterns vary from stage to stage and cultivar to cultivar (Fig. 2). In TD cultivar, the expression level of all the genes was high at one shoot stage; however, a low expression level was observed in AUQ and GAp cultivars (Fig. 2A, 2B). At the tillering stage, the expression level of TaD27 (a tillering negative regulator) was low in TD which can be correlated with the phenotypic data as more tillering was observed in TD compared to AUQ and GAp (Fig. 1E). Similarly, at the jointing stage the expression level of CKX family genes such as TaCKX2.1 and TaCKX2.2 (negative regulators of cytokinin) was low in Gap (Fig. 2B), consequently, an increased phenomenon of jointing was observed in GAp cultivar as compared to TD and AUQ cultivars (Fig. 1F). At the booting stage, the expression levels of all genes showed an enhanced expression pattern compared to other growth stages. However, a similar expression pattern can be observed in all the cultivars at flowering stage. Interestingly, in the flag leaf tissues, the expression level of all genes decreased at the maturing stage in TD and AUQ whereas their expression level was maximum in GAp compared to all other stages. Moreover, the cluster analysis showed a positive correlation in the expression pattern of all the genes at one shoot, jointing, and booting stages (Fig. 2C). A similar yet more strong positive correlation can be observed at tillering, flowering, and maturing stages which represent the stages that highly impact the grain yield. These results also indicate that dynamic transcriptional expression patterns can also be associated with the phenotypic outcome for the agronomically significant traits.

Subsequently, a similar analysis was conducted for the F1 hybrids, generated as a result of reciprocal crosses of these commercially cultivated cultivars (Fig. 1B, 1C), at two significant growth stages i.e., at the flowering and maturing stages to explore whether the expression patterns of these genes stay the similar to their parents or not. In all F1 hybrids (AG, AT, GA, GT, TA, and TG), the expression dynamics of the TaD27 gene stayed consistent at the flowering stage, however, at maturing stage TG hybrid showed higher expression of the TaD27 gene as compared to other hybrids (Fig. 2D, 2E). The expression patterns of the TaGW2 gene demonstrated an increasing notion from flowering to the maturing stage for the AG, AT, and TG hybrids. However, a decreasing notion was observed for the GA, GT, and TA hybrids where the expression of the TaGW2 gene decreased at the maturing stage. The expression of the TaCKX2.1 and TaCKX2.2 genes drastically decreased from the flowering to the maturing stage in the GA hybrid. The cluster analysis indicated a strong positive correlation between the expression patterns of GA and AT hybrids for all the genes in contrast to a slightly less positive correlation that can be observed in GT, TG, and TA hybrids (Fig. 2F). To develop a consensus for transcriptional suppression or dominance and track the behavior of expression patterns for a gene between parents and their hybrids, the expression dynamics of both parents and the hybrids were compared at flowering and maturing stages (Fig. 4). In the case of AG and GA hybrids, the expression of TaD27 gene was same at both stages and significantly different from both parents i.e., AUQ and GAp (Fig. 4A). Moreover, a general trend of enhanced expression of all the genes can be observed at the flowering stage as compared to both parents. Similarly, a general trend of decreased expression in all the genes can be observed in all hybrids from GA, involving GA-2002 as one of the parents, at maturing stage (Fig. 4A, 4C). Interestingly, in the case of TA and AT hybrids, the AT hybrid showed the transcriptional expression dynamics more inclined towards the TD parent at both growth stages where TD served as the male parent (Fig. 4B, 4E). Likewise, an expression pattern similar to the male parent can be reflected for all the genes in TG and GT hybrids (Fig. 4C).

