A novel regulator of wheat tillering LT1 identified by using an innovative BSA method

A novel regulator of wheat tillering LT1 identified by using an innovative BSA method | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A novel regulator of wheat tillering LT1 identified by using an innovative BSA method Yundong Yuan, Bo Lyu, Juan Qi, Xin Liu, Yuanzhi Wang, Pierre Delaplace, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4229022/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Branching/tillering is a critical process for plant architecture and grain yield. However, Branching is intricately controlled by both endogenous and environmental factors. The underlying mechanisms of tillering in wheat remain poorly understood. In this study, we identified Less Tiller 1 ( LT1 ) as a novel regulator of wheat tillering using a newly upgraded bulked segregant analysis (BSA) method called uni-BSA, which is well-suited for wheat. Loss-of-function of LT1 results in fewer tillers due to defects in axillary meristem initiation and bud outgrowth. We mapped LT1 to a 6 Mb region on the chromosome 2D short arm and validated a nucleotide-binding (NB) domain encoding gene as LT1 using CRISPR/Cas9. Furthermore, the lower sucrose concentration in the shoot bases of lt1 might result in inadequate bud outgrowth due to disturbances in the sucrose biosynthesis pathways. Co-expression analysis suggests that LT1 controls tillering by regulating TaROX/TaLAX1 , the ortholog of the Arabidopsis tiller regulator REGULATOR OF AXILLARY MERISTEM FORMATION ( ROX ) or the rice axillary meristem regulator LAX PANICLE1 ( LAX1 ). This study not only offers a novel genetic resource for cultivating optimal plant architecture but also underscores the importance of our innovative BSA method. This uni-BSA method enables the swift and precise identification of pivotal genes associated with significant agronomic traits, thereby hastening gene cloning and crop breeding processes in wheat. Wheat tillering auxin cytokinin sucrose whole-exome resequencing bulked segregant analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Wheat provides approximately one-fifth of human caloric intake worldwide, highlighting the importance of improving grain yield for global food security (Cao et al. 2020 ). Tillering, the process of generating tillers, is a major determinant of yield, as tillers can bear grains (Kebrom et al. 2012 ; Wang et al. 2018a ). While tillering is known to be regulated by external and internal factors (Yuan et al. 2023 ), molecular mechanisms underlying this process remain poorly understood in wheat compared to model species like rice and Arabidopsis . Tillering/branching encompasses the initiation of the axillary meristem (AM) and its subsequent outgrowth. Many genes controlling tillering/branching have been identified and characterized. For example, AM formation involves conserved regulators such as rice MONOCULM1 ( MOC1 ) and its cognate ortholog Arabidopsis LATERAL SUPPRESSOR ( LAS ), which function specifically in the leaf axil to promote meristem development (Li et al. 2003 ; Greb et al. 2003 ). Notably, the Arabidopsis REGULATOR OF AXILLARY MERISTEM FORMATION ( ROX ) basic helix–loop–helix (bHLH) transcription factor is required for AM initiation. The rox mutants display compromised axillary bud formation during vegetable shoot development. The double mutants of rox and las enhance their branching defects, indicating that ROX functions independently of LAS in AM initiation (Yang et al. 2012 ). The ortholog of ROX in rice, LAX PANICLE1 ( LAX1 ), expresses in every AM, which indicates LAX1 is involved in the formation of all types of AMs throughout the ontogeny of a rice plant. In contrast with ROX , LAX1 mainly influences the AM formation of both tillers and panicle branches (Komatsu et al. 2003 ), suggesting a divergence between species. Bud outgrowth is inhibited by the TEOSINTE BRANCHED1 ( TB1 ) transcription factor and its homologs across species, which acts as an integrator of multiple plant hormones, including strigolactones (SLs), auxin, and cytokinins (CKs) (Wang et al. 2018a ; Takeda et al. 2003 ; Matthes et al. 2019 ; Kepinski and Leyser 2005 ; Alder et al. 2012 ; Smith and Li 2014 ; Tanaka et al. 2006 ; Shimizu-Sato et al. 2009 ). Emerging evidence highlights the trophic and signaling roles of sugars in promoting bud outgrowth (Mason et al. 2014 ). For example, the reduced tillering of wheat tiller inhibition ( tin ) mutant is attributed to low sucrose levels (Kebrom et al. 2012 ). Similarly, reduced tiller formation in the rice monoculm 2 ( moc2 ) mutant results from the disruption in the fructose-1,6-bisphosphatase, an enzyme involved in sucrose biosynthesis, leading to decreased sucrose supply (Koumoto et al. 2013 ). The necessity for sugars for bud outgrowth has been demonstrated in rose ( Rosa hybrida ), where sugar is required to trigger bud outgrowth in single nodes cultivated in vitro (Rabot et al. 2012 ; Barbier et al. 2015 ). Additionally, sucrose can also modulate the dynamics of bud outgrowth in a concentration-dependent manner, especially during the transition phase between bud release and sustained bud elongation (Barbier et al. 2015 ). Furthermore, the removal of competing sugar sources or sinks within buds through defoliation provides additional evidence for the regulatory role of sugars in bud release (Mason et al. 2014 ; Kebrom and Mullet 2015 ). In addition to their roles as nutrients, sugars have been shown to influence phytohormone homeostasis. For instance, sucrose stimulates CK biosynthesis in bud-bearing stem segments by upregulating the expression of two CK biosynthesis-related genes (Barbier et al. 2015 ). Sucrose can also modulate auxin metabolism in a concentration-dependent manner in R. hybrida buds (Barbier et al. 2015 ). Furthermore, according to the auxin canalization model, elevated sucrose levels within buds facilitate auxin export from the bud to the stem, promoting bud outgrowth (Barbier et al. 2015 ). These findings collectively demonstrate the crucial role of sugar signaling in regulating bud release. The rapid development of sequencing technologies in recent years has accelerated the cloning of genes associated with important traits in crops. Traditional forward gene mapping methods, such as map-based cloning, are time-consuming and costly, especially in wheat with a large and complex genome (17 G) (Consortium et al. 2018 ). For example, the recent research on the cloned gene ELS3 , which controls leaf senescence, involved 10,133 individuals and spanned several years (Xie et al. 2023 ). Current breakthroughs using high-throughput sequencing techniques have accelerated the identification of genes linked to agronomic traits and made gene isolation more feasible and efficient. For instance, the adaptable method MutMap (Abe et al. 2012 ) has been widely used in identifying genes associated with a variety of traits, including but not limited to salt tolerance (Takagi et al. 2015 ), endosperm development (Wang et al. 2018b ), flowering and seed size (Manchikatla et al. 2021 ), height and spikelet (Huang et al. 2022 ) and more. MutMap-derived methods, such as MutMap+ (Fekih et al. 2013 ), MutMap-Gap (Takagi et al. 2013b ), and QTL-seq (Takagi et al. 2013a ), have also been developed to improve the efficiency and accuracy of gene mapping. However, the immense wheat genome remains cost-prohibitive for gene cloning using next-generation resequencing data (Consortium et al. 2018 ). To tackle this issue, a whole-exome resequencing method was developed, significantly reducing the scope of the wheat genome (Zhang et al. 2021 ). More critically, wheat's over 80% repetitive sequence rate (Consortium et al. 2018 ) poses challenges for unambiguous read mapping, an essential step for gene cloning that must be overcome. Previous research indicates that abscisic acid (ABA) and SLs play a crucial role in wheat tiller development, which mediates by wheat TaD27 (Zhao et al. 2020 ), the orthologs of rice Dwarf27 ( D27 ) (Lin et al. 2009 ), and Tiller Number1 ( TN1 ) which encodes an ankyrin repeat protein (Dong et al. 2023 ), respectively. However, Our understanding of molecular mechanisms regulating tillering in wheat remains limited. This study presents a wheat mutant named ‘ less tiller1’ ( lt1 ), which exhibits reduced tillering. In addition to fewer tillers, lt1 shows reduced stature, chlorotic leaves, and stunted roots. Using an upgraded bulked segregant analysis method called uni-BSA, which is well-suited for wheat, we mapped LT1 to the short arm of chromosome 2D. Further analyses suggested that LT1 encodes a nucleotide-binding domain protein, and LT1 is localized in chloroplasts. Our data shows that LT1 might regulate the expression pattern of TaROX / TaLAX1 and sucrose levels to control tillering. Understanding the role of LT1 will offer valuable perspectives for molecular breeding in wheat. Additionally, our findings provide a new method that allows for the swift and precise identification of crucial genes linked to important agronomic traits in crops. Materials and Methods Plant materials and growth conditions The lt1 mutant is derived from a mutagenesis pool of a landrace Chang6878 (C6878) treated with 1% Ethyl Methanesulfonate (EMS). The lt1 phenotypes were inherited stably after four generations of self-pollination. For gene mapping, lt1 was backcrossed with C6878 and self-fertilized to produce a segregating F 2 population of at least 1000 individuals. Wheat plants are cultivated in the experimental field at Shandong Agriculture University, Tai’an, Shandong, China. The transgenetic plants are grown in a growth chamber maintained at 22/17°C day/night temperatures, 16-h photoperiod, and about 300 µmol m − 2 s − 1 photosynthetically active radiation at 45% humidity. Exome capture sequencing Genomic DNAs were extracted from a minimum of 100 individuals with contrasting extreme phenotypes from an F 2 population, along with 10 lt1 mutants and 10 C6878 plants serving as two control DNA pools, using the CTAB method (Chatterjee et al. 2002 ). The mutant-type and wild-type DNA pools of the F 2 population were generated by bulking at least 50 genomic DNAs in an equal ratio. The lt1 mutant and C6878 DNA pools were also generated in an equal ratio. The datasets generated from Whole-Exome Sequencing (WES) for variation calling in this study were obtained from the Oebiotech company. In principle, the WES generates 260 Mb data per fold of the wheat genome, including 110,000 high-confidence protein-coding genes, 50,000 non-coding genes, and associated promoters. We obtained 26 GB of data per sample, corresponding to 100-fold coverage depth. For more detailed information on WES and the corresponding bioinformatic pipelines, please refer to the Oebiotech website ( https://www.oebiotech.com/ ). The uni-BSA pipeline for rapid gene isolation We developed a novel bulked segregant analysis pipeline called uni-BSA for rapid gene cloning in wheat (Fig. 2 ). This approach consists of the following steps. (1) Develop a segregating population from a backcross between the mutant and the wild-type parental line. (2) Extract and pool DNAs from the mutants, their wild types, and individuals with mutant and wild-type phenotypes of the F 2 population in equal proportions, forming four independent sample pools, respectively. (3) Subject the DNA pools to WES generating deep coverage data (100 folds). (4) Preprocess the raw reads with Fastp (v0.20.1) to remove adapters and low-quality reads (Chen et al. 2018 ). (5) Align the clean reads to the IWGSC RefSeq v3.0 reference genome using BWA (v0.7.17) mem algorithm with default parameters (Li 2013 ). (6) Exclude unmapped and non-primary alignments with Samtools view (v1.7) (Li et al. 2009 ). (7) Use our custom Perl script (Filter.ambi.pl) to filter the primary filtered SAM files to retain unambiguous alignments. (8) Remove PCR duplicates and sort the BAM files with Samtools. (9) Use GATK (v4.0.10.1) (McCormick et al. 2015 ) RealignerTargetCreator and HaplotypeCaller to generate gVCF files, requiring a