“…The encoded protein mainly affects immune response. The deficiency of ITGB2 cause leukocyte adhesion deficiency 51 PTPRC protein tyrosine phosphatase receptor type C The protein encoded by this gene is a member of the protein tyrosine phosphatase family. The family is known to be signaling molecules that regulates a variety of cellular processes including cell growth, differentiation, mitosis, and oncogenic transformation 52 CTSS cathepsin S The preproprotein encoded by this gene is a lysosomal cysteine proteinase that participates in the degradation of antigenic proteins to peptides for presentation on MHC class II molecules 53 LCP2 lymphocyte cytosolic protein 2 This gene encodes an adapter protein,which is thought to play a role in TCR-mediated intracellular signal transduction 54 CD53 CD53 molecule The encoded protein contributes to the transduction of CD2-generated signals in T cells and natural killer cells and has been suggested to play a role in growth regulation 55 Figure 4 GO and KEGG enrichment analysis of the hub genes.…”
The aim of this study was to explore the overlapping key genes, pathway networks and transcription factors (TFs) related to the pathogenesis of rheumatoid arthritis (RA) and atherosclerosis. The gene expression profiles of RA and atherosclerosis were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between RA and atherosclerosis were identified. The biological roles of common DEGs were explored through enrichment analysis. Hub genes were identified using protein–protein interaction networks. TFs were predicted using Transcriptional Regulatory Relationships Unraveled by Sentence Based Text Mining (TRRUST) database. The hub genes and TFs were validated with other datasets. The networks between TFs and hub genes were constructed by CytoScape software. A total of 131 DEGs (all upregulated) were identified. Functional enrichment analyses indicated that DEGs were mostly enriched in leukocyte migration, neutrophil activation, and phagocytosis. CytoScape demonstrated 12 hub genes and one gene cluster module. Four of the 12 hub genes (CSF1R, CD86, PTPRC, and CD53) were validated by other datasets. TRRUST predicted two TFs, including Spi-1 proto-oncogene (SPI1) and RUNX family transcription factor 1(RUNX1). The expression of RUNX1 was validated with another dataset. Our study explored the common pathogenesis of RA and atherosclerosis. These results may guide future experimental research and clinical transformation.
“…The encoded protein mainly affects immune response. The deficiency of ITGB2 cause leukocyte adhesion deficiency 51 PTPRC protein tyrosine phosphatase receptor type C The protein encoded by this gene is a member of the protein tyrosine phosphatase family. The family is known to be signaling molecules that regulates a variety of cellular processes including cell growth, differentiation, mitosis, and oncogenic transformation 52 CTSS cathepsin S The preproprotein encoded by this gene is a lysosomal cysteine proteinase that participates in the degradation of antigenic proteins to peptides for presentation on MHC class II molecules 53 LCP2 lymphocyte cytosolic protein 2 This gene encodes an adapter protein,which is thought to play a role in TCR-mediated intracellular signal transduction 54 CD53 CD53 molecule The encoded protein contributes to the transduction of CD2-generated signals in T cells and natural killer cells and has been suggested to play a role in growth regulation 55 Figure 4 GO and KEGG enrichment analysis of the hub genes.…”
The aim of this study was to explore the overlapping key genes, pathway networks and transcription factors (TFs) related to the pathogenesis of rheumatoid arthritis (RA) and atherosclerosis. The gene expression profiles of RA and atherosclerosis were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between RA and atherosclerosis were identified. The biological roles of common DEGs were explored through enrichment analysis. Hub genes were identified using protein–protein interaction networks. TFs were predicted using Transcriptional Regulatory Relationships Unraveled by Sentence Based Text Mining (TRRUST) database. The hub genes and TFs were validated with other datasets. The networks between TFs and hub genes were constructed by CytoScape software. A total of 131 DEGs (all upregulated) were identified. Functional enrichment analyses indicated that DEGs were mostly enriched in leukocyte migration, neutrophil activation, and phagocytosis. CytoScape demonstrated 12 hub genes and one gene cluster module. Four of the 12 hub genes (CSF1R, CD86, PTPRC, and CD53) were validated by other datasets. TRRUST predicted two TFs, including Spi-1 proto-oncogene (SPI1) and RUNX family transcription factor 1(RUNX1). The expression of RUNX1 was validated with another dataset. Our study explored the common pathogenesis of RA and atherosclerosis. These results may guide future experimental research and clinical transformation.