Identification of preferential expression patterns of homeologs

To predict the preferential expression patterns of homeologs of yield-related negative regulators and identify a high-confidence single-homeolog dominance that might be influencing the expression patterns in different cultivars, we decided to access the relative abundance of transcripts expressed from each subgenome (A, B, and D) for TaCKX2.1, TaCKX2.2, and TaTEF1 genes at all six growth stages in all parents (Fig. 3A, 3B). The expression profiles of the TaTEF1 syntenic triad showed that the relative expression of its homeologs changes at different growth stages in all cultivars, however, in general, a higher relative expression of TaTEF1-7A homeolog was calculated. In GA cultivar, the expression of TaTEF1-7A homeolog was higher than TaTEF1-7B and TaTEF1-7D homeologs at one shoot, booting, and maturing stages. At the jointing stage, all three homeologs showed decreased expression in GA cultivar. Interestingly, at the jointing stage the expression of TaTEF1-7A only plummeted in GA where the expression was very high in AUQ and TD cultivars (Fig. 3B). Contrary to parents’ data, in all F1 hybrids, the expression level of the TaTEF1-7B homeolog was higher at maturing stage except in the TA hybrid where the TaTEF1-7A homeolog showed higher expression (Fig. 3D, 3E). The stage-dependent suppression and dominance of homeologs in a syntenic triad in different cultivars and their hybrids depict the unbiased expressional dynamics of the subgenomes.

To validate the resemblance and/or difference in the expression pattern of homeologs from the CKX family, the expression level of TaCKX2.1 and TaCKX2.2 homeologs was analyzed in parents at all six growth stages. Interestingly, all the homeologs of the TaCKX2.1 gene demonstrated similar patterns of expression at different growth stages in all the cultivars with an enhanced expression level at the booting and maturing stages (Fig. 3A). Likewise, a similar expression pattern was observed for all the homeologs of TaCKX2.2 gene at different growth stages (Fig. 3B). Furthermore, the cluster analysis showed a strong positive correlation in the expression patterns of all the studied homeologs at one shoot, tillering, booting, and flowering stages (Fig. 3C). In the case of F1 hybrids, the expression dynamics of the homeologs of the TaCKX2.1 gene showed an enhanced expression level of the TaCKX2.1A homeolog in all the hybrids at the flowering stage except for the AG hybrid where TaCKX2.1B demonstrated eminently higher expression level (Fig. 3E). The cluster analysis demonstrated a strong positive correlation among AT, GT, and GA hybrids (Fig. 3F). These inconsistent and different expression patterns in hybrids also consolidate the fact that a homolog-expression bias can be overcome by crossing different cultivars. For instance, an enhanced expression level of TaTEF1-7B homeologs can be observed in AG, GA, TG, and GT hybrids in contrast to the GA-2002 parent (GAp) (Fig. 4D, 4F).

Domain analysis of yield-related negative regulators

To predict the interaction of cis-regulatory elements that might be influencing the dynamic transcriptional expression patterns of the genes, we generated the in silico data using online platforms to access the structural and functional distinctions among the yield-related negative regulators and their homeologs. We retrieved three homeologs for each gene i.e., TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 from A, B, and D subgenomes of wheat. Interestingly, we found five homeologs of the TaCKX2.2 gene, namely TaCKX2.2A, TaCKX2.2B, TaCKX2.2D1, TaCKX2.2D2, and TaCKX2.2D3 as shown in Supplementary Data 2 Table S1. To identify their functional characteristics, we used their protein sequences to find out the functional domains (Fig. 5A). The results indicate that the TaGW2 genes have a domain of the Ring U-box superfamily that functions as an E3 ubiquitin ligase and involves specific post-translational modification of proteins known as ubiquitination ^20,21. The TaD27 genes contain the DUF4033 superfamily domain that has been reported in rice, chrysanthemum, and Arabidopsis thaliana and proven to be involved in shoot branching through biosynthesis of strigolactones ^22,23, while TaCKX2.1 and TaCKX2.2 genes possess cytokinin-binding superfamily domains and involved in the inactivation of plant growth hormone- cytokinin ¹². Different domains indicate that these genes are related to different gene families and engaged in different molecular mechanisms related to wheat growth and yield. Moreover, the WoLF PSORT server was accessed to forecast the subcellular localization of genes to identify the main site of their activity ²⁴ (Supplementary Data 2 Table 1).

