Mediating oxidative stress through the Palbociclib/miR-141-3p/STAT4 axis in osteoporosis: a bioinformatics and experimental validation study.

Osteoporosis is a common bone disease characterized by loss of bone mass, reduced bone strength, and deterioration of bone microstructure. ROS-induced oxidative stress plays an important role in osteoporosis. However, the biomarkers and molecular mechanisms of oxidative stress are still unclear. We obtained the datasets from the Gene Expression Omnibus (GEO) database, and performed differential analysis, Venn analysis, and weighted correlation network analysis (WGCNA) analysis out the hub genes. Then, the correlation between inflammatory factors and hub genes was analyzed, and a Mendelian randomization (MR) analysis was performed on cytokines and osteoporosis outcomes. In addition, "CIBERSORT" was used to analyze the infiltration of immune cells and single-cell RNA-seq data was used to analyze the expression distribution of hub genes and cell-cell communications. Finally, we collected human blood samples for RT-qPCR and Elisa experiments, the miRNA-mRNA network was constructed using the miRBase database, the 3D structure was predicted using the RNAfold, Vfold3D database, and the drug sensitivity analysis was performed using the RNAactDrug database. We obtained three differentially expressed genes associated with oxidative stress: DBH, TAF15, and STAT4 by differential, WGCNA clustering, and Venn screening analyses, and further analyzed the correlation of these 3 genes with inflammatory factors and immune cell infiltration and found that STAT4 was significantly and positively correlated with IL-2. Single-cell data analysis showed that the STAT4 gene was highly expressed mainly in dendritic cells and monocytes. In addition, the results of RT-qPCR and Elisa experiments verified that the expression of STAT4 was consistent with the previous analysis, and a significant causal relationship between IL-2 and STAT4 SNPs and osteoporosis was found by Mendelian randomization. Finally, through miRNA-mRNA network and drug sensitivity analysis, we analyzed to get Palbociclib/miR-141-3p/STAT4 axis, which can be used for the prevention and treatment of osteoporosis. In this study, we proposed the Palbociclib/miR-141-3p/STAT4 axis for the first time and provided new insights into the mechanism of oxidative stress in osteoporosis.

Baseline Pydiflumetofen Sensitivity of Fusarium pseudograminearum Isolates Collected from Henan, China, and Potential Resistance Mechanisms.

Fusarium crown rot (FCR), caused by Fusarium pseudograminearum, is one of the most important diseases impacting wheat production in the Huanghuai region, the most important wheat-growing region of China. The current study found that the SDHI fungicide pydiflumetofen, which was recently developed by Syngenta Crop Protection, provided effective control of 67 wild-type F. pseudograminearum isolates in potato dextrose agar, with an average EC50 value of 0.060 ± 0.0098 µg/ml (SE). Further investigation revealed that the risk of fungicide resistance in pydiflumetofen was medium to high. Four F. pseudograminearum mutants generated by repeated exposure to pydiflumetofen under laboratory conditions indicated that pydiflumetofen resistance was associated with fitness penalties. Mutants exhibited significantly (P < 0.05) reduced sporulation in mung bean broth and significantly (P < 0.05) reduced pathogenicity in wheat seedlings. Sequence analysis indicated that the observed pydiflumetofen resistance of the mutants was likely associated with amino acid changes in the different subunits of the succinate dehydrogenase target protein, including R18L and V160M substitutions in the FpSdhA sequence; D69V, D147G, and C257R in FpSdhB; and W78R in FpSdhC. This study found no evidence of cross-resistance between pydiflumetofen and the alternative fungicides tebuconazole, fludioxonil, carbendazim, or fluazinam, which all have distinct modes of action and could therefore be used in combination or rotation with pydiflumetofen to reduce the risk of resistance emerging in the field. Taken together, these results indicate that pydiflumetofen has potential as a novel fungicide for the control of FCR caused by F. pseudograminearum and could therefore be of great significance in ensuring high and stable wheat yields in China.

Progranulin from different gliocytes in the nucleus accumbens exerts distinct roles in FTD- and neuroinflammation-induced depression-like behaviors.

BACKGROUND: Neuroinflammation in the nucleus accumbens (NAc) is well known to influence the progression of depression. However, the molecular mechanisms triggering NAc neuroinflammation in depression have not been fully elucidated. Progranulin (PGRN) is a multifunctional growth factor that is linked to the innate immune response and inflammation, and PGRN plays a key role in neurodegenerative diseases such as frontotemporal dementia (FTD). Here, the purpose of this study was to validate whether PGRN was involved in the NAc neuroinflammation-promoted depressive-like phenotype. METHODS: A NAc neuroinflammation-relevant depression-like model was established using wild-type (WT) and PGRN-knockout (KO) mice after NAc injection with lipopolysaccharide (LPS), and various behavioral tests related to cognition, social recognition, depression and anxiety were performed with WT and PGRNKO mice with or without NAc immune challenge. RT‒PCR, ELISA, western blotting and immunofluorescence staining were used to determine the expression and function of PGRN in the neuroinflammatory reaction in the NAc after LPS challenge. The morphology of neurons in the NAc from WT and PGRNKO mice under conditions of NAc neuroinflammation was analyzed using Golgi-Cox staining, followed by Sholl analyses. The potential signaling pathways involved in NAc neuroinflammation in PGRNKO mice were investigated by western blotting. RESULTS: Under normal conditions, PGRN deficiency induced FTD-like behaviors in mice and astrocyte activation in the NAc, promoted the release of the inflammatory cytokines interleukin (IL)-6 and IL-10 and increased dendritic complexity and synaptic protein BDNF levels in the NAc. However, NAc neuroinflammation enhanced PGRN expression, which was located in astrocytes and microglia within the NAc, and PGRN deficiency in mice alleviated NAc neuroinflammation-elicited depression-like behaviors, seemingly inhibiting astrocyte- and microglia-related inflammatory reactions and neuroplasticity complexity in the NAc via the p38 and nuclear factor of kappa (NF-κB) signaling pathways present in the NAc after neuroinflammation. CONCLUSIONS: Our results suggest that PGRN exerts distinct function on different behaviors, showing protective roles in the FTD-like behavior and detrimental effects on the neuroinflammation-related depression-like behavior, resulting from mediating astrocyte and microglial functions from the NAc in different status.

Catalytic synthesis of 2,5-bis(hydroxymethyl)furan from 5-hydroxymethylfurfual by recombinant Saccharomyces cerevisiae.

2,5-Bis(hydroxymethyl)furan (BHMF) is a versatile building block in the synthesis of polymers, fuels, and macrocycle polyethers. In this work, alcohol dehydrogenases (ADHs) were identified from Meyerozyma guilliermondii SC1103 and were heterologously expressed in Saccharomyces cerevisiae for the synthesis of BHMF from 5-hydroxymethylfurfural (HMF). Of recombinant strains constructed, S. cerevisiae expressing an aryl ADH (MgAAD1669) was observed to be the best catalyst. Upon process optimization, BHMF was afforded with a 99% selectivity and a 94% yield within 24 h at the substrate concentration of 250 mM. The space-time yield up to 3.4 g/L h was achieved in the fed-batch synthesis of BHMF. Inexpensive corncob hydrolysate proved to be a promising alternative to glucose as co-substrate for biocatalytic synthesis of BHMF, thus resulting in the significantly reduced production cost.

DeeReCT-PolyA: a robust and generic deep learning method for PAS identification.

MOTIVATION: Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. RESULTS: In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/likesum/DeeReCT-PolyA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient and accurate inversion of multiple scattering with deep learning.

Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.