“…there are few studies on ITGB2 and AAV. Genetic variations in the ITGB2 gene encoding the β2 integrin subunit are believed to cause leukocyte adhesion deficiency 1 ( 51 ). Toll-like receptors (TLRs), the most characteristic members of pattern recognition receptors (PRRs), play a significant role in innate immunity.…”
BackgroundNETosis is a new form of cell death, marked by DNA chromatin release from dead neutrophils. While it aids in microbe defense, it may worsen inflammation in autoimmune diseases, causing tissue harm. The impact of NETosis on Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN) remains unexplored and requires investigation.MethodsFirst, a weighted gene co-expression network analysis (WGCNA) was conducted to uncover differential expression of neutrophil extranuclear trap-associated genes (DE-NETs) in ANCA-GN. The NETosisScore model was established through the single sample gene set enrichment analysis (ssGSEA), which categorized all patients into high-risk and low-risk groups. The accuracy of model was assessed by ROC curve. The biological function of various subgroups was explored through Gene Set Variation Analysis (GSVA), while the abundance of immune cell infiltration was measured with CIBERSORT. Furthermore, the key NETosis-related genes (NRGs) were identified using three machine learning algorithms, and their relationship with renal function was analyzed through the NephroseqV5 database. Through the application of qPCR and immunohistochemical staining techniques, the mRNA and protein expression levels of NRGs were determined in patients with ANCA-GN and control.ResultsA NETosisScore model was developed from 18 DE-NETs using the ssGSEA algorithm. The model’s ability to predict ANCA-GN patients with a ROC AUC of 0.921. The high-risk group in ANCA-GN showed enrichment of immune-related pathways and greater infiltration of immune cells, as revealed by KEGG enrichment analysis and CIBERSORT. Using three machine learning algorithms, we identified six NRGs. Significant positive correlations were found between NRGs and CCR, macrophages, T-cell co-inhibition, and TIL. Further KEGG analysis revealed that the functions of NRGs may be closely related to the toll-like receptor signaling pathway. The levels of NRGs increased as kidney function declined and were positively correlated with Scr (serum creatinine) and negatively correlated with GFR (glomerular filtration rate), qPCR analysis showed increased expression of most NRGs in ANCA-GN patients. Furthermore, immunohistochemical staining confirmed higher expression of all NRGs in ANCA-GN patients.ConclusionNETosisScore model accurately predicts high-risk patients in ANCA-GN with enriched immune pathways, 6 NRGs identified as potential biomarkers.
“…Leukocyte adhesion deficiency type 1 (LAD‐1) is the most common form of LAD but is nonetheless extremely rare with about 450 unique cases described in the last half century 1–5 . Most cases of LAD‐1 exhibit life‐threatening symptoms.…”
Section: Introductionmentioning
confidence: 99%
“…Leukocyte adhesion deficiency type 1 (LAD‐1) is the most common form of LAD but is nonetheless extremely rare with about 450 unique cases described in the last half century. 1 , 2 , 3 , 4 , 5 Most cases of LAD‐1 exhibit life‐threatening symptoms. Patients often manifest with delayed separation of the umbilical cord, poor wound healing, leukocytosis, recurrent bacterial and fungal infections, periodontitis, sepsis, and other inflammatory conditions due to impaired polymorphonuclear neutrophils (PMN) migration.…”
Key Clinical MessagePartial leukocyte adhesion deficiency type 1 (LAD‐1) deficiency is extremely rare condition with milder infectious manifestation and immune system imbalance leads to increased risks of autoinflammatory complications, such as pyoderma gangrenosum, that can be triggered by trauma or pregnancy. In patients with spice‐site ITGB2 variants, partial expression can occur due to different β2 integrin isophorms expression.AbstractLAD‐1, OMIM ID #116920 is a rare, autosomal recessive disorder that results from mutations in the ITGB2 gene that encodes the CD18 β2 integrin subunit. According to the CD18 expression, LAD‐1 is categorized as severe (<2%), moderate (2%–30%), or mild (>30%). Here, we describe a 22‐year‐old female, who presented with inflammatory skin disease and oral cavity, as well as respiratory tract infections during the first year of life. LAD‐1 was diagnosed at the age of 2 years by low expression of CD18 (1%). Whole‐exome sequencing identified homozygous c. 59‐10C>A variant in the ITGB2 gene. Despite severe phenotype, the patient survived to adulthood without hematopoietic stem cell transplantation and became pregnant at the age of 20 years, with pregnancy complicated by a pyoderma gangrenosum‐like lesion. During her life, CD18 expression increased from 1% to 9%; at 22 years of age, 5% of neutrophils and 9% of lymphocytes were CD18+. All CD18+‐lymphocytes were predominantly memory/effector cytotoxic T cells. However, revertant mosaicism was not being established suggesting that CD18 expression variability may be mediated by other mechanisms such as different β2 integrin isophorms expression.
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