Reprogramming glioblastoma multiforme cells into neurons by protein kinase inhibitors

Abstract Background Reprogramming of cancers into normal-like tissues is an innovative strategy for cancer treatment. Recent reports demonstrate that defined factors can reprogram cancer cells into pluripotent stem cells. Glioblastoma multiforme (GBM) is the most common and aggressive malignant brain tumor in humans. Despite multimodal therapy, the outcome for patients with GBM is still poor. Therefore, developing novel therapeutic strategy is a critical requirement. Methods We have developed a novel reprogramming method that uses a conceptually unique strategy for GBM treatment. We screened a kinase inhibitor library to find which candidate inhibitors under reprogramming condition can reprogram GBM cells into neurons. The induced neurons are identified whether functional and loss of tumorigenicity. Results We have found that mTOR and ROCK kinase inhibitors are sufficient to reprogram GBM cells into neural-like cells and “normal” neurons. The induced neurons expressed neuron-specific proteins, generated action potentials and neurotransmitter receptor-mediated currents. Genome-wide transcriptional analysis showed that the induced neurons had a profile different from GBM cells and were similar to that of control neurons induced by established methods. In vitro and in vivo tumorigenesis assays showed that induced neurons lost their proliferation ability and tumorigenicity. Moreover, reprogramming treatment with ROCK-mTOR inhibitors prevented GBM local recurrence in mice. Conclusion This study indicates that ROCK and mTOR inhibitors-based reprogramming treatment prevents GBM local recurrence. Currently ROCK-mTOR inhibitors are used as anti-tumor drugs in patients, so this reprogramming strategy has significant potential to move rapidly toward clinical trials

Integrated metabolome and transcriptome analysis of the NCI60 dataset

Abstract Background Metabolite profiles can be used for identifying molecular signatures and mechanisms underlying diseases since they reflect the outcome of complex upstream genomic, transcriptomic, proteomic and environmental events. The scarcity of publicly accessible large scale metabolome datasets related to human disease has been a major obstacle for assessing the potential of metabolites as biomarkers as well as understanding the molecular events underlying disease-related metabolic changes. The availability of metabolite and gene expression profiles for the NCI-60 cell lines offers the possibility of identifying significant metabolome and transcriptome features and discovering unique molecular processes related to different cancer types. Methods We utilized a combination of analytical methods in the R statistical package to evaluate metabolic features associated with cancer cell lines from different tissue origins, identify metabolite-gene correlations and detect outliers cell lines based on metabolome and transcriptome data. Statistical analysis results are integrated with metabolic pathway annotations as well as COSMIC and Tumorscape databases to explore associated molecular mechanisms. Results Our analysis reveals that although the NCI-60 metabolome dataset is quite noisy comparing with microarray-based transcriptome data, it does contain tissue origin specific signatures. We also identified biologically meaningful gene-metabolite associations. Most remarkably, several abnormal gene-metabolite relationships identified by our approach can be directly linked to known gene mutations and copy number variations in the corresponding cell lines. Conclusions Our results suggest that integrative metabolome and transcriptome analysis is a powerful method for understanding molecular machinery underlying various pathophysiological processes. We expect the availability of large scale metabolome data in the coming years will significantly promote the discovery of novel biomarkers, which will in turn improve the understanding of molecular mechanism underlying diseases.http://deepblue.lib.umich.edu/bitstream/2027.42/112946/1/12859_2011_Article_4394.pd

Simultaneous determination of ten compounds in two main medicinal plant parts of Tibetan herb, Pterocephalus hookeri (CB Clarke) Höeck, by ultra-high performance liquid chromatography-photodiode array

Purpose: To develop an ultra-high performance liquid chromatography (UPLC) - photodiode array (PDA) method to compare the chemical composition of two different medicinal components of Pterocephalus hookeri.Methods: Samples were chromatographically separated in succession using Waters Acquity UPLCR BEH C18 column (2.1 × 100 mm, 1.7 μm) and gradient elution (0.2 % phosphoric acid aqueous -acetonitrile). Using partial least squares discriminant analysis and one-way analysis of variance, attempts were made to distinguish different medicinal parts of P. hookeri.Results: Regression equation for 10 compounds showed good linear regression (R2 > 0.9994). The relative standard deviations of precision, stability, repeatability and recovery were under 5 %. Compared with the aerial plant part, the root had significantly higher levels of sylvestroside I (p < 0.01), cantleyoside (p < 0.001), dipsanosides B (p < 0.01) and dipsanosides A (p < 0.01), but significantly lower levels of loganic acid (p < 0.001), chlorogenic acid (p < 0.01), and isochlorogenic acid (p < 0.01). There were no significant differences between loganin, sweroside and isochlorogenic acid C.Conclusion: The described method is simple, accurate and reproducible, and can be used for the simultaneous determination of 10 major compounds of P. hookeri. The results demonstrate that there is variation in the chemical composition of the aerialpart and root of P. hookeri and that loganic acid and cantleyoside are the primary chemical biomarkers.Keywords: Tibetan medicine, Pterocephalus hookeri, Medicinal parts, Loganic acid and Cantleyoside, UPLC-PD

