TIGIT as a Promising Therapeutic Target in Autoimmune Diseases

Yue, Chenran; Gao, Sheng; Li, Shuting; Xing, Zhouhang; Qian, Hengrong; Hu, Ying; Wang, Wenqian; Hua, Chunyan

doi:10.3389/fimmu.2022.911919

Cited by 20 publications

(13 citation statements)

References 151 publications

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“…Next, we found a downregulation of TIGIT in cytotoxic CD57 + CD4 + T cells of children from the ≥ 2 Aab first group. The important role of TIGIT was recently highlighted in the context of autoimmune diseases 27 . The increased expression of immune response negative regulators was shown to limit β cell destruction by autoreactive T cells in a mouse model of type 1 diabetes 28 .…”

Section: Discussionmentioning

confidence: 99%

Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies

Starskaia,

Valta,

Pietilä

et al. 2024

Nat Commun

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Previous studies have revealed heterogeneity in the progression to clinical type 1 diabetes in children who develop islet-specific antibodies either to insulin (IAA) or glutamic acid decarboxylase (GADA) as the first autoantibodies. Here, we test the hypothesis that children who later develop clinical disease have different early immune responses, depending on the type of the first autoantibody to appear (GADA-first or IAA-first). We use mass cytometry for deep immune profiling of peripheral blood mononuclear cell samples longitudinally collected from children who later progressed to clinical disease (IAA-first, GADA-first, ≥2 autoantibodies first groups) and matched for age, sex, and HLA controls who did not, as part of the Type 1 Diabetes Prediction and Prevention study. We identify differences in immune cell composition of children who later develop disease depending on the type of autoantibodies that appear first. Notably, we observe an increase in CD161 expression in natural killer cells of children with ≥2 autoantibodies and validate this in an independent cohort. The results highlight the importance of endotype-specific analyses and are likely to contribute to our understanding of pathogenic mechanisms underlying type 1 diabetes development.

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Section: Discussionmentioning

confidence: 99%

Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies

Starskaia,

Valta,

Pietilä

et al. 2024

Nat Commun

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“…S100A12, a member of the small calcium-binding S100 protein family, is secreted primarily by neutrophils, macrophages, and smooth muscle cells; it is released in inflammatory states and functions as a pro-inflammatory molecule and can be used as a diagnostic biomarker for various inflammatory diseases such as rheumatoid ( 22 , 23 ). Co-inhibitory receptor (IR), a recently discovered molecule, protects the host from autoimmune responses and maintains peripheral self-tolerance; in addition, it maintains immune homeostasis, T-cell immunoglobulin, and ITIM structural domains (TIGIT) and plays a crucial role in immune regulation by promoting regulatory T cells ( 24 ). Serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2) is often expressed in lymphoid organs such as the spleen and in inflammatory responses or subjected to induced expression under various conditions of infection and pro-inflammatory stimuli.…”

Section: Discussionmentioning

confidence: 99%

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

2022

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BackgroundThe prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.MethodsThe expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.ResultsDifferential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.ConclusionThe genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.

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“…T-cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT) is highly expressed on Tregs. Research has demonstrated that the interaction of TIGIT with CD155 on human DCs can suppress DC function [39]. T-cell immunoglobulin-3 (TIM-3), a negative regulator of type 1 T helper (Th1) immunity, is expressed by a significant number of tumour-infiltrating lymphocyte (TIL) Tregs.…”

Section: Suppressive Mechanisms Of Tregsmentioning

confidence: 99%

The dual role of regulatory T cells in hepatitis B virus infection and related hepatocellular carcinoma

He,

Miao,

Chen

et al. 2023

Immunology

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Hepatocellular carcinoma (HCC) is a major contributor to cancer‐related deaths worldwide. Hepatitis B virus (HBV) infection is a major etiologic factor leading to HCC. While there have been significant advancements in controlling HBV replication, achieving a complete cure for HBV‐related HCC (HBV‐HCC) remains an intricate challenge. HBV persistence is attributed to a myriad of mechanisms, encompassing both innate and adaptive immune responses. Regulatory T cells (Tregs) are pivotal in upholding immune tolerance and modulating excessive immune activation. During HBV infection, Tregs mediate specific T cell suppression, thereby contributing to both persistent infection and the mitigation of liver inflammatory responses. Studies have demonstrated an augmented expression of circulating and intrahepatic Tregs in HBV‐HCC, which correlates with impaired CD8+ T cell function. Consequently, Tregs play a dual role in the context of HBV infection and the progression of HBV‐HCC. In this comprehensive review, we discuss pertinent studies concerning Tregs in HBV infection, HBV‐related cirrhosis and HCC. Furthermore, we summarize Treg responses to antiviral therapy and provide Treg‐targeted therapies specific to HBV and HCC.

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TIGIT as a Promising Therapeutic Target in Autoimmune Diseases

Cited by 20 publications

References 151 publications

Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies

Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

The dual role of regulatory T cells in hepatitis B virus infection and related hepatocellular carcinoma

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