Dataset Information

Differential transcription profiles of long non-coding RNAs in primary human brain microvascular endothelial cells in response to meningitic Escherichia coli.

ABSTRACT: Accumulating studies have indicated the influence of long non-coding RNAs (lncRNAs) on various biological processes as well as disease development and progression. However, the lncRNAs involved in bacterial meningitis and their regulatory effects are largely unknown. By RNA-sequencing, the transcriptional profiles of host lncRNAs in primary human brain microvascular endothelial cells (hBMECs) in response to meningitic Escherichia coli were demonstrated. Here, 25,257 lncRNAs were identified, including 24,645 annotated lncRNAs and 612 newly found ones. A total of 895 lncRNAs exhibited significant differences upon infection, among which 382 were upregulated and 513 were downregulated (?2-fold, p?

SUBMITTER: Yang R

PROVIDER: S-EPMC5153642 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Differential transcription profiles of long non-coding RNAs in primary human brain microvascular endothelial cells in response to meningitic Escherichia coli.

Yang Ruicheng R Huang Fei F Fu Jiyang J Dou Beibei B Xu Bojie B Miao Ling L Liu Wentong W Yang Xiaopei X Tan Chen C Chen Huanchun H Wang Xiangru X

Scientific reports 20161213

Accumulating studies have indicated the influence of long non-coding RNAs (lncRNAs) on various biological processes as well as disease development and progression. However, the lncRNAs involved in bacterial meningitis and their regulatory effects are largely unknown. By RNA-sequencing, the transcriptional profiles of host lncRNAs in primary human brain microvascular endothelial cells (hBMECs) in response to meningitic Escherichia coli were demonstrated. Here, 25,257 lncRNAs were identified, incl ...[more]

PMID: 27958323

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Project description:BACKGROUND:Bacterial meningitis remains a big threat to the integrity of the central nervous system (CNS), despite the advancements in antimicrobial reagents. Escherichia coli is a bacterial pathogen that can disrupt the CNS function, especially in neonates. E. coli meningitis occurs after bacteria invade the brain microvascular endothelial cells (BMECs) that form a direct and essential barrier restricting the entry of circulating microbes and toxins to the brain. Previous studies have reported on several cellular proteins that function during meningitic E. coli infections; however, more comprehensive investigations to elucidate the potential targets involved in E. coli meningitis are essential to better understand this disease and discover new treatments for it. METHODS:The isobaric tags for relative and absolute quantification (iTRAQ) approach coupled with LC-MS/MS were applied to compare and characterize the different proteomic profiles of BMECs in response to meningitic or non-meningitic E. coli strains. KEGG and gene ontology annotations, ingenuity pathways analysis, and functional experiments were combined to identify the key host molecules involved in the meningitic E. coli-induced tight junction breakdown and neuroinflammatory responses. RESULTS:A total of 13 cellular proteins were found to be differentially expressed by meningitic E. coli strains PCN033 and RS218, including one that was also affected by HB101, a non-meningitic E. coli strain. Through bioinformatics analysis, we identified the macrophage migration inhibitory factor (MIF), granzyme A, NF-?B signaling, and mitogen-activated protein kinase (MAPK) pathways as being biologically involved in the meningitic E. coli-induced tight junction breakdown and neuroinflammation. Functionally, we showed that MIF facilitated meningitic E. coli-induced production of cytokines and chemokines and also helped to disrupt the blood-brain barrier by decreasing the expression of tight junction proteins like ZO-1, occludin. Moreover, we demonstrated the significant activation of NF-?B and MAPK signaling in BMECs in response to meningitic E. coli strains, which dominantly determined the generation of the proinflammatory cytokines including IL-6, IL-8, TNF-?, and IL-1?. CONCLUSIONS:Our work identified 12 host cellular targets that are affected by meningitic E. coli strains and revealed MIF to be an important contributor to meningitic E. coli-induced cytokine production and tight junction disruption, and also the NF-?B and MAPK signaling pathways that are mainly involved in the infection-induced cytokines production. Characterization of these distinct proteins and pathways in BMECs will facilitate further elucidation of meningitis-causing mechanisms in humans and animals, thereby enabling the development of novel preventative and therapeutic strategies against infection with meningitic E. coli.

Project description:BackgroundTranscriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner.ResultsMost existing studies for inferring transcriptional regulatory interactions and the activity of TFs ignore the possible post-transcriptional effects of ncRNAs. In this work, we propose a novel framework to infer the activity of regulators including both TFs and ncRNAs by exploring the expression profiles of target genes and (post)transcriptional regulatory relationships. We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem. The inference process is achieved by an iterative algorithm, in which two linear programming models are efficiently solved. In contrast to available related studies, the effects of ncRNAs on transcription process are considered in this work, and thus more reasonable and accurate reconstruction can be expected. In addition, the approach is suitable for large-scale problems from the viewpoint of computation. Experiments on two synthesized data sets and a model system of Escherichia coli (E. coli) carbon source transition from glucose to acetate illustrate the effectiveness of our model and algorithm.ConclusionOur results show that incorporating the post-transcriptional regulation of ncRNAs into system model can mine the hidden effects from the regulation activity of TFs in transcription processes and thus can uncover the biological mechanisms in gene regulation in a more accurate manner. The software for the algorithm in this paper is available upon request.

Dataset Information

Differential transcription profiles of long non-coding RNAs in primary human brain microvascular endothelial cells in response to meningitic Escherichia coli.

Publications

Differential transcription profiles of long non-coding RNAs in primary human brain microvascular endothelial cells in response to meningitic Escherichia coli.

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