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Background Superoxide dismutase (SOD) can greatly scavenge reactive oxygen species (ROS) in plants. SOD activity is highly related to plant stress tolerance that can be improved by overexpression of SOD genes. Identification of SOD activity-related loci and potential candidate genes is essential for improvement of grain quality in wheat breeding. However, the loci and candidate genes for relating SOD in wheat grains are largely unknown. In the present study, grain SOD activities of 309 recombinant inbred lines (RILs) derived from the ‘Berkut’ × ‘Worrakatta’ cross were assayed by photoreduction method with nitro-blue tetrazolium (NBT) in four environments. Quantitative trait loci (QTL) of SOD activity were identified using inclusive composite interval mapping (ICIM) with the genotypic data of 50 K single nucleotide polymorphism (SNP) array. Results Six QTL for SOD activity were mapped on chromosomes 1BL, 4DS, 5AL (2), and 5DL (2), respectively, explaining 2.2 ~ 7.4% of the phenotypic variances. Moreover, QSOD.xjau-1BL , QSOD.xjau-4DS , QSOD.xjau-5 A.1 , QSOD.xjau-5 A.2 , and QSOD.xjau-5DL.2 identified are likely to be new loci for SOD activity. Four candidate genes TraesCS4D01G059500 , TraesCS5A01G371600 , TraesCS5D01G299900 , TraesCS5D01G343100LC , were identified for QSOD.xjau-4DS , QSOD.xjau-5AL.1 , and QSOD.xjau-5DL.1 (2), respectively, including three SOD genes and a gene associated with SOD activity. Based on genetic effect analysis, this can be used to identify desirable alleles and excellent allele variations in wheat cultivars. Conclusion These candidate genes are annotated for promoting SOD production and inhibiting the accumulation of ROS during plant growth. Therefore, lines with high SOD activity identified in this study may be preferred for future wheat breeding.

To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient ( r ), root means square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those underwater limitation treatment at different fertility stages; several vegetation indices and CC values showed a highly significant correlation, with the highest correlation reaching.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model ( r = 0.63, RMSE = 3.28, NRMSE = 16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model ( r = 0.63, RMSE = 3.47, NRMSE = 19.2%).

Background Superoxide dismutase (SOD) is a significant metalloenzyme that affects the colour and rheological properties of wheat flour. It also enhances the stability of proteins playing a key role in coping with environmental stresses such as high temperatures, drought and salinity. Although SOD plays a crucial role in plant stress tolerance, its molecular mechanisms under various growth environments have not been fully elucidated. In addition, several SOD genes exist in wheat, but the current lack of corresponding molecular markers makes it difficult to effectively select varieties with excellent stress tolerance during breeding. Therefore, in this study, SOD activity in 273 grain sampies from different wheat regions was measured using the nitroblue tetrazolium (NBT) photoreduction assay, and genetic variation at the TaSOD-B2 locus was associated with two co-dominant functional markers . Results There were differences in SOD activity in different wheat regions, with the highest activity in the Southwest winter wheat region, followed by the Northern winter wheat region, the Huanghuai winter wheat region, Foreign varieties, and the Middle and lower reaches of the Yangtze River winter wheat region. In the Foreign varieties and the Huanghuai winter wheat region, the SOD activities of materials containing TaSOD-B2b allelic variants were significantly higher than those of materials containing TaSOD-B2a allelic variants, indicating that TaSOD-B2b is the superior allelic variant type. Conclusion The functional marker SOD2B1/SOD2B2 was effective in distinguishing varieties with different SOD activity levels. Therefore, SOD2B1/SOD2B2 can be applied to molecular marker-assisted breeding in wheat.

