FcγRIIIA-mediated activation of NK cells by IgG heavy chain complexed with MHC class II molecules

Shimizu, Yuta; Kohyama, Masanori; Yorifuji, Hideki; Jin, Hui; Arase, Noriko; Sasajima, Tadahiro; Arase, Hisashi

doi:10.1093/intimm/dxz010

Cited by 5 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The myeloid cell lineage was identified using FCGR3A and the specific marker, CD14. The B cell cluster was classified using the CD19 marker, and plasma cells were identified by SDC1 (CD138) expression (21)(22)(23)(24)(25). After clustering, immune cell proportions were quantified and characterized between datasets using a Chi-squared test (a=0.05).…”

Section: Data Analysis and Visualizationmentioning

confidence: 99%

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

et al. 2021

View full text Add to dashboard Cite

The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.

show abstract

Section: Data Analysis and Visualizationmentioning

confidence: 99%

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Of these, 84 genes are upregulated and 93 genes are downregulated. Among the top five DEGs in Table 3 , the Fc fragment of IgG receptor IIIa (FCGR3A) is linked to rheumatoid arthritis ( Shimizu et al, 2019 ) and is associated with HIV infection ( Poonia et al, 2010 ). Ribosomal protein (RPL22) plays a critical role in regulating lymphoma development ( Rao et al, 2012 ).…”

Section: Resultsmentioning

confidence: 99%

The Analysis of Gene Expression Data Incorporating Tumor Purity Information

2021

View full text Add to dashboard Cite

The tumor microenvironment is composed of tumor cells, stroma cells, immune cells, blood vessels, and other associated non-cancerous cells. Gene expression measurements on tumor samples are an average over cells in the microenvironment. However, research questions often seek answers about tumor cells rather than the surrounding non-tumor tissue. Previous studies have suggested that the tumor purity (TP)—the proportion of tumor cells in a solid tumor sample—has a confounding effect on differential expression (DE) analysis of high vs. low survival groups. We investigate three ways incorporating the TP information in the two statistical methods used for analyzing gene expression data, namely, differential network (DN) analysis and DE analysis. Analysis 1 ignores the TP information completely, Analysis 2 uses a truncated sample by removing the low TP samples, and Analysis 3 uses TP as a covariate in the underlying statistical models. We use three gene expression data sets related to three different cancers from the Cancer Genome Atlas (TCGA) for our investigation. The networks from Analysis 2 have greater amount of differential connectivity in the two networks than that from Analysis 1 in all three cancer datasets. Similarly, Analysis 1 identified more differentially expressed genes than Analysis 2. Results of DN and DE analyses using Analysis 3 were mostly consistent with those of Analysis 1 across three cancers. However, Analysis 3 identified additional cancer-related genes in both DN and DE analyses. Our findings suggest that using TP as a covariate in a linear model is appropriate for DE analysis, but a more robust model is needed for DN analysis. However, because true DN or DE patterns are not known for the empirical datasets, simulated datasets can be used to study the statistical properties of these methods in future studies.

show abstract

“…RNA-containing ICs are involved in RNA-sensing TLR-mediated immune cell activation. On the other hand, FcγR-mediated effector cell responses such as antibody-dependent cellular cytotoxicity, phagocytosis, and cytokine release, do not always require signal transduction through the RNA-sensing TLRs ( 15 , 17 , 25 ). Enhanced phagocytosis of antigens induced by FcγR stimulation increases antigen presentation to T cells ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

“…Plasmids for anti-HEL IgG1 (HyHEL10) and human FCGR3A 176V genotype (accession no. NM_000569.7) were used as previously described ( 15 , 46 ).…”

Section: Methodsmentioning

confidence: 99%

Positive and negative regulation of the Fcγ receptor–stimulating activity of RNA-containing immune complexes by RNase

Naito¹,

Ohmura²,

Higuchi³

et al. 2023

JCI Insight

Self Cite

View full text Add to dashboard Cite

The U1RNP complex, Ro/SSA, and La/SSB are major RNA-containing autoantigens. Immune complexes (ICs) composed of RNA-containing autoantigens and autoantibodies are suspected to be involved in the pathogenesis of some systemic autoimmune diseases. Therefore, RNase treatment, which degrades RNA in ICs, has been tested in clinical trials as a potential therapeutic agent. However, no studies to our knowledge have specifically evaluated the effect of RNase treatment on the Fcγ receptor–stimulating (FcγR-stimulating) activity of RNA-containing ICs. In this study, using a reporter system that specifically detects FcγR-stimulating capacity, we investigated the effect of RNase treatment on the FcγR-stimulating activity of RNA-containing ICs composed of autoantigens and autoantibodies from patients with systemic autoimmune diseases such as systemic lupus erythematosus. We found that RNase enhanced the FcγR-stimulating activity of Ro/SSA- and La/SSB-containing ICs, but attenuated that of the U1RNP complex–containing ICs. RNase decreased autoantibody binding to the U1RNP complex, but increased autoantibody binding to Ro/SSA and La/SSB. Our results suggest that RNase enhances FcγR activation by promoting the formation of ICs containing Ro/SSA or La/SSB. Our study provides insights into the pathophysiology of autoimmune diseases involving anti-Ro/SSA and anti-La/SSB autoantibodies, and into the therapeutic application of RNase treatment for systemic autoimmune diseases.

show abstract

FcγRIIIA-mediated activation of NK cells by IgG heavy chain complexed with MHC class II molecules

Cited by 5 publications

References 40 publications

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

The Analysis of Gene Expression Data Incorporating Tumor Purity Information

Positive and negative regulation of the Fcγ receptor–stimulating activity of RNA-containing immune complexes by RNase

Contact Info

Product

Resources

About