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Written | edited by Su Muqi | XI Immune system problems can cause many diseases, such as systemic lupus erythematosus and rheumatoid arthritis, which are also known as immune-mediated diseases (Immune-mediated diseases).
, IMD).
How do these diseases occur? Genetic studies such as GWAS have found many gene mutation sites related to immune-mediated diseases, most of which are located in non-coding regions, such as enhancers and promoters, but for their specific functions and The regulation mechanism, as we know it, is still a drop in the bucket.
In order to clarify the function of these gene mutation sites, on April 29, 2021, Keishi Fujio and Mineto Ota from the University of Tokyo, Japan published an article Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases on Cell, Through whole-genome sequencing, RNA-Seq and expression quantitative trait loci (eQTL) analysis, gene expression and gene mutation data of 28 immune cells for 10 immune-mediated diseases were generated, explaining the gene mutation The regulation map of gene expression in different diseases and different immune cells.
Prior to this, there were some tissue or cell-specific eQTL databases, such as GTEx, BLUEPEINT, and DICE, but their sample size was usually limited, and they did not include most immune cells, such as dendrites, which are closely related to autoimmunity.
Cells and plasmablasts are not included.
The research conducted by the University of Tokyo included almost all immune cells that we know, a total of 28 types.
The data comes from 9,852 samples from 416 donors, of which 79 are healthy people, and the other 337 are patients with various immune-mediated diseases, including systemic lupus erythematosus (SLE), idiopathic inflammatory myopathy (IIM) ), Systemic Sclerosis (SSc), Mixed Connective Tissue Disease (MCTD), Sjogren’s Disease (SjS), Rheumatoid Arthritis (RA), Behcet’s Disease (BD), Adult Still’s Disease (AOSD) , ANCA-related vasculitis (AAV) and Takayasu arteritis (TAK).
With such a wealth of transcriptome data, the first step is naturally to look at the gene expression profiles of different diseases and different immune cells.
The results found that immune-mediated diseases can be roughly divided into two categories, autoimmune diseases and autoinflammatory diseases.
The up-regulated gene modules in autoimmune diseases overlap with interferon-induced gene pathways; the up-regulated gene modules in autoinflammatory diseases overlap with genes induced by IL-18 or IL-1b.
These data provide rich and useful resources for future research on various immune-mediated diseases and immune cells.
The focus of this study is eQTL analysis.
In each cell type, the researchers performed a stepwise linear regression between the autosomal expressed genes (eGenes) and the gene variants (eVariants) located within 1Mb of the transcription start site to identify eQTLs, and finally determined There are 13,395 protein genes and 3,839 long non-coding RNA (lncRNA) eQTLs.
There are thousands of eGenes in each type of immune cell.
Regardless of the type of immune cell and type of immune disease, except for rare somatic mutations, the DNA is the same, with the same genetic mutations.
As for gene expression, most genes that maintain the basic life activities of cells, such as housekeeping genes, are expressed similarly in different cells and diseases.
The eQTLs related to these genes are the same even in different immune cells and diseases.
The study also found that the vast majority (94%) of eQTLs are the same in health and disease states.
Only part of the gene expression is either cell-specific or disease-specific.
In this case, eQTLs show specificity.
Researchers have found that compared with eQTLs found in healthy people, immune-mediated disease-specific eQTLs have the following characteristics: 1.
They are more common in myeloid cells, such as plasmacytoid dendritic cells (pDC) and neutrophils.
Cells 2.
It is more enriched at the peak signal of enhancer and ATAC-seq 3.
It is more enriched at the GWAS gene mutation signal related to immune-mediated diseases.
What effect do these eQTLs have on the transcription and expression of the entire genome? Next, the researchers analyzed the expression of other genes related to eQTLs (context-dependent eQTLs), and identified pGenes that interact with eGenes.
The association between these pGenes and eGenes is affected by different eVariants, such as: 1.
Pulp Interferon-related genes (STAT1, P1) interact with SLFN5 eQTL in mother cells 2.
