Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.

ß-Elemene enhances the efficacy of gefitinib on glioblastoma multiforme cells through the inhibition of the EGFR signaling pathway.

Glioblastoma multiforme (GBM) is the most common and severe form of primary tumor in the central nervous system of adults which has poor prognosis and limited therapeutic options. Epidermal growth factor receptor (EGFR) inhibitor, such as gefitinib (brand name Iressa, ZD1839), has been approved as a targeted medicine for several types of tumor including glioblastoma multiforme. However, gefitinib exerted very limited effects on some glioblastoma multiforme patients after a period of treatment due to intrinsic and acquired drug resistance. ß-Elemene, a natural plant drug extracted from Curcuma wenyujin, has shown promising anticancer effects against a broad spectrum of tumors. In the present study, we found that ß-elemene could enhance the chemosensitivity of glioblastoma multiforme cells to gefitinib. The combination medication of ß-elemene and gefitinib not only inhibited the survival and proliferation of glioblastoma multiforme cells via inhibition of EGFR signaling pathway but also induced more distinct apoptosis and autophagy in the glioblastoma multiforme cells than the gefitinib monotherapy. These results showed that ß-elemene might be one potential adjuvant to enhance the effect of EGFR inhibitor and reduce the resistance of gefitinib in glioblastoma multiforme.

Protective effects of the total saponins from Rosa laevigata Michx fruit against carbon tetrachloride-induced acute liver injury in mice.

Saponins are the major chemicals from Rosa laevigata Michx fruit. However, there have no papers to report the activities of the natural product against liver damage. In the present work, total saponins from R. laevigata Michx fruit (RLTS) with purity>70% was produced and then the protective effects of it against CCl4-induced acute liver injury in mice was tested. The results showed that RLTS decreased serum ALT and AST activities compared with model group, as well as the relative liver weight and histological findings. In addition, RLTS remarkably increased the levels of SOD, CAT, GSH-Px, GSH and decreased MDA, iNOS and NO levels in liver. Transmission electron microscopy and TUNEL assays showed that RLTS repaired fragmented DNA and mitochondrial change caused by CCl4. Further investigation demonstrated that RLTS pretreatment down-regulated the protein expression of CYP2E1, ATF6, GRP78, EIF2, COX-2, NF-κB, p53, Caspase-3, Caspase-9, Cytokeratin 18 and the levels of MAPKs phosphorylation, up-regulated Bcl-2 expression, and markedly decreased the gene expression of TNF-α, IL-6, Fas/FasL and Bax. This is the first time to reveal the hepatoprotective effect of total saponins from R. laevigata Michx fruit, which should be developed as a new drug for treatment of live injury in future.

[Research progress of effect of anti-diabetic traditional Chinese medicines based on regulation of glucose metabolic enzyme].

Diabetes is a global threat threatening human health in the world, with an increasing incidence rate in recent years. The disorder of glucose metabolism is one of the major factors. As relevant glucose metabolic enzymes such as alpha-glucosidase, glucose-6-phosphatase (G-6-P), glycogen phosphorylase (GP) and glycogen synthase kinase-3 (GSK-3) get involved in and control the process of glucose metabolism, the regulation of the activity of glucose metabolic enzymes is of significance to the treatment of diabetes. Traditional Chinese medicines (TCMs) have been widely researched because of their low toxicology and high efficiency, and many extracts and components from TCMs have been proven to be regulators of glucose metabolic enzymes. Compared with anti-diabetic western medicines, anti-diabetic TCMs feature safety, reliability and low price. This essay summarizes the anti-diabetic effect of TCMs on regulating glucose metabolic enzymes.

Development and validation of a sensitive and rapid non-aqueous LC-ESI-MS/MS method for measurement of diosgenin in the plasma of normal and hyperlipidemic rats: a comparative study.

A sensitive and specific electrospray ionization liquid chromatography-tandem mass spectrometry method was developed to detect diosgenin in the plasma of normal and hyperlipidemic rats. Diosgenin was extracted with n-hexane-ethyl acetate (9:1, v/v) using sarsasapogenin as an internal standard. With multiple reaction monitoring modes, linear calibration curves were obtained in the range 10-1500 ng/mL (r>or=0.9979) and the limit of quantification was 10 ng/mL. Intra- and inter-assay variabilities were within 7.74%, and accuracies were between -5.33% and 1.50%. The assay was successfully applied to study pharmacokinetics in rats after oral administration of diosgenin. Significantly different pharmacokinetics between normal and hyperlipidemic rats were observed, which would be beneficial for the clinical use of diosgenin.