ABSTRACT Bioinformatics tools are imperative for the in depth analysis of heterogeneous high‐throughput data. Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, such software tools may fail to capture “what the community needs in a tool”. Here, we describe a novel community‐driven approach to build a comprehensive functional enrichment analysis tool. Using the existing FunRich tool as a template, we invited researchers to request additional features and/or changes. Remarkably, with the enthusiastic participation of the community, we were able to implement 90% of the requested features. FunRich enables plugin for extracellular vesicles wherein users can download and analyse data from Vesiclepedia database. By involving researchers early through community needs software development, we believe that comprehensive analysis tools can be developed in various scientific disciplines.
Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies. Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining. Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle. The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.
Cancer cachexia is a common condition in many cancer patients, particularly those with advanced disease. Cancer cachexia patients are generally less tolerant to chemotherapies and radiotherapies, largely limiting their treatment options. While the search for treatments of this condition are ongoing, standards for the efficacy of treatments have yet to be developed. Current diagnostic criteria for cancer cachexia are primarily based on loss of body mass and muscle function. However, these criteria are rather limiting, and in time, when weight loss is noticeable, it may be too late for treatment. Consequently, biomarkers for cancer cachexia would be valuable adjuncts to current diagnostic criteria, and for assessing potential treatments. Using high throughput methods such as "omics approaches", a plethora of potential biomarkers have been identified. This article reviews and summarizes current studies of biomarkers for cancer cachexia.
Abstract Recently, the interest in extracellular vesicles released by bacteria has rapidly increased. Bacterial extracellular vesicles (BEVs) have been involved in bacteria‐bacteria and bacteria‐host interactions, which strengthen health or bring about various pathologies. However, BEV separation, characterization, and functional studies require the establishment of guidelines and further optimization in order to stimulate the development of science in BEV research and a following successful transformation into clinical applications. This position paper is authored by the Microbial Vesicles Task Force of the Chinese Society for Extracellular Vesicles (CSEV) composed of experienced medical laboratory specialists, microbiologists, virologists, biologists and material biologists who are actively engaged in BEV research. Herein, we present a concise description of BEV research and discover challenges and critical gaps in current BEV‐based analyses for clinical applications. Finally, we also offer suggestions and considerations to improve experimental reproducibility and interoperability in BEV research to promote progress in the field.
Abstract Extracellular vesicles (EVs) are a heterogeneous group of lipid membrane-enclosed compartments that contain different biomolecules and are released by almost all living cells, including fungal genera. Fungal EVs contain multiple bioactive components that perform various biological functions, such as stimulation of the host immune system, transport of virulence factors, induction of biofilm formation, and mediation of host–pathogen interactions. In this review, we summarize the current knowledge on EVs of human pathogenic fungi, mainly focusing on their biogenesis, composition, and biological effects. We also discuss the potential markers and therapeutic applications of fungal EVs.
Abstract Extracellular vesicles (EVs) are membranous vesicles that are released by cells. In this study, the role of the Endosomal Sorting Complex Required for Transport (ESCRT) machinery in the biogenesis of yeast EVs was examined. Knockout of components of the ESCRT machinery altered the morphology and size of EVs as well as decreased the abundance of EVs. In contrast, strains with deletions in cell wall biosynthesis genes, produced more EVs than wildtype. Proteomic analysis highlighted the depletion of ESCRT components and enrichment of cell wall remodelling enzymes, glucan synthase subunit Fks1 and chitin synthase Chs3, in yeast EVs. Interestingly, EVs containing Fks1 and Chs3 rescued the yeast cells from antifungal molecules. However, EVs from fks1 ∆ or chs3 ∆ or the vps23 ∆ chs3 ∆ double knockout strain were unable to rescue the yeast cells as compared to vps23 ∆ EVs. Overall, we have identified a potential role for yeast EVs in cell wall remodelling.
Developing therapies for cancer cachexia has not been successful to date, in part due to the challenges of achieving robust quantitative measures as a readout of patient treatment. Hence, identifying biomarkers to assess the outcomes of treatments for cancer cachexia is of great interest and important for accelerating future clinical trials.We established a novel xenograft model for cancer cachexia with a cachectic human PC3* cell line, which was responsive to anti-Fn14 mAb treatment. Using RNA-seq and secretomic analysis, genes differentially expressed in cachectic and non-cachectic tumors were identified and validated by digital droplet PCR (ddPCR). Correlation analysis was performed to investigate their impact on survival in cancer patients.A total of 46 genes were highly expressed in cachectic PC3* tumors, which were downregulated by anti-Fn14 mAb treatment. High expression of the top 10 candidates was correlated with low survival and high cachexia risk in different cancer types. Elevated levels of LCN2 were observed in serum samples from cachectic patients compared with non-cachectic cancer patients.The top 10 candidates identified in this study are candidates as potential biomarkers for cancer cachexia. The diagnostic value of LCN2 in detecting cancer cachexia is confirmed in patient samples.
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