Phylogenetic analysis of homeologs

To determine the evolutionary pathway, we identified and retrieved the paralogous genes of GW2, D27, CKX2.1, and CKX2.2 genes from Triticum dicoccoides, Triticum spelta, Triticum turgidum, Triticum urartu, and Arabidopsis thaliana through https://plants.ensembl.org/index.html. According to the results, we divided the phylogenetic tree into three groups (Fig. 5B). The D27, GW2, CKX2.1, and CKX2.2 genes from all the above-mentioned species were classified into three different groups (I, II, and III) based on their evolutionary relationships. Interestingly, five homeologs of the TaCKX2.2 genes were obtained through protein sequence-based analysis (Supplementary Data 2 Fig. S1).

Gene structure and motif analysis

According to gene structure analysis, all homeologs of TaCKX2.1 and TaCKX2.2 genes contain three exons and two introns, and the homeologs of TaD27 genes consist of six exons and five introns, while all the homeologs of TaGW2 genes contain eight exons and seven introns (Fig. 5C). Furthermore, we identified conserved motifs of TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes by accessing MEME platform ²⁵. Results indicated that all TaGW2 genes have eight motifs distributed on N and C terminals. The TaD27 contains a maximum of six and a minimum of five motifs while TaCKX2.1 and TaCKX2.2 genes have a maximum of 13 motifs, eight on the N terminal and six on the C terminal (Fig. 5D). Motif analyses showed that these structurally different genes possessed five conserved motifs. Moreover, these structural and motif similarities in the homeologs of different genes explain their similar molecular and biological functions.

Functional gene ontology annotation analysis

To enhance our understanding of the functional performance of these genes, we used gene ontology (GO) enrichment analysis based on cellular component (CC), molecular function (MF), and biological process (BP) classes to estimate the roles of the TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes in polyploid Triticum aestivum. These concepts enabled us to forecast the molecular actions of these genes in the growth and development of the plant. Figure 6A demonstrates comprehensive information on the findings of the annotation.

The GO-CC enrichment annotation analysis detected three enriched terms, nucleus (GO:0005634), chloroplast (GO:0009507), and extracellular space (GO:0005615). The GO-BP enrichment detected a total of seven terms such as ligase activity (GO:0016874), ubiquitin-protein ligase activity (GO:0061630), macromolecule metabolic process (GO:0043170), primary metabolic process (GO:0044238), secondary shoot formation (GO:0010223), strigolactone biosynthetic process (GO:1901601), and cytokinin metabolic process (GO:0009690). While GO-MF annotation analysis identified a total of 12 enrichment terms, macromolecule metabolic process (GO:0043170), primary metabolic process (GO:0044238), regulation of organ growth (GO:0046620), regulation of seed growth (GO:0080113), iron ion binding (GO:0005506), isomerase activity (GO:0016853), cis-trans isomerase activity (GO:0016859), catalytic activity (GO:0003824), oxidoreductase activity (GO:0016491), cytokinin dehydrogenase activity (GO:0019139), flavin adenine dinucleotide binding (GO:0050660), and FAD binding (GO:0071949) (Fig. 6A, Supplementary Data 1 Table S1). The TaCKX2.1 and TaCKX2.2 genes demonstrated their involvement in catalytic activity and cytokinin metabolic processes encompassing their function as negative regulators of cytokinin hormones. The TaD27 gene showed its involvement in the strigolactone biosynthetic process and secondary shoot formation. Similarly, the TaGW2 gene showed its contribution to the regulation of seed growth.

Identification of putative miRNA targets

Various plants’ reactions to biotic and abiotic stress as well as phytohormones have associations with microRNA (miRNA)-directed regulators. Therefore, we decided to fetch bioinformatics analysis based on miRNA-gene sequence interaction through the psRNATarget database. We discovered many miRNAs that target the genes we were interested in to further expand our understanding of the miRNAs regulating these genes in Triticum aestivum. Figure 6B shows comprehensive information about targeting miRNAs. The TaGW2 genes are targeted by a maximum number of miRNAs as 15 different miRNAs are targeting the homeolog TaGW2D. Despite being the homeologs of the same gene (TaD27), only two miRNAs (TaemiRN4410 and TaemiRN4300) and one miRNA (TaemiRN4300) target the TaD27B and TaD27D gene, respectively. Interestingly, the miRNA target data also emphasize that homeologs of the same gene have varying numbers of targeting miRNAs and the same miRNA can target homeologs of different genes (Supplementary Data 1 Table S2). This observation also explains the impact of targeting miRNAs that might result in the variable expression pattern of these genes and/or homeologs at different growth and developmental stages of the plant.

