Genome-wide identification of MATH-BTB gene family and expression analysis in rye (Secale cereale L.)

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Zhenbo Zhai, Yonghe Che, Yunjie Yang, Xirui Wei, Yanping Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7922703/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Proteins encoded by the MATH-BTB gene family regulate plant grain development by mediating the degradation of transcription factors. This study identified the MATH-BTB family in rye through a homology-based search and performed a bioinformatic analysis to investigate its functions. We identified 56 MATH-BTB genes in the rye genome. Phylogenetic analysis classified these genes into 10 distinct subfamilies. Synteny analysis revealed​no collinear genes with Arabidopsis or maize, one with rice, and 16 with the A subgenome of wheat. Cluster analysis of expression data revealed that this gene family is predominantly expressed in spikes and grains, suggesting its potential role in grain development. Integration with our previous findings on yield-related traits pinpointed a specific MATH-BTB gene, ScWN4R01G352500.1 , which was previously associated with grain width.. This strengthens the hypothesis that this gene family plays a role in grain development. Furthermore, we developed a Kompetitive Allele-Specific PCR (KASP) marker that effectively genotypes the grain width trait. Collectively, these results provide a valuable resource for future functional studies of the MATH-BTB family in rye and offer a directly applicable marker for molecular breeding programs aimed at improving grain width. rye MATH-BTB gene family bioinformatics analysis grain width kASP marker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Grain yield, a primary objective in cereal breeding, is governed by a suite of traits, including panicle size, grain dimensions (length and width), and thousand-grain weight (Kumar et al. 2016 ; Wang et al. 2019; Guan et al. 2020 ). Numerous genes controlling these traits have been characterized across cereals. A prominent example is the GS3 gene in rice ( Oryza sativa L.), a major quantitative trait locus (QTL) for grain length and weight, whose functional homologs in wheat ( Triticum aestivum L.) and maize ( Zea mays L.) modulate similar traits (Fan et al. 2006 ; Mao et al. 2010 ; Li et al. 2010 ; Zhang et al. 2014 ). Likewise, loss-of-function mutations in GW2 and GW5 increase grain weight (Song et al. 2007 ; Shomura et al. 2008 ; Huang et al. 2013 ), whereas GS5 , which encodes a serine carboxypeptidase, acts as a positive regulator of grain size (Li et al. 2011 ). The MATH-BTB proteins constitute a subfamily of the BTB (Broad-complex, Tramtrack, and Bric-à-brac) family, characterized by an N-terminal MATH (Meprin and TRAF homology) domain and a C-terminal BTB/POZ domain that forms a characteristic fold (Artem et al. 2023 ). This protein family is implicated in maintaining cellular homeostasis and regulating the development of various plant organs. Initial characterization of the MATH-BTB family in plants was performed in maize and Arabidopsis thaliana (L.) Heynh (Juranič et al. 2012 ; Chen et al. 2014 ). In Arabidopsis , the six family members influence fatty acid metabolism and seed development by regulating the ethylene-responsive transcription factor WRI1 (WRINKLED1) (Weber et al. 2009; Chen et al. 2013 ). In maize, MATH-BTB proteins are critical for reproductive development, among other key biological processes (Juranič et al. 2014). In wheat, the gene TaBTB-5A has been identified as influencing yield-related traits such as plant height, grain length, and grain width (Dong, 2021 ). However, the functions of this protein family remain uncharacterized in rye ( Secale cereale L.). Rye, a member of the Triticeae tribe ( Poaceae family), is often considered a secondary crop due to its close morphological resemblance to wheat and barley ( Hordeum vulgare L.). It was domesticated in the coastal areas of the eastern Mediterranean (McElroy, 2014 ; Institute, 2022 ). Crucially, rye shares high genomic homology with wheat, which has facilitated its extensive use in wheat improvement programs, for instance, through the introgression of desirable traits (Bauer et al., 2017 ; Rabanus-Wallace et al., 2021 ). The KASP (Kompetitive Allele-Specific PCR) system is a fluorescence-based genotyping technology that enables high-throughput, precise bi-allelic scoring of target SNPs and InDels (Dipta et al., 2024 ). It is widely applied in plant breeding to screen for desirable agronomic traits. For example, five KASP markers highly associated with smut resistance were identified in sugarcane (Gao et al., 2022 ), and 15 markers linked to powdery mildew resistance were developed in melon (Cao et al., 2021 ). In wheat breeding, KASP markers have been successfully used to select genotypes with enhanced drought tolerance (Eltaher et al., 2023 ). This study performs a systematic identification and comprehensive bioinformatic analysis of the MATH-BTB gene family in rye. Furthermore, we developed KASP markers for yield-related traits, providing a practical tool for the molecular screening of wheat plants with improved agronomic characteristics. Materials and methods Plant materials The three rye accessions used in this study (89R13, Z1672, and 90R13). There were obtained from and maintained by Hebei Normal University of Science and Technology. All accessions were grown under standard field conditions at the university's agricultural experiment station in Changli, Hebei Province, China, during the 2022 growing season. Identification of the MATH-BTB gene family in rye ​ The protein sequences of known MATH-BTB genes from Arabidopsis obtained from the TAIR database, while those from rice and maize were retrieved from Ensembl Plants. The genome sequences of rye (Weining) and the A subgenome of wheat were downloaded from the National Genomics Data Center (NGDC) database (Li et al., 2021 ; Ling et al., 2013 ). Identification was performed in two steps. First, the Arabidopsis MATH-BTB protein sequences were used as queries to perform a local BLASTP search against the rye and wheat A subgenome protein databases using TBtools software (Chen et al., 2020 ). Second, an online BLASTP search was conducted via the NCBI website to identify additional potential homologs. The conserved domains of all candidate proteins from Arabidopsis, wheat, and rye were verified using the NCBI’s conserved domain database (CDD) batch search tool. Results were visualized with TBtools, and only proteins containing both the MATH and BTB domains were retained for subsequent analysis. Following identification, the physicochemical properties of the deduced rye MATH-BTB proteins were predicted using the ExPASy ProtParam tool (Gasteiger et al., 2003 ). Phylogenetic analysis A phylogenetic tree was constructed from the aligned MATH-BTB protein sequences of rye, rice, wheat, maize, and Arabidopsis MEGA software. Multiple sequence alignment was performed with the MUSCLE algorithm, and the tree was built using the Neighbor-Joining (NJ) method. The topological robustness of the tree was assessed with 1000 bootstrap replicates. The final tree was visualized and annotated using FigTree software (Kumar et al. 2016 ). Conserved motif analysis Motif analysis of the rye MATH-BTB proteins was performed with the MEME Suite (max motifs = 10, default settings), followed by visualization of the motif architectures using TBtools (Bailey et al. 2009 ). Collinearity analysis Collinearity analysis of the MATH-BTB gene family was performed between rye and four reference species: Arabidopsis , maize, rice, and the A subgenome of wheat. Expression analysis and functional validation of rye MATH-BTB genes Expression profiles of the identified MATH-BTB gene family members across different rye tissues were analyzed using publicly available transcriptome data from the WheatOmics database. The expression patterns were visualized as a heatmap using TBtools to identify genes with tissue-specific or developmentally regulated expression (Ma et al., 2021 ). To leverage prior genetic evidence, we integrated these findings with results from our previous study based on SLAF-seq of a rye hybrid population, which mapped QTLs for yield-related traits and identified associated candidate genes (Che et al., 2023 ). The functional validation in the present study consisted of identifying the intersection