Conditional DETR for Fast Training Convergence

The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.Comment: Accepted by ICCV 2021. The first two authors share first authorship, and the order was determined by rolling dic

R-process beta-decay neutrino flux from binary neutron star merger and collapsar

This study investigates the antineutrinos production by $\beta$-decay of $r$-process nuclei in two astrophysical sites that are capable of producing gamma-ray bursts (GRBs): binary neutron star mergers (BNSMs) and collapsars, which are promising sites for heavy element nucleosynthesis. We employ a simplified method to compute the $\beta$-decay $\bar\nu_e$ energy spectrum and consider two representative thermodynamic trajectories for $r$-process simulations, each with four sets of $Y_e$ distribution. The time evolution of the $\bar\nu_e$ spectrum is derived for both the dynamical ejecta and the disk wind for BNSMs and collapsar outflow, based on approximated mass outflow rates. Our results show that the $\bar\nu_e$ has an average energy of approximately 3 to 9~MeV, with a high energy tail of up to 20 MeV. The $\bar\nu_e$ flux evolution is primarily determined by the outflow duration, and can thus remain large for $\mathcal{O}(10)$~s and $\mathcal{O}(100)$~s for BNSMs and collapsars, respectively. For a single merger or collapsar at 40~Mpc, the $\bar\nu_e$ flux is $\mathcal{O}(10-100)$~cm$^{-2}$~s$^{-1}$, indicating a possible detection horizon up to $0.1-1$~Mpc for Hyper-kamiokande. We also estimate their contributions to the diffuse $\bar\nu_e$ background. Our results suggest that although the flux from BNSMs is roughly 4--5 orders of magnitude lower than that from the regular core-collapse supernovae, those from collapsars can possibly contribute a non-negligible fraction to the total diffuse $\bar\nu_e$ flux at energy $\lesssim 1$~MeV, with a large uncertainty depending on the unknown rate of collapsars capable of hosting the $r$-process.Comment: 13 pages, 7 figure

1,2,4,5-Tetra­phenyl-1H-imidazole

The asymmetric unit of the title compound, C27H20N2, contains two independent mol­ecules, A and B. In both mol­ecules, the N atom in the 1-position and the C atom in the 5-position are statistically disordered [as 0.571 (8):0.429 (8) in A and 0.736 (9):0.264 (9) in B]. The phenyl rings in the 1-, 2-, 4- and 5-positions in A are twisted from the central imidazole ring by 84.3 (2), 21.6 (2), 21.5 (2) and 75.7 (2)°, respectively. The corresponding dihedral angles in B are 85.5 (2), 3.8 (2), 2.4 (2) and 81.7 (2)°, respectively

Network pharmacology and UPLC-Q-TOF/MS studies on the anti-arthritic mechanism of Pterocephalus hookeri

Purpose: To investigate the mechanism underlying the anti-arthritic properties of Pterocephalus hookeri used for treatment of rheumatoid arthritis (RA).Methods: Aqueous methanol extract of P. hookeri was analyzed using UPLC-Q-TOF/MS, a Waters Acquity UPLCR BEH C18 column (2.1 × 100 mm, 1.7 μm) and gradient elution with acetonitrile-formic acid-water. Targets and related pathways were predicted by PharmMapper database and Molecule Annotation System, respectively. The network was built with Cytoscape software.Results: Forty compounds were identified, comprising 17 iridoid glycosides, 7 phenolic acids, 13 triterpenes, and 3 other compounds. A total of 38 targets and 44 pathways associated with RA were obtained. These involved mainly MAPK signaling pathway, adherens junction, and colorectal cancer.Conclusion: These results from network pharmacology suggest that P. hookeri exerts therapeutic effect on RA via multiple components, multiple targets and multiple pathways.Keywords: Pterocephalus hookeri, Rheumatoid arthritis, UPLC-Q-TOF/MS, Chemical composition, Network pharmacolog