Identification of transcription factors binding sites

Transcriptional regulations also play a pivotal role in the coordinated expression of genes and these regulations encompass the activation and suppression of genes ²⁶. Such events are controlled through cis-acting elements (CREs) present in the promoter region located upstream of the gene ²⁷. The transcriptional machinery consists of transcriptional factors and co-activators, bears the tendency to bind with transcription factor binding sites (TFBS), and decides the fate of the gene’s expression pattern ²⁶. To understand a landscape of differential expression patterns of the TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes, we decided to predict the molecular map of CREs present in their promoter regions.

Although all the genes belong to different gene families and conduct distinct molecular and biological functions, we were able to find six common CREs, namely AP2/ERF, GATA/tify, GATA, Dof, bZIP, and MYB/SANT, in the promoter region of all the genes (Fig. 7). Among these CREs, the AP2/ERF (APETALA2/ethylene-responsive element binding factors) transcription factors are a large group of plant transcription factors that are highly conserved throughout the plant kingdom and regulate several biological and physiological processes such as plant-responsive mechanisms against various stresses, morphogenesis, metabolite regulation, and hormone signal transduction in plants ²⁸. Interestingly, the TaCKX2.1 and TaCKX2.2 genes belonging to the CKX family only showed one common TFBS for SHI-related sequence (SRS) transcription factor that is reported to be involved in growth and development, phytohormone biosynthesis, and stress response in plants ²⁹. However, the TaD27 gene showed the highest number (74) of TFBS while the TaCKX2.2D2 gene showed merely seven TFBS (Fig. 4B, Supplementary Data 1 Table S3, S4, S5). Like targeting miRNAs, the inconsistency in the number of CREs present in the homeologs of the same gene endorses the fact that they hold the tendency to influence the expression pattern of these homeologs at different growth stages.

Protein structure analyses

The advancement of the graphical display of genetic data has enabled the comparative study of proteins and describe their properties using 3D structures because proteins possessing similar domains frequently have similar functions despite their structural differences. To explore the architectures of the protein, the protein structures were predicted using the c2khoA template at a 90–95 percent confidence level. Based on the principles of protein structure guide design, binding ligands that are required to bind with either exterior or internal residues can be found. The TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes exhibit the same monomer structure with alpha (α) helices and beta (β) sheets (Supplementary Data 2 Fig. S1). These genes are also anticipated to have physical connections, independent divergence sequences, and conserved biological traits because of the tertiary structures of proteins. Moreover, no domains or motifs were observed that warrant the interactions among the proteins encoded by these genes with each other that may hinder their normal functionality. Despite having variable gene structures, the protein structures of all the homeologs are similar to each other which explains their capacity to carry out the assigned task in a similar fashion, apart from their expression profile that shows which homeolog is being expressed at a higher level than others.