between the MATH-BTB gene family and the candidate genes previously implicated in yield traits. Development of molecular markers for candidate genes Conventional PCR primers flanking the candidate gene sequences were designed using Primer Premier 5 software. Total RNA was extracted from randomly selected individuals of the rye accessions 89R41 and Z1672 and reverse-transcribed into cDNA. The target sequences were amplified by PCR, and the products were resolved by agarose gel electrophoresis. The resulting bands were examined for size polymorphisms and/or intensity variations to preliminarily assess sequence variation in the candidate genes. If no clear polymorphism was detected, KASP markers were developed based on the SNP loci previously linked to the candidate gene. The KASP assay, designed using the PolyMarker website, comprised two allele-specific forward primers (each labeled with a distinct fluorescent dye, FAM or HEX) and a common reverse primer. Genotyping was performed via KASP-PCR, and the fluorescence signals were quantified to identify markers associated with the target phenotypic trait. Results Identification and characterization of the MATH-BTB gene family in rye A total of 56 MATH-BTB genes were identified in the rye genome and systematically annotated as ScMB1 - ScMB56 . These genes were distributed across all seven rye chromosomes. The physicochemical properties of the encoded 56 proteins were analyzed (Table 1 ). The isoelectric points (pI) ranged from 4.84 ( ScMB14 ) − 8.83 ( ScMB45 ). The majority (50 proteins) had a pI below 7, indicating that most MATH-BTB proteins in rye are acidic, while only six are alkaline. Analysis of the grand average of hydropathicity (GRAVY) showed that the proteins encoded by ScMB3 and ScMB27 are hydrophilic, whereas the rest are hydrophobic. The molecular weights ranged from 36.05 kDa ( ScMB45 ) − 59.48 kDa ( ScMB48 ), corresponding to amino acid lengths of 327–546 residues. The aliphatic index varied from 75.03 ( ScMB18 ) − 93.98 ( ScMB27 ). Notably, 52 proteins had an index greater than 80, suggesting that the majority of the MATH-BTB family members are thermostable, a feature that may contribute to their functional adaptability under diverse environmental conditions. Table 1 Physical and chemical characteristics of rye MATH-BTB genes and its encoded protein Name Gene pI MW / kD No.ofaminoacid Aliphaticindex GRAVY ScMB1 ScWN3R01G289700.1 5.66 39 990.62 364 87.58 -0.09 ScMB2 ScWN2R01G399400.1 5.87 40 351.34 361 85.87 -0.27 ScMB3 ScWN6R01G378100.1 6.24 40 198.27 365 90.93 0.01 ScMB4 ScWN2R01G562500.1 6.16 41 254.30 369 80.08 -0.11 ScMB5 ScWN5R01G406400.1 5.94 39 308.09 352 83.64 -0.08 ScMB6 ScWN1R01G194300.1 5.73 41 489.49 371 90.40 -0.08 ScMB7 ScWN5R01G425000.1 5.97 45 463.25 412 89.44 -0.03 ScMB8 ScWN4R01G155400.1 5.74 39 437.03 360 88.03 -0.10 ScMB9 ScWN6R01G033300.1 7.06 39 057.69 349 79.94 -0.31 ScMB10 ScWN7R01G487200.1 6.99 36 624.09 332 91.39 -0.06 ScMB11 ScWN3R01G232100.1 6.16 38 370.79 349 86.39 -0.06 ScMB12 ScWN1R01G077700.1 5.15 39 837.15 363 89.67 -0.03 ScMB13 ScWN3R01G289900.1 5.79 40 945.61 372 82.55 -0.19 ScMB14 ScWN6R01G530500.1 4.84 43 527.54 392 79.85 -0.14 ScMB15 ScWN4R01G385200.1 6.56 42 432.38 387 80.39 -0.12 ScMB16 ScWN7R01G486000.1 5.35 41 300.36 370 93.57 -0.04 ScMB17 ScWN5R01G504300.1 6.12 39 096.00 353 88.41 -0.10 ScMB18 ScWN5R01G496400.1 5.09 47 363.71 437 75.03 -0.32 ScMB19 ScWN2R01G516700.1 8.7 42 640.99 384 81.30 -0.14 ScMB20 ScWN5R01G424700.1 5.66 40 010.80 365 85.21 -0.06 ScMB21 ScWN7R01G486600.1 6.99 39 676.64 360 91.03 -0.05 ScMB22 ScWN1R01G411700.1 5.73 39 961.87 356 81.35 -0.11 ScMB23 ScWN3R01G472700.1 5.05 39 606.08 360 92.14 -0.02 ScMB24 ScWN5R01G424500.1 6.38 44 702.21 404 84.98 -0.20 ScMB25 ScWN4R01G352500.1 6.4 39 779.01 355 87.94 -0.16 ScMB26 ScWN7R01G108900.1 5.89 40 954.55 370 81.97 -0.23 ScMB27 ScWN7R01G487700.1 5.53 38 200.83 352 93.98 0.07 ScMB28 ScWN7R01G388800.1 5.69 39 209.73 362 87.40 -0.04 ScMB29 ScWN6R01G535700.1 5.04 41 178.65 367 81.63 -0.24 ScMB30 ScWN1R01G116400.2 4.9 43 385.72 386 91.37 -0.04 ScMB31 ScWN5R01G453500.1 5.44 38 544.85 350 81.69 -0.04 ScMB32 ScWN7R01G487100.1 7.52 39 697.69 360 92.67 -0.04 ScMB33 ScWN6R01G535900.1 6.43 41 698.18 371 82.83 -0.22 ScMB34 ScWN3R01G289800.1 5.63 44 283.34 400 80.42 -0.10 ScMB35 ScWN7R01G484500.1 6.17 38 808.40 348 89.14 -0.20 ScMB36 ScWN5R01G051900.1 5.85 40 551.44 364 85.49 -0.19 ScMB37 ScWN3R01G402000.1 5.61 41 322.48 365 86.30 -0.17 ScMB38 ScWN5R01G533900.1 5.77 40 764.95 363 84.63 -0.16 ScMB39 ScWN5R01G036500.1 6.41 39 319.53 353 88.39 -0.03 ScMB40 ScWN7R01G109800.1 5.89 40 954.55 370 81.97 -0.23 ScMB41 ScWN3R01G536900.1 5.24 41 786.58 377 80.40 -0.11 ScMB42 ScWN3R01G243300.1 5.29 38 473.04 345 89.33 -0.05 ScMB43 ScWN6R01G235200.1 6.19 39 669.87 351 87.49 -0.23 ScMB44 ScWN2R01G469700.1 6.57 42 605.49 376 89.73 -0.19 ScMB45 ScWN5R01G424600.1 5.78 44 265.85 400 89.92 -0.02 ScMB46 ScWN1R01G411600.3 5.83 39 448.31 356 83.88 -0.09 ScMB47 ScWN5R01G044200.1 5.95 49 786.20 446 79.01 -0.34 ScMB48 ScWN2R01G516600.1 6.58 59 477.82 546 81.98 -0.10 ScMB49 ScWN5R01G504200.1 6.4 44 029.41 393 87.66 -0.19 ScMB50 ScWN2R01G292300.1 6.08 44 140.15 396 83.18 -0.20 ScMB51 ScWN6R01G535400.1 5.04 41 178.65 367 81.63 -0.24 ScMB52 ScWN7R01G485000.1 5.78 39 862.65 360 88.08 -0.13 ScMB53 ScWN5R01G532000.1 7.58 41 156.33 364 85.22 -0.33 ScMB54 ScWN7R01G297300.1 5.67 41 097.08 366 83.66 -0.13 ScMB55 ScWN6R01G233900.1 6.11 41 251.63 366 87.65 -0.23 ScMB56 ScWN3R01G019000.1 7.16 41 249.43 374 86.60 -0.04 Phylogenetic analysis of rye MATH-BTB proteins A phylogenetic tree was constructed using MATH-BTB protein sequences from rye (56), wheat A subgenome (72), rice (74), maize (40), and Arabidopsis (6) to elucidate their evolutionary relationships (Fig. 1). The proteins clustered into ten distinct subfamilies ( G1 – G10 ). Proteins from the monocot species (rye, wheat, rice, and maize) were distributed across all subfamilies, whereas those from the dicot Arabidopsis confined to G5 . This distribution suggests that the diversification of the MATH-BTB family occurred after the monocot-dicot divergence. The number of rye genes in each subfamily ( G1 – G10 ) was 4, 3, 9, 1, 2, 4, 8, 5, 6, and 14. Conserved motif analysis Conserved motif analysis of the 56 rye MATH-BTB proteins revealed a high degree of sequence conservation (Fig. 2). All members shared motifs 1, 2, 3, 4, 5, 7, and 10. Only minor variations were observed: the proteins encoded by ScMB30 and ScMB18 lacked motif 6, and the protein from ScMB10 lacked motif 8. The overall minimal variation in domain composition across subfamilies indicates strong structural conservation within this protein family in rye. Analysis of genomic synteny Synteny analysis of the rye MATH-BTB genes with four reference species revealed a distinct pattern: no collinearity with Arabidopsis and maize, one syntenic pair with rice, and 16 pairs with the wheat A subgenome (Fig. 3 ). The strong collinearity observed specifically with wheat underscores their close phylogenetic relationship and highlights the value of rye genetic resources for wheat improvement. Tissue-specific expression analysis of rye MATH-BTB genes Expression patterns of the MATH-BTB genes were analyzed using RNA-seq data from the WheatOmics database, which included root, stem, leaf, and spike tissues at the heading stage, as well as seeds at 10, 20, 30, and 40 days after pollination. A heatmap generated with TBtools revealed distinct expression profiles (Fig. 4). Fourteen genes ( ScMB2, ScMB6, ScMB9, ScMB15, ScMB19, ScMB24, ScMB26, ScMB35, ScMB37, ScMB40, ScMB41, ScMB44, ScMB45, ScMB47 ) showed no expression across all examined tissues. In contrast, the remaining genes were expressed in various tissues, with particularly high expression levels in spikes and developing seeds, collectively suggesting a potential role for this gene family in grain development. Functional validation of the rye MATH-BTB gene family Building on our previous SLAF-seq study that identified QTLs for yield-related traits, we integrated the genetic data with the 56 MATH-BTB genes identified here. This integration pinpointed a strong candidate gene, ScWN4R01G352500.1 ( ScMB25 ), which is located within a QTL interval for grain width. The QTL was mapped to chromosome 4 (77.871 cM), with a LOD score of 3.63 and a PVE of 15%. A total of 27 SNPs were linked to this locus. This result provides genetic evidence that the MATH-BTB gene