minimum mapping quality of 30. Use GATK GenomicsDBImport and VariantsToTable to compile variants from all samples. (10) Use the mean δ-index values (Abe et al. 2012 ) from 2 Mb sliding windows (0.1 Mb per slide) to define the candidate region. The linkage interval is the region framed by the positions whose corresponding mean δ-index values exceed the 95th percentile of the mean of all δ-index values. (11) Annotate variants using ANNOVAR (Wang et al. 2010 ) to determine functional effects in coding and non-coding regions. Construction of CRISPR/Cas9 vector to knock out LT1 To disrupt LT1 function, conserved coding regions in LT1 are selected as editing targets to induce frameshift or premature stop codon mutations. The CRISPR MultiTargeter web tool (Prykhozhij et al. 2015 ) is utilized for guide RNA (gRNA) design against LT1 . Two gRNA target sites flanking the original LT1 mutation are chosen within its coding sequence (Target 1 sequence (5’-3’): AGTCATATAAACTACATGA, Target 2 sequence (5’-3’): ATAGTGACAACAAGATCTG). The two gRNAs are cloned into the pUE413 plasmid following digestion with Bsa I (NEB: R0535S) and ligation with T4 ligase (NEB: M0202S). Wheat transformation Using the Agrobacterium-mediated genetic transformation method, the pUE413 plasmid containing two gRNA targets was used to transform immature embryos of a spring cultivar wheat Fielder. This plasmid has the cauliflower mosaic virus 35S promoter and nos terminator regulating expression of the BAR gene, which confers bialaphos herbicide resistance for transformant selection. Immature embryo transformation and tissue culture were performed following the protocol described by Sivamani et al . (Ishida et al. 2015 ). The LT1 target region was PCR amplified and sequenced to identify mutations. The number of tillers was recorded in both edited and non-edited T 2 progeny lines for comparison. Quantification of sucrose content The quantification of sucrose utilizes acid hydrolysis to break down sucrose into glucose and fructose. The fructose then reacts with phenol to form a colored product that can be detected at a 480-nanometer wavelength. Shoot base samples were harvested from 30 lt1 mutants and wild-type C6878 plants at the developmental stages of two, three, and four leaves, with three biological replicates per genotype per stage. Approximately 100 mg of fresh shoot base tissue was ground in liquid nitrogen for each sample. Sucrose extraction and colorimetric detection were performed following the detailed protocol provided in the sucrose assay kit from Solarbio (item no. BC2465). Dynamic observation of AM Development and its subsequent outgrowth in wheat To evaluate AM development in wheat, seedlings were examined at the developmental stages of 1) only coleoptile emerged, 2) two leaves, and 3) four leaves. At each stage, shoot base samples were collected randomly from several seedlings. After carefully removing the leaves, the shoot bases were directly visualized using a stereomicroscope. The number of visible axillary meristems was counted at each timepoint. We also examined the plants after the heading stage for axillary bud outgrowth to observe if they had ceased axillary buds, like the process of AM number counting. Co-expression analysis Tissues used in gene expression analysis were harvested from shoot bases where AMs arise. These materials belong to lt1 and C6878 at three different development stages: two-leaf, three-leaf, and four-leaf stages, with three replicates per time point. mRNA for each sample was extracted using TRIzol Reagent (Invitrogen) and then subjected to RNA sequencing performed by the ANOROAD company. Clustering analysis was performed using the Mfuzz R package (Kumar and Futschik 2007 ). The data containing all sample Transcripts Per Million Mapped Reads (TPM) values was first standardized using z-score normalization. Soft clustering was then carried out using the “mfuzz” R package with default parameters. Differentially expressed genes (DEGs) were identified using the R package DEGseq2 (Love et al. 2014 ). Genes with absolute log 2 fold change greater than one and adjusted p-value (padj) less than 0.05 relative to the wild-type plant were considered statistically significantly expressed. Gene ontology (GO) analysis was conducted to categorize DEGs into functional groups. GO annotation library for each gene of wheat was calculated from the eggNOG database (version 4.5) using default parameters (Huerta-Cepas et al. 2019 ). These annotations were then used to build a custom R package, “ org.Taestivum.eg.db ”, containing the GO information for all genes analyzed. The R package clusterProfiler (Wu et al. 2021 ) was utilized along with “ org.Taestivum.eg.db ” to perform GO enrichment analysis on DEGs. Details on the use of clusterProfiler can be found in its documentation. Subcellular localization assay To investigate the subcellular localization of the LT1 protein, we performed an in vitro localization experiment using wheat protoplasts. We fused the C-terminal of LT1 from the Chinese Spring wheat landrace to GFP plasmid pBL21 and transformed the fusion construct LT1-GFP into wheat protoplasts via polyethylene glycol-mediated transfection, as described previously by Xiong et al. ( 2022 ) (Xiong et al. 2022 ). After incubating transformed protoplasts at 23°C for 12–16 hours, we visualized GFP fluorescence by confocal laser scanning microscopy (LSM 880, Carl Zeiss, Germany) to determine the intracellular localization of the LT1-GFP protein. Quantitative RT-PCR Quantitative real-time PCR (qRT-PCR) was performed to assess gene expression levels, as described previously (Xiong et al. 2022 ). Briefly, total RNA was extracted using TRIzol Reagent (Invitrogen), followed by DNase I (Takara) treatment to remove residual DNA, and then the RNA was purified using an RNA purification kit (Tiangen). First-strand cDNA synthesis was carried out using the iScript cDNA synthesis kit (Bio-Rad). qRT-PCR was conducted using the SsoFast EvaGreen Supermix kit (Bio-Rad) on a CFX 96 real-time PCR system (Bio-Rad) with the following amplification program: 95°C for 2 min, 40 cycles of 95°C for 5 s, and 60°C for 35 s. Primers used for qRT-PCR are listed in Supplemental Table S1 . The wheat ACTTIN ( TraesCS1A02G020500 ) gene served as an internal control. Relative gene expression was calculated by the 2 −ΔΔCt method (Livak and Schmittgen 2001 ). Each experiment was performed with at least three biological replicates. Results Phenotypes of the wheat tillering mutant lt1 The lt1 mutant was derived from an EMS mutagenesis pool of the elite wheat landrace C6878. This recessive mutant exhibits reduced tillering, typically producing four tillers compared to eighteen of C6878 at the heading stage (Fig. 1 A-B). To explain whether the reduced tillers are due to defects in bud initiation or bud elongation, we observed the dynamic development process of tiller buds. At first, we found that the number of AMs remained consistent during the coleoptile and two-leaf stages but started to diverge by the four-leaf stage, with four in the wild type and two in lt1 (Fig. 1 C-D). This revealed that the reduced tillering number of lt1 is partially due to the defective AM initiation. Furthermore, we examined the number of ceased lateral buds at the heading stage. This thorough examination revealed a reduced outgrowth ratio of lt1 compared to C6878 (Fig. 1 E-F). Taken together, the tillering defect of lt1 appears attributable to both its reduced AM formation and bud outgrowth. Additional pleiotropic defects in lt1 , including decreased stature, short roots, chlorotic leaves, and wrinkled seeds, are concomitant (Fig. S1 ). These global impacts on lt1 development suggest that LT1 plays significant roles in multiple processes. Isolation of LT1 by an upgraded bulked segregant method, uni-BSA The LT1 gene was proven to be a recessive gene via assessing the F 2 segregating population. However, the hexaploidy wheat genome is highly complex, which makes traditional map-based cloning more time-consuming. To expedite the cloning of LT1 , we utilized the BSA-based method uni-BSA, using the big data from WES combined with our newly developed algorithm, making it cost-friendly and effective. Firstly, the WES data was used to minimize the genome size without the penalty of losing protein-encoding genes while guaranteeing enough SNPs to carry out linkage analysis. Secondly, to address the ambiguous mapping when alignment is performed due to the high duplication proportion of the wheat genome, which may result in aligning one read to multiple loci, we tailor-make a Perl script called Filter.ambi.pl integrated into the uni-BSA protocol (Fig. 2 , Fig. S4A). This algorithm potentially leverages reads as more as possible that are uniquely mapped and their mate reads, even if their mate reads are mapped ambiguously to several locations. Accordingly, this filtering method retained 61% of total reads, compared to 48% when discarding all ambiguous reads (Fig. S2 B). As a result, the average percentage of each gene coverage was over 81%, with the majority of genes covered at 100% (Fig. S2 C). The average coding sequencing depth reached 70X, implying robust sequencing quality for accurate variant calling (Fig. S2 D). Application of uni-BSA narrowed LT1 to a 6 Mb region on the short arm of chromosome 2D (Fig. 3 A), compared to 8 Mb without ambiguous read filtering (Fig. S4D). This interval contains 140 genes, of which 65 genes have variations, including SNPs and Indels. As EMS tends to cause SNPs over Indels, 17 Indels were excluded, thus eliminating five genes. Additionally, 41 SNPs of lt1 matching the reference Chinese Spring are unlikely EMS-induced mutations. Ultimately, four genes were identified as candidate genes (Table S2 ). Interestingly, one gene, TraesCS2D03G0082100 , encoding a nucleotide-binding (NB) domain protein (Fig. 3 C), harbors an SNP mutation in the 793rd base (C-T), causing a premature of this gene in lt1 (Fig. 3 C), while the other four genes had UTR mutations. In addition, the individuals of the F 2 population with this homozygous mutation displayed lt1 phenotypes (Fig. 3 B). Further, TraesCS2D03G0082100 was not expressed in 2-leaf, 3-leaf, and 4-leaf of lt1 , compared to the wildtype (Fig. 3 D). We initially considered TraesCS2D03G0082100 the likely causal LT1 gene, given its severe mutation and undetectable expression. Verification of LT1 To validate TraesCS2D03G0082100 as the LT1 gene regulating tillering in wheat, we used CRISPR/Cas9 to create knock-out mutants in Fielder. The three independent edited lines with different mutations within its coding sequences were obtained (Fig. 4 A). The LT1-CR1 and LT1-CR2 show the mutations at gRNA targeted sites, and LT1-CR3 has 239 bp deletion (Fig. 4 A). Intriguingly, all three edited homozygous individuals produced fewer tillers than the wildtype (Fig. 4 B-D). Moreover, these three lines exhibit other defects of lt1 , like yellow leaves (Fig. 4 B-D), thus confirming TraesCS2D03G0082100 as the LT1 locus. Therefore, we hereby designate TraesCS2D03G0082100 as LT1. To elucidate the possible reasons for pleiotropic phenotypes of the lt1 mutant, we assessed the expression levels of LT1 in various tissues. qPCR analysis revealed ubiquitous expression of LT1 , with exceptionally high levels in leaves (Fig. 4 E). Given its high level in leaves, it is not strange that lt1 has yellow leaves once LT1 is disrupted. LT1 was detectable in tiller buds, albeit at relatively lower levels (Fig. 4 E). The broad expression pattern of LT1 suggests its multiple roles in wheat development. Overall, these data indicate that LT1 likely influences tillering and other developmental processes indirectly or directly. To determine the sublocation of LT1, we carried out a transient expression experiment of LT1 in wheat protoplasts. In contrast with the control, which is ubiquitous in protoplast cells, the LT1-GFP fusion protein was predominantly localized in chloroplasts (Fig. 4 F). The chloroplast location of LT1 implies that LT1 may operate nutrition production, like sucrose, to control