Morphological trait analysis of F1 hybrids and F2 plants

Harnessing the potential of integrated approaches involving genetics, breeding, and statistical tools enables the fast-track introduction of elite cultivars in hybrid development programs with prior knowledge about their phenotypic performance especially related to yield. To exploit our genomic and transcriptomic analyses in the breeding program, phenotypically distinct cultivars were crossed in a reciprocal manner to observe their morphological behavior in F1 progeny. The data of agronomically significant traits such as yield were collected from parents, the F1 hybrids developed through reciprocal crosses, and F2 segregating population (Fig. 8). The data showed significant variations in parents and F1 hybrids that were grown in the pots with a basic dose of fertilizers and consistent agronomic practices throughout the experiment. An impeccable hybrid vigor was observed with a 17% increase in the average number of grains and a 50% increase in average thousand grains weight in the F1 hybrids of GA (GAp x AUQ) and TA (TD x AUQ), respectively (Fig. 1D, 1E, 1F). To assess the correlation among selected yield-related traits principal component analysis (PCA) was conducted (Fig. 8). The scree plot indicated that the first two principal components explained more than 60% of the total variability for both years, based on which PCA biplot was constructed to analyze the variability (Fig. 8A). During 1st year (F1 hybrid progeny), plant height (PH) and spike length (SL), non-productive (NP)-tillers vectors lie close to each other and have greater than 90° with flag leaf length (LL), flag leaf area (LA), and tillers which indicates that both sets of traits have an antagonistic relationship with each other (Fig. 8B, 8C, 8D). During the 2nd year, the growth of segregating population of F2 wheat plants, sown in the field, showed a mixed response from declined to enhanced yield notion compared to parents. The number of tillers, total grains per plant (TGP), thousand grains weight (TGW), LA, and SL were higher for all the genotypes except the cross between AUQ and TD (AT) which performed comparatively better in the F1 progeny in the previous year (Fig. 8E, 8F, 8G, 8H). Moreover, LL and NP-tillers function synergistically but lie opposite to TGW which indicates that TGW will be less with the plants having high LL and NP-tillers (Fig. 8F). Correlation analysis also showed that during 1st-year the number of tillers was positively correlated with LA, TGW, TGP, and spikelet, and negatively correlated with PH and NP-tillers (Fig. 8C, 8D). During 2nd year, the number of tillers was positively correlated with flag leaf width (LW), SL, and TGP, and negatively correlated with LL and NP-tillers whereas, TGW was negatively correlated with LL and positively correlated with a number of tillers (Fig. 8G, 8H). During the 1st year, cluster analysis grouped the GAp and TD parents together, and hybrids TG, TA, GT, GA, AT, and AG were put into separate groups based on their performance similarity (Fig. 8C). However, during the 2nd year, the TD and AUQ parents behaved similarly and were put in one group whereas, among traits, TGW and tiller were grouped in one group which showed that they have a synergistic relationship with each other (Fig. 8G).

Upon polyploidization, the hexaploid bread wheat (AABBDD) underwent a series of genetic modifications owing to the gene flow from wild relatives that resulted in conferring agronomically significant phenotypes and widening its adaptability ^30,31. The gene duplication altered the transcriptional landscape of various genes at different developmental and growth stages ². Furthermore, the dynamic expression profile constantly varies in different tissues that are influenced by such developmental changes at various growth stages ³². In this study, we decipher the dynamic transcriptional expression patterns of yield-related negative regulators such as TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 in three elite cultivars of hexaploid wheat at six different growth stages and their F1 hybrids and unravel the molecular actors such as miRNAs and transcription factors targeting cis-regulatory elements as potential candidates to influence dynamic expression patterns. To elucidate their evolutionary pathways, the phylogenetic analysis for the homeologs of the TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes was conducted. The syntenic triads portrayed the origin of each homeolog from its putative wild progenitor. Interestingly, an occurrence of five homeologs of TaCKX2.2 genes was noted, a single copy on A and B subgenomes, namely TaCKX2.2A and TaCKX2.2B, while three copies on the chromosome 3 of D subgenome, namely TaCKX2.2D1, TaCKX2.2D2, and TaCKX2.2D3 (Fig. 2) that explains the duplication of homeologs during interspecific hybridization of three different diploid species ³³. The duplication of genes underpinned the neofunctionalization of different homeologs in the form of altered expression patterns in different tissues at various growth stages ^34,35. The gene and protein structural analyses demonstrate that despite having variable gene structures, all the homeologs of a gene possess similar protein conformation and play a similar function in the development processes of the cell, which was further validated by in silico protein structural analysis that shows the involvement of the homeologs of TaCKX2.2 gene in the regulation plant growth hormone cytokinin ¹².