In initial B cells, FCRL3-related genes (P7) interact with SCD5 eQTL 3.
In Th1 cells, cell proliferation-related genes (CDCA7) , P8) and ENTPD1 eQTL interaction 4.
In EM CD8 cells, the aging-related genes SATB1 (P4) interacts with CCNG2 eQTL.
Researchers have also explored the relationship between disease-specific eQTL and GWAS, and verified various Immune diseases are mediated by which immune cells, such as: 1.
Fr.
II regulatory T cells-rheumatoid arthritis and type 1 diabetes 2.
B cells-systemic lupus erythematosus 3.
Initial B cells-systemic sclerosis Diseases and type 1 diabetes 4.
CD8+ T cells and celiac disease 5.
NK cells and biliary cirrhosis Finally, the researchers took systemic lupus erythematosus as an example to explain that the co-localization analysis of eQTL and GWAS can help optimize the risk of ranking diseases gene.
For example, ARHGAP31, one of the risk genes for systemic lupus erythematosus, has its eQTL effect only in plasmablasts; the other risk gene is LBH, and its rs7599760 eQTL has the opposite effect in different immune cells.
The gene expression is down-regulated in bone marrow cells.
In plasmablasts, it is up-regulated. LBH is related to cell proliferation and DNA repair.
Its high expression in plasmablasts may help its expansion, while its down-regulation in other cell types may cause DNA damage.
PTPRC, which encodes CD45, and its eQTL also show completely different effects in different immune cells.
In short, this study portrays the unique gene expression profiles of different immune cells and immune diseases, reveals the dynamic changes of eQTL under different conditions, and provides rich inspiration and resources for the study of disease and immune-related genetic variation and gene expression.
Original link: https://doi.
org/10.
1016/j.
cell.
2021.
03.
056 Platemaker: Instructions for reprinting on the 11th [Original article] BioArt original article, personal forwarding and sharing are welcome, reprinting without permission is prohibited, all published The copyright of the work is owned by BioArt.
BioArt reserves all statutory rights and offenders must be investigated.
, IMD).
How do these diseases occur? Genetic studies such as GWAS have found many gene mutation sites related to immune-mediated diseases, most of which are located in non-coding regions, such as enhancers and promoters, but for their specific functions and The regulation mechanism, as we know it, is still a drop in the bucket.
In order to clarify the function of these gene mutation sites, on April 29, 2021, Keishi Fujio and Mineto Ota from the University of Tokyo, Japan published an article Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases on Cell, Through whole-genome sequencing, RNA-Seq and expression quantitative trait loci (eQTL) analysis, gene expression and gene mutation data of 28 immune cells for 10 immune-mediated diseases were generated, explaining the gene mutation The regulation map of gene expression in different diseases and different immune cells.
Prior to this, there were some tissue or cell-specific eQTL databases, such as GTEx, BLUEPEINT, and DICE, but their sample size was usually limited, and they did not include most immune cells, such as dendrites, which are closely related to autoimmunity.
Cells and plasmablasts are not included.
The research conducted by the University of Tokyo included almost all immune cells that we know, a total of 28 types.
The data comes from 9,852 samples from 416 donors, of which 79 are healthy people, and the other 337 are patients with various immune-mediated diseases, including systemic lupus erythematosus (SLE), idiopathic inflammatory myopathy (IIM) ), Systemic Sclerosis (SSc), Mixed Connective Tissue Disease (MCTD), Sjogren’s Disease (SjS), Rheumatoid Arthritis (RA), Behcet’s Disease (BD), Adult Still’s Disease (AOSD) , ANCA-related vasculitis (AAV) and Takayasu arteritis (TAK).
With such a wealth of transcriptome data, the first step is naturally to look at the gene expression profiles of different diseases and different immune cells.
The results found that immune-mediated diseases can be roughly divided into two categories, autoimmune diseases and autoinflammatory diseases.