tillering. The regulatory pathways of LT1 in tillering development LT1 controls lateral bud formation by targeting TaROX / TaLAX1 directly or indirectly To investigate modular relationships involving LT1 further, we conducted a co-expression analysis using TPM values from shoot base tissues at three developmental stages: the two-leaf, three-leaf, and four-leaf. An initial survey of these RNA-seq datasets revealed that samples belonging to each group clustered well (Fig. S3). The transcripts were grouped into eight clusters representing distinct gene expression trends (Fig. 5 A). LT1 expression, which belongs to cluster five, was highest at the two-leaf stage and then decreased at the three- and four-leaf stages. We considered the two-leaf stage to be essential for AM initiation since genes active in this stage showed a pulse expression and then decreased in the following stages. Thus, we performed GO analysis on genes with significant changes between lt1 and C6878, revealing perturbation of various pathways in lt1 . We then specially examined genes belonging to the overlap between cluster five and the two-leaf stage to determine which pathways were affected (Fig. 5 B). Notably, in this stage, various pathways (Fig. 5 C) related to AM formation shared the locus TraesCS3B02G383000 , an ortholog of Arabidopsis ROX and LAX1 in rice that regulate AM formation. These pathways include “morphogenesis of a branching structure”, “secondary shoot formation”, and “shoot axis formation”. Moreover, TraesCS3B02G383000 , namely Ta3BLAX1 , is undetectable in lt1 (Fig. 5 D). This is consistent with our previous observation of significant differences in tiller numbers at the four-leaf stage in lt1 mutants (Fig. 1 C). Taken together, LT1 might regulate AM initiation by affecting TaROX / TaLAX1 directly or indirectly. Auxin and cytokinin are involved in tiller development in lt1 Auxin and CK play antagonistic roles in regulating tillering (Yuan et al. 2023 ). We performed GO analysis on the genes in the intersection between the three developmental stages and cluster 5, respectively. The results revealed perturbation in several phytohormone-related pathways, including auxin, CK, salicylic acid, and jasmonic acid (Fig. 6 A). Among these pathways, the indole-containing compound biosynthesis process, in which auxin is biosynthesized, was enriched at all three developmental stages. For example, TrpA family genes Ta5BTrpA and Ta5DTrpA exhibited significant upregulation in lt1 . This suggests that higher auxin levels may inhibit tillering in lt1 (Fig. 6 B). In addition to auxin, CK levels were suggestively decreased, as indicated by the upregulation of TaCKX5 ( cytokinin dehydrogenase 5 ) genes ( Ta3ACKX5, Ta3BCKX5 , and Ta3DCKX5 ) mediating CK degradation (Fig. 6 B). These CKX5 genes were also enriched in pathways related to secondary shoot formation (Bartrina et al. 2011 ), implying CK metabolism may play an important role in tillering in wheat controlled by LT1 . Taken together, LT1 may regulate tillering through the involvement of auxin and cytokinin-related pathways. LT1 may function through the sucrose biosynthesis pathway As with all organisms, plants require energy for growth. They achieve this by intercepting light and fixing it into usable chemical forms via photosynthesis. The resulting carbohydrate (sugar) energy is then utilized as substrates for growth or stored as reserves (Eveland and Jackson 2012 ), thus influencing various aspects of plant development, such as tillering (Rabot et al. 2012 ). Our co-expression analysis revealed perturbations in the fructose 1,6-bisphosphate (FBP) pathway at the four-leaf stage, which is involved in sucrose biosynthesis (Fig. 6 C). Coincidentally, RNA-seq analysis using whole seedlings with two leaves also showed perturbations of the FBP pathway genes (Fig. 6 D). Within this pathway, three closely related TaFBPase genes involved in sucrose biosynthesis were down-regulated in lt1 mutants (Fig. 6 E), implying lower sucrose levels. To determine if the sucrose levels have changed in the lt1 mutant, we collected the shoot base at the two-, three-, and four-leaf stages and measured the sucrose level. Indeed, it decreased significantly in lt1 mutants compared to wildtype (Fig. 6 F). Together, these datasets suggest LT1 may exert its influence on tillering and other phenotypes by targeting FBPases , thereby impacting sucrose levels. Discussion In higher plants, the degree and pattern of tillering/branching are major determinants of plant architecture and grain yield, especially in crops. Significant advances have been made in identifying genes controlling branching in model plants like Arabidopsis and rice, but fewer genes controlling tillering have been identified in wheat. This study used a new approach called uni-BSA to clone LT1 , a chloroplast protein with an NB-containing domain. Functional analysis revealed that LT1 modulates auxin, CK, and sucrose levels to control tillering in wheat (Fig. 7 ). The uni-BSA method is well-suited for wheat gene cloning BSA is a cost-effective and robust approach for identifying causal genes from segregating populations. BSA-based methods, such as bulked segregant RNA sequencing (BSR-seq) (del Viso et al. 2012 ), Mutmap (Abe et al. 2012 ), and Graded-seq (Wang et al. 2019 ), enable the rapid development of genetic markers and gene cloning. However, few genes have been mapped using BSA-based methods in wheat. This is mainly due to the high cost of whole genome resequencing for BSA, which becomes prohibitive given the large genome size of wheat and the high proportion of repetitive regions that lead to ambiguous read mapping. To address these challenges, firstly, we implemented WES to identify variations while ensuring sufficient markers for the linkage analysis and, thus, reducing the genome from 17 Gb to 260 Mb. Secondly, we developed an effective uni-BSA algorithm to filter ambiguous reads while retaining as many reads as possible, improving mapping accuracy and narrowing down smaller candidate gene intervals (Fig. S4D). Namely, uni-BSA can produce more sensitive δ index values than those with no-filtering or strict-filtering methods, making it easier to define linkage areas (Fig. S4C). While the linkage interval defined by the strict-filering method is same as uni-BSA, the uni-BSA covers more genomic areas by using its algorithm (Fig. S2 A). Collectively, our uni-BSA method is a powerful and preferable approach for gene cloning in wheat. LT1 shares an NB domain with plant resistance proteins The NB domain is a common feature of many plant resistance proteins, also known as NB-LRR proteins, named after their central NB domain and C-terminal leucine-rich repeat (LRR) domain (Takken and Tameling 2009 ). Because R proteins can trigger host cell death, their activity requires tight regulation. Studies of R protein interactions and mutagenesis revealed that both the NB and LRR domains play a role in the auto-inhibition of these proteins (Rairdan and Moffett 2006 ; Rairdan and Moffett 2007 ). Additionally, the LRR domain likely functions in recognizing avirulence effectors produced by pathogens (Takken and Tameling 2009 ). Despite their role in disease resistance, dysregulation of R proteins also impacts developmental processes, resulting in phenotypes like stunted dwarfism (Yang and Hua 2004 ; Michael Weaver et al. 2006 ), increased branching (Igari et al. 2008 ), early leaf senescence (Xie et al. 2023 ), altered plant height (Borrill et al. 2022 ), and abnormal panicle development (Pan et al. 2022 ). Unlike other R proteins, the LT1 gene identified in our study encodes only an NB domain, lacking the LRR domain. Our analysis showed 2035 NB domain-containing genes in the wheat genome, with 964 lacking LRR domains (Fig. S5). The evolutionary mechanisms leading to the high number of NB-only proteins require further investigation. We hypothesize that disruption of LT1 removes its auto-inhibition, thereby activating resistance responses and impacting developmental pathways like tillering. Alternatively, LT1 may presumably play a direct role in the regulation of tillering, independent of disease resistance. The chloroplast location of LT1 provides a link between its effect on disease resistance and plant development. Chloroplasts are energy production sites, so localization to this organelle implies that LT1 may impact developmental processes by influencing energy production. This is consistent with the pleiotropic phenotypes observed in lt1 , such as reduced tillering, plant height, and short roots. Further investigation of how a chloroplast-localized protein like LT1 influences energy production and downstream developmental pathways will shed important light on its roles in plant growth and disease resistance. LT1 is essential in controlling wheat developments, especially in tillering Crop tillering is a trait closely related to yield. LT1 is a new gene controlling wheat tiller number through both disrupting bud initiation and its outgrowth. CRISPR/Cas9-generated transformants phenocopied lt1 phenotypes, including reduced tiller number, shorter stature, yellow leaves, and additional traits. However, some progeny derived from certain heterozygous individuals, especially those with large truncations of LT1 , like LT-CR3 , displayed lethal phenotypes in seedlings (Fig. 4 D), as evidenced by yellowing and withered leaves. This seedling lethality in some genotypes likely explains the inability to find lines exhibiting lt1 phenotypes in segregating populations in the field conditions, as these lines died at early developmental stages. Overall, the pleiotropic effects caused by LT1 disruption, including lethality in severe cases, demonstrate that LT1 plays an essential role in regulating diverse aspects of wheat development. In our study, we observed significant alterations in sucrose levels and phytohormone metabolism throughout the dynamic developmental stages of lt1 (Fig. 7 ). Notably, LT1 is also shown to influence the TaROX / TaLAX1 gene, a key mediator of axillary meristem initiation. Together, these results provide new insights into the molecular mechanisms governing tillering in wheat. Elucidation of LT1 's multifaceted roles in this process, from energy metabolism to hormone signaling, will enable more targeted breeding efforts to optimize tiller number and wheat yields. Further exploration of LT1 and its interacting partners will enhance our understanding of the intricate regulatory systems that will illuminate the complex regulatory networks controlling tillering and plant architecture in cereal crops. Declarations Acknowledgements We sincerely thank Prof. ZhongFu Ni for providing the lt1 mutant and the CRISPR/Cas9 vector. We cordially express our gratitude to Prof. Yongwang Wang for her insightful advice on experimental design. Author contribution Yundong Yuan: Conceptualization, Investigation, Writing – original draft, Writing – review & editing. Bo Lyu: Conceptualization, Plant transformation. Juan Qi: Field investigation and CRISPR/Cas9 vector constructing. Yuanzhi Wang and Xin Liu: Plant management. Pierre Delaplace: Supervision. Yanfang Du: Funding acquisition, Writing – review & editing, Supervision. Funding This work was funded by the National Natural Science Foundation of China (32201840), the National Natural Science Foundation of Shandong province (ZR2022QC048 and ZR2022MC199), and the National Key Research and Development Program of China (2021YFD1200601-08). Data availability All data are enclosed either in the main text or as supplementary materials. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4229022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290513642,"identity":"a9a3902c-ee7e-4302-9ac2-9700bde4df31","order_by":0,"name":"Yundong Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACwwcJFRJyQAbjAQaGBKK0GBt8OGNhzMDAzEC0FjPJmW0ViQ1EazG4kbxBmodNIr1fIv/AgQ8VaQz87d349RncSCsw5uGRyJ05I5nh4IwzOQwSZ85uIKAlxyCZR0Iid8ONZIbDvG0VDAZANkEth3kMJNINSNFi2DgjQSIBqiWHsBbJM8+KGT4ckDCc2fPYAOiXNB6CfuE7nrz9R+K/Onl+9sSHDz5UJMvxt/fi16JwIQFVgAevchCQ7z9AUM0oGAWjYBSMdAAAzRJNdj1nGHoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5163-4383","institution":"Shandong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Yundong","middleName":"","lastName":"Yuan","suffix":""},{"id":290513644,"identity":"540dcf0a-ba24-47fc-af4e-0b9133378d2f","order_by":1,"name":"Bo Lyu","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Lyu","suffix":""},{"id":290513646,"identity":"ada418d2-8f68-425a-aceb-768d1f3bd900","order_by":2,"name":"Juan Qi","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Qi","suffix":""},{"id":290513648,"identity":"b01554cf-b09d-4c0d-89a7-0144d8a50d73","order_by":3,"name":"Xin Liu","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":290513649,"identity":"9dd40a0e-2358-4741-8c9a-69a0ee5a4bdf","order_by":4,"name":"Yuanzhi Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuanzhi","middleName":"","lastName":"Wang","suffix":""},{"id":290513650,"identity":"0aa688ec-4d18-43dd-b9b4-26d204d47207","order_by":5,"name":"Pierre Delaplace","email":"","orcid":"","institution":"University of Liege: Universite de Liege","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Delaplace","suffix":""},{"id":290513651,"identity":"4ae4e018-7fed-4805-b1cd-ead5248115d2","order_by":6,"name":"Yanfang Du","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yanfang","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-04-07 01:03:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4229022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4229022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54836073,"identity":"206e10c7-d9b5-42c0-aed1-1e8e3be7833f","added_by":"auto","created_at":"2024-04-17 12:36:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408553,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypes of \u003cem\u003elt1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Tiller number comparison between \u003cem\u003elt1\u003c/em\u003e and C6878. Bar = 15 cm.\u003c/p\u003e\n\u003cp\u003e(B) The statistical values representing the tiller number of \u003cem\u003elt1\u003c/em\u003e and C6878. Values are means ± SD (n = 10). *** P \u0026lt; 0.001, Student’s t-test.\u003c/p\u003e\n\u003cp\u003e(C) Dynamic observation of AM formation between \u003cem\u003elt1\u003c/em\u003e and C6878, namely, coleoptile, two- and four-leaf stages. The yellow arrow indicates the bud primordium. Bar = 200 µm.\u003c/p\u003e\n\u003cp\u003e(D) Axillary bud number of \u003cem\u003elt1\u003c/em\u003eand C6878 at each stage. Values are means ± SD (n = 5). * P \u0026lt; 0.05, Student’s t-test.\u003c/p\u003e\n\u003cp\u003e(E) Ceased bud observation at the heading stage. The ceased buds are closed up in the white box. Bar = 1 cm.\u003c/p\u003e\n\u003cp\u003e(F) Ceased bud ratios at the heading stage. Values are means ± SD (n = 4). ** P \u0026lt; 0.01, Student’s t-test.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/47d476c3b3c535a13262570c.png"},{"id":54835588,"identity":"2c63ca93-7802-4df4-a657-ee03747a7a6c","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241601,"visible":true,"origin":"","legend":"\u003cp\u003ePipeline of the innovative BSA method In this flowchart, seeds of wheat C6878 were mutagenized by EMS. The resulting M\u003csub\u003e2\u003c/sub\u003e or higher generation mutant plant \u003cem\u003elt1\u003c/em\u003e was backcrossed to C6878 and self-pollinated to generate an F\u003csub\u003e2\u003c/sub\u003e segregating population. Four bulks indicated by red circled digits were subjected to Whole-Exone sequencing (WES). The big datasets were aligned against the Chinese Spring version 3.0. Then, the resulting sam files were primarily filtered and deep treated by the Perl script “Filter.ambi.pl” to leverage as more as possible reads. Sequentially, the GVCF files containing all variants were generated. The causal variants responsible for the mutant phenotype were identified in the candidate interval.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/05372f1dddd5ecbeba94fc01.png"},{"id":54835590,"identity":"8448b682-1483-4e40-ae38-cdd9cecd3d00","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121455,"visible":true,"origin":"","legend":"\u003cp\u003eGene cloning of \u003cem\u003eLT1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Gene linkage analysis. It indicates that \u003cem\u003eLT1\u003c/em\u003e is framed in a 6 Mb interval on the short arm of the chromosome 2D between 14-16 Mb. The horizontal dotted line represents the 95\u003csup\u003eth\u003c/sup\u003e mean of all the delta indexes.\u003c/p\u003e\n\u003cp\u003e(B) Mutation validified by Sanger sequencing. The nucleotide change is shaded. The individuals of the F\u003csub\u003e2\u003c/sub\u003e population with the homozygous mutation display \u003cem\u003elt1\u003c/em\u003e phenotypes.\u003c/p\u003e\n\u003cp\u003e(C) Schematic diagram of LT1. This protein contains an RX-CC (N-terminal coiled-coil) domain and the NB-ARC (nucleotide-binding domain) domain. The stop gain position is 265\u003csup\u003eth\u003c/sup\u003e amino acid.\u003c/p\u003e\n\u003cp\u003e(D) Relative expression of \u003cem\u003eLT1\u003c/em\u003e at three stages of \u003cem\u003elt1\u003c/em\u003e and the C6878. Values are means ± SD (n = 3). *** P \u0026lt; 0.001, Student’s t-test.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/6f6a32470116db7edc603f82.png"},{"id":54835589,"identity":"25e347a6-5b11-4e0a-9287-e86122588e52","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":681066,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of \u003cem\u003eLT1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic diagrams indicating targets of CRISPR/Cas9 (\u003cem\u003eLT1-CRs\u003c/em\u003e) and the mutations in each line. PAM sites are depicted with underlines. Deletions are indicated with words in red color, and \u003cem\u003eLT1-CR3\u003c/em\u003e had a 293 deletion.\u003c/p\u003e\n\u003cp\u003e(B) Phenotypes of \u003cem\u003eLT1-CRs\u003c/em\u003e. All the \u003cem\u003eLT1-CRs \u003c/em\u003eshow \u003cem\u003elt1\u003c/em\u003e phenotypes, including yellow leaves and fewer tillers in the field conditions. The wild-type plants circled by red are Fielder. The mutants with \u003cem\u003eLT1\u003c/em\u003efunction disrupted are boxed by blue circles and closed up in the right pictures.\u003c/p\u003e\n\u003cp\u003e(C) Tiller number comparison between\u003cem\u003e LT1-CRs\u003c/em\u003e and the wild type. Values are means ± SD (n = 4). *** P \u0026lt; 0.001, Student’s t-test.\u003c/p\u003e\n\u003cp\u003e(D) Phenotypes of \u003cem\u003eLT1-CRs\u003c/em\u003e in the cabinet. Progenies\u003cem\u003e \u003c/em\u003eof \u003cem\u003eLT-CR1\u003c/em\u003e and \u003cem\u003eLT-CR2\u003c/em\u003e grown in the growth cabinet at the seedling stage show fewer tillers. Progenies of \u003cem\u003eLT-CR3\u003c/em\u003e are lethal in seedlings. Bar = 7.5 cm.\u003c/p\u003e\n\u003cp\u003e(E) Relative expression levels of \u003cem\u003eLT1\u003c/em\u003e in various tissues. The Values are relative to \u003cem\u003eACTIN\u003c/em\u003e. Values are mean ± SD (n = 3).\u003c/p\u003e\n\u003cp\u003e(F) Sublocation of LT1 using wheat protoplasts. Scale bars correspond to 10 μm.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/e07ed9d1866aeb214a3d1def.png"},{"id":54835586,"identity":"3774557d-1476-47bc-a4b7-a5995273414b","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":336835,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression analysis of DEGs in C6878 and \u003cem\u003elt1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Expression cluster analysis. Eight clusters of all the gene expressions are grouped distinctly. The cluster 5 contains \u003cem\u003eLT1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(B) Venn diagram showing the proportion of overlapped genes between the leaf2 stage (significantly changed) and cluster 5.\u003c/p\u003e\n\u003cp\u003e(C) Go analysis of the genes belonging to the intersection between the leaf 2 stage and cluster 5 containing \u003cem\u003eLT1\u003c/em\u003e. Pathways sharing \u003cem\u003eTaLAX1\u003c/em\u003e are framed by the black box. P. adjust values indicating significance are colored gradually from blue to red.\u003c/p\u003e\n\u003cp\u003e(D) Expression levels of \u003cem\u003eTa3BLAX1\u003c/em\u003e. Values are mean ± SD (n = 4). ** P \u0026lt; 0.01, Student’s t-test.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/3aae0126ab052a920bf99d84.png"},{"id":54835593,"identity":"b0cf87f4-232e-4c85-ade1-2395794df90a","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":368637,"visible":true,"origin":"","legend":"\u003cp\u003eGO analysis of genes related to auxin, cytokinin, and sucrose\u003c/p\u003e\n\u003cp\u003e(A) Various phytohormones enriched by GO analysis. The p.adjust values are portrayed by blue to red gradient colors. The gene number represented by values on the X-axis is the count number belonging to the pathways enriched.\u003c/p\u003e\n\u003cp\u003e(B) Auxin and cytokinin related gene expression (TPM) values. Values are means ± SD (n = 3). *** P \u0026lt; 0.001, Student’s t-test.\u003c/p\u003e\n\u003cp\u003e(C) and (D) Go analysis of DEGs at the four-leaf stage and DEGs from seedlings. The sucrose biosynthesis pathways perturbed are arrowed. The gene number represented by values of the X-axis is the count number belonging to the pathways enriched. P.ajust values indicating significance are colored gradually from blue to red.\u003c/p\u003e\n\u003cp\u003e(E) Relative expression levels of \u003cem\u003eFBPase\u003c/em\u003e. The Values are relative to \u003cem\u003eACTIN\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(F) Sucrose content comparison. Sucrose levels of \u003cem\u003elt1\u003c/em\u003e in three developmental stages are all reduced significantly than that in C6878. Values are means ± SD (n = 3). *** P \u0026lt; 0.001, Student’s t-test.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/14d205463305b51c02b9767f.png"},{"id":54835592,"identity":"8f856af2-fce4-4e9f-9ad2-6869947392cc","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119970,"visible":true,"origin":"","legend":"\u003cp\u003eA proposed model of \u003cem\u003eLT1\u003c/em\u003eregulating tillering\u003c/p\u003e\n\u003cp\u003eIn this model, \u003cem\u003eLT1\u003c/em\u003epromotes \u003cem\u003eTaLAX1\u003c/em\u003e to regulate AM formation. \u003cem\u003eLT1\u003c/em\u003e can affect \u003cem\u003eTaFBPase\u003c/em\u003eexpression levels, thus mediating internal sucrose content to facilitate axillary bud outgrowth. \u003cem\u003eLT1\u003c/em\u003e also impacts phytohormone-related genes to control tillering, as it inhibits \u003cem\u003eTrpA\u003c/em\u003e to reduce auxin levels and inhibits \u003cem\u003eTaCKX5s\u003c/em\u003e to increase cytokinin levels.