family, particularly ScMB25 , may regulate grain width in rye. Development of molecular markers To validate the presence of the candidate gene ScWN4R01G352500.1 , we designed a gene-specific primer pair. cDNA from the wide-grain wild rye 89R41 and the narrow-grain cultivated rye Z1672 was used as a template for PCR amplification. Electrophoresis confirmed the presence of the target gene in both accessions, but no polymorphism was detected. Consequently, we developed seven KASP markers (KM38–KM44) targeting SNP loci linked to the grain width QTL. Initial screening in a hybrid population (89R41 × Z1672) showed that KM38 and KM42 could genotype the trait. These two markers were subsequently validated in a larger population from the wild rye 90R13 (n = 311). A t-test comparing the genotyping results with the grain width phenotypic data revealed a significant association between the KM42 marker and grain width ( P = 0.018, P < 0.05), indicating its utility for selecting rye plants with desirable grain width. The SNP corresponding to this marker is located at position 749,349,504 bp on chromosome 4. This marker can therefore be used for selecting rye plants with desirable grain width. Phenotypic analysis revealed that allele 1 of KM42 positively influences grain width compared to allele 2, with a contribution rate of 102.2%. Detailed results are presented in Tables 2 – 3 and Fig. 5. The environmental stability of KM42 is under investigation. Table 2 KASP primer information table Primer Sequences Pre-primer sequence 1 ( 5＇－3＇) GAAGGTGACCAAGTTCATGCTtgatagaggagtcgtcgacC Pre-primer sequence 2 ( 5＇－3＇) GAAGGTCGGAGTCAACGGATTtgatagaggagtcgtcgacT Co-primers ( 5＇－3＇) cttgatggtgacctcggagg Note: Pre-primer sequence 1 connects the FAM-type fluorescent motif sequence GAAGGTGACCAAGTTCATGCT at the 5' end, and pre-primer sequence 2 connects the HEX-type fluorescent motif sequence GAAGGTCGGGAGTCAACGGATT at the 5' end, and C/T stands for SNP site. Table 3 The results of KASP molecular marker test Typing Amplification rate Amplified samples Phenotypic data (cm) Contribution rate p Homozygous allele 1 86.50% 122 0.232 ± 0.019 102.2% 0.018 Homozygous allele 2 147 0.227 ± 0.018 Discussion We identified 56 MATH-BTB genes in the rye genome, a number comparable to other grasses but larger than in Arabidopsis , likely reflecting a Poaceae specific expansion. We systematically analyzed the gene structures, phylogenetic relationships, conserved protein motifs, synteny, and expression patterns of these 56 genes. Our findings establish a crucial foundation for future functional studies, such as the cloning and validation of individual rye MATH-BTB genes, and provide a valuable resource for elucidating their roles in rye growth and development. Our integrated analysis yields several key insights into the MATH-BTB family in rye. First, the prevalence of acidic, hydrophobic, and thermostable proteins suggests an adaptation to diverse environmental stresses. Second, the phylogenetic isolation of the Arabidopsis genes in a single subfamily, contrasted with the broad distribution in cereals, strongly supports a lineage-specific expansion after the monocot-dicot split. The conserved domain architecture within subfamilies implies conserved biological functions. Most significantly, the extensive collinearity between rye and wheat provides compelling genomic evidence of their close evolutionary relationship. These findings establish the MATH-BTB family as a subject for future functional studies in rye and firmly position rye as a valuable reservoir of genetic diversity for the enhancement of wheat. The tight linkage between gene expression and function was evident in our analysis. The predominant expression of rye MATH-BTB genes in spikes and seeds strongly suggests a primary role in reproductive development. An intriguing pattern emerged from integrating phylogenetic and expression data: two members of the G5 subfamily displayed significantly higher and more ubiquitous expression. The fact that the entire Arabidopsis MATH-BTB family also clusters within G5 provides a key evolutionary context. This allows us to infer that these two rye G5 members are orthologs of the ancestral genes present before the monocot-dicot divergence, and their strong expression may reflect a fundamental, conserved function. In contrast, the more restricted expression of other subfamily members likely indicates functional specialization following lineage-specific expansions. SLAF-seq technology enables high-throughput development and genotyping of molecular markers (SNPs and InDels) and is widely applied in QTL mapping and crop breeding (Sun et al. 2013 ; Guo et al. 2022 ; Anilkumar et al. 2022 ; Zhang et al. 2016 ). By integrating previously identified yield-related QTLs, linked SNPs, and candidate genes obtained through SLAF-seq with the 56 MATH-BTB genes identified in this study, we identified a candidate gene, ​ ScWN4R01G352500.1 ( ScMB25 ), which was associated with grain width in the SLAF-seq results. PCR analysis of this gene revealed no sequence variation among different rye varieties; however, under consistent cDNA concentrations, the band intensity was stronger in seeds with wider grains compared to those with narrower grains (Fig. 6). Quantification using ImageJ showed average grayscale values of 123.0, 155.7, 110.3, and 158 for lanes 2, 3, 4, and 5, respectively, indicating differential expression of this gene in seeds of varying sizes. Higher expression levels appear to promote increased grain width, a hypothesis that warrants further validation through transgenic experiments. These findings suggest that the function of the MATH-BTB protein encoded by this gene in rye is similar to that observed in Arabidopsis and wheat, supporting the hypothesis that MATH-BTB proteins influence grain width in rye. Additionally, the KASP marker KM42 developed in this study provides a practical tool for selecting rye varieties with desirable grain width traits. Note Lane 1 shows the DNA Marker (50–500 bp), with bands from top to bottom corresponding to 500 bp, 400 bp, 300 bp, 250 bp, 200 bp, 150 bp, 100 bp, and 50 bp. Lanes 2–5 display the target gene band (410 bp) in the upper region, lanes 2 and 4 are electrophoresis bands of low-grain width rye plants, the average gray levels were 123 and 110.3, lanes 3 and 5 are electrophoresis bands of high-grain width rye plants, the average gray levels were 155.7 and 158. Figure 6 Electrophoresis map of ScMB25 gene Conclusions This study provides the first genome-wide identification and characterization of the MATH-BTB gene family in rye, comprising 56 members. Integrated bioinformatic and phylogenetic analyses, combined with prior genetic mapping data, pinpointed ScWN4R01G352500.1 as a key candidate gene regulating grain width. Furthermore, we developed and validated a functional KASP marker (KM42) for this gene, offering a practical molecular tool for the rapid selection of improved grain width in rye breeding programs. Declarations Author contributions LW, ZZ, YC: contributed to the management and manuscript review. ZZ, YY, XW: performed the experiment; ZZ, XW, YY: designed experiments as well as provided the methodology of data collection and analysis. All authors have read and agreed to the published version of the manuscript. Funding This research supported by the “S&T Program of Hebei Department of Science and Technology” (21326340D). Data availability The protein sequences of Arabidopsis MATH-BTB gene family members were obtained from the TAIR database (http://www.arabidopsis.org), the protein sequences of the MATH-BTB gene family members of rice and maize were obtained from the Ensembl Plants database (http://plants.ensembl.org/index.html). the data of wheat A genome and rye (Weining) genome were obtained from the NGDC database (https://ngdc. cncb.ac.cn/), the transcript expression profiles of different tissues of rye were obtained from the WheatOmics database (http://wheatomics.sdau.edu.cn/). All data generated or analysed during this study are included in this published article. References Anilkumar C, Sah RP, Azharudheen TM, Behera S, Singh N, Prakash NR, Sunitha NC, Devanna BN, Marndi BC, Patra BC, Nair SK (2022) Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification. 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The FEBS Journal 276(22): 6624–6635. https://doi.org/10.1111/j.1742-4658.2009.07373.x Zhang Z, Shang H, Shi Y, Huang L, Li J, Ge Q, Gong J, Liu A, Chen T, Wang D, Wang Y, Palanga KK, Muhammad J, Li W, Lu Q, Deng X, Tan Y, Song W, Cai J, Li P, Rashid Ho, Gong W, Yuan Y (2016) Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to Quantitative Trait Loci (QTL) analysis for boll weight in upland cotton ( Gossypium hirsutum .). BMC Plant Biology 16: 79. https://doi.org/10.1186/s12870-016-0741-4 Zhang YJ, Liu JD, Xia XC, He ZH (2014) TaGS-D1 , an ortholog of rice OsGS3 , is associated with grain weight and grain length in common wheat. Molecular Breeding 34(3): 1097. https://doi.org/10.1007/s11032-014-0102-7 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":3657099,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;Phylogenetic analysis of the \u003cem\u003eMATH-BTB\u003c/em\u003egene family in rye\u003c/p\u003e\n\u003cp\u003eNote: Sc: \u003cem\u003eS. cereale\u003c/em\u003e ; At: \u003cem\u003eArabidopsis thaliana\u003c/em\u003e; Ta: \u003cem\u003eTriticum aestivum\u003c/em\u003e; Os: \u003cem\u003eOryza sativa\u003c/em\u003e ; Zm: \u003cem\u003eZea mays\u003c/em\u003e; \u003cem\u003eG1\u003c/em\u003e~\u003cem\u003eG10\u003c/em\u003erepresent 10 subclasses.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/8eac719264b958b38494a90c.png"},{"id":95757361,"identity":"ebce0950-ec31-4e43-9f35-a8ad7a8238b3","added_by":"auto","created_at":"2025-11-12 16:55:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3789531,"visible":true,"origin":"","legend":"\u003cp\u003eConserved motif analysis of rye \u003cem\u003eMATH-BTB\u003c/em\u003eprotein\u003c/p\u003e\n\u003cp\u003eNote: Motif1-10 represents a conserved domain.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/429dfad43877a8326a67187a.png"},{"id":95802008,"identity":"fc9243c8-fb91-4d1d-b3de-f42b0d60c564","added_by":"auto","created_at":"2025-11-13 08:26:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334813,"visible":true,"origin":"","legend":"\u003cp\u003eSyntenic relationships of \u003cem\u003eMATH-BTB\u003c/em\u003e Genes between rye and four other plant species\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/6517660694b56ea4dd8b96dd.png"},{"id":95757366,"identity":"785b8baa-f391-44dd-b49f-4a191a83e46a","added_by":"auto","created_at":"2025-11-12 16:55:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157141,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of 56 \u003cem\u003eMATH-BTB\u003c/em\u003egenes in different tissues of rye\u003c/p\u003e\n\u003cp\u003eNote: 1. Root: Root; 2. Leaf: Leaf blade; 3. Stem: Stem; 4. Spikelet: Spike; 5. Seed.10DAF: Seed at 10d after flowering; 6. Seed.20DAF: Seed at 20d after flowering; 7. Seed.30DAF: Seed at 30d after flowering; 8. Seed.40DAF: Seed at 40d after flowering; 9. Red for high expression, blue for low expression.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/b294b001eaba5377dbf02a6f.png"},{"id":95801666,"identity":"3c82e051-473e-4cf2-9aa1-902077399d20","added_by":"auto","created_at":"2025-11-13 08:25:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251797,"visible":true,"origin":"","legend":"\u003cp\u003eKASP labeling 3 sampling typing results\u003c/p\u003e\n\u003cp\u003eNote: 1. X-axis, allele 1, reported by FAM-type fluorescence; 2. Y-axis, allele 2, reported by HEX-type fluorescence; 3. Blue dots represent homozygous allele group 1; 4. Orange dots represent homozygous allele group 2; and 5. Black dots represent no response.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/2ecc61eb7f5d7b16991aa579.png"},{"id":95801659,"identity":"256808c7-2e39-427a-b129-036a517a5fb6","added_by":"auto","created_at":"2025-11-13 08:25:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1551932,"visible":true,"origin":"","legend":"\u003cp\u003eElectrophoresis map of \u003cem\u003eScMB25\u003c/em\u003e gene\u003c/p\u003e\n\u003cp\u003eNote: Lane 1 shows the DNA Marker (50–500 bp), with bands from top to bottom corresponding to 500 bp, 400 bp, 300 bp, 250 bp, 200 bp, 150 bp, 100 bp, and 50 bp. Lanes 2-5 display the target gene band (410 bp) in the upper region, lanes 2 and 4 are electrophoresis bands of low-grain width rye plants, the average gray levels were 123 and 110.3, lanes 3 and 5 are electrophoresis bands of high-grain width rye plants, the average gray levels were 155.7 and 158.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/057f9c8cb710e87f33ec6721.png"},{"id":103517209,"identity":"1cf8b5dd-043c-48b0-a7e5-91c511850a3b","added_by":"auto","created_at":"2026-02-26 14:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2586022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7922703/v1/0f64157c-a8d3-4ec0-8444-63d3c4a11575.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide identification of MATH-BTB gene family and expression analysis in rye (Secale cereale L.)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGrain yield, a primary objective in cereal breeding, is governed by a suite of traits, including panicle size, grain dimensions (length and width), and thousand-grain weight (Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. 2019; Guan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Numerous genes controlling these traits have been characterized across cereals. A prominent example is the \u003cem\u003eGS3\u003c/em\u003e gene in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), a major quantitative trait locus (QTL) for grain length and weight, whose functional homologs in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) and maize (\u003cem\u003eZea mays\u003c/em\u003e L.) modulate similar traits (Fan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mao et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Likewise, loss-of-function mutations in \u003cem\u003eGW2\u003c/em\u003e and \u003cem\u003eGW5\u003c/em\u003e increase grain weight (Song et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Shomura et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), whereas \u003cem\u003eGS5\u003c/em\u003e, which encodes a serine carboxypeptidase, acts as a positive regulator of grain size (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eMATH-BTB\u003c/em\u003e proteins constitute a subfamily of the BTB (Broad-complex, Tramtrack, and Bric-\u0026agrave;-brac) family, characterized by an N-terminal MATH (Meprin and TRAF homology) domain and a C-terminal BTB/POZ domain that forms a characteristic fold (Artem et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This protein family is implicated in maintaining cellular homeostasis and regulating the development of various plant organs. Initial characterization of the \u003cem\u003eMATH-BTB\u003c/em\u003e family in plants was performed in maize and \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (L.) Heynh (Juranič et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In \u003cem\u003eArabidopsis\u003c/em\u003e, the six family members influence fatty acid metabolism and seed development by regulating the ethylene-responsive transcription factor WRI1 (WRINKLED1) (Weber et al. 2009; Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In maize, \u003cem\u003eMATH-BTB\u003c/em\u003e proteins are critical for reproductive development, among other key biological processes (Juranič et al. 2014). In wheat, the gene \u003cem\u003eTaBTB-5A\u003c/em\u003e has been identified as influencing yield-related traits such as plant height, grain length, and grain width (Dong, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the functions of this protein family remain uncharacterized in rye (\u003cem\u003eSecale cereale\u003c/em\u003e L.).