Altered expression of SIRPγ on the T-cells of relapsing remitting multiple sclerosis and type 1 diabetes patients could potentiate effector responses from T-cells

Sinha, Sushmita; Renavikar, Pranav S; Crawford, Michael P.; Brate, Ashley A.; Tsalikian, Eva; Tansey, Michael; Shivapour, E Torage; Cho, Tracey A.; Kamholz, John; Karandikar, Nitin J.

doi:10.1371/journal.pone.0238070

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Our study highlights SIRPG as a significant marker for DR and its advanced form, PDR, supported by strong evidence. SIRPG, a member of the SIRP protein family, is primarily found on T cells and a subset of B cells ( 23 ). Genomic studies have linked two specific genetic variations of SIRPG, rs2281808 (C > T; intronic) and rs6043409 (G > A; A263 V), to type 1 diabetes ( 24 , 25 ).…”

Section: Discussionmentioning

confidence: 99%

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis

Zou,

Ye,

Tan

2024

Front. Endocrinol.

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BackgroundDiabetic retinopathy (DR), a leading cause of vision loss, has limited options for effective prevention and treatment. This study aims to utilize genomics and proteomics data to identify potential drug targets for DR.MethodsWe utilized plasma protein quantitative trait loci data from the Atherosclerosis Risk in Communities Study and the Icelandic Decoding Genetics Study for discovery and replication, respectively. Genetic associations with DR, including its subtypes, were derived from the FinnGen study. Mendelian Randomization (MR) analysis estimated associations between protein levels and DR risk, complemented by colocalization analysis to examine shared causal variants.ResultsOur MR analysis identified significant associations of specific plasma proteins with DR and proliferative DR (PDR). Elevated genetically predicted levels of WARS (OR = 1.16; 95% CI = 0.095-0.208, FDR = 1.31×10-4) and SIRPG (OR = 1.15; 95% CI = 0.071-0.201, FDR = 1.46×10-2) were associated with higher DR risk, while increased levels of ALDOC (OR = 1.56; 95% CI = 0.246-0.637, FDR = 5.48×10-3) and SIRPG (OR = 1.15; 95% CI = 0.068-0.208, FDR = 4.73×10-2) were associated with higher PDR risk. These findings were corroborated by strong colocalization evidence.ConclusionsOur study highlights WARS, SIRPG, and ALDOC as significant proteins associated with DR and PDR, providing a basis for further exploration in drug development. Additional studies are needed to validate these proteins as disease biomarkers across diverse populations.

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Section: Discussionmentioning

confidence: 99%

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis

Zou,

Ye,

Tan

2024

Front. Endocrinol.

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“… 79 Accumulating evidence showed that altered expression of genes such as midkine (MDK), 80 CCR2, 81 SAA1, 82 C3, 83 CD19, 84 CCR5, 85 CXCR3, 86 FABP4, 87 GDF15, 88 IGF2, 89 IGFBP1, 90 and IL6 91 is important in the progression of DN. A previous study has shown that UBASH3A, 92 signal regulatory protein gamma (SIRPG), 93 IKZF3, 94 CD1D, 95 CD2, 96 CD48, 97 CD247, 98 and CYP27B1 99 are liable for progression of type 1 diabetes mellitus. The studies have shown that expression of SIT1, 100 junction adhesion molecule like (JAML), 101 TIMP1, 102 protein kinase C beta (PRKCB), 103 MMP7, 104 WNT7B, 105 WNT10A, 106 DUSP1, 107 WT1, 108 APOC3, 109 ERRFI1, 110 HCN2, 111 membrane metalloendopeptidase (MME), 112 STRA6, 113 SLC12A3, 114 and GC vitamin D-binding protein (GC) 115 expedites the epithelial-to-mesenchymal transition and renal fibrosis in DN.…”