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/a6dac28153a23c9da4ada9be.png"},{"id":54836531,"identity":"cd9bee28-922a-4674-9bef-f06ecc70fd3d","added_by":"auto","created_at":"2024-04-17 12:44:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3092332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/d38a4205-ed0d-4786-9425-900156e18f98.pdf"},{"id":54835587,"identity":"f3e51fc1-2abd-40a2-bec2-5c5e588f3508","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2207392,"visible":true,"origin":"","legend":"","description":"","filename":"Supfig1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/86f131fc8bfa61f2dea90343.docx"},{"id":54835584,"identity":"9ec2ce9d-e53a-41bf-b6bb-a859c214c0d2","added_by":"auto","created_at":"2024-04-17 12:28:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16780,"visible":true,"origin":"","legend":"","description":"","filename":"Supptables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4229022/v1/bbea6d39ee2aa4815a0fd840.docx"}],"financialInterests":"","formattedTitle":"A novel regulator of wheat tillering LT1 identified by using an innovative BSA method","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat provides approximately one-fifth of human caloric intake worldwide, highlighting the importance of improving grain yield for global food security (Cao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tillering, the process of generating tillers, is a major determinant of yield, as tillers can bear grains (Kebrom et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). While tillering is known to be regulated by external and internal factors (Yuan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), molecular mechanisms underlying this process remain poorly understood in wheat compared to model species like rice and \u003cem\u003eArabidopsis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTillering/branching encompasses the initiation of the axillary meristem (AM) and its subsequent outgrowth. Many genes controlling tillering/branching have been identified and characterized. For example, AM formation involves conserved regulators such as rice \u003cem\u003eMONOCULM1\u003c/em\u003e (\u003cem\u003eMOC1\u003c/em\u003e) and its cognate ortholog \u003cem\u003eArabidopsis LATERAL SUPPRESSOR\u003c/em\u003e (\u003cem\u003eLAS\u003c/em\u003e), which function specifically in the leaf axil to promote meristem development (Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Greb et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Notably, the \u003cem\u003eArabidopsis REGULATOR OF AXILLARY MERISTEM FORMATION\u003c/em\u003e (\u003cem\u003eROX\u003c/em\u003e) basic helix\u0026ndash;loop\u0026ndash;helix (bHLH) transcription factor is required for AM initiation. The \u003cem\u003erox\u003c/em\u003e mutants display compromised axillary bud formation during vegetable shoot development. The double mutants of \u003cem\u003erox\u003c/em\u003e and \u003cem\u003elas\u003c/em\u003e enhance their branching defects, indicating that \u003cem\u003eROX\u003c/em\u003e functions independently of \u003cem\u003eLAS\u003c/em\u003e in AM initiation (Yang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The ortholog of \u003cem\u003eROX\u003c/em\u003e in rice, \u003cem\u003eLAX PANICLE1\u003c/em\u003e (\u003cem\u003eLAX1\u003c/em\u003e), expresses in every AM, which indicates \u003cem\u003eLAX1\u003c/em\u003e is involved in the formation of all types of AMs throughout the ontogeny of a rice plant. In contrast with \u003cem\u003eROX\u003c/em\u003e, \u003cem\u003eLAX1\u003c/em\u003e mainly influences the AM formation of both tillers and panicle branches (Komatsu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), suggesting a divergence between species.\u003c/p\u003e \u003cp\u003eBud outgrowth is inhibited by the \u003cem\u003eTEOSINTE BRANCHED1\u003c/em\u003e (\u003cem\u003eTB1\u003c/em\u003e) transcription factor and its homologs across species, which acts as an integrator of multiple plant hormones, including strigolactones (SLs), auxin, and cytokinins (CKs) (Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Takeda et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Matthes et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kepinski and Leyser \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Alder et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Smith and Li \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tanaka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Shimizu-Sato et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Emerging evidence highlights the trophic and signaling roles of sugars in promoting bud outgrowth (Mason et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For example, the reduced tillering of wheat \u003cem\u003etiller inhibition\u003c/em\u003e (\u003cem\u003etin\u003c/em\u003e) mutant is attributed to low sucrose levels (Kebrom et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similarly, reduced tiller formation in the rice \u003cem\u003emonoculm 2\u003c/em\u003e (\u003cem\u003emoc2\u003c/em\u003e) mutant results from the disruption in the fructose-1,6-bisphosphatase, an enzyme involved in sucrose biosynthesis, leading to decreased sucrose supply (Koumoto et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The necessity for sugars for bud outgrowth has been demonstrated in rose (\u003cem\u003eRosa hybrida\u003c/em\u003e), where sugar is required to trigger bud outgrowth in single nodes cultivated \u003cem\u003ein vitro\u003c/em\u003e (Rabot et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barbier et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, sucrose can also modulate the dynamics of bud outgrowth in a concentration-dependent manner, especially during the transition phase between bud release and sustained bud elongation (Barbier et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, the removal of competing sugar sources or sinks within buds through defoliation provides additional evidence for the regulatory role of sugars in bud release (Mason et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kebrom and Mullet \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to their roles as nutrients, sugars have been shown to influence phytohormone homeostasis. For instance, sucrose stimulates CK biosynthesis in bud-bearing stem segments by upregulating the expression of two CK biosynthesis-related genes (Barbier et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sucrose can also modulate auxin metabolism in a concentration-dependent manner in R. \u003cem\u003ehybrida\u003c/em\u003e buds (Barbier et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, according to the auxin canalization model, elevated sucrose levels within buds facilitate auxin export from the bud to the stem, promoting bud outgrowth (Barbier et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings collectively demonstrate the crucial role of sugar signaling in regulating bud release.\u003c/p\u003e \u003cp\u003eThe rapid development of sequencing technologies in recent years has accelerated the cloning of genes associated with important traits in crops. Traditional forward gene mapping methods, such as map-based cloning, are time-consuming and costly, especially in wheat with a large and complex genome (17 G) (Consortium et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, the recent research on the cloned gene \u003cem\u003eELS3\u003c/em\u003e, which controls leaf senescence, involved 10,133 individuals and spanned several years (Xie et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Current breakthroughs using high-throughput sequencing techniques have accelerated the identification of genes linked to agronomic traits and made gene isolation more feasible and efficient. For instance, the adaptable method MutMap (Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) has been widely used in identifying genes associated with a variety of traits, including but not limited to salt tolerance (Takagi et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), endosperm development (Wang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e), flowering and seed size (Manchikatla et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), height and spikelet (Huang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and more. MutMap-derived methods, such as MutMap+ (Fekih et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), MutMap-Gap (Takagi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e), and QTL-seq (Takagi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e), have also been developed to improve the efficiency and accuracy of gene mapping. However, the immense wheat genome remains cost-prohibitive for gene cloning using next-generation resequencing data (Consortium et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To tackle this issue, a whole-exome resequencing method was developed, significantly reducing the scope of the wheat genome (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More critically, wheat's over 80% repetitive sequence rate (Consortium et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) poses challenges for unambiguous read mapping, an essential step for gene cloning that must be overcome.\u003c/p\u003e \u003cp\u003ePrevious research indicates that abscisic acid (ABA) and SLs play a crucial role in wheat tiller development, which mediates by wheat \u003cem\u003eTaD27\u003c/em\u003e (Zhao et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the orthologs of rice \u003cem\u003eDwarf27\u003c/em\u003e (\u003cem\u003eD27\u003c/em\u003e) (Lin et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and \u003cem\u003eTiller Number1\u003c/em\u003e (\u003cem\u003eTN1\u003c/em\u003e) which encodes an ankyrin repeat protein (Dong et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), respectively. However, Our understanding of molecular mechanisms regulating tillering in wheat remains limited. This study presents a wheat mutant named \u0026lsquo;\u003cem\u003eless tiller1\u0026rsquo;\u003c/em\u003e (\u003cem\u003elt1\u003c/em\u003e), which exhibits reduced tillering. In addition to fewer tillers, \u003cem\u003elt1\u003c/em\u003e shows reduced stature, chlorotic leaves, and stunted roots. Using an upgraded bulked segregant analysis method called uni-BSA, which is well-suited for wheat, we mapped \u003cem\u003eLT1\u003c/em\u003e to the short arm of chromosome 2D. Further analyses suggested that \u003cem\u003eLT1\u003c/em\u003e encodes a nucleotide-binding domain protein, and LT1 is localized in chloroplasts. Our data shows that \u003cem\u003eLT1\u003c/em\u003e might regulate the expression pattern of \u003cem\u003eTaROX\u003c/em\u003e/\u003cem\u003eTaLAX1\u003c/em\u003e and sucrose levels to control tillering. Understanding the role of \u003cem\u003eLT1\u003c/em\u003e will offer valuable perspectives for molecular breeding in wheat. Additionally, our findings provide a new method that allows for the swift and precise identification of crucial genes linked to important agronomic traits in crops.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and growth conditions\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003elt1\u003c/em\u003e mutant is derived from a mutagenesis pool of a landrace Chang6878 (C6878) treated with 1% Ethyl Methanesulfonate (EMS). The \u003cem\u003elt1\u003c/em\u003e phenotypes were inherited stably after four generations of self-pollination. For gene mapping, \u003cem\u003elt1\u003c/em\u003e was backcrossed with C6878 and self-fertilized to produce a segregating F\u003csub\u003e2\u003c/sub\u003e population of at least 1000 individuals. Wheat plants are cultivated in the experimental field at Shandong Agriculture University, Tai\u0026rsquo;an, Shandong, China. The transgenetic plants are grown in a growth chamber maintained at 22/17\u0026deg;C day/night temperatures, 16-h photoperiod, and about 300 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e photosynthetically active radiation at 45% humidity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExome capture sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNAs were extracted from a minimum of 100 individuals with contrasting extreme phenotypes from an F\u003csub\u003e2\u003c/sub\u003e population, along with 10 \u003cem\u003elt1\u003c/em\u003e mutants and 10 C6878 plants serving as two control DNA pools, using the CTAB method (Chatterjee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The mutant-type and wild-type DNA pools of the F\u003csub\u003e2\u003c/sub\u003e population were generated by bulking at least 50 genomic DNAs in an equal ratio. The \u003cem\u003elt1\u003c/em\u003e mutant and C6878 DNA pools were also generated in an equal ratio.\u003c/p\u003e \u003cp\u003eThe datasets generated from Whole-Exome Sequencing (WES) for variation calling in this study were obtained from the Oebiotech company. In principle, the WES generates 260 Mb data per fold of the wheat genome, including 110,000 high-confidence protein-coding genes, 50,000 non-coding genes, and associated promoters. We obtained 26 GB of data per sample, corresponding to 100-fold coverage depth. For more detailed information on WES and the corresponding bioinformatic pipelines, please refer to the Oebiotech website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oebiotech.com/\u003c/span\u003e\u003cspan address=\"https://www.oebiotech.