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Androgen independency is associated with poor prostate cancer (PCa) survival. Here we report that silencing of transglutaminase-2 (TG2) expression by CRISPR-Cas9 is associated with upregulation of androgen receptor (AR) transcription in PCa cell lines. Knockout of TG2 reversed the migratory potential and anchorage independency of PC3 and DU145 cells and revealed a reduced level of mucin-1 (MUC1) RNA transcript through unbiased multi-omics profiling, which was restored by selective add-back of the truncated TG2 isoform (TGM2_v2). Silencing of AR resulted into increased MUC1 in TG2KO PC3 cells showing that TG2 affects transcriptional regulation of MUC1 via repressing AR expression. Treatment of PC3 WT cell line with TG2 inhibitor ZDON led to a significant increase in AR expression and decrease in MUC1. ZDON also blocked the formation of MUC1-multimers labelled with TG amine-donor substrates in reducing conditions, revealing for the first time a role for TG2, which we show to be externalised via extracellular vesicles, in MUC1 stabilisation via calcium-dependent transamidation. A specific antibody towards TGM2_v2 revealed its restricted nuclear location compared to the canonical long form of TG2 (TGM2_v1), which is predominantly cytosolic, suggesting that this form contributes to the previously suggested TG2-mediated NF-κB activation and AR transcriptional repression. As TGM2_v2 transcription was increased in biopsies of early-stage prostate adenocarcinoma (PRAD) patients compared to subjects presenting inflammatory prostatitis, and total TG2 protein expression significantly increased in PRAD versus normal tissue, the role of TG2 and its truncated form as a prostate malignancy marker is suggested. In conclusion, this investigation has provided the first unbiased discovery of a novel pathway mediated by TG2 via MUC1, which is shown to contribute to androgen insensitivity and malignancy of PCa cells and be upregulated in PCa biopsies, with potential relevance to cancer immune evasion.

Purpose of Review This manuscript summarizes novel clinical and interventional approaches in the management of chronic, nociceptive, and neuropathic pain. Recent Findings Pain can be defined as a feeling of physical or emotional distress caused by an external stimulus. Pain can be grouped into distinct types according to characteristics including neuropathic pain, which is a pain caused by disease or lesion in the sensory nervous system; nociceptive pain, which is pain that can be sharp, aching, or throbbing and is caused by injury to bodily tissues; and chronic pain, which is long lasting or persisting beyond 6 months. With improved understanding of different signaling systems for pain in recent years, there has been an upscale of methods of analgesia to counteract these pathological processes. Novel treatment methods such as use of cannabinoids, stem cells, gene therapy, nanoparticles, monoclonal antibodies, and platelet-rich plasma have played a significant role in improved strategies for therapeutic interventions. Summary Although many management options appear to be promising, extensive additional clinical research is warranted to determine best practice strategies in the future for clinicians.

A parsing algorithm visualizer is a tool that visualizes the construction of a parser for a given context-free grammar and then illustrates the use of that parser to parse a given string. Parsing algorithm visualizers are used to teach the course on compiler construction which in invariably included in all undergraduate computer science curricula. This paper presents a new parsing algorithm visualizer that can visualize six parsing algorithms, viz. predictive parsing, simple LR parsing, canonical LR parsing, look-ahead LR parsing, Earley parsing and CYK parsing. The tool logically explains the process of parsing showing the calculations involved in each step. The output of the tool has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. The tool has been used to teach the course on compiler construction at both undergraduate and graduate levels. An overall positive feedback was received from the students with 89% of them saying that the tool helped them in understanding the parsing algorithms. The tool is capable of visualizing multiple parsing algorithms and 88% students used it to compare the algorithms.