Two Secreted Proteoglycans, Activators of Urothelial Cell-Cell Adhesion, Negatively Contribute to Bladder Cancer Initiation and Progression.

Osteomodulin (OMD) and proline/arginine-rich end leucine repeat protein (PRELP) are secreted extracellular matrix proteins belonging to the small leucine-rich proteoglycans family. We found that OMD and PRELP were specifically expressed in umbrella cells in bladder epithelia, and their expression levels were dramatically downregulated in all bladder cancers from very early stages and various epithelial cancers. Our in vitro studies including gene expression profiling using bladder cancer cell lines revealed that OMD or PRELP application suppressed the cancer progression by inhibiting TGF-β and EGF pathways, which reversed epithelial-mesenchymal transition (EMT), activated cell-cell adhesion, and inhibited various oncogenic pathways. Furthermore, the overexpression of OMD in bladder cancer cells strongly inhibited the anchorage-independent growth and tumorigenicity in mouse xenograft studies. On the other hand, we found that in the bladder epithelia, the knockout mice of OMD and/or PRELP gene caused partial EMT and a loss of tight junctions of the umbrella cells and resulted in formation of a bladder carcinoma in situ-like structure by spontaneous breakdowns of the umbrella cell layer. Furthermore, the ontological analysis of the expression profiling of an OMD knockout mouse bladder demonstrated very high similarity with those obtained from human bladder cancers. Our data indicate that OMD and PRELP are endogenous inhibitors of cancer initiation and progression by controlling EMT. OMD and/or PRELP may have potential for the treatment of bladder cancer

Nascent RNA interaction keeps PRC2 activity poised and in check

Polycomb-repressive complex 2 (PRC2) facilitates the maintenance and inheritance of chromatin domains repressive to transcription through catalysis of methylation of histone H3 at Lys27 (H3K27me2/3). However, through its EZH2 subunit, PRC2 also binds to nascent transcripts from active genes that are devoid of H3K27me2/3 in embryonic stem cells. Here, biochemical analyses indicated that RNA interaction inhibits SET domain-containing proteins, such as PRC2, nonspecifically in vitro. However, CRISPR-mediated truncation of a PRC2-interacting nascent RNA rescued PRC2-mediated deposition of H3K27me2/3. That PRC2 activity is inhibited by interactions with nascent transcripts supports a model in which PRC2 can only mark for repression those genes silenced by transcriptional repressors

Requirements of the RNA Polymerase II C-Terminal Domain for Reconstituting Pre-mRNA 3′ Cleavage

RNA polymerase II (RNAP II) has previously been shown to be required for the pre-mRNA polyadenylation cleavage reaction in vitro. This activity was found to reside solely in the C-terminal domain (CTD) of the enzyme's largest subunit. Using a deletion analysis of glutathione S-transferase-CTD fusion proteins, we searched among the CTD's 52 imperfectly repetitive heptapeptides for the minimal subset that possesses this property. We found that heptads in the vicinity of 30 to 37 contribute modestly more than other sections, but that no specific subsection of the CTD is necessary or sufficient for cleavage. To investigate further the heptad requirements for cleavage, we constructed a series of all-consensus CTDs having 13, 26, 39, and 52 YSPTSPS repeats. We found that the nonconsensus CTD heptads are together responsible for only 20% of the wild-type cleavage activity. Analysis of the all-consensus CTD series revealed that the remaining 80% of the CTD-dependent cleavage activity directly correlates with CTD length, with significant activity requiring ≈26 or more repeats. These results are consistent with a scaffolding role for the RNAP II CTD in the pre-mRNA cleavage reaction

BRCA1/BARD1 inhibition of mRNA 3′ processing involves targeted degradation of RNA polymerase II

Mammalian cells exhibit a complex response to DNA damage. The tumor suppressor BRCA1 and associated protein BARD1 are thought to play an important role in this response, and our previous work demonstrated that this includes transient inhibition of the pre-mRNA 3′ processing machinery. Here we provide evidence that this inhibition involves proteasomal degradation of a component necessary for processing, RNA polymerase II (RNAP II). We further show that RNAP IIO, the elongating form of the enzyme, is a specific in vitro target of the BRCA1/BARD1 ubiquitin ligase activity. Significantly, siRNA-mediated knockdown of BRCA1 and BARD1 resulted in stabilization of RNAP II after DNA damage. In addition, inhibition of 3′ cleavage induced by DNA damage was reverted in extracts of BRCA1-, BARD1-, or BRCA1/BARD1-depleted cells. We also describe corresponding changes in the nuclear localization and/or accumulation of these factors following DNA damage. Our results support a model in which a BRCA1/BARD1-containing complex functions to initiate degradation of stalled RNAP IIO, inhibiting the coupled transcription-RNA processing machinery and facilitating repair

Phosphorylation of the PRC2 component Ezh2 is cell cycle-regulated and up-regulates its binding to ncRNA

Ezh2 functions as a histone H3 Lys 27 (H3K27) methyltransferase when comprising the Polycomb-Repressive Complex 2 (PRC2). Trimethylation of H3K27 (H3K27me3) correlates with transcriptionally repressed chromatin. The means by which PRC2 targets specific chromatin regions is currently unclear, but noncoding RNAs (ncRNAs) have been shown to interact with PRC2 and may facilitate its recruitment to some target genes. Here we show that Ezh2 interacts with HOTAIR and Xist. Ezh2 is phosphorylated by cyclin-dependent kinase 1 (CDK1) at threonine residues 345 and 487 in a cell cycle-dependent manner. A phospho-mimic at residue 345 increased HOTAIR ncRNA binding to Ezh2, while the phospho-mimic at residue 487 was ineffectual. An Ezh2 domain comprising T345 was found to be important for binding to HOTAIR and the 5′ end of Xist

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential

Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning

Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography