Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.
Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.
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DOI：
10.3389/fgene.2021.648329
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发表时间：
2021
影响因子：
3.7
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Ning W;Acharya A;Sun Z;Ogbuehi AC;Li C;Hua S;Ou Q;Zeng M;Liu X;Deng Y;Haak R;Ziebolz D;Schmalz G;Pelekos G;Wang Y;Hu X
牙周炎是一种慢性免疫炎症性疾病，其特征是牙齿支持组织的炎性破坏。其发病机制涉及调节失调的局部宿主免疫应答，其在对抗微生物挑战方面无效。基于多方面的研究，对牙周炎中参与介导免疫反应抑制的基因进行综合研究，可以揭示牙周炎发病机制的关键基因。在这里，我们的目标是应用基于深度学习（DL）的自动编码器（AE），通过整合多个组学数据集来预测牙周炎相关的免疫抑制基因。包括两个牙周炎相关的GEO转录组数据集（GSE 16134和GSE 10334）和从DisGeNET和HisgAtlas中鉴定的免疫抑制基因。使用GSE 16134中与牙周炎相关的免疫抑制基因作为输入来构建AE，以识别最具疾病代表性的免疫抑制基因特征。利用K均值聚类和方差分析，免疫亚型标签分配到疾病样本和支持向量机（SVM）分类器的构建。将该分类器应用于验证集（GSE 10334中与牙周炎相关的免疫抑制基因），用于预测样本标签，评估AE的准确性。此外，差异表达的基因（DEG），信号通路和转录因子（TF）参与免疫抑制和牙周炎的生物信息学分析的数组。与区分牙周炎和对照的DEG以及区分免疫亚型的DEG共有的共享DEG被认为是牙周炎中关键的免疫抑制基因。我们产生了具有代表性的分子特征，并确定了两个免疫亚型牙周炎使用AE。两个亚型也预测在验证集与SVM分类器。三个“主”免疫抑制基因，PECAM 1，FCGR 3A，和FOS被确定为候选人的免疫抑制机制在牙周炎的关键。六个转录因子，NFKB 1，FOS，JUN，HIF 1A，STAT 5 B和STAT 4，被鉴定为TFs-DEGs相互作用网络的中心。这两种免疫亚型的调节途径不同。本研究首次应用基于DL的AE来鉴定牙周炎的免疫亚型和区分牙周炎与健康人的关键免疫抑制基因。确定了在牙周炎中介导免疫抑制的关键信号通路和TF靶向DEG。PECAM 1、FCGR 3A和FOS成为牙周炎的高价值生物标志物和候选治疗靶点。
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. 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. 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. 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.
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