\u003c/p\u003e\u003cp\u003eRye, a member of the \u003cem\u003eTriticeae\u003c/em\u003e tribe (\u003cem\u003ePoaceae\u003c/em\u003e family), is often considered a secondary crop due to its close morphological resemblance to wheat and barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.). It was domesticated in the coastal areas of the eastern Mediterranean (McElroy, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Institute, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Crucially, rye shares high genomic homology with wheat, which has facilitated its extensive use in wheat improvement programs, for instance, through the introgression of desirable traits (Bauer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rabanus-Wallace et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe KASP (Kompetitive Allele-Specific PCR) system is a fluorescence-based genotyping technology that enables high-throughput, precise bi-allelic scoring of target SNPs and InDels (Dipta et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is widely applied in plant breeding to screen for desirable agronomic traits. For example, five KASP markers highly associated with smut resistance were identified in sugarcane (Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and 15 markers linked to powdery mildew resistance were developed in melon (Cao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In wheat breeding, KASP markers have been successfully used to select genotypes with enhanced drought tolerance (Eltaher et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study performs a systematic identification and comprehensive bioinformatic analysis of the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family in rye. Furthermore, we developed KASP markers for yield-related traits, providing a practical tool for the molecular screening of wheat plants with improved agronomic characteristics.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eThe three rye accessions used in this study (89R13, Z1672, and 90R13). There were obtained from and maintained by Hebei Normal University of Science and Technology. All accessions were grown under standard field conditions at the university's agricultural experiment station in Changli, Hebei Province, China, during the 2022 growing season.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of the\u003c/b\u003e \u003cb\u003eMATH-BTB\u003c/b\u003e \u003cb\u003egene family in rye\u003c/b\u003e​\u003c/p\u003e\u003cp\u003eThe protein sequences of known \u003cem\u003eMATH-BTB\u003c/em\u003e genes from \u003cem\u003eArabidopsis\u003c/em\u003e obtained from the TAIR database, while those from rice and maize were retrieved from Ensembl Plants. The genome sequences of rye (Weining) and the A subgenome of wheat were downloaded from the National Genomics Data Center (NGDC) database (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ling et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIdentification was performed in two steps. First, the \u003cem\u003eArabidopsis MATH-BTB\u003c/em\u003e protein sequences were used as queries to perform a local BLASTP search against the rye and wheat A subgenome protein databases using TBtools software (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, an online BLASTP search was conducted via the NCBI website to identify additional potential homologs. The conserved domains of all candidate proteins from Arabidopsis, wheat, and rye were verified using the NCBI\u0026rsquo;s conserved domain database (CDD) batch search tool. Results were visualized with TBtools, and only proteins containing both the MATH and BTB domains were retained for subsequent analysis.\u003c/p\u003e\u003cp\u003eFollowing identification, the physicochemical properties of the deduced rye \u003cem\u003eMATH-BTB\u003c/em\u003e proteins were predicted using the ExPASy ProtParam tool (Gasteiger et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eA phylogenetic tree was constructed from the aligned \u003cem\u003eMATH-BTB\u003c/em\u003e protein sequences of rye, rice, wheat, maize, and \u003cem\u003eArabidopsis\u003c/em\u003e MEGA software. Multiple sequence alignment was performed with the MUSCLE algorithm, and the tree was built using the Neighbor-Joining (NJ) method. The topological robustness of the tree was assessed with 1000 bootstrap replicates. The final tree was visualized and annotated using FigTree software (Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eConserved motif analysis\u003c/h3\u003e\n\u003cp\u003eMotif analysis of the rye \u003cem\u003eMATH-BTB\u003c/em\u003e proteins was performed with the MEME Suite (max motifs\u0026thinsp;=\u0026thinsp;10, default settings), followed by visualization of the motif architectures using TBtools (Bailey et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCollinearity analysis\u003c/h3\u003e\n\u003cp\u003eCollinearity analysis of the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family was performed between rye and four reference species: \u003cem\u003eArabidopsis\u003c/em\u003e, maize, rice, and the A subgenome of wheat.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression analysis and functional validation of rye\u003c/b\u003e \u003cb\u003eMATH-BTB\u003c/b\u003e \u003cb\u003egenes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExpression profiles of the identified \u003cem\u003eMATH-BTB\u003c/em\u003e gene family members across different rye tissues were analyzed using publicly available transcriptome data from the WheatOmics database. The expression patterns were visualized as a heatmap using TBtools to identify genes with tissue-specific or developmentally regulated expression (Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo leverage prior genetic evidence, we integrated these findings with results from our previous study based on SLAF-seq of a rye hybrid population, which mapped QTLs for yield-related traits and identified associated candidate genes (Che et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional validation in the present study consisted of identifying the intersection between the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family and the candidate genes previously implicated in yield traits.\u003c/p\u003e\n\u003ch3\u003eDevelopment of molecular markers for candidate genes\u003c/h3\u003e\n\u003cp\u003eConventional PCR primers flanking the candidate gene sequences were designed using Primer Premier 5 software. Total RNA was extracted from randomly selected individuals of the rye accessions 89R41 and Z1672 and reverse-transcribed into cDNA. The target sequences were amplified by PCR, and the products were resolved by agarose gel electrophoresis. The resulting bands were examined for size polymorphisms and/or intensity variations to preliminarily assess sequence variation in the candidate genes.\u003c/p\u003e\u003cp\u003eIf no clear polymorphism was detected, KASP markers were developed based on the SNP loci previously linked to the candidate gene. The KASP assay, designed using the PolyMarker website, comprised two allele-specific forward primers (each labeled with a distinct fluorescent dye, FAM or HEX) and a common reverse primer. Genotyping was performed via KASP-PCR, and the fluorescence signals were quantified to identify markers associated with the target phenotypic trait.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification and characterization of the\u003c/strong\u003e \u003cstrong\u003eMATH-BTB\u003c/strong\u003e \u003cstrong\u003egene family in rye\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 56 \u003cem\u003eMATH-BTB\u003c/em\u003e genes were identified in the rye genome and systematically annotated as \u003cem\u003eScMB1\u003c/em\u003e - \u003cem\u003eScMB56\u003c/em\u003e. These genes were distributed across all seven rye chromosomes.\u003c/p\u003e\n\u003cp\u003eThe physicochemical properties of the encoded 56 proteins were analyzed (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The isoelectric points (pI) ranged from 4.84 (\u003cem\u003eScMB14\u003c/em\u003e) \u0026minus;\u0026thinsp;8.83 (\u003cem\u003eScMB45\u003c/em\u003e). The majority (50 proteins) had a pI below 7, indicating that most \u003cem\u003eMATH-BTB\u003c/em\u003e proteins in rye are acidic, while only six are alkaline. Analysis of the grand average of hydropathicity (GRAVY) showed that the proteins encoded by \u003cem\u003eScMB3\u003c/em\u003e and \u003cem\u003eScMB27\u003c/em\u003e are hydrophilic, whereas the rest are hydrophobic. The molecular weights ranged from 36.05 kDa (\u003cem\u003eScMB45\u003c/em\u003e) \u0026minus;\u0026thinsp;59.48 kDa (\u003cem\u003eScMB48\u003c/em\u003e), corresponding to amino acid lengths of 327\u0026ndash;546 residues. The aliphatic index varied from 75.03 (\u003cem\u003eScMB18\u003c/em\u003e) \u0026minus;\u0026thinsp;93.98 (\u003cem\u003eScMB27\u003c/em\u003e). Notably, 52 proteins had an index greater than 80, suggesting that the majority of the \u003cem\u003eMATH-BTB\u003c/em\u003efamily members are thermostable, a feature that may contribute to their functional adaptability under diverse environmental conditions.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysical and chemical characteristics of rye \u003cem\u003eMATH-BTB\u003c/em\u003e genes and its encoded protein\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMW / kD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.ofaminoacid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAliphaticindex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRAVY\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G289700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 990.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G399400.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 351.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G378100.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 198.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G562500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 254.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G406400.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 308.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN1R01G194300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 489.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G425000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 463.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN4R01G155400.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 437.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G033300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 057.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G487200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 624.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G232100.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 370.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN1R01G077700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 837.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G289900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 945.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G530500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 527.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN4R01G385200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 432.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G486000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 300.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G504300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 096.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G496400.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 363.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G516700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 640.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G424700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 010.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G486600.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 676.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB22\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN1R01G411700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 961.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB23\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G472700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 606.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G424500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 702.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN4R01G352500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 779.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G108900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 954.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB27\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G487700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 200.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB28\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G388800.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 209.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G535700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 178.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB30\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN1R01G116400.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 385.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB31\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G453500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 544.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G487100.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 697.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB33\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G535900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 698.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G289800.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 283.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB35\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G484500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 808.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB36\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G051900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 551.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB37\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G402000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 322.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB38\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G533900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 764.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G036500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 319.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G109800.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40 954.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB41\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G536900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 786.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB42\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G243300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 473.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB43\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G235200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 669.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G469700.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 605.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G424600.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 265.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB46\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN1R01G411600.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 448.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB47\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G044200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 786.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB48\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G516600.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 477.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB49\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G504200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 029.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB50\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN2R01G292300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 140.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB51\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G535400.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 178.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB52\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G485000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 862.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN5R01G532000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 156.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN7R01G297300.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 097.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB55\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN6R01G233900.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 251.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScMB56\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScWN3R01G019000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 249.