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Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions.

ABSTRACT: COVID-19 has become a global pandemic. Immune dysregulation has been implicated, but immune responses remain poorly understood. We analyzed 71 COVID-19 patients compared to recovered and healthy subjects using high dimensional cytometry. Integrated analysis of ~200 immune and >30 clinical features revealed activation of T cell and B cell subsets, but only in some patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses could reach >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable to uninfected subjects. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. These analyses identified three "immunotypes" associated with poor clinical trajectories versus improving health. These immunotypes may have implications for therapeutics and vaccines.

SUBMITTER: Mathew D

PROVIDER: S-EPMC7263500 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions.

Mathew Divij D Giles Josephine R JR Baxter Amy E AE Greenplate Allison R AR Wu Jennifer E JE Alanio Cécile C Oldridge Derek A DA Kuri-Cervantes Leticia L Pampena M Betina MB D'Andrea Kurt K Manne Sasikanth S Chen Zeyu Z Huang Yinghui Jane YJ Reilly John P JP Weisman Ariel R AR Ittner Caroline A G CAG Kuthuru Oliva O Dougherty Jeanette J Nzingha Kito K Han Nicholas N Kim Justin J Pattekar Ajinkya A Goodwin Eileen C EC Anderson Elizabeth M EM Weirick Madison E ME Gouma Sigrid S Arevalo Claudia P CP Bolton Marcus J MJ Chen Fang F Lacey Simon F SF Hensley Scott E SE Apostolidis Sokratis S Huang Alexander C AC Vella Laura A LA Betts Michael R MR Meyer Nuala J NJ Wherry E John EJ

bioRxiv : the preprint server for biology 20200523

COVID-19 has become a global pandemic. Immune dysregulation has been implicated, but immune responses remain poorly understood. We analyzed 71 COVID-19 patients compared to recovered and healthy subjects using high dimensional cytometry. Integrated analysis of ~200 immune and >30 clinical features revealed activation of T cell and B cell subsets, but only in some patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses could reach > ...[more]

PMID: 32511371

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Project description:BackgroundPeriodontitis 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.MethodsTwo 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.ResultsWe 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.ConclusionThis 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.

Project description:BackgroundNeuroblastoma (NB) is a childhood cancer originating from immature nerve cells in the sympathetic nervous system. Current clinical and molecular subtyping methods for NB have limitations in providing accurate prognostic information and guiding treatment decisions.ResultsTo overcome these challenges, we explored the microenvironment of NB based on the knowledge-based functional gene expression signatures (Fges), which revealed heterogeneous subtypes. Consensus clustering of Fges activity scores identified three subtypes (Cluster 1, Cluster 2, and Cluster 3) that demonstrated significant differences in prognosis compared to mainstream subtypes. We assessed the immune infiltration, immunogenicity, CD8T cytotoxicity, and tumor purity of these subtypes, uncovering their distinct biological functions. Cluster 1 and Cluster 2 exhibited higher immunoreactivity, while Cluster 3 displayed higher tumor purity and poor prognosis. Gene ontology annotation and pathway analysis identified immune activation in Cluster 1, epithelial-mesenchymal transition (EMT) in Cluster 2, and cell cycle processes in Cluster 3. Notably, the impact of EMT activity on prognosis may vary across NB subtypes. A classification model using XGBoost accurately predicted subtypes in independent NB cohorts, with significant prognostic differences. GPR125, CDK4, and GREB1 emerged as potential therapeutic targets in Cluster 3. CD4K inhibitors showed subtype-specific responses, suggesting tailored treatment strategies. Single-cell analysis highlighted unfavorable clinical features in Cluster 3, including high-risk classification and reduced cytotoxicity. Suppressed interactions between monocytes, macrophages, and regulatory T cells were observed, affecting immune regulation and patient prognosis.ConclusionTo summarize, we have identified a new independent prognostic factor in NB that underscores the significant correlation between tumor phenotype and immune contexture. These findings deepen our understanding of NB subtypes and immune cell interactions, paving the way for more effective treatment approaches.

Dataset Information

Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions.

Publications

Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions.

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