Inflammatory response has been reported to contribute to the renal lesions in rat Thy-1 nephritis (Thy-1N) as an animal model of human mesangioproliferative glomerulonephritis (MsPGN). Besides C5b-9 complex, C5a is also a potent pro-inflammatory mediator and correlated to severity of various nephritic diseases. However, the role of C5a in mediating pro-inflammatory cytokine production in rats with Thy-1N is poorly defined. In the present studies, the levels of C5a, interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were first determined in the renal tissues of rats with Thy-1N. Then, the expression of IL-6 and TNF-α was detected in rat glomerular mesangial cells (GMC) stimulated with our recombinant rat C5a in vitro. Subsequently, the activation of mitogen-activated protein kinase (MAPK) signaling pathways (p38 MAPK, ERK1/2 and JNK) and their roles in the regulation of IL-6 and TNF-α production were examined in the GMC induced by C5a. The results showed that the levels of C5a, IL-6 and TNF-α were markedly increased in the renal tissues of Thy-1N rats. Rat C5a stimulation in vitro could up-regulate the expression of IL-6 and TNF-α in rat GMC, and the activation of MAPK signaling pathways was involved in the induction of IL-6 and TNF-α. Mechanically, p38 MAPK activation promoted IL-6 production, while either ERK1/2 or JNK activation promoted TNF-α production in the GMC with exposure to C5a. Taken together, these data implicate that C5a induces the synthesis of IL-6 and TNF-α in rat GMC through the activation of MAPK signaling pathways.

Interleukin 17 (IL-17) is increasingly recognized as a key factor that contributes to the pathogenesis of multiple sclerosis (MS) and its experimental mouse autoimmune encephalomyelitis (EAE) model. However, the roles and regulatory mechanisms of IL-17-induced pro-inflammatory cytokine production in EAE mice remain largely unclear. In this study, the expression of IL-17, hypoxia inducible factor-1α (HIF-1α), IL-1β, IL-6 and microRNA-497 (miR-497), as well as their intrinsic associations, was investigated using EAE model mice and cultured astrocytes exposed to IL-17 in vitro. We observed markedly increased production of IL-17, HIF-1α, IL-1β and IL-6 in the brain tissues of EAE mice, while the expression and secretion of HIF-1α, IL-1β and IL-6 were also significantly increased when cultured primary astrocytes from mice were stimulated with IL-17. Meanwhile, the expression of miR-497 was downregulated both in vivo and in vitro. Subsequent in vitro experiments revealed that IL-17 induced the production of IL-1β and IL-6 in astrocytes through the upregulation of HIF-1α as a transcriptional factor, indicating that IL-17-mediated downregulation of miR-497 enhanced HIF-1α expression. Furthermore, astrocyte-specific knockdown of IL-17RA and HIF-1α or astrocyte-specific overexpression of miR-497 by infection with different lentiviral vectors containing an astrocyte-specific promotor markedly decreased IL-1β and IL-6 production in brain tissues and alleviated the pathological changes and score of EAE mice. Collectively, these findings indicate that decreased miR-497 expression is responsible for IL-17-triggered high HIF-1α expression and consequent IL-1β and IL-6 production by astrocytes in EAE mice.Cellular & Molecular Immunology advance online publication, 1 May 2017; doi:10.1038/cmi.2017.12.

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Background A promising intervention for heart diseases is the application of cardiomyocytes differentiated from pluripotent stem cells. However, the driving factors of the differentiation process still remain unclear. This study aims to map the transcriptional landscapes of directional differentiation of pluripotent stem cells at multiple stages, using single‐cell sequencing data, in order to identify the key drivers of the differentiation process. Method Single‐cell sequencing data from pluripotent stem cell‐derived cardiomyocytes at age day 0, day 2, day 5, day 15 and day 30 were included and normalized. UMAP algorithm was then used to reduce the dimensionality of the transcriptome profiles, followed by community detection to group the cells and cell type identification using marker genes. Graph‐autocorrelation analysis was implemented to identify differentially expressed genes among cell groups with Gene Ontology term enrichment. Next, we calculated the pseudotimes of each cardiomyocyte to construct the pseudotime trajectories. Finally, knowledge based dynamic changes in gene expression were analyzed to identify the key factors driving the cardiomyocyte development. Result We identified 9 different cell types in the process of cardiomyocyte development, including cardiomyocytes, induced pluripotent stem cells, cardiogenic mesoderm cells, cardiovascular progenitor cells, embryonic stem cells, endoderm cells, epithelial progenitor cells, cardiomyocyte progenitor cells and multipotent progenitor cells. Identification of cardiomyocyte was confirmed with specific gene expression profiles such as significant enrichment in the biological processes of cell adhesion and supramolecular fiber organization. The expression of NKX2‐5, which encodes a homeobox‐containing transcription factor, was found to be at a lower level in the early stages and continually increased as cardiomyocyte develop, suggesting that NKX2‐5 was one of the driving factors of cardiac development, acting through promoting myocardial maturation and development. Conclusion Single cell sequencing data revealed the dynamic changes of gene expression during cardiomyocyte development differentiation. NKX2‐5 was identified as a key factor in cardiac development. Our finding contributes to the understanding of the mechanism behind the cardiomyocyte development, and may serve as the basis for stem cell based therapies. Support or Funding Information This work was supported by National Natural Science Foundation of China 81774152 (to RZ) and 81770571 (to LZ).

Neutrophil extracellular traps (NETs), products of neutrophil death when exposed to certain stimuli, were first proposed as a type of response to bacterial infection in infectious diseases. Since then, extensive studies have discovered its involvement in other non-infectious inflammatory diseases including thromboembolism, autoimmune diseases, and cancer. Colorectal cancer (CRC) is one of the most common malignancies in the world. NET formation is closely associated with tumorigenesis, progression, and metastasis in CRC. Therefore, the application of NETs in clinical practice as diagnostic biomarkers, therapeutic targets, and prognostic predictors has a promising prospect. In addition, therapeutics targeting NETs are significantly efficient in halting tumor progression in preclinical cancer models, which further indicates its potential clinical utility in cancer treatment. This review focuses on the stimuli of NETosis, its pro-tumorigenic activity, and prospective clinical utility primarily in but not limited to CRC.

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.

Microbial signatures have emerged as promising biomarkers and targets for disease diagnosis, prognosis, and remission. However, these biomarkers exhibit contradictory results in different studies, necessitating the identification of universally robust microbial biomarkers. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation, and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Although xMarkerFinder is initially developed for gut microbiome study, it is generalizable to different omics layers, as well as other habitats. Execution time varies depending on the sample size, selected algorithm, and computational resource. xMarkerFinder can be accessed at https://github.com/tjcadd2020/xMarkerFinder. FAQs, detailed tutorials, issue boards, and a ready-to-use Docker image are provided for users with no prior bioinformatics or statistics training.