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis of rye\u003c/strong\u003e \u003cstrong\u003eMATH-BTB\u003c/strong\u003e \u003cstrong\u003eproteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA phylogenetic tree was constructed using \u003cem\u003eMATH-BTB\u003c/em\u003e protein sequences from rye (56), wheat A subgenome (72), rice (74), maize (40), and \u003cem\u003eArabidopsis\u003c/em\u003e (6) to elucidate their evolutionary relationships (Fig. 1). The proteins clustered into ten distinct subfamilies (\u003cem\u003eG1\u003c/em\u003e\u0026ndash;\u003cem\u003eG10\u003c/em\u003e). Proteins from the monocot species (rye, wheat, rice, and maize) were distributed across all subfamilies, whereas those from the dicot \u003cem\u003eArabidopsis\u003c/em\u003e confined to \u003cem\u003eG5\u003c/em\u003e. This distribution suggests that the diversification of the MATH-BTB family occurred after the monocot-dicot divergence. The number of rye genes in each subfamily (\u003cem\u003eG1\u003c/em\u003e\u0026ndash;\u003cem\u003eG10\u003c/em\u003e) was 4, 3, 9, 1, 2, 4, 8, 5, 6, and 14.\u003c/p\u003e\n\u003ch3\u003eConserved motif analysis\u003c/h3\u003e\n\u003cp\u003eConserved motif analysis of the 56 rye \u003cem\u003eMATH-BTB\u003c/em\u003e proteins revealed a high degree of sequence conservation (Fig. 2). All members shared motifs 1, 2, 3, 4, 5, 7, and 10. Only minor variations were observed: the proteins encoded by \u003cem\u003eScMB30\u003c/em\u003e and \u003cem\u003eScMB18\u003c/em\u003e lacked motif 6, and the protein from \u003cem\u003eScMB10\u003c/em\u003e lacked motif 8. The overall minimal variation in domain composition across subfamilies indicates strong structural conservation within this protein family in rye.\u003c/p\u003e\n\u003ch3\u003eAnalysis of genomic synteny\u003c/h3\u003e\n\u003cp\u003eSynteny analysis of the rye \u003cem\u003eMATH-BTB\u003c/em\u003e genes with four reference species revealed a distinct pattern: no collinearity with \u003cem\u003eArabidopsis\u003c/em\u003e and maize, one syntenic pair with rice, and 16 pairs with the wheat A subgenome (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The strong collinearity observed specifically with wheat underscores their close phylogenetic relationship and highlights the value of rye genetic resources for wheat improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue-specific expression analysis of rye\u003c/strong\u003e \u003cstrong\u003eMATH-BTB\u003c/strong\u003e \u003cstrong\u003egenes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression patterns of the MATH-BTB genes were analyzed using RNA-seq data from the WheatOmics database, which included root, stem, leaf, and spike tissues at the heading stage, as well as seeds at 10, 20, 30, and 40 days after pollination. A heatmap generated with TBtools revealed distinct expression profiles (Fig.\u0026nbsp;4). Fourteen genes (\u003cem\u003eScMB2, ScMB6, ScMB9, ScMB15, ScMB19, ScMB24, ScMB26, ScMB35, ScMB37, ScMB40, ScMB41, ScMB44, ScMB45, ScMB47\u003c/em\u003e) showed no expression across all examined tissues. In contrast, the remaining genes were expressed in various tissues, with particularly high expression levels in spikes and developing seeds, collectively suggesting a potential role for this gene family in grain development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional validation of the rye\u003c/strong\u003e \u003cstrong\u003eMATH-BTB\u003c/strong\u003e \u003cstrong\u003egene family\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on our previous SLAF-seq study that identified QTLs for yield-related traits, we integrated the genetic data with the 56 \u003cem\u003eMATH-BTB\u003c/em\u003e genes identified here. This integration pinpointed a strong candidate gene, \u003cem\u003eScWN4R01G352500.1\u003c/em\u003e (\u003cem\u003eScMB25\u003c/em\u003e), which is located within a QTL interval for grain width. The QTL was mapped to chromosome 4 (77.871 cM), with a LOD score of 3.63 and a PVE of 15%. A total of 27 SNPs were linked to this locus. This result provides genetic evidence that the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family, particularly \u003cem\u003eScMB25\u003c/em\u003e, may regulate grain width in rye.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of molecular markers\u003c/h2\u003e\n \u003cp\u003eTo validate the presence of the candidate gene \u003cem\u003eScWN4R01G352500.1\u003c/em\u003e, we designed a gene-specific primer pair. cDNA from the wide-grain wild rye 89R41 and the narrow-grain cultivated rye Z1672 was used as a template for PCR amplification. Electrophoresis confirmed the presence of the target gene in both accessions, but no polymorphism was detected.\u003c/p\u003e\n \u003cp\u003eConsequently, we developed seven KASP markers (KM38\u0026ndash;KM44) targeting SNP loci linked to the grain width QTL. Initial screening in a hybrid population (89R41 \u0026times; Z1672) showed that KM38 and KM42 could genotype the trait. These two markers were subsequently validated in a larger population from the wild rye 90R13 (n\u0026thinsp;=\u0026thinsp;311). A t-test comparing the genotyping results with the grain width phenotypic data revealed a significant association between the KM42 marker and grain width (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating its utility for selecting rye plants with desirable grain width. The SNP corresponding to this marker is located at position 749,349,504 bp on chromosome 4. This marker can therefore be used for selecting rye plants with desirable grain width.\u003c/p\u003e\n \u003cp\u003ePhenotypic analysis revealed that allele 1 of KM42 positively influences grain width compared to allele 2, with a contribution rate of 102.2%. Detailed results are presented in Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. 5. The environmental stability of KM42 is under investigation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; KASP primer information table\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eSequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePre-primer sequence 1 ( 5＇－3＇)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 370px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTtgatagaggagtcgtcgacC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePre-primer sequence 2 ( 5＇－3＇)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 370px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTtgatagaggagtcgtcgacT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eCo-primers ( 5＇－3＇)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 370px;\"\u003e\n \u003cp\u003ecttgatggtgacctcggagg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Pre-primer sequence 1 connects the FAM-type fluorescent motif sequence GAAGGTGACCAAGTTCATGCT at the 5\u0026apos; end, and pre-primer sequence 2 connects the HEX-type fluorescent motif sequence GAAGGTCGGGAGTCAACGGATT at the 5\u0026apos; end, and C/T stands for SNP site.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe results of KASP molecular marker test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTyping\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmplification rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmplified samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotypic data (cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContribution rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomozygous allele 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e86.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.232\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e102.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomozygous allele 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe identified 56 \u003cem\u003eMATH-BTB\u003c/em\u003e genes in the rye genome, a number comparable to other grasses but larger than in \u003cem\u003eArabidopsis\u003c/em\u003e, likely reflecting a \u003cem\u003ePoaceae\u003c/em\u003e specific expansion. We systematically analyzed the gene structures, phylogenetic relationships, conserved protein motifs, synteny, and expression patterns of these 56 genes. Our findings establish a crucial foundation for future functional studies, such as the cloning and validation of individual rye \u003cem\u003eMATH-BTB\u003c/em\u003e genes, and provide a valuable resource for elucidating their roles in rye growth and development.