Dataset Information

LncRNA-mRNA expression profiles and functional networks in osteoclast differentiation.

ABSTRACT: Human osteoclasts are differentiated from CD14+ monocytes and are responsible for bone resorption. Long non-coding RNAs (lncRNAs) have been proved to be significantly involved in multiple biologic processes, especially in cell differentiation. However, the effect of lncRNAs in osteoclast differentiation is less appreciated. In our study, RNA sequencing (RNA-seq) was used to identify the expression profiles of lncRNAs and mRNAs in osteoclast differentiation. The results demonstrated that expressions of 1117 lncRNAs and 296 mRNAs were significantly altered after osteoclast differentiation. qRT-PCR assays were performed to confirm the expression profiles, and the results were almost consistent with the RNA-seq data. GO and KEGG analyses were used to predict the functions of these differentially expressed mRNA and lncRNAs. The Path-net analysis demonstrated that MAPK pathway, PI3K-AKT pathway and NF-kappa B pathway played important roles in osteoclast differentiation. Co-expression networks and competing endogenous RNA networks indicated that ENSG00000257764.2-miR-106a-5p-TIMP2 may play a central role in osteoclast differentiation. Our study provides a foundation to further understand the role and underlying mechanism of lncRNAs in osteoclast differentiation, in which many of them could be potential targets for bone metabolic disease.

SUBMITTER: Liu W

PROVIDER: S-EPMC7520269 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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LncRNA-mRNA expression profiles and functional networks in osteoclast differentiation.

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

Journal of cellular and molecular medicine 20200726 17

Human osteoclasts are differentiated from CD14<sup>+</sup> monocytes and are responsible for bone resorption. Long non-coding RNAs (lncRNAs) have been proved to be significantly involved in multiple biologic processes, especially in cell differentiation. However, the effect of lncRNAs in osteoclast differentiation is less appreciated. In our study, RNA sequencing (RNA-seq) was used to identify the expression profiles of lncRNAs and mRNAs in osteoclast differentiation. The results demonstrated th ...[more]

PMID: 32715654

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Project description: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.

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 in osteoclast differentiation.

Publications

LncRNA-mRNA expression profiles and functional networks in osteoclast differentiation.

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