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, a fragrant purple-red wild lily endemic to Northeast Asia, represents both ecological significance (as a key protected species) and horticultural value. While its white variant ( var. ) exhibits distinct flower color and fragrance traits, the molecular mechanisms underlying these variations remain poorly understood. Previous studies attributed the low anthocyanin content in the white variant to LcMYB12 downregulation, yet comprehensive analyses of associated genes and metabolic pathways are lacking. This study employed integrated transcriptomics, metabolomics, and volatile metabolomics to systematically compare and its white variant. We analyzed differential gene expression in the phenylpropanoid and flavonoid biosynthesis pathways, quantified anthocyanin/flavonoid metabolites, and assessed volatile organic compound profiles. The white variant showed significant reductions in flavonoids (catechin, epicatechin) and anthocyanins (cyanidin, pelargonidin, peonidin), linked to the downregulation of 58 genes in the flavonoid pathway-including , , , and UFGT. Critically, UFGT suppression disrupted anthocyanin glycosylation, promoting degradation and vacuolar accumulation failure. Concurrently, phenylpropanoid pathway inhibition reduced p-coumaric acid synthesis, diminishing downstream anthocyanins and volatile compounds (eugenol/methyleugenol). Our multi-omics approach reveals that flower color loss in var. album results from synergistic effects of transcriptional regulation and metabolic flux redirection. The UFGT-mediated glycosylation defect provides a novel explanation for anthocyanin instability in white petals. These findings complement prior genetic studies and establish a framework for targeted breeding of ornamental traits in Lilium species.

Hosta ventricosa is a robust ornamental perennial plant that can tolerate low temperatures, and which is widely used in urban landscaping design in Northeast China. However, the mechanism of cold-stress tolerance in this species is unclear. A combination of transcriptomic and metabolomic analysis was used to explore the mechanism of low-temperature tolerance in H . ventricosa . A total of 12 059 differentially expressed genes and 131 differentially expressed metabolites were obtained, which were mainly concentrated in the signal transduction and phenylpropanoid metabolic pathways. In the process of low-temperature signal transduction, possibly by transmitting Ca 2+ inside and outside the cell through the ion channels on the three cell membranes of COLD, CNGCs and CRLK, H . ventricosa senses temperature changes and stimulates SCRM to combine with DREB through the MAPK signal pathway and Ca 2+ signal sensors such as CBL, thus strengthening its low-temperature resistance. The pathways of phenylpropanoid and flavonoid metabolism represent the main mechanism of low-temperature tolerance in this species. The plant protects itself from low-temperature damage by increasing its content of genistein, scopolentin and scopolin. It is speculated that H . ventricosa can also adjust the content ratio of sinapyl alcohol and coniferyl alcohol and thereby alter the morphological structure of its cell walls and so increase its resistance to low temperatures.When subjected to low-temperature stress, H . ventricosa perceives temperature changes via COLD, CNGCs and CRLK, and protection from low-temperature damage is achieved by an increase in the levels of genistein, scopolentin and scopolin through the pathways of phenylpropanoid biosynthesis and flavonoid biosynthesis.

Backgrounds A growing number of expression quantitative trait loci (eQTLs) have been found to be linked with tumorigenesis. In this article, we employed integrated Mendelian randomization (MR) analyses to identify novel susceptibility genes in renal cancer (RC) and reveal their potential mechanisms. Methods Two-sample MR analyses were performed to infer causal relationships between eQTLs, metabolites, and RC risks through the “TwoSampleMR” R package. Sensitivity analyses, such as heterogeneity, pleiotropy, and leave-one-out analysis, were used to assess the stability of our outcomes. Summary-data-based MR (SMR) analyses were used to verify the causal relationships among cis-eQTLs and RC risks via the SMR 1.3.1 software. Results Our results provided the first evidence for AFF3 eQTL elevating RC risks, suggesting its oncogenic roles (IVW method; odds ratio (OR) = 1.0005; 95% confidence interval (CI) = 1.0001–1.0010; P  = 0.0285; heterogeneity = 0.9588; pleiotropy = 0.8397). Further SMR analysis validated the causal relationships among AFF3 cis-eQTLs and RC risks ( P  < 0.05). Moreover, the TCGA-KIRC, the ICGC-RC, and the GSE159115 datasets verified that the AFF3 gene was more highly expressed in RC tumors than normal control via scRNA-sequencing and bulk RNA-sequencing ( P  < 0.05). Gene set enrichment analysis (GSEA) analysis identified six potential biological pathways of AFF3 involved in RC. As for the potential mechanism of AFF3 in RC, we concluded in this article that AFF3 eQTL could negatively modulate the levels of the X-11,315 metabolite (IVW method; OR = 0.9127; 95% CI = 0.8530–0.9765; P  = 0.0081; heterogeneity = 0.4150; pleiotropy = 0.8852), exhibiting preventive effects against RC risks (IVW method; OR = 0.9987; 95% CI = 0.9975–0.9999; P  = 0.0380; heterogeneity = 0.5362; pleiotropy = 0.9808). Conclusions We concluded that AFF3 could serve as a novel eQTL-mediated susceptibility gene in RC and reveal its potential mechanism of elevating RC risks via negatively regulating the X-11,315 metabolite levels.

