J Korean Med Sci.  2012 Oct;27(10):1129-1136. 10.3346/jkms.2012.27.10.1129.

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

• Park YR
• Chung TS
• Lee YJ
• Song YW
• Lee EY
• Sohn YW
• Song S
• Park WY
• Kim JH

• KMID: 1778816
• DOI: http://doi.org/10.3346/jkms.2012.27.10.1129

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).

Keyword

Prediction Model; Microbial Infection; DNA Microarray; Mycoplasma

MeSH Terms

Algorithms
Cell Line
Chondrocytes/cytology/metabolism/microbiology
Databases, Genetic
Gene Expression Profiling
Host-Pathogen Interactions
Humans
Keratinocytes/cytology/metabolism/microbiology
*Models, Genetic
Mycoplasma/genetics/metabolism
Oligonucleotide Array Sequence Analysis

Figure

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  Fig. 1 The microarray experimental design in three dimensional spaces according to source of infection (x axis), cell type (y axis) and day of culture (z axis).

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  Fig. 2 Dendrogram for samples clustered by hierarchical clustering algorithm using centered correlation and average linkage.

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  Fig. 3 Determining the optimal number of marker genes for microbial infection (A) or Mycoplasma-specific infection (B). The cross-validation score CV(n) for a positive integer represents the prediction power when we select n genes as marker genes.

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