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Background: Interleukin (IL)-17-producing T lymphocytes play a role in pulmonary fibrosis, but the possible mechanism of IL-17 on lung fibroblasts remains uncertain. Objectives: To explore the role and possible mechanism of IL-17 on lung fibroblasts. Methods: A mouse model of pulmonary fibrosis was established by intratracheal administration of 5 mg/kg bleomycin. At 14 days following bleomycin administration the pulmonary fibroblasts were isolated, cultured and identified. siRNA for activator 1 (Act1) were transfected into lung fibroblasts, which were cocultured with IL-17. The NF-κB pathway was detected for IL-17 on the lung fibroblasts. Results: IL-17R was increased significantly at 14 days in the bleomycin-induced pulmonary fibroblast model, exogenous IL-17 significantly promoted the proliferation of the pulmonary fibroblasts in primary culture and obviously increased the expression of α-smooth muscle actin and type I and type III collagen in the fibroblasts. We found that IL-17 rapidly activated the NF-κB signaling pathway through activated phosphorylated p65 and IκB, and all roles of IL-17 on lung fibroblasts were inhibited under the interference for the expression of Act1 in lung fibroblasts. Conclusion: IL-17 may directly promote the proliferation, transformation and collagen synthesis of lung fibroblasts via the NF-kB signaling pathway, which can be inhibited by the interference for the expression of Act1.

PurposeRecently, Cyclin O (CCNO) has been reported to be a novel protein of the cyclin family. However, the clinical significance and functional roles of CCNO in human cancer, including gastric cancer (GC), remain largely unexplored. In this study, we investigated the clinical and functional roles of CCNO in GC.MethodsWe analyzed CCNO expression patterns in GC patients. To investigate the role of CCNO in malignancy of GC, we used lentivirus-delivered short hairpin RNA to knockdown CCNO expression in GC cell lines. Then multiparametric high-content screening and MTT incorporation assay were used to assess the cell proliferation capability. Cell apoptosis was detected by flow cytometry and Caspase 3/7 assays. Furthermore, the effect of CCNO on tumorigenicity of GC was also determined in vivo. Finally, microarray analysis was performed to elucidate the molecular mechanisms by which shCCNO inhibited the malignancy of GC cells.ResultsThe analysis from The Cancer Genome Atlas database revealed elevated CCNO mRNA expression in GC tissue than in the adjacent normal tissue. Immunohistochemical studies also showed that stronger cytoplasmic staining of CCNO was detected in GC tissues. Downregulation of CCNO in GC cells efficiently, through infection with the lentivirus-mediated specific short hairpin RNA, could significantly induce cell apoptosis and inhibit the proliferative properties both in vitro and in vivo. Microarray analysis further revealed 652 upregulated genes and 527 downregulated genes in the shCCNO group compared with control, and indicated that CCNO knockdown could inhibit the malignancy of GC cells through inducing genome-wide gene expression changes.ConclusionOur work is the first to reveal that elevated CCNO expression is closely associated with human GC development and that CCNO knockdown could efficiently inhibit the malignant properties of GC cells by inducing cell apoptosis. Therefore, CCNO could be used as a potential biomarker for prognosis or even as a therapeutic target in human GC.

Nearly a dozen papers have been published over the past five years aimed at approximately speeding up the exact stochastic simulation algorithm ͑SSA͒ for chemical systems that evolve on widely different time scales. Such systems are termed stiff, a term whose meaning in a stochastic context was recently elucidated by Rathinam et al. 1 Here we focus on just two of these papers, namely, Ref. 2 by the present authors, which introduced the slow-scale stochastic simulation algorithm ͑ssSSA͒, and Ref. 3 by E et al., which was published almost a year later and introduced the nested stochastic simulation algorithm ͑nSSA͒. We first correct an incorrect claim that was made in Ref. 3 concerning the ssSSA. We then show that the misunderstanding that led the authors of Ref. 3 to make that incorrect claim also prevented them from seeing that, although there are indeed differences in how the ssSSA and the nSSA are implemented, those two algorithms share a common theoretical foundation-one that was fully established in Ref. 2. The incorrect claim in Ref. 3 occurred in its discussion of how to speed up the simulation of the model reaction set S 1 c 2 c 1 S 2 c 4 c 3 S 3 c 6 c 5 S 4 , ͑1͒

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