Section: Discussionmentioning

confidence: 99%

“…79 Accumulating evidence showed that altered expression of genes such as midkine (MDK), 80 CCR2, 81 SAA1, 82 C3, 83 CD19, 84 CCR5, 85 CXCR3, 86 FABP4, 87 GDF15, 88 IGF2, 89 IGFBP1, 90 and IL6 91 is important in the progression of DN. A previous study has shown that UBASH3A, 92 signal regulatory protein gamma (SIRPG), 93 IKZF3, 94 CD1D, 95 CD2, 96 CD48, 97 CD247, 98 and CYP27B1 99 are liable for progression 128 and UGT2B7 129 to be expressed in obesity. A study has confirmed that altered expression of FCRL3, 130 FCGR2B, 131 cartilage oligomeric matrix protein (COMP), 132 erythroferrone (ERFE), 133 and NPHS1 134 is involved in the progression of nephropathy.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

et al. 2022

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Objectives: The underlying molecular mechanisms of diabetic nephropathy have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of diabetic nephropathy. Methods: We downloaded next-generation sequencing data set GSE142025 from Gene Expression Omnibus database having 28 diabetic nephropathy samples and nine normal control samples. The differentially expressed genes between diabetic nephropathy and normal control samples were analyzed. Biological function analysis of the differentially expressed genes was enriched by Gene Ontology and REACTOME pathways. Then, we established the protein–protein interaction network, modules, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network. Hub genes were validated by using receiver operating characteristic curve analysis. Results: A total of 549 differentially expressed genes were detected including 275 upregulated and 274 downregulated genes. The biological process analysis of functional enrichment showed that these differentially expressed genes were mainly enriched in cell activation, integral component of plasma membrane, lipid binding, and biological oxidations. Analyzing the protein–protein interaction network, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB, and NR4A1 by the Cytoscape software. The receiver operating characteristic curve analysis confirmed that hub genes were of diagnostic value. Conclusions: Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in diabetic nephropathy, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for diabetic nephropathy.

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“…Accumulating evidence showed that altered expression of genes such as MDK (midkine) 80 , CCR2 81 , SAA1 82 , C3 83 , CD19 84 , CCR5 85 , CXCR3 86 , FABP4 87 , GDF15 88 , IGF2 89 , IGFBP1 90 and IL6 91 are important in the progression of DN. A previous study has shown that UBASH3A 92 , SIRPG (signal regulatory protein gamma) 93 , IKZF3 94 , CD1D 95 , CD2 96 , CD48 97 , CD247 98 and CYP27B1 99 are liable for progression of type 1 diabetes mellitus. The studies have shown that expression of SIT1 100 , JAML (junction adhesion molecule like) 101 , TIMP1 102 , PRKCB (protein kinase C beta) 103 , MMP7 104 , WNT7B 105 , WNT10A 106 , DUSP1 107 , WT1 108 , APOC3 109 , ERRFI1 110 , HCN2 111 , MME (membrane metalloendopeptidase) 112 , STRA6 113 , SLC12A3 114 and GC (GC vitamin D binding protein) 115 expedites epithelial to mesenchymal transition and renal brosis in DN.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Joshi

Vastrad²,

Joshi

et al. 2022

Preprint

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Objectives The underlying molecular mechanisms of diabetic nephropathy (DN) have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of DN. Methods We downloaded next generation sequencing (NGS) dataset GSE142025 from Gene Expression Omnibus (GEO) database having 28 DN samples and 9 normal control samples. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed. Biological function analysis of the DEGs was enriched by GO and REACTOME pathway. Then we established the protein-protein interaction (PPI) network, modules, miRNA-DEG regulatory network and TF-DEG regulatory network. Hub genes were validated by using receiver operating characteristic (ROC) curve analysis. Results A total of 549 DEGs were detected including 275 up regulated and 274 down regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in cell activation, integral component of plasma membrane, lipid binding and biological oxidations. Analyzing the PPI network, miRNA-DEG regulatory network and TF-DEG regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 by the Cytoscape software. The ROC curve analysis confirmed that hub genes were of diagnostic value. Conclusions Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in DN, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for DN.

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Altered expression of SIRPγ on the T-cells of relapsing remitting multiple sclerosis and type 1 diabetes patients could potentiate effector responses from T-cells

Cited by 7 publications

References 33 publications

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

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