Transcriptome Sequencing Unravels Potential Biomarkers at Different Stages of Cerebral Ischemic Stroke

Cai, Yuanheng; Zhang, Yufen; Ke, Xiao; Guo, Yu; Yao, Chengye; Tang, Na; Pang, Pei; Xie, Gangcai; Li, Fang; Zhang, Zhe; Li, Jincheng; Fan, Yixian; He, Ximiao; Wen, Ruojian; Pei, Lei; Lu, Youming

doi:10.3389/fgene.2019.00814

Cited by 30 publications

(22 citation statements)

References 56 publications

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“…In addition, previous studies have reported that changes in lipid metabolism are associated with mitochondrial dysfunction caused by oxidative stress ( Tobe, 2013 ), which is one of the three main pathophysiological reactions (neurotoxicity, oxidative stress, and inflammation) in IS ( Fukuyama et al, 1998 ; Chamorro et al, 2012 ; Lai et al, 2014 ). This result is consistent with the transcriptomic profiling results of IS ( Li et al, 2015 ; Cai et al, 2019 ). Cai et al (2019) assessed the patterns of transcriptomic changes at different stages of IS using a mouse model.…”

Section: Discussionsupporting

confidence: 92%

“…This result is consistent with the transcriptomic profiling results of IS ( Li et al, 2015 ; Cai et al, 2019 ). Cai et al (2019) assessed the patterns of transcriptomic changes at different stages of IS using a mouse model. The results showed that mmu-miR-199a-5p and mmu-miR-199b-3p inhibit the inflammatory response during the recovery phase of IS and exert neuroprotective effects by regulating the Taok1 gene ( Cai et al, 2019 ).…”

Section: Discussionsupporting

confidence: 92%

“… Cai et al (2019) assessed the patterns of transcriptomic changes at different stages of IS using a mouse model. The results showed that mmu-miR-199a-5p and mmu-miR-199b-3p inhibit the inflammatory response during the recovery phase of IS and exert neuroprotective effects by regulating the Taok1 gene ( Cai et al, 2019 ). This may imply that fatty acid metabolism is related to the regulation of these genes.…”

Section: Discussionmentioning

confidence: 99%

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Changes of Metabolites in Acute Ischemic Stroke and Its Subtypes

et al. 2021

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Existing techniques have many limitations in the diagnosis and classification of ischemic stroke (IS). Considering this, we used metabolomics to screen for potential biomarkers of IS and its subtypes and to explore the underlying related pathophysiological mechanisms. Serum samples from 99 patients with acute ischemic stroke (AIS) [the AIS subtypes included 49 patients with large artery atherosclerosis (LAA) and 50 patients with small artery occlusion (SAO)] and 50 matched healthy controls (HCs) were analyzed by non-targeted metabolomics based on liquid chromatography–mass spectrometry. A multivariate statistical analysis was performed to identify potential biomarkers. There were 18 significantly different metabolites, such as oleic acid, linoleic acid, arachidonic acid, L-glutamine, L-arginine, and L-proline, between patients with AIS and HCs. These different metabolites are closely related to many metabolic pathways, such as fatty acid metabolism and amino acid metabolism. There were also differences in metabolic profiling between the LAA and SAO groups. There were eight different metabolites, including L-pipecolic acid, 1-Methylhistidine, PE, LysoPE, and LysoPC, which affected glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, histidine metabolism, and lysine degradation. Our study effectively identified the metabolic profiles of IS and its subtypes. The different metabolites between LAA and SAO may be potential biomarkers in the context of clinical diagnosis. These results highlight the potential of metabolomics to reveal new pathways for IS subtypes and provide a new avenue to explore the pathophysiological mechanisms underlying IS and its subtypes.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Changes of Metabolites in Acute Ischemic Stroke and Its Subtypes

et al. 2021

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“…Most CVD and MD transcriptome analyses [61][62][63][64][65][66][67] are restricted to mouse models or a limited sample size of human expression data. In this study, we systematically analyzed expression data for over 16,000 healthy human samples across multiple tissues from GTEx, investigating global transcriptomic alterations on the human body in cases with a history of CVD or MD.…”