Grain peroxidase (POD) has a pivotal role in determining wheat flour color and end-use products. High POD activity in wheat grains can improve the whiteness and commercial value of flour. The POD genes TaPod-7AS and TaPod-4AL have high effects on POD activity in wheat. Function marker for POD genes can be used to improve flour color attributes accurately and efficiently in wheat molecular breeding. Here, wheat POD genes TaPod-7AS and TaPod-4AL were sequenced and characterized, gene expression analysis indicated that TaPod-7AS , TaPod-4AL , TaPod-D1 genes were highly expressed at 21 days after flowering. Function markers POD-7AS1 and POD-7AS2 were developed for TaPod-7AS , amplifying 424-bp and 464-bp PCR fragments in the cultivars with low and high POD activities, respectively. The markers POD-4AL1 and POD-4AL2 for TaPod-4AL amplified 541-bp and 391-bp fragments in the cultivars with low and high POD activities, respectively. The functional markers were validated in a diverse panel of 268 winter wheat cultivars and advanced lines, indicating significant associations between genotypes and POD activity ( P  < 0.05). The genotypes with favorable alleles TaPod-7ASb/TaPod-4ALb/TaPod-D1b had higher POD activity (749.9 U.g -1 .min -1 ) than those with alleles TaPod-7ASa/TaPod-4ALa/TaPod-D1a (mean POD activity 619.4 U.g -1 .min -1 ).

The MADS-box genes encode transcription factors with key roles in plant growth and development. A comprehensive analysis of the MADS-box gene family in bread wheat (Triticum aestivum) has not yet been conducted, and our understanding of their roles in stress is rather limited. Here, we report the identification and characterization of the MADS-box gene family in wheat. A total of 180 MADS-box genes classified as 32 Mα, 5 Mγ, 5 Mδ, and 138 MIKC types were identified. Evolutionary analysis of the orthologs among T. urartu, Aegilops tauschii and wheat as well as homeologous sequences analysis among the three sub-genomes in wheat revealed that gene loss and chromosomal rearrangements occurred during and/or after the origin of bread wheat. Forty wheat MADS-box genes that were expressed throughout the investigated tissues and development stages were identified. The genes that were regulated in response to both abiotic stresses (i.e., phosphorus deficiency, drought, heat, and combined drought and heat) and biotic stresses (i.e., Fusarium graminearum, Septoria tritici, stripe rust and powdery mildew) were detected as well. A few notable MADS-box genes were specifically expressed in a single tissue and those showed relatively higher expression differences between the stress and control treatment. The expression patterns of considerable MADS-box genes differed from those of their orthologs in Brachypodium, rice, and Arabidopsis. Collectively, the present study provides new insights into the possible roles of MADS-box genes in response to stresses and will be valuable for further functional studies of important candidate MADS-box genes.

This article examines the institutional evolution of university data governance in China through the lens of historical institutionalism, offering a novel perspective on this critical topic. This framework provides a structured approach to analyzing the role of institutional factors, power dynamics, and path dependence in shaping university data governance. Since the onset of the information age, Chinese university data governance has evolved through three distinct phases: functional departmentalism, cross-departmental collaborative governance with hierarchical structures, and governance focused on data openness and application. At a deeper level, shifts in governmental data governance serve as key indicators of transformations in university data governance, demonstrating the interplay between institutional frameworks and power structures. Path dependence is evident, with rational choices made by both the government and universities driving the persistence of existing governance models. Legitimacy emerges as the core driving force behind these institutional changes, while efficiency acts as an accelerator, contingent on legitimacy. To advance data governance, Chinese universities must break free from path dependence, reform institutional frameworks, and adapt data power structures to meet the evolving demands of data openness and effective application.

Dexamethasone (Dex) has been widely used as a potent anti-inflammatory, antishock, and immunosuppressive agent. However, high dose or long-term use of Dex is accompanied by side effects including skeletal muscle atrophy, whose underlying mechanisms remain incompletely understood. A number of microRNAs (miRNAs) have been shown to play key roles in skeletal muscle atrophy. Previous studies showed significantly increased miR-322 expression in Dex-treated C2C12 myotubes. In our study, the glucocorticoid receptor (GR) was required for Dex to increase miR-322 expression in C2C12 myotubes. miR-322 mimic or miR-322 inhibitor was used for regulating the expression of miR-322. Insulin-like growth factor 1 receptor (IGF1R) and insulin receptor (INSR) were identified as target genes of miR-322 using luciferase reporter assays and played key roles in Dex-induced muscle atrophy. miR-322 overexpression promoted atrophy in Dex-treated C2C12 myotubes and the gastrocnemius muscles of mice. Conversely, miR-322 inhibition showed the opposite effects. These data suggested that miR-322 contributes to Dex-induced muscle atrophy via targeting of IGF1R and INSR. Furthermore, miR-322 might be a potential target to counter Dex-induced muscle atrophy. miR-322 inhibition might also represent a therapeutic approach for Dex-induced muscle atrophy.