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe uni-BSA pipeline for rapid gene isolation\u003c/h2\u003e \u003cp\u003eWe developed a novel bulked segregant analysis pipeline called uni-BSA for rapid gene cloning in wheat (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This approach consists of the following steps. (1) Develop a segregating population from a backcross between the mutant and the wild-type parental line. (2) Extract and pool DNAs from the mutants, their wild types, and individuals with mutant and wild-type phenotypes of the F\u003csub\u003e2\u003c/sub\u003e population in equal proportions, forming four independent sample pools, respectively. (3) Subject the DNA pools to WES generating deep coverage data (100 folds). (4) Preprocess the raw reads with Fastp (v0.20.1) to remove adapters and low-quality reads (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). (5) Align the clean reads to the IWGSC RefSeq v3.0 reference genome using BWA (v0.7.17) mem algorithm with default parameters (Li \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). (6) Exclude unmapped and non-primary alignments with Samtools view (v1.7) (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). (7) Use our custom Perl script (Filter.ambi.pl) to filter the primary filtered SAM files to retain unambiguous alignments. (8) Remove PCR duplicates and sort the BAM files with Samtools. (9) Use GATK (v4.0.10.1) (McCormick et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) RealignerTargetCreator and HaplotypeCaller to generate gVCF files, requiring a minimum mapping quality of 30. Use GATK GenomicsDBImport and VariantsToTable to compile variants from all samples. (10) Use the mean δ-index values (Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) from 2 Mb sliding windows (0.1 Mb per slide) to define the candidate region. The linkage interval is the region framed by the positions whose corresponding mean δ-index values exceed the 95th percentile of the mean of all δ-index values. (11) Annotate variants using ANNOVAR (Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to determine functional effects in coding and non-coding regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of CRISPR/Cas9 vector to knock out\u003c/b\u003e \u003cb\u003eLT1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo disrupt \u003cem\u003eLT1\u003c/em\u003e function, conserved coding regions in \u003cem\u003eLT1\u003c/em\u003e are selected as editing targets to induce frameshift or premature stop codon mutations. The CRISPR MultiTargeter web tool (Prykhozhij et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is utilized for guide RNA (gRNA) design against \u003cem\u003eLT1\u003c/em\u003e. Two gRNA target sites flanking the original \u003cem\u003eLT1\u003c/em\u003e mutation are chosen within its coding sequence (Target 1 sequence (5\u0026rsquo;-3\u0026rsquo;): AGTCATATAAACTACATGA, Target 2 sequence (5\u0026rsquo;-3\u0026rsquo;): ATAGTGACAACAAGATCTG). The two gRNAs are cloned into the pUE413 plasmid following digestion with \u003cem\u003eBsa\u003c/em\u003eI (NEB: R0535S) and ligation with T4 ligase (NEB: M0202S).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eWheat transformation\u003c/h2\u003e \u003cp\u003eUsing the Agrobacterium-mediated genetic transformation method, the pUE413 plasmid containing two gRNA targets was used to transform immature embryos of a spring cultivar wheat Fielder. This plasmid has the cauliflower mosaic virus \u003cem\u003e35S\u003c/em\u003e promoter and nos terminator regulating expression of the \u003cem\u003eBAR\u003c/em\u003e gene, which confers bialaphos herbicide resistance for transformant selection. Immature embryo transformation and tissue culture were performed following the protocol described by Sivamani \u003cem\u003eet al\u003c/em\u003e. (Ishida et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The \u003cem\u003eLT1\u003c/em\u003e target region was PCR amplified and sequenced to identify mutations. The number of tillers was recorded in both edited and non-edited T\u003csub\u003e2\u003c/sub\u003e progeny lines for comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of sucrose content\u003c/h2\u003e \u003cp\u003eThe quantification of sucrose utilizes acid hydrolysis to break down sucrose into glucose and fructose. The fructose then reacts with phenol to form a colored product that can be detected at a 480-nanometer wavelength. Shoot base samples were harvested from 30 \u003cem\u003elt1\u003c/em\u003e mutants and wild-type C6878 plants at the developmental stages of two, three, and four leaves, with three biological replicates per genotype per stage. Approximately 100 mg of fresh shoot base tissue was ground in liquid nitrogen for each sample. Sucrose extraction and colorimetric detection were performed following the detailed protocol provided in the sucrose assay kit from Solarbio (item no. BC2465).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDynamic observation of AM Development and its subsequent outgrowth in wheat\u003c/h2\u003e \u003cp\u003eTo evaluate AM development in wheat, seedlings were examined at the developmental stages of 1) only coleoptile emerged, 2) two leaves, and 3) four leaves. At each stage, shoot base samples were collected randomly from several seedlings. After carefully removing the leaves, the shoot bases were directly visualized using a stereomicroscope. The number of visible axillary meristems was counted at each timepoint. We also examined the plants after the heading stage for axillary bud outgrowth to observe if they had ceased axillary buds, like the process of AM number counting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCo-expression analysis\u003c/h2\u003e \u003cp\u003eTissues used in gene expression analysis were harvested from shoot bases where AMs arise. These materials belong to \u003cem\u003elt1\u003c/em\u003e and C6878 at three different development stages: two-leaf, three-leaf, and four-leaf stages, with three replicates per time point. mRNA for each sample was extracted using TRIzol Reagent (Invitrogen) and then subjected to RNA sequencing performed by the ANOROAD company. Clustering analysis was performed using the Mfuzz R package (Kumar and Futschik \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The data containing all sample Transcripts Per Million Mapped Reads (TPM) values was first standardized using z-score normalization. Soft clustering was then carried out using the \u0026ldquo;mfuzz\u0026rdquo; R package with default parameters. Differentially expressed genes (DEGs) were identified using the R package DEGseq2 (Love et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Genes with absolute log\u003csub\u003e2\u003c/sub\u003e fold change greater than one and adjusted p-value (padj) less than 0.05 relative to the wild-type plant were considered statistically significantly expressed. Gene ontology (GO) analysis was conducted to categorize DEGs into functional groups. GO annotation library for each gene of wheat was calculated from the eggNOG database (version 4.5) using default parameters (Huerta-Cepas et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These annotations were then used to build a custom R package, \u0026ldquo;\u003cem\u003eorg.Taestivum.eg.db\u003c/em\u003e\u0026rdquo;, containing the GO information for all genes analyzed. The R package clusterProfiler (Wu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was utilized along with \u0026ldquo;\u003cem\u003eorg.Taestivum.eg.db\u003c/em\u003e\u0026rdquo; to perform GO enrichment analysis on DEGs. Details on the use of clusterProfiler can be found in its documentation.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eSubcellular localization assay\u003c/h2\u003e \u003cp\u003eTo investigate the subcellular localization of the LT1 protein, we performed an \u003cem\u003ein vitro\u003c/em\u003e localization experiment using wheat protoplasts. We fused the C-terminal of LT1 from the Chinese Spring wheat landrace to GFP plasmid pBL21 and transformed the fusion construct LT1-GFP into wheat protoplasts via polyethylene glycol-mediated transfection, as described previously by Xiong et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Xiong et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After incubating transformed protoplasts at 23\u0026deg;C for 12\u0026ndash;16 hours, we visualized GFP fluorescence by confocal laser scanning microscopy (LSM 880, Carl Zeiss, Germany) to determine the intracellular localization of the LT1-GFP protein.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative RT-PCR\u003c/h2\u003e \u003cp\u003eQuantitative real-time PCR (qRT-PCR) was performed to assess gene expression levels, as described previously (Xiong et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, total RNA was extracted using TRIzol Reagent (Invitrogen), followed by DNase I (Takara) treatment to remove residual DNA, and then the RNA was purified using an RNA purification kit (Tiangen). First-strand cDNA synthesis was carried out using the iScript cDNA synthesis kit (Bio-Rad). qRT-PCR was conducted using the SsoFast EvaGreen Supermix kit (Bio-Rad) on a CFX 96 real-time PCR system (Bio-Rad) with the following amplification program: 95\u0026deg;C for 2 min, 40 cycles of 95\u0026deg;C for 5 s, and 60\u0026deg;C for 35 s. Primers used for qRT-PCR are listed in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The wheat \u003cem\u003eACTTIN\u003c/em\u003e (\u003cem\u003eTraesCS1A02G020500\u003c/em\u003e) gene served as an internal control. Relative gene expression was calculated by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method (Livak and Schmittgen \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Each experiment was performed with at least three biological replicates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePhenotypes of the wheat tillering mutant\u003c/b\u003e \u003cb\u003elt1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003elt1\u003c/em\u003e mutant was derived from an EMS mutagenesis pool of the elite wheat landrace C6878. This recessive mutant exhibits reduced tillering, typically producing four tillers compared to eighteen of C6878 at the heading stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). To explain whether the reduced tillers are due to defects in bud initiation or bud elongation, we observed the dynamic development process of tiller buds. At first, we found that the number of AMs remained consistent during the coleoptile and two-leaf stages but started to diverge by the four-leaf stage, with four in the wild type and two in \u003cem\u003elt1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D). This revealed that the reduced tillering number of \u003cem\u003elt1\u003c/em\u003e is partially due to the defective AM initiation. Furthermore, we examined the number of ceased lateral buds at the heading stage. This thorough examination revealed a reduced outgrowth ratio of \u003cem\u003elt1\u003c/em\u003e compared to C6878 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). Taken together, the tillering defect of \u003cem\u003elt1\u003c/em\u003e appears attributable to both its reduced AM formation and bud outgrowth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional pleiotropic defects in \u003cem\u003elt1\u003c/em\u003e, including decreased stature, short roots, chlorotic leaves, and wrinkled seeds, are concomitant (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These global impacts on \u003cem\u003elt1\u003c/em\u003e development suggest that \u003cem\u003eLT1\u003c/em\u003e plays significant roles in multiple processes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIsolation of\u003c/b\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003eby an upgraded bulked segregant method, uni-BSA\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eLT1\u003c/em\u003e gene was proven to be a recessive gene via assessing the F\u003csub\u003e2\u003c/sub\u003e segregating population. However, the hexaploidy wheat genome is highly complex, which makes traditional map-based cloning more time-consuming. To expedite the cloning of \u003cem\u003eLT1\u003c/em\u003e, we utilized