Inhibitory Effect of D-chiro-inositol on Both Growth and Recurrence of Breast Tumor from MDA-MB-231 Cancer Cells

D-chiro-inositol (DCI) is a secondary messenger in insulin signal transduction. It is produced in vivo from myo-inositol via action of epimerase. In this study, we evaluated antitumor activity of DCI against human breast cancer both in vitro and in vivo. In order to determine the inhibitory effects of DCI on growth of human breast cancer cells (MDA-MB-231), two different assessment methods were implemented: MTT assay and mouse xenograft assay. MTT assay demonstrated downturn in cell proliferation by DCI treatment (1, 5, 10, 20 and 40 mM) groups by 18.3% (p<0.05), 17.2% (p<0.05), 17.5% (p<0.05), 18.4% (p<0.05), and 24.9% (p<0.01), respectively. Also, inhibition of tumor growth was investigated in mouse xenograft model. DCI was administered orally at the dose of 500 mg/kg and 1000 mg/kg body weight to treat nude mouse for 45 consecutive days. On the 45th day, tumor growth of DCI (500 mg/kg and 1000 mg/kg) groups was suppressed by 22.1% and 67.6% as mean tumor volumes were 9313.8 ± 474.1 mm³ and 3879.1 ± 1044.1 mm³, respectively. Furthermore, breast cancer stem cell (fCSC) phenotype (CD44⁺/CD24⁻) was measured using flow cytometry. On the 46th day, CSC ratios of DCI (500 mg/kg) and co-treatment with doxorubicin (4 mg/kg) and DCI (500 mg/kg) group decreased by 24.7% and 53.9% (p<0.01), respectively. Finally, from tumor recurrence assay, delay of 5 days in the co-treatment group compared to doxorubicin (4 mg/kg) alone group was observed. Based on these findings, we propose that DCI holds potential as an anti-cancer drug for treatment of breast cancer.

Prediction of Microbial Infection of Cultured Cells Using DNA Microarray Gene-Expression Profiles of Host Responses

Infection by microorganisms may cause fatally erroneous interpretations in the biologic researches based on cell culture. The contamination by microorganism in the cell culture is quite frequent (5% to 35%). However, current approaches to identify the presence of contamination have many limitations such as high cost of time and labor, and difficulty in interpreting the result. In this paper, we propose a model to predict cell infection, using a microarray technique which gives an overview of the whole genome profile. By analysis of 62 microarray expression profiles under various experimental conditions altering cell type, source of infection and collection time, we discovered 5 marker genes, NM_005298, NM_016408, NM_014588, S76389, and NM_001853. In addition, we discovered two of these genes, S76389, and NM_001853, are involved in a Mycolplasma-specific infection process. We also suggest models to predict the source of infection, cell type or time after infection. We implemented a web based prediction tool in microarray data, named Prediction of Microbial Infection (http://www.snubi.org/software/PMI).