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

Abstract

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).

Epidemiologic Factors and Urogenital Infections Associated With Preterm Birth in a Midwestern U.S. Population

“…Furthermore, using antibiotic prescriptions as a surrogate for health care utilization, antibiotics were more frequently prescribed in the non–large urban cohorts for nongenitourinary infections. Confirming negative effects seen by others (5,28,29,31), our study did not show a reduced risk for preterm birth with the non-randomized prescription of antibiotics considered active against urogenital mollicutes, possibly due to reinfection, antibacterial resistance, inadequate drug levels at site of infection, or lack of eradication secondary to non-bacteriocidal therapy (5,31,32,33,34). …”