It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18-24.45 and 1.68-28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly \"pleiotropic.\" This study provided important information for MAS and candidate gene functional studies.

Background Gossypium barbadense (Sea Island, Egyptian or Pima cotton) cotton has high fiber quality, however, few studies have investigated the genetic basis of its traits using molecular markers. Genome complexity reduction approaches such as genotyping-by-sequencing have been utilized to develop abundant markers for the construction of high-density genetic maps to locate quantitative trait loci (QTLs). Results The Chinese G. barbadense cultivar 5917 and American Pima S-7 were used to develop a recombinant inbred line (RIL) population with 143 lines. The 143 RILs together with their parents were tested in three replicated field tests for lint yield traits (boll weight and lint percentage) and fiber quality traits (fiber length, fiber elongation, fiber strength, fiber uniformity and micronaire) and then genotyped using GBS to develop single-nucleotide polymorphism (SNP) markers. A high-density genetic map with 26 linkage groups (LGs) was constructed using 3557 GBS SNPs spanning a total genetic distance of 3076.23 cM at an average density of 1.09 cM between adjacent markers. A total of 42 QTLs were identified, including 24 QTLs on 12 LGs for fiber quality and 18 QTLs on 7 LGs for lint yield traits, with LG1 (9 QTLs), LG10 (7 QTLs) and LG14 (6 QTLs) carrying more QTLs. Common QTLs for the same traits and overlapping QTLs for different traits were detected. Each individual QTLs explained 0.97 to 20.7% of the phenotypic variation. Conclusions This study represents one of the first genetic mapping studies on the fiber quality and lint yield traits in a RIL population of G. barbadense using GBS-SNPs. The results provide important information for the subsequent fine mapping of QTLs and the prediction of candidate genes towards map-based cloning and marker-assisted selection in cotton.

High-throughput phenotypic analysis using multispectral unmanned aerial vehicle (UAV) technology is a critical approach for enhancing the efficiency and accuracy of gene mining. This study aimed to evaluate the feasibility of UAV-based remote sensing techniques in predicting chlorophyll content and conducting genome-wide association studies (GWAS) for winter wheat under both normal and drought stress conditions. The study was conducted in the fall of 2019 at the Zepu and Manas experimental bases using winter wheat. Chlorophyll content was measured manually during the heading, flowering, and grain filling stages and compared with data obtained via UAV-mounted multispectral sensors. A predictive model for chlorophyll content was developed using UAV data and validated against manual measurements. The predicted and measured chlorophyll values were then integrated into a GWAS to identify loci associated with chlorophyll content.Chlorophyll content values differed across growth stages, with both measured and predicted values increasing from the heading to grain filling stages. Under normal conditions, manual measurements ranged from 43.96 to 65.85, while UAV-predicted values ranged from 47.59 to 62.29. Under drought conditions, manual measurements ranged from 45.00 to 66.33, and UAV-predicted values ranged from 47.83 to 65.89. Correlation coefficients between measured and predicted values were high under normal conditions (0.90–0.93 during heading, 0.91–0.92 during flowering, and 0.88–0.90 during filling) and moderate to high under drought stress (0.57–0.70, 0.89–0.91, and 0.94–0.96, respectively). A neural network model demonstrated high accuracy in predicting chlorophyll content. GWAS revealed 308 loci associated with chlorophyll content, with UAV-predicted data identifying 206 loci across 21 chromosomes, explaining 7.58%–19.58% of the phenotypic variation. Measured values identified 102 loci across 21 chromosomes, accounting for 9.31%–15.83% of the variation. Eighteen overlapping loci were detected on chromosomes 1A, 1B, 2B, 3B, 4B, 5A, 5B, 5D, 6B, 6D, 7A, and 7B. This study confirms the reliability of UAV-based multispectral data for chlorophyll content inversion and GWAS. Site-specific differences in prediction quality were observed, with site P showing stronger correlations and higher prediction accuracy. Analysis of loci identified 21 candidate genes potentially related to chlorophyll content, including those encoding chlorophyll a/b-binding proteins, aquaporins, and chlorophyll kinases. These findings demonstrate the potential of UAV technology for high-throughput, efficient, and accurate phenotyping, facilitating better understanding of the genetic mechanisms underlying chlorophyll content variation.