Various studies have discussed the significance of capturing the tissue-specific expression of agronomically significant genes at different developmental stages ^32,36. In addition, similar expression patterns of such genes demonstrated a conserved role in discrete biological pathways. Our study shows that the differential expression profile of the same gene varies at the same growth stage. The expression level of the gene presents a varying notion that increases and plummets at the same growth stage in different wheat cultivars as well. Similarly, the increased or decreased expression level of the gene vanishes in the very next growth stage which suggests the ephemeral suppression that might explain the involvement of epigenetic modifications and/or transcription regulatory elements such as microRNAs or transcription factors ^37–39. Furthermore, owing to evolutionary divergence, the preferential expression of homeologs of a gene at a certain development stage showed biased shifts between subgenomes and plays a significant role in controlling various quantitative and/or qualitative traits in wheat ^2,32. This expression bias towards specific subgenome homeolog has also been reported in introgressed and non-introgressed genomic regions of the hexaploid wheat as a result of plasticity conferred by polyploidization ^14,40. In agreement with these previous findings, our study suggests the prevalence of preferential expression of homeologs at different growth stages that shows varying expression dynamics not only in parents but also in their F1 hybrids. However, there exists no bias in the expression pattern of the homeologs from different subgenomes, a varying trend of suppressed or dominant expression patterns can be observed at different growth stages. In addition, the expression pattern also demonstrated the suppression of homeologs of negative yield regulators at different growth stages that support the phenotypic outcome in those cultivars such as increased yield in terms of the number of tillers, grain number, or grain size.

Our findings also consolidate the fact that these genes show putative miRNA-binding sites that might influence the differential expression patterns, eventually controlling the cell physiology. Although being the homeologs of the same gene, homeologs of the A subgenome showed a high number of regulatory miRNA targets as compared to B and D subgenomes. In contrast, the transcription factor binding sites (TFBS) demonstrated dynamic scenarios where TaGW2D, TaD27A, TaCKX2.1D, and TaCKX2.2B showed the presence of more TFBS as compared to their respective homeologs. However, further investigation would elucidate the recognition, expression, and binding patterns of miRNAs and transcription factors to assign their vivid biological impact, and how they are involved in the regulation of these yield-related negative regulators in wheat.

Based on the phenotypic and transcriptional data, we tried to study the behavior of the trade-off traits such as grain number and grain weight in reciprocal crosses between high grain weight producing wheat cultivar TD with high grain number producing wheat cultivars GAp and AUQ (Fig. 1B). The F1 hybrid data demonstrate a better performance than the mid-parent in any cross combination. Moreover, our F1 hybrid data strengthen the fact that the trade-off traits can be combined with an improved version of those phenotypes than both parents (Fig. 8). During both years, tillers, TGW, and TG indicated a similar trend which shows that these traits perform synergistically and are antagonistic to LL, SL, and PH which also reveals that these traits are limiting factors in achieving high yield. Moreover, cluster analysis also grouped tillers and TGW together for both years validating that the simultaneous selection of these traits is important and correlation analysis showed that these traits are negatively associated with LA, LL, PH, and NP-tillers. In a nutshell, heterosis in wheat hybrids, developed from cultivars showing suppressed expression dynamics of yield-related negative regulators, can significantly improve grain yield. Moreover, new germplasm can be introduced by knocking out the negatively regulating genes hindering the grain numbers and weight through targeted mutagenesis by deploying various CRISPR-Cas systems, simultaneously ^41–43. Also, future breeding programs can be improved by selecting traits that play an exponential role in boosting grain yields such as tillers, TG, and TGW.

Plant materials

Three bread wheat cultivars were selected for the study, namely TD-1, Auqaab-2000, and GA-2002. The seeds were provided by Ayub Agriculture Research Institute (AARI), Faisalabad, Pakistan. For the transcriptional study, the plants were grown in clay pots under basic agronomical management protocols for wheat in the winter wheat growing season at National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan. The reciprocal crosses were designed and carried out at AARI under field conditions. The F1 hybrids (20 seeds from each cross) were sown in clay pots under basic agronomical management protocols at NIBGE. The data was collected from 15 plants representing a single cross. The F2 progeny (60 seeds from each cross) was grown in the field under basic agronomical management protocols. The data were collected from 50 plants of each cross.

