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/v1

Cited by 7 publications

(9 citation statements)

References 33 publications

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“…Many studies have reported the genome-wide expression profiling of sepsis [ 13 , 16 , 17 ]. The differential gene expression has been well characterized between sepsis versus non-sepsis inflammatory responses [18] , survivors versus non-survivors [ 19 , 20 ], and sepsis of viral versus bacterial causes [21] . Two endotypes of sepsis were identified [ 20 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

Zhang

Pan

et al. 2020

eBioMedicine

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Background Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets ( n = 780) to evaluate its prognostic and predictive performance. Findings A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort ( n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 – 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 – 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 – 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). Interpretation Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. Funding The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

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Section: Introductionmentioning

confidence: 99%

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

Zhang

Pan

et al. 2020

eBioMedicine

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

Cited by 7 publications

References 33 publications

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

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

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

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