Sonic Hedgehog Pathway Is Essential for Maintenance of Cancer Stem-Like Cells in Human Gastric Cancer

Abnormal activation of the Sonic hedgehog (SHH) pathway has been described in a wide variety of human cancers and in cancer stem cells (CSCs), however, the role of SHH pathway in gastric CSCs has not been reported. In this study, we investigated the possibility that abnormal activation of the SHH pathway maintained the characteristics of gastric CSCs. First, we identified cancer stem-like cells (CSLCs) from human gastric cancer cell lines (HGC-27, MGC-803 and MKN-45) using tumorsphere culture. Compared with adherent cells, the floating tumorsphere cells had more self-renewing capacity and chemoresistance. The cells expressing CSCs markers (CD44, CD24 and CD133) were also significantly more in tumorsphere cells than in adherent cells. More importantly, in vivo xenograft studies showed that tumors could be generated with 2×104 tumorsphere cells, which was 100-fold less than those required for tumors seeding by adherent cells. Next, RT-PCR and Western blot showed that the expression levels of Ptch and Gli1 (SHH pathway target genes) were significantly higher in tumorsphere cells than in adherent cells. The results of quantitative real-time PCR were similar to those of RT-PCR and Western blot. Further analysis revealed that SHH pathway blocked by cyclopamine or 5E1 caused a higher reduction in self-renewing capacity of HGC-27 tumorsphere cells than that of adherent cells. We also found that SHH pathway blocking strongly enhanced the efficacy of chemotherapeutic drugs in HGC-27 tumorsphere cells in vitro and in vivo but had no significant effect in adherent cells. Finally, we isolated the tumorspheres from gastric cancer specimen, these cells also had chemoresistance and tumorigenic capacity, and SHH pathway maintained the gastric CSLCs characteristics of tumorsphere cells from primary tumor samples. In conclusion, our data suggested that SHH pathway was essential for maintenance of CSLCs in human gastric cancer

Methyltransferase Dnmt3a upregulates HDAC9 to deacetylate the kinase TBK1 for activation of antiviral innate immunity

The DNA methyltransferase Dnmt3a has high expression in terminally differentiated macrophages; however, its role in innate immunity remains unknown. Here we report that deficiency in Dnmt3a selectively impaired the production of type I interferons triggered by pattern-recognition receptors (PRRs), but not that of the proinflammatory cytokines TNF and IL-6. Dnmt3a-deficient mice exhibited enhanced susceptibility to viral challenge. Dnmt3a did not directly regulate the transcription of genes encoding type I interferons; instead, it increased the production of type I interferons through an epigenetic mechanism by maintaining high expression of the histone deacetylase HDAC9. In turn, HDAC9 directly maintained the deacetylation status of the key PRR signaling molecule TBK1 and enhanced its kinase activity. Our data add mechanistic insight into the crosstalk between epigenetic modifications and post-translational modifications in the regulation of PRR signaling and activation of antiviral innate immune responses

Using machine learning to support debugging with Tarantula

Using a specific machine learning technique, this paper proposes a way to identify suspicious statements during debugging. The technique is based on principles similar to Tarantula but addresses its main flaw: its difficulty to deal with the presence of multiple faults as it assumes that failing test cases execute the same fault(s). The improvement we present in this paper results from the use of C4.5 decision trees to identify various failure conditions based on information regarding the test cases' inputs and outputs. Failing test cases executing under similar conditions are then assumed to fail due to the same fault(s). Statements are then considered suspicious if they are covered by a large proportion of failing test cases that execute under similar conditions. We report on a case study that demonstrates improvement over the original Tarantula technique in terms of statement ranking. Another contribution of this paper is to show that failure conditions as modeled by a C4.5 decision tree accurately predict failures and can therefore be used as well to help debugging