A long non‐coding RNA signature for diagnostic prediction of sepsis upon ICU admission

Liu, Xueyan; Zheng, Xubin; Wang, Jun; Zhang, Ning; Leung, Kwong‐Sak; Ye, Xiufeng; Cheng, Lixin

doi:10.1002/ctm2.123

Cited by 27 publications

(14 citation statements)

References 9 publications

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“…Sepsis is a common condition that can lead to ICU hospitalization and cause damage to multiple organs, among which the lung is the most vulnerable ( Kuebler, 2019 ; Liu et al, 2020b ). Acute respiratory distress syndrome (ARDS) and acute lung injury (ALI) are common complications of sepsis that are often life-threatening ( Van Wessem and Leenen, 2018 ; Forrester 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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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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“…The normalization step is the same as gene expression ( Cheng et al, 2016a , b ; Liu et al, 2019 ) and hence not discussed in this paper. After that, researchers can perform downstream analysis depending on their research purposes, such as building machine learning classifiers for diagnosis or prognosis ( Liu et al, 2020a , c ; Wang et al, 2020a , b ).…”

Section: Resultsmentioning

confidence: 99%

Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

Zheng

et al. 2021

Front. Cell Dev. Biol.

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Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

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“…High‐throughput gene expression data have been widely applied to characterize the global expression patterns to understand the molecular mechanisms (Cheng et al ., 2016a; Cheng et al ., 2016b; Liu et al ., 2019; Cheng et al ., 2020; Liu et al ., 2020c). We sought to leverage the available senescence transcriptomics datasets to provide independent support for the involvement of the identified ARMs in autophagy.…”

Section: Methodsmentioning

confidence: 99%

Systematic prediction of autophagy‐related proteins using Arabidopsis thaliana interactome data

et al. 2020

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SUMMARY Autophagy is a self‐degradative process that is crucial for maintaining cellular homeostasis by removing damaged cytoplasmic components and recycling nutrients. Such an evolutionary conserved proteolysis process is regulated by the autophagy‐related (Atg) proteins. The incomplete understanding of plant autophagy proteome and the importance of a proteome‐wide understanding of the autophagy pathway prompted us to predict Atg proteins and regulators in Arabidopsis. Here, we developed a systems‐level algorithm to identify autophagy‐related modules (ARMs) based on protein subcellular localization, protein–protein interactions, and known Atg proteins. This generates a detailed landscape of the autophagic modules in Arabidopsis. We found that the newly identified genes in each ARM tend to be upregulated and coexpressed during the senescence stage of Arabidopsis. We also demonstrated that the Golgi apparatus ARM, ARM13, functions in the autophagy process by module clustering and functional analysis. To verify the in silico analysis, the Atg candidates in ARM13 that are functionally similar to the core Atg proteins were selected for experimental validation. Interestingly, two of the previously uncharacterized proteins identified from the ARM analysis, AGD1 and Sec14, exhibited bona fide association with the autophagy protein complex in plant cells, which provides evidence for a cross‐talk between intracellular pathways and autophagy. Thus, the computational framework has facilitated the identification and characterization of plant‐specific autophagy‐related proteins and novel autophagy proteins/regulators in higher eukaryotes.

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A long non‐coding RNA signature for diagnostic prediction of sepsis upon ICU admission

Cited by 27 publications

References 9 publications

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

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

Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

Systematic prediction of autophagy‐related proteins using Arabidopsis thaliana interactome data

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