Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Cheng, Lixin; Nan, Chuanchuan; Kang, Lin; Zhang, Ning; Liu, Sheng; Chen, Huaisheng; Hong, Chengying; Chen, Youlian; Liang, Ziwen; Liu, Xueyan

doi:10.21203/rs.2.24685/v2

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…Regulatory mechanisms of some important modules involving transcription regulators and miRNA were explored by validated databases. Consistent with previously published studies employing high-throughput dataset to examine sepsis, these modules were enriched for pathways related to immune response [ 28 , 29 ]. The black module was significantly associated with survival outcome and the transcription factor CEBPB was the master regulator of this module.…”

Section: Discussionsupporting

confidence: 82%

Gene correlation network analysis to identify regulatory factors in sepsis

Zhang

Chen

et al. 2020

J Transl Med

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Background and objectives Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. Methods The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. Results A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). Conclusions The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

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Section: Discussionsupporting

confidence: 82%

Gene correlation network analysis to identify regulatory factors in sepsis

Zhang

Chen

et al. 2020

J Transl Med

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“…Long non-coding RNAs may regulate the biological function of miRNAs by acting as competitive endogenous RNAs (ceRNAs) (Liu et al, 2018;Cheng et al, 2020). Prior findings have indicated that miR-194-5p regulates apoptosis and genes targeted by miR-194-5p are enriched in apoptotic and inflammatory cell pathways, such as MAPK (Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Knockdown of lncRNA MALAT1 Alleviates LPS-Induced Acute Lung Injury via Inhibiting Apoptosis Through the miR-194-5p/FOXP2 Axis

Nan

Zhang

Cheung

et al. 2020

Front. Cell Dev. Biol.

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Purpose: We aimed to identify and verify the key genes and lncRNAs associated with acute lung injury (ALI) and explore the pathogenesis of ALI. Research showed that lower expression of the lncRNA metastasis-associated lung carcinoma transcript 1 (MALAT1) alleviates lung injury induced by lipopolysaccharide (LPS). Nevertheless, the mechanisms of MALAT1 on cellular apoptosis remain unclear in LPS-stimulated ALI. We investigated the mechanism of MALAT1 in modulating the apoptosis of LPS-induced human pulmonary alveolar epithelial cells (HPAEpiC). Methods: Differentially expressed lncRNAs between the ALI samples and normal controls were identified using gene expression profiles. ALI-related genes were determined by the overlap of differentially expressed genes (DEGs), genes correlated with lung, genes correlated with key lncRNAs, and genes sharing significantly high proportions of microRNA targets with MALAT1. Quantitative real-time PCR (qPCR) was applied to detect the expression of MALAT1, microRNA (miR)-194-5p, and forkhead box P2 (FOXP2) mRNA in 1 µg/ml LPS-treated HPAEpiC. MALAT1 knockdown vectors, miR-194-5p inhibitors, and ov-FOXP2 were constructed and used to transfect HPAEpiC. The influence of MALAT1 knockdown on LPS-induced HPAEpiC proliferation and apoptosis via the miR-194-5p/FOXP2 axis was determined using Cell counting kit-8 (CCK-8) assay, flow cytometry, and Western blotting analysis, respectively. The interactions between MALAT1, miR-194-5p, and FOXP2 were verified using dualluciferase reporter gene assay. Results: We identified a key lncRNA (MALAT1) and three key genes (EYA1, WNT5A, and FOXP2) that are closely correlated with the pathogenesis of ALI. LPS stimulation promoted MALAT1 expression and apoptosis and also inhibited HPAEpiC viability. MALAT1 knockdown significantly improved viability and suppressed the apoptosis of LPS-stimulated HPAEpiC. Moreover, MALAT1 directly targeted miR-194-5p, a downregulated miRNA in LPS-stimulated HPAEpiC, when FOXP2 was overexpressed.

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“…The WGCNA approach is a bioinformatics technique that allows the extraction and grouping into modules of a list of genes involved in a given biological process [19]. This technique has been used for the credible discovery of a number of genes associated with various diseases and their sub-characteristics [20][21][22][23]. WGCNA allows the correlative identification of genes with a similar expression profile to a given characteristic trait.…”

Section: Introductionmentioning

confidence: 99%

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

et al. 2020

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Background: Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. Methods: A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. Results: A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrixassociated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

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Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Cited by 10 publications

References 18 publications

Gene correlation network analysis to identify regulatory factors in sepsis

Gene correlation network analysis to identify regulatory factors in sepsis

Knockdown of lncRNA MALAT1 Alleviates LPS-Induced Acute Lung Injury via Inhibiting Apoptosis Through the miR-194-5p/FOXP2 Axis

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

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