To investigate the effect of baicalin on diabetic nephropathy (DN) rats and podocytes and its mechanism. The rat models with DN were established by high-fat and high-sugar diet and intraperitoneal injection of streptozotocin. The fasting blood glucose (FBG) and weight of rats in each group were measured at 0, 1, 2, 3, and 4 weeks. Their biochemical indicators, expression of inflammatory, and antioxidant factors were measured using an automatic biochemical analyzer together with ELISA. Hematoxylin-eosin staining and periodic acid-schiff staining were used to observe the morphological changes in the kidneys of rats in each group. Finally, the expressions of related molecules and PI3K/Akt/mTOR signaling pathway proteins in renal tissues and podocytes were examined by qRT-PCR and Western blot. Compared with the DN group, the FBG and weight, serum creatinine, blood urea nitrogen, total cholesterol, triacylglycerol, microalbumin, and albumin/creatinine ratio were all significantly decreased in the Baicalin treatment groups in a concentration-dependent manner. The levels of inflammatory factors in kidney tissue and podocytes were decreased. In addition, the activities of lactate dehydrogenase and malondialdehyde in tissue were decreased, while the superoxide dismutase was increased. The pathological sections showed that glomerular atrophy and glomerular basement membrane thickening caused by hyperglycemia were improved in the Baicalin treatment groups. Meanwhile, baicalin inhibited the downregulation of Nephrin and Podocin expressions and upregulation of Desmin expression caused by DN, and inhibited the expressions of p-PI3K, p-Akt, and p-mTOR proteins. Baicalin slows down podocyte injury caused by DN by inhibiting the activity of PI3K/Akt/mTOR signaling pathway.

Blueberry ( Vaccinium ssp.) is a perennial shrub belonging to the family Ericaceae, which is highly tolerant of acid soils and heavy metal pollution. In the present study, blueberry was subjected to cadmium (Cd) stress in simulated pot culture. The transcriptomics and rhizosphere fungal diversity of blueberry were analyzed, and the iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and cadmium (Cd) content of blueberry tissues, soil and DGT was determined. A correlation analysis was also performed. A total of 84 374 annotated genes were identified in the root, stem, leaf and fruit tissue of blueberry, of which 3370 were DEGs, and in stem tissue, of which 2521 were DEGs. The annotation data showed that these DEGs were mainly concentrated in a series of metabolic pathways related to signal transduction, defense and the plant–pathogen response. Blueberry transferred excess Cd from the root to the stem for storage, and the highest levels of Cd were found in stem tissue, consistent with the results of transcriptome analysis, while the lowest Cd concentration occurred in the fruit, Cd also inhibited the absorption of other metal elements by blueberry. A series of genes related to Cd regulation were screened by analyzing the correlation between heavy metal content and transcriptome results. The roots of blueberry rely on mycorrhiza to absorb nutrients from the soil. The presence of Cd has a significant effect on the microbial community composition of the blueberry rhizosphere. The fungal family Coniochaetaceae, which is extremely extremelytolerant, has gradually become the dominant population. The results of this study increase our understanding of the plant regulation mechanism for heavy metals, and suggest potential methods of soil remediation using blueberry.

Ferro-coke, as a new burden of blast furnace (BF), can not only greatly reduce the energy consumption and CO2 emission, but also promote the resource utilization by using the low-quality iron ore and low-grade coal. However, the strength of ferro-coke decreased with the increasing amount of iron ore powder. In order to maintain the strength of ferro-coke while increasing the amount of iron ore powder, it is necessary to add binder during the coking process to enhance the strength of ferro-coke. In this paper, phenolic resin, silicon metal powder, corn starch, and coal tar pitch were used as binder for the fabrication of ferro-coke. I-type drum machine (I 600), scanning electron microscope (SEM), and X-ray diffraction (XRD) were applied to test the crushing strength, morphology, and microcrystalline structure of the ferro-coke. The results showed that the increasing amount of iron ore powder resulted in lower crushing strength, higher porosity, and the worse macroscopic morphology of ferro-coke. When the amount of iron ore powder reached 40%, obvious cracks appeared on the surface of ferro-coke. When the amount of iron ore was 30%, the crushing strength of ferro-coke dropped to 18.15%. Among the four binders, coal tar pitch could significantly enhance the cold crushing strength of ferro-coke through decreasing the porosity of ferro-coke and improving the bonding effect between carbon matrix particles. In the case of the 10% coal tar pitch addition, the cold crushing strength of ferro-coke was increased from 18.15% to 76.41%; meanwhile, its hot compression strength during gasification improved by 100N.

