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Bacterial Gene Expression Analysis Using Microarrays
Published on: May 28, 2007
Gene-Expression Profiles in Generalized Aggressive Periodontitis: A Gene Network-Based Microarray Analysis
Esra Guzeldemir-Akcakanat1, Deniz Sunnetci-Akkoyunlu2, Begum Orucguney1
1Department of Periodontology, Faculty of Dentistry, Kocaeli University, Kocaeli, Turkey.
Background:
In this study, molecular biomarkers that play a role in the development of generalized aggressive periodontitis (GAgP) are investigated using gingival tissue samples through omics-based whole-genome transcriptomics while using healthy individuals as background controls.
Methods:
Gingival tissue biopsies from 23 patients with GAgP and 25 healthy individuals were analyzed using gene-expression microarrays with network and pathway analyses to identify gene-expression patterns. To substantiate the results of the microarray studies, real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to assess the messenger RNA (mRNA) expression of MZB1 and DSC1. The microarrays and qRT-PCR resulted in similar gene-expression changes, confirming the reliability of the microarray results at the mRNA level.
Results:
As a result of the gene-expression microarray studies, four significant gene networks were identified. The most upregulated genes were found as MZB1, TNFRSF17, PNOC, FCRL5, LAX1, BMS1P20, IGLL5, MMP7, SPAG4, and MEI1; the most downregulated genes were found as LOR, LAMB4, AADACL2, MAPT, ARG1, NPR3, AADAC, DSC1, LRRC4, and CHP2.
Conclusions:
Functions of the identified genes that were involved in gene networks were cellular development, cell growth and proliferation, cellular movement, cell-cell signaling and interaction, humoral immune response, protein synthesis, cell death and survival, cell population and organization, organismal injury and abnormalities, molecular transport, and small-molecule biochemistry. The data suggest new networks that have important functions as humoral immune response and organismal injury/abnormalities. Future analyses may facilitate proteomic profiling analyses to identify gene-expression patterns related to clinical outcome.
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