RNA isolation and cDNA synthesis

Total RNA isolation was done by taking fresh leaf samples from different stages of wheat plants at one shoot, tillering, jointing, booting, flowering, and maturing stages. Similarly, for F1 hybrids, the total RNA was isolated from flag leaves at the flowering and maturing stages. RNA extraction protocol recommended by the supplier (Invitrogen, USA) of RNA extraction kit TRIzol Plant RNA isolation reagent (Invitrogen, Carlsbad, CA, USA) was used for the isolation of total RNA from tissues. RNA samples were quantified followed by treatment with DNase to remove any traces of plant genomic DNA. The cDNA was prepared from RNA using the RevertAid First-strand cDNA Synthesis Kit (Thermo Fisher Scientific, Cat No. K1621).

Real-time PCR-based expression analysis

Real-time quantitative PCR (qPCR) analyses were performed to evaluate the difference in the expression of TaGW2, TaCKX2.1, and TaCKX2.2 genes and the differential/preferential expression of the homeologs of the TaCKX2.1, and TaCKX2.2 genes in A, B, and D subgenomes along with the TaTEF1 gene at different stages of wheat plants. Primers from Supplementary Data 1 Table S6 were used for qPCR analysis. The qPCR was performed by using the SYBER green real-time PCR master mix (Thermo-Fisher Scientific). The cDNA was used as the template. The reactions were performed in triplicates. The profile was set as 95℃ for 5 minutes (min); followed by 40 cycles of 95℃ for 30 seconds (sec); 55℃ for 30 sec, and 72℃ for 30 sec. A melt curve analysis was conducted following the completion of the amplification cycles. The TaActin gene was used as an internal control to normalize the results and through the ΔΔCt method, the relative fold difference for each sample in each experiment was calculated. Similarly, in F1 hybrids the differential expression was calculated through the ΔΔCt method and demonstrated/visualized as a log fold change by using RStudio ⁴⁴.

Identification of a specific domain

To recognize specific domains in TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 proteins, we performed domain analysis by accessing the NCBI conversed domain (CDD) database (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi/) and TBtools software V 1.098 (https://github.com/CJ-Chen/TBtools/) ⁴⁵. To predict the Sub-Cellular localization, the WoLF PSORT server https://wolfpsort.hgc.jp/ was accessed ²⁴.

Phylogenetic tree analysis

A phylogenetic tree was created using the protein sequences of the GW2, D27, CKX2.1, and CKX2.2 genes from the following species: Triticum aestivum, Triticum dicoccoides, Triticum spelta, Triticum turgidum, Triticum urartu, and Arabidopsis thaliana. Utilizing MEGA-X, the Neighbor-joining (NJ) approach with 1000 bootstrap replicates was utilized to align and build peptide sequences. Furthermore, the phylogenetic trees of the homeologs of the TaCKX2.2 gene were developed from the following species: Triticum aestivum, Triticum turgidum, Triticum spelta, Triticum uartu, Aegilops tauschii, Hordeum valgare, Oryza sativa subgroup japonica, Sorgham bicolor, Zea mays, and Glycine max.

Identification of gene, motif, and 3D protein structure

Online tools (http://gsds.gao-lab.org/) ⁴⁶ and (https://meme-suite.org/meme/tools/meme) ²⁵ were accessed to identify the gene structure and conserved motifs, respectively, of TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genes in Triticum aestivum. The PHYRE2 Protein Fold Recognition Server (ic.ac.uk) online web-based program was used to create the graphical representation of the three-dimensional (3D) structure of the TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 proteins while selecting the fold recognition method.

Putative miRNA predictions and functional annotation analysis

The TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 genomic sequences were collected via the psRNATarget database (http://plantgrn.noble.org/psRNATarget/) to classify potential miRNAs. GO annotation was carried out online using the DAVID website (https://david.ncifcrf.gov). Graphs were generated by using online tools provided by RAWGraphs (https://www.rawgraphs.io/). A summary of the number and putative miRNAs that target the genes TaGW2, TaD27, TaCKX2.1, and TaCKX2.2 as well as the GO annotation is provided (Supplementary Data 1 Table S2).

