IADR Abstract Archives
Gene Expression Profiles in Patients With Aggressive Periodontitis: A Gene Network-based Microarray Analysis
Objectives: To investigate molecular biomarkers which play a role in the development of generalized aggressive periodontitis (GAgP) using gingival tissue samples through omics-based whole-genome transcriptomic while using healthy individuals as background controls.
Methods: Gingival tissue biopsies from 25 patients with GAgP and 26 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, Q-RT-PCR was performed to assess the mRNA expression of MZB1, MMP7, LOR and DSC1. The microarrays and real-time PCR resulted in similar gene expression changes, confirming the reliability of our microarray results at mRNA level.
Results: As a result of the gene expression microarray studies, 5 significant gene networks were identified. The most up-regulated genes were found as MZB1, TNFRSF17, PNOC, FCRL5, LAX1, BMS1P20, IGLL1 / IGLL5, MMP7, SPAG4, MEI1; the most down-regulated genes were found as LOR, LAMB4, AADACL2, MAPT, ARG1, NPR3, AADAC, DSC1, LRRC4, CHP2.
Conclusions: Functions of the identified genes that 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, small molecule biochemistry. Our data suggest new networks that have important functions as humoral immune response and organismal injury/abnormalities. Future analyses may facilitate for proteomic profiling analyses to identify gene expression patterns related to clinical outcome.
Methods: Gingival tissue biopsies from 25 patients with GAgP and 26 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, Q-RT-PCR was performed to assess the mRNA expression of MZB1, MMP7, LOR and DSC1. The microarrays and real-time PCR resulted in similar gene expression changes, confirming the reliability of our microarray results at mRNA level.
Results: As a result of the gene expression microarray studies, 5 significant gene networks were identified. The most up-regulated genes were found as MZB1, TNFRSF17, PNOC, FCRL5, LAX1, BMS1P20, IGLL1 / IGLL5, MMP7, SPAG4, MEI1; the most down-regulated genes were found as LOR, LAMB4, AADACL2, MAPT, ARG1, NPR3, AADAC, DSC1, LRRC4, CHP2.
Conclusions: Functions of the identified genes that 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, small molecule biochemistry. Our data suggest new networks that have important functions as humoral immune response and organismal injury/abnormalities. Future analyses may facilitate for proteomic profiling analyses to identify gene expression patterns related to clinical outcome.