\u003c/p\u003e\u003cp\u003eOur integrated analysis yields several key insights into the \u003cem\u003eMATH-BTB\u003c/em\u003e family in rye. First, the prevalence of acidic, hydrophobic, and thermostable proteins suggests an adaptation to diverse environmental stresses. Second, the phylogenetic isolation of the \u003cem\u003eArabidopsis\u003c/em\u003e genes in a single subfamily, contrasted with the broad distribution in cereals, strongly supports a lineage-specific expansion after the monocot-dicot split. The conserved domain architecture within subfamilies implies conserved biological functions. Most significantly, the extensive collinearity between rye and wheat provides compelling genomic evidence of their close evolutionary relationship. These findings establish the \u003cem\u003eMATH-BTB\u003c/em\u003e family as a subject for future functional studies in rye and firmly position rye as a valuable reservoir of genetic diversity for the enhancement of wheat.\u003c/p\u003e\u003cp\u003eThe tight linkage between gene expression and function was evident in our analysis. The predominant expression of rye \u003cem\u003eMATH-BTB\u003c/em\u003e genes in spikes and seeds strongly suggests a primary role in reproductive development. An intriguing pattern emerged from integrating phylogenetic and expression data: two members of the \u003cem\u003eG5\u003c/em\u003e subfamily displayed significantly higher and more ubiquitous expression. The fact that the entire \u003cem\u003eArabidopsis MATH-BTB\u003c/em\u003e family also clusters within \u003cem\u003eG5\u003c/em\u003e provides a key evolutionary context. This allows us to infer that these two rye \u003cem\u003eG5\u003c/em\u003e members are orthologs of the ancestral genes present before the monocot-dicot divergence, and their strong expression may reflect a fundamental, conserved function. In contrast, the more restricted expression of other subfamily members likely indicates functional specialization following lineage-specific expansions.\u003c/p\u003e\u003cp\u003eSLAF-seq technology enables high-throughput development and genotyping of molecular markers (SNPs and InDels) and is widely applied in QTL mapping and crop breeding (Sun et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anilkumar et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). By integrating previously identified yield-related QTLs, linked SNPs, and candidate genes obtained through SLAF-seq with the 56 \u003cem\u003eMATH-BTB\u003c/em\u003e genes identified in this study, we identified a candidate gene, ​\u003cem\u003eScWN4R01G352500.1\u003c/em\u003e (\u003cem\u003eScMB25\u003c/em\u003e), which was associated with grain width in the SLAF-seq results. PCR analysis of this gene revealed no sequence variation among different rye varieties; however, under consistent cDNA concentrations, the band intensity was stronger in seeds with wider grains compared to those with narrower grains (Fig.\u0026nbsp;6). Quantification using ImageJ showed average grayscale values of 123.0, 155.7, 110.3, and 158 for lanes 2, 3, 4, and 5, respectively, indicating differential expression of this gene in seeds of varying sizes. Higher expression levels appear to promote increased grain width, a hypothesis that warrants further validation through transgenic experiments. These findings suggest that the function of the \u003cem\u003eMATH-BTB\u003c/em\u003e protein encoded by this gene in rye is similar to that observed in \u003cem\u003eArabidopsis\u003c/em\u003e and wheat, supporting the hypothesis that \u003cem\u003eMATH-BTB\u003c/em\u003e proteins influence grain width in rye. Additionally, the KASP marker KM42 developed in this study provides a practical tool for selecting rye varieties with desirable grain width traits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eLane 1 shows the DNA Marker (50\u0026ndash;500 bp), with bands from top to bottom corresponding to 500 bp, 400 bp, 300 bp, 250 bp, 200 bp, 150 bp, 100 bp, and 50 bp. Lanes 2\u0026ndash;5 display the target gene band (410 bp) in the upper region, lanes 2 and 4 are electrophoresis bands of low-grain width rye plants, the average gray levels were 123 and 110.3, lanes 3 and 5 are electrophoresis bands of high-grain width rye plants, the average gray levels were 155.7 and 158.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e Electrophoresis map of \u003cem\u003eScMB25\u003c/em\u003e gene\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first genome-wide identification and characterization of the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family in rye, comprising 56 members. Integrated bioinformatic and phylogenetic analyses, combined with prior genetic mapping data, pinpointed \u003cem\u003eScWN4R01G352500.1\u003c/em\u003e as a key candidate gene regulating grain width. Furthermore, we developed and validated a functional KASP marker (KM42) for this gene, offering a practical molecular tool for the rapid selection of improved grain width in rye breeding programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLW, ZZ, YC: contributed to the management and manuscript review. ZZ, YY, XW: performed the experiment; ZZ, XW, YY: designed experiments as well as provided the methodology of data collection and analysis. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research supported by the \u0026ldquo;S\u0026amp;T Program of Hebei Department of Science and Technology\u0026rdquo; (21326340D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein sequences of Arabidopsis \u003cem\u003eMATH-BTB\u003c/em\u003e gene family members were obtained from the TAIR database (http://www.arabidopsis.org), the protein sequences of the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family members of rice and maize were obtained from the Ensembl Plants database (http://plants.ensembl.org/index.html). the data of wheat A genome and rye (Weining) genome were obtained from the NGDC database (https://ngdc. cncb.ac.cn/), the transcript expression profiles of different tissues of rye were obtained from the WheatOmics database (http://wheatomics.sdau.edu.cn/). 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Molecular Breeding 34(3): 1097. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-014-0102-7\u003c/span\u003e\u003cspan address=\"10.1007/s11032-014-0102-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rye, MATH-BTB gene family, bioinformatics analysis, grain width, kASP marker","lastPublishedDoi":"10.21203/rs.3.rs-7922703/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7922703/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProteins encoded by the \u003cem\u003eMATH-BTB\u003c/em\u003e gene family regulate plant grain development by mediating the degradation of transcription factors. This study identified the \u003cem\u003eMATH-BTB\u003c/em\u003e family in rye through a homology-based search and performed a bioinformatic analysis to investigate its functions. We identified 56 \u003cem\u003eMATH-BTB\u003c/em\u003e genes in the rye genome. Phylogenetic analysis classified these genes into 10 distinct subfamilies. Synteny analysis revealed​no collinear genes with \u003cem\u003eArabidopsis\u003c/em\u003e or maize, one with rice, and 16 with the A subgenome of wheat. Cluster analysis of expression data revealed that this gene family is predominantly expressed in spikes and grains, suggesting its potential role in grain development. Integration with our previous findings on yield-related traits pinpointed a specific \u003cem\u003eMATH-BTB\u003c/em\u003e gene, \u003cem\u003eScWN4R01G352500.1\u003c/em\u003e, which was previously associated with grain width.. This strengthens the hypothesis that this gene family plays a role in grain development. Furthermore, we developed a Kompetitive Allele-Specific PCR (KASP) marker that effectively genotypes the grain width trait. Collectively, these results provide a valuable resource for future functional studies of the \u003cem\u003eMATH-BTB\u003c/em\u003e family in rye and offer a directly applicable marker for molecular breeding programs aimed at improving grain width.\u003c/p\u003e","manuscriptTitle":"Genome-wide identification of MATH-BTB gene family and expression analysis in rye (Secale cereale L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 16:55:44","doi":"10.21203/rs.3.rs-7922703/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e58b168-8f16-4ee0-9c18-7d8acb95b5b7","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T14:19:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 16:55:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7922703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7922703","identity":"rs-7922703","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}