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Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, proteinprotein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a r e g u l a t o r y r e l a t i o n s h i p . C 2 2 o r f 3 4h s a -m i R -6 7 8 5 -5 p -R A B 3 7 , hsacirc_013887-hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p-RAB37, MIR4435-1HG-hsa-miR-30b-3p-RAB37, and GAS5-hsa-miR-30b-Frontiers in Endocrinology frontiersin.org 01

Two new nortriterpenoids, paeonenoides D (Ia) and E (Ib), are isolated from the roots of Paeonia lactiflora.

Plant lectins have been investigated to elucidate their complicated mechanisms due to their remarkable anticancer activities. Although plant lectins seems promising as a potential anticancer agent for further preclinical and clinical uses, further research is still urgently needed and should include more focus on molecular mechanisms. Herein, a Naïve Bayesian model was developed to predict the protein-protein interaction (PPI), and thus construct the global human PPI network. Moreover, multiple sources of biological data, such as smallest shared biological process (SSBP), domain-domain interaction (DDI), gene co-expression profiles and cross-species interolog mapping were integrated to build the core apoptotic PPI network. In addition, we further modified it into a plant lectin-induced apoptotic cell death context. Then, we identified 22 apoptotic hub proteins in mesothelioma cells according to their different microarray expressions. Subsequently, we used combinational methods to predict microRNAs (miRNAs) which could negatively regulate the abovementioned hub proteins. Together, we demonstrated the ability of our Naïve Bayesian model-based network for identifying novel plant lectin-treated cancer cell apoptotic pathways. These findings may provide new clues concerning plant lectins as potential apoptotic inducers for cancer drug discovery.

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Tw onew nortriterpenoids,paeonenoides Dand E(1 and 2,resp.),together with seven known compounds,were isolated from the roots of Paeonia lactiflora.T heir structures were elucidated on the basis of spectroscopic evidence.C ompounds 1 -7 were screened for inhibitory effects against NO productioninLPS-induced RAW246.7 macrophages and for cytotoxic activities against HL-60,Hep-G2,and SK-OV-3cell lines.Compounds 1 -3 and 5 -7 exhibited inhibitory activities with IC 50 valuesinthe range of 9.6 -32.2 mm.T riterpenoids with an epoxide ring and af ree COOH function, 1 -3,s howede ffectively increased activities compared with other pentacyclic triterpenoids. Compounds 1 -6 showedsignificant cytotoxic activities against the Hep-G2cell line and modest cytotoxic activities against HL-60and SK-OV-3cell lines.

Background:Human epidermal growth factor receptor-2 (HER2) is a trans-membrane receptor like protein, and aberrant signaling of HER2 is implicated in many human cancers, such as ovarian cancer, gastric cancer, and prostate cancer, most notably breast cancer. Moreover, it has been in the spotlight in the recent years as a promising new target for therapy of breast cancer.Objective:Since virtual screening has become an integral part of the drug discovery process, it is of great significant to identify novel HER2 inhibitors by structure-based virtual screening.Materials and Methods:In this study, we carried out a series of elegant bioinformatics approaches, such as virtual screening and molecular dynamics (MD) simulations to identify HER2 inhibitors from Food and Drug Administration-approved small molecule drug as potential “new use” drugs. Results: Molecular docking identified top 10 potential drugs which showed spectrum affinity to HER2. Moreover, MD simulations suggested that ZINC08214629 (Nonoxynol-9) and ZINC03830276 (Benzonatate) might exert potential inhibitory effects against HER2-targeted anti-breast cancer therapeutics.Conclusion:Together, our findings may provide successful application of virtual screening studies in the lead discovery process, and suggest that our discovered small molecules could be effective HER2 inhibitor candidates for further study.SUMMARY A series of elegant bioinformatics approaches, including virtual screening and molecular dynamics (MD) simulations were took advantage to identify human epidermal growth factor receptor-2 (HER2) inhibitors. Molecular docking recognized top 10 candidate compounds, which showed spectrum affinity to HER2. Further, MD simulations suggested that ZINC08214629 (Nonoxynol-9) and ZINC03830276 (Benzonatate) in candidate compounds were identified as potential “new use” drugs against HER2-targeted anti-breast cancer therapeutics. Abbreviations used: HER2: Human epidermal growth factor receptor-2, FDA: Food and Drug Administration, PDB: Protein Database Bank, RMSDs: Root mean square deviations, SPC: Single point charge, PME: Particle mesh Ewald, NVT: Constant volume, NPT: Constant pressure, RMSF: Root-mean-square fluctuation.