Rainfall is a key triggering factor for numerous geotechnical hazards. Hence, it is necessary to investigate the degradation characteristics of rock–soil strength under different water contents. The existing macro–mesoscopic analysis methods for rock–soil strength degradation neglect the intrinsic connection between water content variations caused by external rainfall and mesoscopic mechanical mechanisms. In addition, there is a lack of discrete element method (DEM) mesoscopic parameter calibration methods for rock–soil strength under the influence of external environmental factors. Hence, this study aims to perform a macro–mesoscopic analysis and develop a parameter calibration model for the degradation of rock–soil strength under different water contents. First, the mesoscopic mechanical characteristics under different water contents are investigated by analyzing particle displacement, the bond failure rate, and the anisotropy coefficient. Interrelationships among shear strength, water content, and mesoscopic parameters are qualitatively analyzed, which indicated a macro–mesoscopic synergistic mechanism. A macro–meso-environment data set is constructed. Key mesoscopic parameters are determined using Pearson correlation (Pearson) and mutual information (MI) methods. Then, the mapping relationships are established based on ordinary least squares. The model accuracy is verified by comparing the calibrated simulation results with direct shear test results. The results show that the shear strength increases with vertical pressure under a constant water content. However, as the water content varies, the strength initially increases and then decreases. The average displacement of central particles and bond failure rate both decrease initially and then increase with rising water content, while the anisotropy coefficients show the opposite trend. Normal bond strength, tangential bond strength, and friction coefficient are determined as the key parameters. The goodness-of-fit R2 of the parameter calibration model exceeds 0.92. Among 45 validation working conditions, only two are found to have errors of 12.4% and 13.6%, and the remainder have errors below 5%.

Background Biological laboratories and companies involved in antibody development need convenient and versatile methods to detect highly active antibodies. Methods To develop a mammalian cell-based ZZ display system for antibody quantification, the eukaryotic ZZ-displayed plasmid was constructed and transfected into CHO cells. After screening by flow cytometric sorting, the stable ZZ display cells were incubated with reference IgG and samples with unknown IgG content for 40 min at 4℃, the relative fluorescence intensity of cells was analyzed and the concentration of IgG was calculated. Results By investigating the effects of different display-associated genetic elements, a eukaryotic ZZ-displaying plasmid with the highest display efficiency were constructed. After transfection and screening, almost 100% of the cells were able to display the ZZ peptide (designated CHO-ZZ cells). These stable CHO-ZZ cells were able to capture a variety of IgG, including human, rabbit, donkey and even mouse and goat. CHO-ZZ cells could be used to quantify human IgG in the range of approximately 12.5–1000 ng/mL, and to identify high-yielding engineered monoclonal cell lines. Conclusions We have established a highly efficient CHO-ZZ display system in this study, which enables the quantification of IgG from various species under physiological conditions. This system offers the advantage of eliminating the need for antibody purification and will contribute to antibody development.

Tumor metastasis is the primary cause of mortality in most cancer patients. Before disassociation from the tumors, most of malignant tumor cells undergo the epithelial–mesenchymal transition to break away from the adhesions between the cells and the surrounding extracellular matrix. Recently, activating enhancer-binding protein (AP4) has been shown to be a mediator of EMT in colorectal cancer and high level of AP4 correlates with poor prognosis in cancer patients. It has been found that AP4 upregulates the genes involved in EMT and cell proliferation in colorectal cancer cells and that the aggressive human breast cancer cells MDA-MB-231 are highly metastatic. Therefore, we tested the hypothesis that AP4 may also affect cell migration and EMT in this cell type. Three different assays, including the wound-healing assay, the Boyden chamber assay, and the cell tracking assay, were employed to confirm that AP4 activated both cell migration and invasion. Immunofluorescence staining and Western blot analysis revealed that the cells underwent EMT when AP4 was upregulated. In contrast, overexpression of dominant-negative AP4, lacking the DNA-binding domain, inactivated the DNA-binding ability of endogenous AP4 and led to lower cell motility. Furthermore, we found that AP4 enhanced p53 expression at both transcriptional and translational levels. Knockdown of p53 by siRNA significantly diminished the activation of cell migration by AP4, indicating that AP4 can regulate cell migration via the activity of p53.

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).