Identification of transcription factors binding sites

Transcriptional factor binding sites (TFBS) were found in the promoters of TaGW2A, TaGW2B, TaGW2D, TaD27A, TaD27B, TaD27D, TaCKX2.1A, TaCKX2.1B, TaCKX2.1D, TaCKX2.2A, TaCKX2.2B, TaCKX2.2D1, TaCKX2.2D2, and TaCKX2.2D3 using the 5 kb of upstream sequence. Web tool PlantPAN 2.0 available online at http://plantpan2.itps.ncku.edu.tw/promoter.php (Chow et al., 2019) was used to forecast TFBS using previously discovered locations across several plants.

Data visualization

Graphs were generated by using online tools provided by RAWGraphs (https://www.rawgraphs.io/) and modified in Adobe Illustrator. VEN graph was generated by using the online interface VENNY 2.0 provided at https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html.

Statistical analysis

All the statistical analyses were carried out in RStudio ⁴⁴.

TGW = thousand-grain weight; CKX = cytokinin oxidase/dehydrogenase; SL = strigolactones; NJ = neighbor-joining; miRNAs = micro-RNAs; GO = gene ontology; TFBS = transcriptional factor binding sites; qPCR = Real-time quantitative PCR; CC = cellular component; MF = molecular function; BP = biological process; CREs = cis-acting elements; AP2/ERF = APETALA2/ethylene-responsive element binding factors; SRS = SHI-related sequence; TD = TD-1; Gap = GA-2002; AUQ = Auqaab-2000; PCA = principal component analysis; PH = plant height; SL = spike length; LL = leaf length; LA = flag leaf area; TGP = total grains per plant; TGW = thousand grains weight.

Acknowledgments

The authors acknowledge Mr. Atiq-ur-Rehman (NIBGE), Dr. Muhammad Asif (NIBGE, Faisalabad), Dr. Mudassir Ahmed (NIBGE), Dr. Asma Imran (NIBGE), Dr. Imtiaz (NIBGE), Dr. Fatiha (NIBGE), Dr. Javed Ahmad (AARI, Faisalabad) & Dr. Aziz-ur-Rehman (AARI, Faisalabad) for their technical support.

Author contributions

SM, IA, AR, and NAS provided the concept, guidance, resources, and supervised the whole project. MJAA, MAF, RZN, UK, and SARB performed the in-silico data analysis and validation. MJAA, MABW, MAM, and MIB conducted field experiments and collected data. MJAA and RZN conducted the RT-PCR experiments. MAF, UK, and SARB performed the statistical data analysis. MJAA, MAF, RZN, and UK prepared the manuscript. All authors have contributed to the article and approved the submitted version.

Data Availability

The authors have utilized open-access databases and tools for data retrieval and analysis. All the databases and tools or software utilized with their access web links have been properly given in the relevant material and methods section for clarity and use by other researchers. The software and links to databases are provided here; EnsemblPlant Triticum aestivum (https://plants.ensembl.org/Triticum_aestivum/Info/Index), MEGA-X, RStudio, NCBI conversed domain (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi/), TBtools software V 1.098 (https://github.com/CJ-Chen/TBtools/), WoLF PSORT (https://wolfpsort.hgc.jp/), (http://gsds.gao-lab.org/), MEME (https://meme-suite.org/meme/tools/meme), PHYRE2 (ic.ac.uk), psRNATarget database (http://plantgrn.noble.org/psRNATarget/), DAVID website (https://david.ncifcrf.gov), RAWGraphs (https://www.rawgraphs.io/), PlantPAN 2.0 (http://plantpan2.itps.ncku.edu.tw/promoter.php), VENNY 2.0 (https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html).

Compliance with international, national, and/or institutional guidelines

The present experimental research and field studies comply with relevant institutional, national, and international guidelines and legislation.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat and F1 hybrids at different growth stages

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