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Effect of glycolysis on water holding capacity during postmortem aging of Jersey cattle-yak meat.

Postmortem muscle moisture loss leads to a decrease in carcass weight and can adversely impact overall meat quality. Therefore, it is critical to investigate water holding capacity (WHC) to enhance meat quality. Current research has primarily focused on examining the correlation between signaling molecules and meat quality in relation to the glycolysis effect on muscle WHC. But there exists a significant knowledge gap regarding the mechanism of WHC in Jersey cattle-yak meat. Jersey cattle-yak meat pH decreased and then increased during postmortem aging. Lactate content, cooking loss, pressing loss, drip loss and centrifuging loss of Jersey cattle-yak meat increased and then decreased during postmortem aging. The glycogen content of Jersey cattle-yak meat was significantly higher than that of yak meat at 6-120 h, being 8.40% higher than that of yak meat at 120 h. The activity of key glycolytic enzymes hexokinase (HK), pyruvate kinase (PK), phosphofructokinase (PFK) and lactate dehydrogenase (LDH) in Jersey cattle-yak meat was lower than that in yak meat. Correlation analysis showed that Jersey cattle-yak meat WHC was positively correlated with the activity of HK, PK, PFK and LDH. The WHC of Jersey cattle-yak meat was higher than that of Gannan yak meat, and it was significantly positively correlated with the activity of key enzymes of the glycolytic signaling pathway. Therefore, the glycolysis rate can be reduced by inhibiting enzyme activity to improve Jersey cattle-yak meat WHC and meat quality. © 2023 Society of Chemical Industry.

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Transcription factor ZmDof22 enhances drought tolerance by regulating stomatal movement and antioxidant enzyme activities in maize (Zea mays L.)

Abstract Identifying drought stress regulatory genes is essential for the genetic improvement of maize yield. Deoxyribonucleic acid binding with one finger (Dof), a plant-specific transcription factor family, is involved in signal transduction, morphogenesis, and environmental stress responses. In the present study, by weighted correlation network analysis (WGCNA) and gene coexpression network analysis, 15 putative Dof genes that respond to drought and rewatering were identified from maize. A real-time fluorescence quantitative PCR showed that these 15 genes were strongly induced by drought and ABA treatment, and among them, ZmDof22 was highly induced by drought and ABA treatment. Its expression level increased nearly 200 times after drought stress and more than 50 times after ABA treatment. After the normal conditions were restored, the expression levels were almost 100 times and 40 times those before treatment, respectively. The Gal4-LexA/UAS system and transcriptional activation analysis indicate that ZmDof22 is a transcriptional activator regulating maize drought tolerance and recovery ability. Further, overexpressed transgenic and mutant plants of ZmDof22 by CRISPR/Cas9 show that the ZmDof22 improves maize drought tolerance by promoting stomatal closure, reduces water loss, and enhances antioxidant enzyme activity by participating in the ABA pathways. Taken together, our findings laid a foundation for further functional studies of the ZmDof gene family and provided insights into the role of the ZmDof22 regulatory network in controlling drought tolerance and recovery ability of maize.

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Breed-Related Differential microRNA Expression and Analysis of Colostrum and Mature Milk Exosomes in Bamei and Landrace Pigs.

Breast milk, an indispensable source of immunological and nutrient components, is essential for the growth and development of newborn mammals. MicroRNAs (miRNAs) are present in various tissues and body fluids and are selectively packaged inside exosomes, a type of membrane vesicle. Milk exosomes have potential regulatory effects on the growth, development, and immunity of newborn piglets. To explore the differences in milk exosomes related to the breed and milk type, we isolated exosomes from colostrum and mature milk from domestic Bamei pigs and foreign Landrace pigs by using density gradient centrifugation and then characterized them by transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Furthermore, the profiles and functions of miRNAs in the two types of pig milk exosomes were investigated using miRNA-seq and bioinformatics analysis. We identified a total of 1081 known and 2311 novel miRNAs in pig milk exosomes from Bamei and Landrace pigs. These differentially expressed miRNAs (DE-miRNAs) are closely associated with processes such as cell signaling, cell physiology, and immune system development. Functional enrichment analysis showed that DE-miRNA target genes were significantly enriched in endocytosis, the T cell receptor signaling pathway, and the Th17 cell differentiation signaling pathway. The exosomal miRNAs in both the colostrum and mature milk of the two pig species showed significant differences. Based on related signaling pathways, we found that the colostrum of local pig breeds contained more immune-system-development-related miRNAs. This study provides new insights into the possible function of milk exosomal miRNAs in the development of the piglet immune system.

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PDE9A polymorphism and association analysis with growth performance and gastrointestinal weight of Hu sheep

Phosphodiesterase 9A (PDE9A) plays a crucial role in activating the cGMP-dependent signaling pathway and may have important effects on the growth and development of the gastrointestinal tract in Hu sheep. In this study, we analyzed the single nucleotide polymorphisms of PDE9A in 988 Hu sheep and their correlation with growth performance, feed efficiency, and gastrointestinal development. Additionally, we examined the expression level of different PDE9A genotypes in the gastrointestinal tract of Hu sheep by using fluorescence quantitative PCR. The results revealed a moderate level of polymorphism (0.25 < PIC < 0.50) at the g.286248617 T > C mutation site located in the first intron of PDE9A in Hu sheep, with three genotypes: CC, CT, and TT. The weights of the omasum, colon, and cecum were significantly greater in the CC genotype than in the TT genotype (P < 0.05), and the expression level of PDE9A in the tissues of the rumen, ileum, cecum, and colon was notably lower in the CC genotype individuals (P < 0.05). These findings suggest that the polymorphism of PDE9A affects the weight of the stomach, colon, and cecum in Hu sheep through expression regulation. Overall, the results of this study suggest that the g.286248617 T > C mutation site in the first intron of PDE9A can serve as a potential molecular marker for breeding practices related to the gastrointestinal weight of Hu sheep.

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Integrating Unmanned Aerial Vehicle-Derived Vegetation and Texture Indices for the Estimation of Leaf Nitrogen Concentration in Drip-Irrigated Cotton under Reduced Nitrogen Treatment and Different Plant Densities

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters.

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