Gene‐Expression Profiles in Generalized Aggressive Periodontitis: A Gene Network‐Based Microarray Analysis. Issue 1 (1st January 2016) - Guzeldemir‐Akcakanat, Esra; Sunnetci‐Akkoyunlu, Deniz; Orucguney, Begum; Cine, Naci; Kan, Bahadır; Yılmaz, Elif Büsra; Gümüşlü, Esen; Savli, Hakan
Abstract : 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.