Effect of Beclin-1 gene silencing on autophagy and apoptosis of the prostatic hyperplasia epithelial cells

Objectives: This study aims to explore the effect of silencing Beclin-1 gene on autophagy and apoptosis of Benign Prostatic Hyperplasia (BPH) (BPH-1) cells under the condition of Androgen Deprivation (AD) and Autophagy Inhibition (AI). Methods: Control group (BPH-1 group), empty carrier group (sh-RNA-BPH-1 group) and Beclin-1 silenced group (sh-Beclin1-BPH-1 group) were set. The Beclin-1 gene silencing efficiency was detected by RT-PCR and Western blot. Autophagic flux was monitored by GFP-LC3 cleavage assay and cell apoptosis was analyzed by flow cytometry. The protein expression levels of LC3, Caspase-3, PARP-1, Bcl-2, and Bax were detected by Western blot. Results: The transfection of sh-Beclin-1 obviously down-regulated the expression of Beclin-1 at both mRNA and protein levels. Under the conditions of AD and AI, silencing of Beclin-1 restrained the autophagy of BPH-1 cells, as evidenced by a decreased number of autophagosomes and down-regulation of LC3-II protein (p < 0.001). The results of flow cytometry showed that the apoptotic rate of sh-Beclin-1 group was elevated significantly compared to the other two groups (p < 0.01). Western blot results showed that silencing of Beclin-1 promoted 89 kd fragmentation of PARP-1 (p < 0.001) and Caspase-3 activation (p < 0.01). Moreover, silencing of Beclin-1 resulted in declined Bcl-2 and augmented Bax protein expression in BPH-1 cells (p < 0.01), which ultimately led to a decreased Bcl-2/Bax ratio. Conclusions: The results indicated that the silencing of Beclin-1 gene hampered autophagy while activating apoptosis in BPH-1 cells. Thus, Beclin-1 may participate in an antagonistic relationship between autophagy and apoptosis in BPH

The emerging role of deubiquitylating enzymes as therapeutic targets in cancer metabolism.

Cancer cells must rewire cellular metabolism to satisfy the unbridled proliferation, and metabolic reprogramming provides not only the advantage for cancer cell proliferation but also new targets for cancer treatment. However, the plasticity of the metabolic pathways makes them very difficult to target. Deubiquitylating enzymes (DUBs) are proteases that cleave ubiquitin from the substrate proteins and process ubiquitin precursors. While the molecular mechanisms are not fully understood, many DUBs have been shown to be involved in tumorigenesis and progression via controlling the dysregulated cancer metabolism, and consequently recognized as potential drug targets for cancer treatment. In this article, we summarized the significant progress in understanding the key roles of DUBs in cancer cell metabolic rewiring and the opportunities for the application of DUBs inhibitors in cancer treatment, intending to provide potential implications for both research purpose and clinical applications

An epigenetic switch induced by Shh signalling regulates gene activation during development and medulloblastoma growth

The Sonic hedgehog (Shh) signalling pathway plays important roles during development and in cancer. Here we report a Shh-induced epigenetic switch that cooperates with Gli to control transcription outcomes. Before induction, poised Shh target genes are marked by a bivalent chromatin domain containing a repressive histone H3K27me3 mark and an active H3K4me3 mark. Shh activation induces a local switch of epigenetic cofactors from the H3K27 methyltransferase polycomb repressive complex 2 (PRC2) to an H3K27me3 demethylase Jmjd3/Kdm6b-centred coactivator complex. We also find that non-enzymatic activities of Jmjd3 are important and that Jmjd3 recruits the Set1/MLL H3K4 methyltransferase complexes in a Shh-dependent manner to resolve the bivalent domain. In vivo, changes of the bivalent domain accompanied Shh-activated cerebellar progenitor proliferation. Overall, our results reveal a regulatory mechanism that underlies the activation of Shh target genes and provides insight into the causes of various diseases and cancers exhibiting altered Shh signalling

Chromosome-scale genomics, metabolomics, and transcriptomics provide insight into the synthesis and regulation of phenols in Vitis adenoclada grapes

Vitis adenoclada is a wild grape unique to China. It exhibits well resistance to heat, humidity, fungal disease, drought, and soil infertility. Here, we report the high-quality, chromosome-level genome assembly of GH6 (V. adenoclada). The 498.27 Mb genome contained 221.78 Mb of transposable elements, 28,660 protein-coding genes, and 481.44 Mb of sequences associated with 19 chromosomes. GH6 shares a common ancestor with PN40024 (Vitis vinifera) from approximately 4.26–9.01 million years ago, whose divergence occurred later than Vitis rotundifolia and Vitis riparia. Widely-targeted metabolome and transcriptome analysis revealed that the profiles and metabolism of phenolic compounds in V. adenoclada varieties significantly were differed from other grape varieties. Specifically, V. adenoclada varieties were rich in phenolic acids and flavonols, whereas the flavan-3-ol and anthocyanin content was lower compared with other varieties that have V. vinifera consanguinity in this study. In addition, ferulic acid and stilbenes content were associated with higher expressions of COMT and STSs in V. adenoclada varieties. Furthermore, MYB2, MYB73-1, and MYB73-2 were presumably responsible for the high expression level of COMT in V. adenoclada berries. MYB12 (MYBF1) was positively correlated with PAL, CHS, FLS and UFGT.Meanwhile, MYB4 and MYBC2-L1 may inhibit the synthesis of flavan-3-ols and anthocyanins in two V. adenoclada varieties (YN2 and GH6). The publication of the V. adenoclada grape genome provides a molecular foundation for further revealing its flavor and quality characteristics, is also important for identifying favorable genes of the East Asian species for future breeding

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic information. In this work, we have proposed a novel deep learning-based graph model network which can automatically fuse dual-modality information for tumor area segmentation. Our method rationally utilizes the advantage of each imaging modality(PET: the superior contrast, CT: the superior spatial resolution). We formulate this task as a Conditional Random Field(CRF) based on multi-scale fusion and dual-modality co-segmentation of object image with a normalization term which balances the segmentation divergence between PET and CT. This mechanism considers that the spatial varying characteristics acquire different scales, which encode various feature information over different modalities. The ability of our method was evaluated to detect and segment tumor regions with different fusion approaches using a dataset of PET/CT clinical tumor images. The results illustrated that our method effectively integrates both PET and CT modalities information, deriving segmentation accuracy result of 0.86 in DSC and the sensitivity of 0.83, which is 3.61% improvement compared to the W-Net