2
|
Zhang H, Cao S, Xu Y, Sun X, Fei M, Jing Q, Xu X, Tang J, Niu B, Li C. Landscape of immune infiltration in entorhinal cortex of patients with Alzheimerʼs disease. Front Pharmacol 2022; 13:941656. [PMID: 36249779 PMCID: PMC9557331 DOI: 10.3389/fphar.2022.941656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases and manifests as progressive memory loss and cognitive dysfunction. Neuroinflammation plays an important role in the development of Alzheimer’s disease and anti-inflammatory drugs reduce the risk of the disease. However, the immune microenvironment in the brains of patients with Alzheimer’s disease remains unclear, and the mechanisms by which anti-inflammatory drugs improve Alzheimer’s disease have not been clearly elucidated. This study aimed to provide an overview of the immune cell composition in the entorhinal cortex of patients with Alzheimer’s disease based on the transcriptomes and signature genes of different immune cells and to explore potential therapeutic targets based on the relevance of drug targets. Transcriptomics data from the entorhinal cortex tissue, derived from GSE118553, were used to support our study. We compared the immune-related differentially expressed genes (irDEGs) between patients and controls by using the limma R package. The difference in immune cell composition between patients and controls was detected via the xCell algorithm based on the marker genes in immune cells. The correlation between marker genes and immune cells and the interaction between genes and drug targets were evaluated to explore potential therapeutic target genes and drugs. There were 81 irDEGs between patients and controls that participated in several immune-related pathways. xCell analysis showed that most lymphocyte scores decreased in Alzheimer’s disease, including CD4+ Tc, CD4+ Te, Th1, natural killer (NK), natural killer T (NKT), pro-B cells, eosinophils, and regulatory T cells, except for Th2 cells. In contrast, most myeloid cell scores increased in patients, except in dendritic cells. They included basophils, mast cells, plasma cells, and macrophages. Correlation analysis suggested that 37 genes were associated with these cells involved in innate immunity, of which eight genes were drug targets. Taken together, these results delineate the profile of the immune components of the entorhinal cortex in Alzheimer’s diseases, providing a new perspective on the development and treatment of Alzheimer’s disease.
Collapse
Affiliation(s)
- Hui Zhang
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- Department of Anesthesiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Silu Cao
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- Department of Anesthesiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yaru Xu
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- Department of Anesthesiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaoru Sun
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Miaomiao Fei
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Qi Jing
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- Department of Anesthesiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaodong Xu
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- Department of Anesthesiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jinxuan Tang
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Bing Niu, ; Cheng Li,
| | - Cheng Li
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai, China
- Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- Clinical Research Center for Anesthesiology and Perioperative Medicine, Tongji University, Shanghai, China
- *Correspondence: Bing Niu, ; Cheng Li,
| |
Collapse
|
3
|
Hong GH, Guan Q, Peng H, Luo XH, Mao Q. Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches. Front Genet 2022; 13:979213. [PMID: 36204312 PMCID: PMC9531151 DOI: 10.3389/fgene.2022.979213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation. Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability. Results: We identified CD4+ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases. Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.
Collapse
Affiliation(s)
- Guo-Hu Hong
- Department of Infectious Disease, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Qing Guan
- Department of Dermatology, The First People’s Hospital of Guiyang, Guiyang, China
| | - Hong Peng
- Department of Infectious Disease, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Xin-Hua Luo
- Department of Infectious Disease, Guizhou Provincial People’s Hospital, Guiyang, China
- *Correspondence: Xin-Hua Luo, ; Qing Mao,
| | - Qing Mao
- Department of Infectious Disease, The First Hospital Affiliated to Army Medical University, Chongqing, China
- *Correspondence: Xin-Hua Luo, ; Qing Mao,
| |
Collapse
|