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End-stage renal disease (ESRD) patients are at high risk for coronary artery disease (CAD). The optimal revascularization strategy remains unknown. We performed a meta-analysis of retrospective observational trials to compare coronary artery bypass grafting (CABG) with percutaneous coronary intervention (PCI) for ESRD patients with CAD. A search of published reports was conducted to identify clinical studies comparing CABG with PCI in ESRD patients with CAD with a minimal follow-up of 12 months. Sixteen studies included 32 350 ESRD patients with revascularization. Compared with PCI, CABG was associated with a lower risk for late mortality [relative risk (RR) 0.90, 95% confidence interval (CI) 0.87–0.93], myocardial infarction event (RR 0.64, 95% CI: 0.61–0.68), repeat revascularization event (RR 0.22, 95% CI: 0.16–0.31) and cumulative events (RR 0.69, 95% CI: 0.65–0.73), despite having a higher risk for early mortality (RR 1.98, 95% CI: 1.51–2.60). In conclusion, the long-term results of PCI in ESRD patients are dismal, and CABG is significantly superior to PCI in this subset of patients. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However, the further efficacy brought by radical radiotherapy has reached the bottleneck in advanced patients, who are prone to develop recurrence and distant metastasis after treatment. The application of photon therapy makes it possible for radiation dose escalation in refractory cases and may provide second chance for recurrent patients with less unrecoverable tissue damage. The concept of adaptive radiotherapy is put forward in consideration of target volume shrinkage during treatment. The replanning procedure offers better protection for the organ at risk. However, the best timing and candidates for adaptive radiotherapy is still under debate. The current tendency of artificial intelligence in NPC mainly focuses on image recognition, auto-segmentation and dose prediction. Although artificial intelligence is still in developmental stage, the future of it is promising. To further improve the efficacy of NPC, multimodality treatment is encouraged. In-depth studies on genetic and epigenetic variations help to explain the great heterogeneity among patients, and could further be applied to precise screening and prediction, personalized radiotherapy and the evolution of targeted drugs. Given the clinical benefit of immunotherapy in other cancers, the application of immunotherapy, especially immune checkpoint inhibitor, in NPC is also of great potential. Results from ongoing clinical trials combining immunotherapy with radiotherapy in NPC are expected. [ABSTRACT FROM AUTHOR]

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies. [ABSTRACT FROM AUTHOR]

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology. [ABSTRACT FROM AUTHOR]

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service. [ABSTRACT FROM AUTHOR]

Summary UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer. Availability and implementation UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The increasing demand for triacylglycerol (TAG) enriching polyunsaturated fatty acids (PUFAs) has led to a surge of interest in microalgal TAG metabolism. Polar membrane lipids serve as the desaturation carrier for PUFA, and the functional group of PUFA can be incorporated into TAG. Monogalactoglycerolipid has been found to provide the de novo synthesized oleate acyl group or the nascent polyunsaturated diacylglycerol backbone for TAG biosynthesis in the model green alga, Chlamydomonas reinhardtii. However, whether other membrane lipids take part in the formation of PUFA-attached TAG has not been clearly discovered. A time course study of glycerolipidomics in the starchless mutant of C. reinhardtii , BAFJ5, which hyper-accumulates TAG, revealed that digalactosyldiacylglycerol (DGDG) and diacylglycerol- N , N , N -trimethylhomoserine (DGTS) turned into the main components of membrane lipids, accounting for 62% of the total polar lipids, under nitrogen deprivation combined with high light conditions. In addition, the membrane lipid molecules DGDG 18:3n3/16:0 and DGTS 16:0/18:3n6 were presumed to be involved in the consecutive integration of the de novo synthesized linolenates into TAG. Based on the stoichiometry calculation, DGDG and DGTS were demonstrated to provide a major contribution to the accumulation of linolenate-attached TAG. Our study gives insights into the potential PUFA-attached TAG formation pathway mediated by the turnover of de novo synthesized DGDG and DGTS in the starchless mutant of Chlamydomonas. [ABSTRACT FROM AUTHOR]

Summary Mutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution for de novo signature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution, doublet base substitution, small insertion and deletion and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. Sigflow pipeline can be applied to both human and mouse genomes. Availability and implementation Sigflow is an open source software under academic free license v3.0 and it is freely available at https://github.com/ShixiangWang/sigflow or https://hub.docker.com/r/shixiangwang/sigflow. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A low-temperature-active alkaline esterase, Est12, from a marine sediment metagenomic fosmid library was identified. Est12 prefers short- and middle-chain p-nitrophenol esters as substrate with optimum temperature and pH value of 50 °C and 9.0, respectively, and nearly 50 % of maximum activity retained at 5 °C. The hydrolysis activity of Est12 was stable at 40 °C. Ca especially activated the activity of Est12 to about 151 % of the control. DEPC and PMSF inhibited the activity of Est12 to 34 and 25 %, respectively. In addition, Est12 was more tolerable to methanol compared to other organic solvents tested. The crystal structure of Est12 at 1.39 Å resolution showed that the cap domain which is composed of an α-helix and a flexible region resulted in a relatively wide spectrum of substrate, with p-nitrophenol caproate as the preferred one. Furthermore, the flexible cap domain and the high percentage of Gly, Ser, and Met may play important roles in the adaptation of Est12 to low temperature. [ABSTRACT FROM AUTHOR]

Aims SUMO-specific protease 1 (SENP1) removes SUMO from proteins and plays important roles in the regulation of multiple cellular signalling pathways. However, little is known about the role of SENP1 in coronary heart disease. In this study, we tested the hypothesis that SENP1 protects against myocardial ischaemia/reperfusion (I/R) injury and investigated the underlying molecular mechanisms involved. Methods and results First, we found that SENP1 levels increased after I/R in human and mouse myocardium in vivo and in rat cardiomyocytes in vitro. We then performed coronary artery ligation to induce I/R injury in wild-type (WT) and heterozygous SENP1-knockdown (SENP1+/−) mice. Compared with WT mice, SENP1+/− mice had normal cardiac function at baseline but lower systolic function after I/R. Post-I/R myocardial infarction sizes were larger in SENP1+/− mice. Furthermore, we demonstrated that SENP1 regulates the expression of hypoxia-inducible factor 1 α (HIF1α), a critical protective factor during I/R, in vivo and in vitro. Overexpression of HIF1α reversed the deteriorating effect of SENP1 knockdown on cellular death. Conclusion Our results suggest that SENP1 deficiency exacerbates I/R injury in cardiomyocytes via a HIF1α-dependent pathway. [ABSTRACT FROM PUBLISHER]

Under stressful conditions, the non-model marine microalga Tetraselmis subcordiformis can accumulate a substantial amount of starch, making it a potential feedstock for the production of fuel ethanol. Investigating the interactions of the enzymes and the regulatory factors involved in starch metabolism will provide potential genetic manipulation targets for optimising the starch productivity of T. subcordiformis. For this reason, the proteome of T. subcordiformis was utilised to predict the first protein-protein interaction (PPI) network for this marine alga based on orthologous interactions, mainly from the general PPI repositories. Different methods were introduced to evaluate the credibility of the predicted interactome, including the confidence value of each PPI pair and Pfam-based and subcellular location-based enrichment analysis. Functional subnetworks analysis suggested that the two enzymes involved in starch metabolism, starch phosphorylase and trehalose-phosphate synthase may be the potential ideal genetic engineering targets. [ABSTRACT FROM AUTHOR]