The up-regulated gene modules in autoimmune diseases overlap with interferon-induced gene pathways; the up-regulated gene modules in autoinflammatory diseases overlap with genes induced by IL-18 or IL-1b.
These data provide rich and useful resources for future research on various immune-mediated diseases and immune cells.
The focus of this study is eQTL analysis.
In each cell type, the researchers performed a stepwise linear regression between the autosomal expressed genes (eGenes) and the gene variants (eVariants) located within 1Mb of the transcription start site to identify eQTLs, and finally determined There are 13,395 protein genes and 3,839 long non-coding RNA (lncRNA) eQTLs.
There are thousands of eGenes in each type of immune cell.
Regardless of the type of immune cell and type of immune disease, except for rare somatic mutations, the DNA is the same, with the same genetic mutations.
As for gene expression, most genes that maintain the basic life activities of cells, such as housekeeping genes, are expressed similarly in different cells and diseases.
The eQTLs related to these genes are the same even in different immune cells and diseases.
The study also found that the vast majority (94%) of eQTLs are the same in health and disease states.
Only part of the gene expression is either cell-specific or disease-specific.
In this case, eQTLs show specificity.
Researchers have found that compared with eQTLs found in healthy people, immune-mediated disease-specific eQTLs have the following characteristics: 1.
They are more common in myeloid cells, such as plasmacytoid dendritic cells (pDC) and neutrophils.
Cells 2.
It is more enriched at the peak signal of enhancer and ATAC-seq 3.
It is more enriched at the GWAS gene mutation signal related to immune-mediated diseases.
What effect do these eQTLs have on the transcription and expression of the entire genome? Next, the researchers analyzed the expression of other genes related to eQTLs (context-dependent eQTLs), and identified pGenes that interact with eGenes.
The association between these pGenes and eGenes is affected by different eVariants, such as: 1.
Pulp Interferon-related genes (STAT1, P1) interact with SLFN5 eQTL in mother cells 2.
In initial B cells, FCRL3-related genes (P7) interact with SCD5 eQTL 3.
In Th1 cells, cell proliferation-related genes (CDCA7) , P8) and ENTPD1 eQTL interaction 4.
In EM CD8 cells, the aging-related genes SATB1 (P4) interacts with CCNG2 eQTL.
Researchers have also explored the relationship between disease-specific eQTL and GWAS, and verified various Immune diseases are mediated by which immune cells, such as: 1.
Fr.
II regulatory T cells-rheumatoid arthritis and type 1 diabetes 2.
B cells-systemic lupus erythematosus 3.
Initial B cells-systemic sclerosis Diseases and type 1 diabetes 4.
CD8+ T cells and celiac disease 5.
NK cells and biliary cirrhosis Finally, the researchers took systemic lupus erythematosus as an example to explain that the co-localization analysis of eQTL and GWAS can help optimize the risk of ranking diseases gene.
For example, ARHGAP31, one of the risk genes for systemic lupus erythematosus, has its eQTL effect only in plasmablasts; the other risk gene is LBH, and its rs7599760 eQTL has the opposite effect in different immune cells.
The gene expression is down-regulated in bone marrow cells.
In plasmablasts, it is up-regulated. LBH is related to cell proliferation and DNA repair.
Its high expression in plasmablasts may help its expansion, while its down-regulation in other cell types may cause DNA damage.
PTPRC, which encodes CD45, and its eQTL also show completely different effects in different immune cells.
In short, this study portrays the unique gene expression profiles of different immune cells and immune diseases, reveals the dynamic changes of eQTL under different conditions, and provides rich inspiration and resources for the study of disease and immune-related genetic variation and gene expression.
Original link: https://doi.
org/10.
1016/j.
cell.
2021.
03.
056 Platemaker: Instructions for reprinting on the 11th [Original article] BioArt original article, personal forwarding and sharing are welcome, reprinting without permission is prohibited, all published The copyright of the work is owned by BioArt.
BioArt reserves all statutory rights and offenders must be investigated.
