Background: Periodontitis is a chronic immuno-inflammatory disease characterized
by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a
dysregulated local host immune response that is ineffective in combating microbial
challenges. An integrated investigation of genes involved in mediating immune response
suppression in periodontitis, based on multiple studies, can reveal genes pivotal to
periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based
autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by
integrating multiples omics datasets.
Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and
GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas
were included. Immunosuppression genes related to periodontitis in GSE16134
were used as input to build an AE, to identify the top disease-representative
immunosuppression gene features. Using K-means clustering and ANOVA, immune
subtype labels were assigned to disease samples and a support vector machine
(SVM) classifier was constructed. This classifier was applied to a validation set
(Immunosuppression genes related to periodontitis in GSE10334) for predicting
sample labels, evaluating the accuracy of the AE. In addition, differentially expressed
genes (DEGs), signaling pathways, and transcription factors (TFs) involved in
immunosuppression and periodontitis were determined with an array of bioinformatics
analysis. Shared DEGs common to DEGs differentiating periodontitis from controls
and those differentiating the immune subtypes were considered as the key
immunosuppression genes in periodontitis.
Results: We produced representative molecular features and identified two immune
subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation
set with the SVM classifier. Three “master” immunosuppression genes, PECAM1,
FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive
mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A,
STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network.
The two immune subtypes were distinct in terms of their regulating pathways.
Conclusion: This study applied a DL-based AE for the first time to identify immune
subtypes of periodontitis and pivotal immunosuppression genes that discriminated
periodontitis from the healthy. Key signaling pathways and TF-target DEGs that
putatively mediate immune suppression in periodontitis were identified. PECAM1,
FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic
targets for periodontitis.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84283 |
| Date | 24 March 2023 |
| Creators | Ning, Wanchen, Acharya, Aneesha, Sun, Zhengyang, Ogbuehi, Anthony Chukwunonso, Li, Cong, Hua, Shiting, Ou, Qianhua, Zeng, Muhui, Liu, Xiangqiong, Deng, Yupei, Haak, Rainer, Ziebolz, Dirk, Schmalz, Gerhard, Pelekos, George, Wang, Yang, Hu, Xianda |
| Publisher | Frontiers Research Foundation |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1664-8021, 648329 |
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