the BSA-based method uni-BSA, using the big data from WES combined with our newly developed algorithm, making it cost-friendly and effective. Firstly, the WES data was used to minimize the genome size without the penalty of losing protein-encoding genes while guaranteeing enough SNPs to carry out linkage analysis. Secondly, to address the ambiguous mapping when alignment is performed due to the high duplication proportion of the wheat genome, which may result in aligning one read to multiple loci, we tailor-make a Perl script called Filter.ambi.pl integrated into the uni-BSA protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. S4A). This algorithm potentially leverages reads as more as possible that are uniquely mapped and their mate reads, even if their mate reads are mapped ambiguously to several locations. Accordingly, this filtering method retained 61% of total reads, compared to 48% when discarding all ambiguous reads (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). As a result, the average percentage of each gene coverage was over 81%, with the majority of genes covered at 100% (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). The average coding sequencing depth reached 70X, implying robust sequencing quality for accurate variant calling (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eApplication of uni-BSA narrowed \u003cem\u003eLT1\u003c/em\u003e to a 6 Mb region on the short arm of chromosome 2D (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), compared to 8 Mb without ambiguous read filtering (Fig. S4D). This interval contains 140 genes, of which 65 genes have variations, including SNPs and Indels. As EMS tends to cause SNPs over Indels, 17 Indels were excluded, thus eliminating five genes. Additionally, 41 SNPs of \u003cem\u003elt1\u003c/em\u003e matching the reference Chinese Spring are unlikely EMS-induced mutations. Ultimately, four genes were identified as candidate genes (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Interestingly, one gene, \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e, encoding a nucleotide-binding (NB) domain protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), harbors an SNP mutation in the 793rd base (C-T), causing a premature of this gene in \u003cem\u003elt1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), while the other four genes had UTR mutations. In addition, the individuals of the F\u003csub\u003e2\u003c/sub\u003e population with this homozygous mutation displayed \u003cem\u003elt1\u003c/em\u003e phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Further, \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e was not expressed in 2-leaf, 3-leaf, and 4-leaf of \u003cem\u003elt1\u003c/em\u003e, compared to the wildtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We initially considered \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e the likely causal \u003cem\u003eLT1\u003c/em\u003e gene, given its severe mutation and undetectable expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eVerification of\u003c/b\u003e \u003cb\u003eLT1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e as the \u003cem\u003eLT1\u003c/em\u003e gene regulating tillering in wheat, we used CRISPR/Cas9 to create knock-out mutants in Fielder. The three independent edited lines with different mutations within its coding sequences were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The \u003cem\u003eLT1-CR1\u003c/em\u003e and \u003cem\u003eLT1-CR2\u003c/em\u003e show the mutations at gRNA targeted sites, and \u003cem\u003eLT1-CR3\u003c/em\u003e has 239 bp deletion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Intriguingly, all three edited homozygous individuals produced fewer tillers than the wildtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D). Moreover, these three lines exhibit other defects of \u003cem\u003elt1\u003c/em\u003e, like yellow leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D), thus confirming \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e as the \u003cem\u003eLT1\u003c/em\u003e locus. Therefore, we hereby designate \u003cem\u003eTraesCS2D03G0082100\u003c/em\u003e as \u003cem\u003eLT1.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the possible reasons for pleiotropic phenotypes of the \u003cem\u003elt1\u003c/em\u003e mutant, we assessed the expression levels of \u003cem\u003eLT1\u003c/em\u003e in various tissues. qPCR analysis revealed ubiquitous expression of \u003cem\u003eLT1\u003c/em\u003e, with exceptionally high levels in leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Given its high level in leaves, it is not strange that \u003cem\u003elt1\u003c/em\u003e has yellow leaves once \u003cem\u003eLT1\u003c/em\u003e is disrupted. \u003cem\u003eLT1\u003c/em\u003e was detectable in tiller buds, albeit at relatively lower levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The broad expression pattern of \u003cem\u003eLT1\u003c/em\u003e suggests its multiple roles in wheat development. Overall, these data indicate that \u003cem\u003eLT1\u003c/em\u003e likely influences tillering and other developmental processes indirectly or directly.\u003c/p\u003e \u003cp\u003eTo determine the sublocation of LT1, we carried out a transient expression experiment of LT1 in wheat protoplasts. In contrast with the control, which is ubiquitous in protoplast cells, the LT1-GFP fusion protein was predominantly localized in chloroplasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The chloroplast location of LT1 implies that \u003cem\u003eLT1\u003c/em\u003e may operate nutrition production, like sucrose, to control tillering.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe regulatory pathways of\u003c/b\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003ein tillering development\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003econtrols lateral bud formation by targeting\u003c/b\u003e \u003cb\u003eTaROX\u003c/b\u003e\u003cb\u003e/\u003c/b\u003e\u003cb\u003eTaLAX1\u003c/b\u003e \u003cb\u003edirectly or indirectly\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate modular relationships involving \u003cem\u003eLT1\u003c/em\u003e further, we conducted a co-expression analysis using TPM values from shoot base tissues at three developmental stages: the two-leaf, three-leaf, and four-leaf. An initial survey of these RNA-seq datasets revealed that samples belonging to each group clustered well (Fig. S3). The transcripts were grouped into eight clusters representing distinct gene expression trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). \u003cem\u003eLT1\u003c/em\u003e expression, which belongs to cluster five, was highest at the two-leaf stage and then decreased at the three- and four-leaf stages. We considered the two-leaf stage to be essential for AM initiation since genes active in this stage showed a pulse expression and then decreased in the following stages. Thus, we performed GO analysis on genes with significant changes between \u003cem\u003elt1\u003c/em\u003e and C6878, revealing perturbation of various pathways in \u003cem\u003elt1\u003c/em\u003e. We then specially examined genes belonging to the overlap between cluster five and the two-leaf stage to determine which pathways were affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, in this stage, various pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) related to AM formation shared the locus \u003cem\u003eTraesCS3B02G383000\u003c/em\u003e, an ortholog of \u003cem\u003eArabidopsis ROX\u003c/em\u003e and \u003cem\u003eLAX1\u003c/em\u003e in rice that regulate AM formation. These pathways include \u0026ldquo;morphogenesis of a branching structure\u0026rdquo;, \u0026ldquo;secondary shoot formation\u0026rdquo;, and \u0026ldquo;shoot axis formation\u0026rdquo;. Moreover, \u003cem\u003eTraesCS3B02G383000\u003c/em\u003e, namely \u003cem\u003eTa3BLAX1\u003c/em\u003e, is undetectable in \u003cem\u003elt1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). This is consistent with our previous observation of significant differences in tiller numbers at the four-leaf stage in \u003cem\u003elt1\u003c/em\u003e mutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Taken together, \u003cem\u003eLT1\u003c/em\u003e might regulate AM initiation by affecting \u003cem\u003eTaROX\u003c/em\u003e/\u003cem\u003eTaLAX1\u003c/em\u003e directly or indirectly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAuxin and cytokinin are involved in tiller development in\u003c/b\u003e \u003cb\u003elt1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAuxin and CK play antagonistic roles in regulating tillering (Yuan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We performed GO analysis on the genes in the intersection between the three developmental stages and cluster 5, respectively. The results revealed perturbation in several phytohormone-related pathways, including auxin, CK, salicylic acid, and jasmonic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among these pathways, the indole-containing compound biosynthesis process, in which auxin is biosynthesized, was enriched at all three developmental stages. For example, TrpA family genes \u003cem\u003eTa5BTrpA\u003c/em\u003e and \u003cem\u003eTa5DTrpA\u003c/em\u003e exhibited significant upregulation in \u003cem\u003elt1\u003c/em\u003e. This suggests that higher auxin levels may inhibit tillering in \u003cem\u003elt1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In addition to auxin, CK levels were suggestively decreased, as indicated by the upregulation of \u003cem\u003eTaCKX5\u003c/em\u003e (\u003cem\u003ecytokinin dehydrogenase 5\u003c/em\u003e) genes (\u003cem\u003eTa3ACKX5, Ta3BCKX5\u003c/em\u003e, \u003cem\u003eand Ta3DCKX5\u003c/em\u003e) mediating CK degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These \u003cem\u003eCKX5\u003c/em\u003e genes were also enriched in pathways related to secondary shoot formation (Bartrina et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), implying CK metabolism may play an important role in tillering in wheat controlled by \u003cem\u003eLT1\u003c/em\u003e. Taken together, \u003cem\u003eLT1\u003c/em\u003e may regulate tillering through the involvement of auxin and cytokinin-related pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003emay function through the sucrose biosynthesis pathway\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs with all organisms, plants require energy for growth. They achieve this by intercepting light and fixing it into usable chemical forms via photosynthesis. The resulting carbohydrate (sugar) energy is then utilized as substrates for growth or stored as reserves (Eveland and Jackson \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), thus influencing various aspects of plant development, such as tillering (Rabot et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Our co-expression analysis revealed perturbations in the fructose 1,6-bisphosphate (FBP) pathway at the four-leaf stage, which is involved in sucrose biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Coincidentally, RNA-seq analysis using whole seedlings with two leaves also showed perturbations of the FBP pathway genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Within this pathway, three closely related \u003cem\u003eTaFBPase\u003c/em\u003e genes involved in sucrose biosynthesis were down-regulated in \u003cem\u003elt1\u003c/em\u003e mutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), implying lower sucrose levels. To determine if the sucrose levels have changed in the \u003cem\u003elt1\u003c/em\u003e mutant, we collected the shoot base at the two-, three-, and four-leaf stages and measured the sucrose level. Indeed, it decreased significantly in \u003cem\u003elt1\u003c/em\u003e mutants compared to wildtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Together, these datasets suggest \u003cem\u003eLT1\u003c/em\u003e may exert its influence on tillering and other phenotypes by targeting \u003cem\u003eFBPases\u003c/em\u003e, thereby impacting sucrose