Dataset Information

LncRNA-mRNA expression profiles and functional networks of mesenchymal stromal cells involved in monocyte regulation.

ABSTRACT: BACKGROUND:The goals of this study were to explore the expression profiles and functional networks of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in mesenchymal stromal cells (MSCs) involved in regulating the function of monocytes and to clarify the mechanisms by which MSCs exert immunoregulatory effects on monocytes. METHODS:MSCs and CD14+ monocytes were separately isolated. The immunoregulatory effects of MSCs on monocytes were determined by flow cytometry. lncRNAs and mRNAs that were differentially expressed (DE) between the control group (MSCs only) and co-culture group (MSCs co-cultured with monocytes) were identified through high-throughput sequencing and bioinformatic analyses and were confirmed by qRT-PCR. Bioinformatic analyses were performed to identify the critical biological functions and signalling pathways involved in MSC-mediated monocyte regulation and to identify the functional networks formed between DE mRNAs and lncRNAs. RESULTS:MSCs showed a strong ability to induce monocyte migration but inhibited monocyte differentiation into M1 macrophages. A total of 145 DE lncRNAs and 768 DE mRNAs were identified between the control and co-culture groups. Significant fold changes in lncRNAs and mRNAs were confirmed by qRT-PCR. GO analysis demonstrated that DE mRNAs and lncRNAs were highly associated with terms related to binding and biological regulation. KEGG analysis revealed 122 significantly regulated pathways, including the cytokine-cytokine receptor pathway and chemokine signalling pathway. Interaction and co-expression networks were constructed for DE mRNAs and lncRNAs, and several key microRNAs were identified in the competitive endogenous RNA (ceRNA) network. Target genes of the DE lncRNAs were analysed to predict critical mRNA-lncRNA axes involved in the immunoregulatory function of MSCs. CONCLUSIONS:Our research describes the lncRNA and mRNA expression profiles and functional networks involved in MSC-mediated regulation of monocytes. These results provide possible molecular mechanisms for the immunoregulatory function of MSCs and may help to elucidate possible molecular therapeutic targets in MSCs for the treatment of autoimmune diseases.

SUBMITTER: Li M

PROVIDER: S-EPMC6636070 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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lncRNA-mRNA expression profiles and functional networks of mesenchymal stromal cells involved in monocyte regulation.

Li Ming M Xie Zhongyu Z Cai Zhaopeng Z Su Fang F Zheng Guan G Li Jinteng J Wang Shan S Cen Shuizhong S Liu Wenjie W Tang Su'an S Ye Guiwen G Li Zhaofeng Z Mi Rujia R Pan Yiqian Y Wang Peng P Wu Yanfeng Y Shen Huiyong H

Stem cell research & therapy 20190716 1

<h4>Background</h4>The goals of this study were to explore the expression profiles and functional networks of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in mesenchymal stromal cells (MSCs) involved in regulating the function of monocytes and to clarify the mechanisms by which MSCs exert immunoregulatory effects on monocytes.<h4>Methods</h4>MSCs and CD14+ monocytes were separately isolated. The immunoregulatory effects of MSCs on monocytes were determined by flow cytometry. lncRNAs ...[more]

PMID: 31311593

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Project description:BackgroundLong non-coding RNA (lncRNA) plays crucial role in osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs), involving in regulation of competing endogenous RNA (ceRNA) mechanisms and conduction of signaling pathways. However, its mechanisms are poorly understood. This study aimed to investigate lncRNAs, miRNAs and mRNAs expression profiles in rat BMMSCs (rBMMSCs) osteogenic differentiation, screen the potential key lncRNA-miRNA-mRNA networks, explore the putative functions and identify the key molecules, as the basis of studying potential mechanism of rBMMSCs osteogenic differentiation driven by lncRNA, providing molecular targets for the management of bone defect.MethodsHigh-throughput RNA sequencing (RNA-seq) was used to determine lncRNAs, miRNAs, and mRNAs expression profiles at 14-day rBMMSCs osteogenesis. The pivotal lncRNA-miRNA and miRNA-mRNA networks were predicted from sequencing data and bioinformatic analysis, and the results were exported by Cytoscape 3.9.0 software. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for functional exploration. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate lncRNAs, miRNAs and mRNAs.ResultsrBMMSCs were identified, and the osteogenic and adipogenic differentiation ability were detected. A total of 8634 lncRNAs were detected by RNA-seq, and 1524 differential expressed lncRNAs, of which 812 up-regulated and 712 down-regulated in osteo-inductive groups compared with control groups. 30 up-regulated and 61 down-regulated miRNAs, 91 miRNAs were differentially expressed in total. 2453 differentially expressed mRNAs including 1272 up-expressed and 1181 down-expressed were detected. 10 up-regulated lncRNAs were chosen to predict 21 down-regulated miRNAs and 650 up-regulated mRNAs. 49 lncRNA-miRNA and 1515 miRNA-mRNA interactive networks were constructed. GO analysis showed the most important enrichment in cell component and molecular function were "cytoplasm" and "protein binding", respectively. Biological process related to osteogenic differentiation such as "cell proliferation", "wound healing", "cell migration", "osteoblast differentiation", "extracellular matrix organization" and "response to hypoxia" were enriched. KEGG analysis showed differentially expressed genes were mainly enriched in "PI3K-Akt signaling pathway", "Signaling pathway regulating pluripotency of stem cells", "cGMP-PKG signaling pathway", "Axon guidance" and "Calcium signaling pathway". qRT-PCR verified that lncRNA Tug1, lncRNA AABR07011996.1, rno-miR-93-5p, rno-miR-322-5p, Sgk1 and Fzd4 were consistent with the sequencing results, and 4 lncRNA-miRNA-mRNA networks based on validations were constructed, and enrichment pathways were closely related to "PI3K-Akt signaling pathway", "Signaling pathway regulating pluripotency of stem cells" and "Wnt signaling pathway".ConclusionslncRNAs, miRNAs and mRNAs expression profiles provide clues for future studies on their roles for BMMSCs osteogenic differentiation. Furthermore, lncRNA-miRNA-mRNA networks give more information on potential new mechanisms and targets for management on bone defect.

Project description:Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.

Dataset Information

LncRNA-mRNA expression profiles and functional networks of mesenchymal stromal cells involved in monocyte regulation.

Publications

lncRNA-mRNA expression profiles and functional networks of mesenchymal stromal cells involved in monocyte regulation.

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