Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis

Li, Qiaoqiao; Gao, Xueping; Luo, Xue-Shan; Wu, Qingrui; He, Jintao; Liu, Yang; Xue, Yan; Wu, Shulin; Rao, Fang

doi:10.1155/2022/8086991

Cited by 4 publications

(4 citation statements)

References 37 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also discovered 3 SNPs each known to code the ATG16L1 and THADA genes in chromosome 2, IKZF3 and PGAP3 genes in chromosome 17, DOCK8 gene in chromosome 9, TSPAN14 gene in chromosome 10 and ETS1 gene in chromosome 11. FAM53B is known to be associated with humoral immune reponse, regulation of immune effector process, and regulation of lymphocyte activation [ 57 ]; reduced expression of PGAP3 is known to be related to impaired clearance of apoptotic cells and has been observed in CD and UC patients [ 44 ]; TSPAN14 is expressed in immune cell types participating in immunity and inflammation, and is positively correlated with microphages and neutrophils and negatively correlated with T cells CD8 [ 58 ]; and finally, ETS1 is known to be over-expressed in intestinal epithelial cells of patients with UC [ 59 ], and has also been linked to fistula formation, an epithelial defect caused by destructive inflammation, in the pathogenesis of CD [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

Khatiwada,

Yilmaz,

Wolf

et al. 2023

PLoS Comput Biol

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to ‘pleiotropy’ and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn’s disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

show abstract

Section: Resultsmentioning

confidence: 99%

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

Khatiwada,

Yilmaz,

Wolf

et al. 2023

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…CLEC4D is one of the hub genes that is differentially expressed between IS patients and controls 21 and may be involved in mir-27a-3p−CLEC4D regulation. 22 In addition, it was previously found that CLEC4D and CD163 are immune biomarkers for IS, and these genes were used to construct classification models based on a neural network algorithm.…”

Section: ■ Discussionmentioning

confidence: 99%

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Zhao,

Zeng,

Zhang

et al. 2024

J. Proteome Res.

View full text Add to dashboard Cite

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895−1.000) and 0.954 (95% CI: 0.884−1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801−0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K−AKT signaling pathway, and the B-cell receptor signaling pathway.

show abstract

“…Enrichment of a wide host of functions including leukocyte activation, filopodium assembly, cytoskeleton organization, regulation of cellular response to transforming growth factor beta receptor, and gene silencing point to the breadth of cell types in blood undergoing cell proliferation, activation, and migration in the first 72 h after ischemic stroke. Through different approaches, Li et al [ 89 ] identified several stroke-related hub genes, where one of them is common to our analysis in whole blood hub genes ( TSPAN14 ). This gene is involved in the regulation of Notch signaling pathway [ 90 ] and may be a promising target in CE stroke.…”

Section: Discussionmentioning

confidence: 99%

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

Carmona-Mora¹,

Knepp²,

Jickling

et al. 2023

BMC Med

View full text Add to dashboard Cite

Background After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy. Methods The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0–24 h, 24–48 h, and >48 h following stroke. Results Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood. Conclusions Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

show abstract

Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis

Cited by 4 publications

References 37 publications

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

Contact Info

Product

Resources

About