levels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn higher plants, the degree and pattern of tillering/branching are major determinants of plant architecture and grain yield, especially in crops. Significant advances have been made in identifying genes controlling branching in model plants like \u003cem\u003eArabidopsis\u003c/em\u003e and rice, but fewer genes controlling tillering have been identified in wheat. This study used a new approach called uni-BSA to clone \u003cem\u003eLT1\u003c/em\u003e, a chloroplast protein with an NB-containing domain. Functional analysis revealed that \u003cem\u003eLT1\u003c/em\u003e modulates auxin, CK, and sucrose levels to control tillering in wheat (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe uni-BSA method is well-suited for wheat gene cloning\u003c/h2\u003e \u003cp\u003eBSA is a cost-effective and robust approach for identifying causal genes from segregating populations. BSA-based methods, such as bulked segregant RNA sequencing (BSR-seq) (del Viso et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Mutmap (Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Graded-seq (Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), enable the rapid development of genetic markers and gene cloning. However, few genes have been mapped using BSA-based methods in wheat. This is mainly due to the high cost of whole genome resequencing for BSA, which becomes prohibitive given the large genome size of wheat and the high proportion of repetitive regions that lead to ambiguous read mapping. To address these challenges, firstly, we implemented WES to identify variations while ensuring sufficient markers for the linkage analysis and, thus, reducing the genome from 17 Gb to 260 Mb. Secondly, we developed an effective uni-BSA algorithm to filter ambiguous reads while retaining as many reads as possible, improving mapping accuracy and narrowing down smaller candidate gene intervals (Fig. S4D). Namely, uni-BSA can produce more sensitive δ index values than those with no-filtering or strict-filtering methods, making it easier to define linkage areas (Fig. S4C). While the linkage interval defined by the strict-filering method is same as uni-BSA, the uni-BSA covers more genomic areas by using its algorithm (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Collectively, our uni-BSA method is a powerful and preferable approach for gene cloning in wheat.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003eshares an NB domain with plant resistance proteins\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe NB domain is a common feature of many plant resistance proteins, also known as NB-LRR proteins, named after their central NB domain and C-terminal leucine-rich repeat (LRR) domain (Takken and Tameling \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Because R proteins can trigger host cell death, their activity requires tight regulation. Studies of R protein interactions and mutagenesis revealed that both the NB and LRR domains play a role in the auto-inhibition of these proteins (Rairdan and Moffett \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rairdan and Moffett \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, the LRR domain likely functions in recognizing avirulence effectors produced by pathogens (Takken and Tameling \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite their role in disease resistance, dysregulation of R proteins also impacts developmental processes, resulting in phenotypes like stunted dwarfism (Yang and Hua \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Michael Weaver et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), increased branching (Igari et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), early leaf senescence (Xie et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), altered plant height (Borrill et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and abnormal panicle development (Pan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnlike other R proteins, the \u003cem\u003eLT1\u003c/em\u003e gene identified in our study encodes only an NB domain, lacking the LRR domain. Our analysis showed 2035 NB domain-containing genes in the wheat genome, with 964 lacking LRR domains (Fig. S5). The evolutionary mechanisms leading to the high number of NB-only proteins require further investigation. We hypothesize that disruption of \u003cem\u003eLT1\u003c/em\u003e removes its auto-inhibition, thereby activating resistance responses and impacting developmental pathways like tillering. Alternatively, \u003cem\u003eLT1\u003c/em\u003e may presumably play a direct role in the regulation of tillering, independent of disease resistance.\u003c/p\u003e \u003cp\u003eThe chloroplast location of LT1 provides a link between its effect on disease resistance and plant development. Chloroplasts are energy production sites, so localization to this organelle implies that LT1 may impact developmental processes by influencing energy production. This is consistent with the pleiotropic phenotypes observed in \u003cem\u003elt1\u003c/em\u003e, such as reduced tillering, plant height, and short roots. Further investigation of how a chloroplast-localized protein like LT1 influences energy production and downstream developmental pathways will shed important light on its roles in plant growth and disease resistance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLT1\u003c/b\u003e \u003cb\u003eis essential in controlling wheat developments, especially in tillering\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCrop tillering is a trait closely related to yield. \u003cem\u003eLT1\u003c/em\u003e is a new gene controlling wheat tiller number through both disrupting bud initiation and its outgrowth. CRISPR/Cas9-generated transformants phenocopied \u003cem\u003elt1\u003c/em\u003e phenotypes, including reduced tiller number, shorter stature, yellow leaves, and additional traits. However, some progeny derived from certain heterozygous individuals, especially those with large truncations of \u003cem\u003eLT1\u003c/em\u003e, like \u003cem\u003eLT-CR3\u003c/em\u003e, displayed lethal phenotypes in seedlings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), as evidenced by yellowing and withered leaves. This seedling lethality in some genotypes likely explains the inability to find lines exhibiting \u003cem\u003elt1\u003c/em\u003e phenotypes in segregating populations in the field conditions, as these lines died at early developmental stages. Overall, the pleiotropic effects caused by \u003cem\u003eLT1\u003c/em\u003e disruption, including lethality in severe cases, demonstrate that \u003cem\u003eLT1\u003c/em\u003e plays an essential role in regulating diverse aspects of wheat development.\u003c/p\u003e \u003cp\u003eIn our study, we observed significant alterations in sucrose levels and phytohormone metabolism throughout the dynamic developmental stages of \u003cem\u003elt1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, \u003cem\u003eLT1\u003c/em\u003e is also shown to influence the \u003cem\u003eTaROX\u003c/em\u003e/\u003cem\u003eTaLAX1\u003c/em\u003e gene, a key mediator of axillary meristem initiation. Together, these results provide new insights into the molecular mechanisms governing tillering in wheat. Elucidation of \u003cem\u003eLT1\u003c/em\u003e's multifaceted roles in this process, from energy metabolism to hormone signaling, will enable more targeted breeding efforts to optimize tiller number and wheat yields. Further exploration of \u003cem\u003eLT1\u003c/em\u003e and its interacting partners will enhance our understanding of the intricate regulatory systems that will illuminate the complex regulatory networks controlling tillering and plant architecture in cereal crops.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe sincerely thank Prof. ZhongFu Ni for providing the \u003cem\u003elt1\u003c/em\u003e mutant and the CRISPR/Cas9 vector. We cordially express our gratitude to Prof. Yongwang Wang for her insightful advice on experimental design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003eYundong Yuan: Conceptualization, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Bo Lyu: Conceptualization, Plant transformation. Juan Qi: Field investigation and CRISPR/Cas9 vector constructing. Yuanzhi Wang and Xin Liu: Plant management. Pierre Delaplace: Supervision.\u0026nbsp;Yanfang Du: Funding acquisition, Writing \u0026ndash; review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was funded by the National Natural Science Foundation of China (32201840), the National Natural Science Foundation of Shandong province (ZR2022QC048 and ZR2022MC199), and the National Key Research and Development Program of China (2021YFD1200601-08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eAll data are enclosed either in the main text or as supplementary materials. Other data can be requested from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. 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Plant Biotechnol J 18 (2):513-525. http://doi.org/10.1111/pbi.13220.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wheat, tillering, auxin, cytokinin, sucrose, whole-exome resequencing, bulked segregant analysis","lastPublishedDoi":"10.21203/rs.3.rs-4229022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4229022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBranching/tillering is a critical process for plant architecture and grain yield. However, Branching is intricately controlled by both endogenous and environmental factors. The underlying mechanisms of tillering in wheat remain poorly understood. In this study, we identified \u003cem\u003eLess Tiller 1\u003c/em\u003e (\u003cem\u003eLT1\u003c/em\u003e) as a novel regulator of wheat tillering using a newly upgraded bulked segregant analysis (BSA) method called uni-BSA, which is well-suited for wheat. Loss-of-function of \u003cem\u003eLT1\u003c/em\u003e results in fewer tillers due to defects in axillary meristem initiation and bud outgrowth. We mapped \u003cem\u003eLT1\u003c/em\u003e to a 6 Mb region on the chromosome 2D short arm and validated a nucleotide-binding (NB) domain encoding gene as \u003cem\u003eLT1\u003c/em\u003e using CRISPR/Cas9. Furthermore, the lower sucrose concentration in the shoot bases of \u003cem\u003elt1\u003c/em\u003e might result in inadequate bud outgrowth due to disturbances in the sucrose biosynthesis pathways. Co-expression analysis suggests that \u003cem\u003eLT1\u003c/em\u003e controls tillering by regulating \u003cem\u003eTaROX/TaLAX1\u003c/em\u003e, the ortholog of the Arabidopsis tiller regulator \u003cem\u003eREGULATOR OF AXILLARY MERISTEM FORMATION\u003c/em\u003e (\u003cem\u003eROX\u003c/em\u003e) or the rice axillary meristem regulator \u003cem\u003eLAX PANICLE1\u003c/em\u003e (\u003cem\u003eLAX1\u003c/em\u003e). This study not only offers a novel genetic resource for cultivating optimal plant architecture but also underscores the importance of our innovative BSA method. This uni-BSA method enables the swift and precise identification of pivotal genes associated with significant agronomic traits, thereby hastening gene cloning and crop breeding processes in wheat.\u003c/p\u003e","manuscriptTitle":"A novel regulator of wheat tillering LT1 identified by using an innovative BSA method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 12:28:01","doi":"10.21203/rs.3.rs-4229022/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-04-12T12:49:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-12T12:05:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-07T23:47:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2024-04-06T21:02:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ce5dab45-09b9-472a-b26f-a39bd6918790","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-30T03:05:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 12:28:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4229022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4229022","identity":"rs-4229022","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}