Section: Discussionmentioning

confidence: 99%

Exploring the impact of complex diseases on non-diseased human tissue transcriptomes

Poon

Chen

2021

Preprint

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Background The development of complex diseases is contributed by the combination of multiple factors and complicated interactions between them. Inflammation has recently been associated with many complex diseases and may cause long-term damage to the human body. In this study, we examined whether two types of complex disease systematically altered the transcriptomes of non-diseased human tissues and whether inflammation was linked to identifiable molecular signatures, using post-mortem samples from the Genotype-Tissue Expression (GTEx) project. Results Following a series of differential expression (DE) analyses, dozens to hundreds of DE genes were identified in multiple tissues between subjects with and without a history of cerebrovascular disease (CVD) or major depression (MD). DE genes from these disease-associated tissues—the visceral adipose, tibial artery, caudate, and spinal cord for CVD; and the hypothalamus, putamen, and spinal cord for MD—were further analyzed for functional enrichment. Many pathways associated with immunological events were positively enriched in the DEGs of the CVD-associated tissues, as were the neurological and metabolic pathways in the MD-associated tissues. Eight gene–tissue pairs were found to overlap with those prioritized by our transcriptome-wide association studies (TWAS), indicating a potential genetic effect on gene expression for circulating cytokine phenotypes. Conclusions Complex diseases like CVD and MD may cause observable changes in the gene expression of non-diseased tissues, suggesting that a long-term impact of diseases, lifestyles and environmental factors may together contribute to the appearance of transcriptomic “scars” on the human body. Furthermore, inflammation is probably one of the systemic and long-lasting effects of cerebrovascular events.

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“…Simultaneously analysing gene ontology (GO) and the pathway enrichment associated with the altered gene set will provide further information about the molecular networks involved in the diseased state. Transcriptome analyses in animal model of IS have revealed differentially expressed messenger ribonucleic acid (mRNA) and micro ribonucleic acid (miRNA), presenting as clustered sets of unique markers that are pathologically expressed and correlate well with different disease stages ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Differentially Expressed Genes and Their Clinical Significance in Ischaemic Stroke: An In-Silico Study

Appunni

Rubens

Ramamoorthy

et al. 2020

MJMS

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Background: Ischaemic stroke (IS), a multifactorial neurological disorder, is mediated by interplay between genes and the environment and, thus, blood-based IS biomarkers are of significant clinical value. Therefore, this study aimed to find global differentially expressed genes (DEGs) in-silico, to identify key enriched genes via gene set enrichment analysis (GSEA) and to determine the clinical significance of these genes in IS. Methods: Microarray expression dataset GSE22255 was retrieved from the Gene Expression Omnibus (GEO) database. It includes messenger ribonucleic acid (mRNA) expression data for the peripheral blood mononuclear cells of 20 controls and 20 IS patients. The bioconductor-package ‘affy’ was used to calculate expression and a pairwise t-test was applied to screen DEGs (P < 0.01). Further, GSEA was used to determine the enrichment of DEGs specific to gene ontology (GO) annotations. Results: GSEA analysis revealed 21 genes to be significantly plausible gene markers, enriched in multiple pathways among all the DEGs (n = 881). Ten gene sets were found to be core enriched in specific GO annotations. JunD, NCX3 and fibroblast growth factor receptor 4 (FGFR4) were under-represented and glycoprotein M6-B (GPM6B) was persistently over- represented. Conclusion: The identified genes are either associated with the pathophysiology of IS or they affect post-IS neuronal regeneration, thereby influencing clinical outcome. These genes should, therefore, be evaluated for their utility as suitable markers for predicting IS in clinical scenarios.

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Transcriptome Sequencing Unravels Potential Biomarkers at Different Stages of Cerebral Ischemic Stroke

Cited by 30 publications

References 56 publications

Changes of Metabolites in Acute Ischemic Stroke and Its Subtypes

Changes of Metabolites in Acute Ischemic Stroke and Its Subtypes

Exploring the impact of complex diseases on non-diseased human tissue transcriptomes

Differentially Expressed Genes and Their Clinical Significance in Ischaemic Stroke: An In-Silico Study

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