Table_1_The Construction and Analysis of lncRNA–miRNA–mRNA Competing Endogenous RNA Network of Schwann Cells in Diabetic Peripheral Neuropathy.DOCX
Diabetes mellitus is a worldwide disease with high incidence. Diabetic peripheral neuropathy (DPN) is one of the most common but often ignored complications of diabetes mellitus that cause numbness and pain, even paralysis. Recent studies demonstrate that Schwann cells (SCs) in the peripheral nervous system play an essential role in the pathogenesis of DPN. Furthermore, various transcriptome analyses constructed by RNA-seq or microarray have provided a comprehensive understanding of molecular mechanisms and regulatory interaction networks involved in many diseases. However, the detailed mechanisms and competing endogenous RNA (ceRNA) network of SCs in DPN remain largely unknown.
MethodsWhole-transcriptome sequencing technology was applied to systematically analyze the differentially expressed mRNAs, lncRNAs and miRNAs in SCs from DPN rats and control rats. Gene ontology (GO) and KEGG pathway enrichment analyses were used to investigate the potential functions of the differentially expressed genes. Following this, lncRNA-mRNA co-expression network and ceRNA regulatory network were constructed by bioinformatics analysis methods.
ResultsThe results showed that 2925 mRNAs, 164 lncRNAs and 49 miRNAs were significantly differently expressed in SCs from DPN rats compared with control rats. 13 mRNAs, 7 lncRNAs and 7 miRNAs were validated by qRT-PCR and consistent with the RNA-seq data. Functional and pathway analyses revealed that many enriched biological processes of GO terms and pathways were highly correlated with the function of SCs and the pathogenesis of DPN. Furthermore, a global lncRNA–miRNA–mRNA ceRNA regulatory network in DPN model was constructed and miR-212-5p and the significantly correlated lncRNAs with high degree were identified as key mediators in the pathophysiological processes of SCs in DPN. These RNAs would contribute to the diagnosis and treatment of DPN.
ConclusionOur study has shown that differentially expressed RNAs have complex interactions among them. They also play critical roles in regulating functions of SCs involved in the pathogenesis of DPN. The novel competitive endogenous RNA network provides new insight for exploring the underlying molecular mechanism of DPN and further investigation may have clinical application value.
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References
- https://doi.org//10.1126/science.1173299
- https://doi.org//10.1016/j.metabol.2015.11.002
- https://doi.org//10.1038/ncomms7364
- https://doi.org//10.1093/nar/gks1099
- https://doi.org//10.1016/j.ejphar.2018.06.034
- https://doi.org//10.1016/j.neuroscience.2018.12.040
- https://doi.org//10.1016/j.neuron.2017.02.005
- https://doi.org//10.1038/nrneurol.2016.201
- https://doi.org//10.3389/fneur.2018.00268
- https://doi.org//10.1002/jcb.28802
- https://doi.org//10.1530/JME-16-0179
- https://doi.org//10.1038/nprot.2006.486
- https://doi.org//10.1038/jid.2014.371
- https://doi.org//10.1161/CIRCULATIONAHA.104.511964
- https://doi.org//10.1038/srep28422
- https://doi.org//10.1093/bioinformatics/btm045
- https://doi.org//10.1038/nprot.2012.118
- https://doi.org//10.1016/j.vaa.2018.12.009
- https://doi.org//10.1038/nmeth.3317
- https://doi.org//10.1097/AAP.0b013e3181e82df0
- https://doi.org//10.2337/db18-0084
- https://doi.org//10.1186/1471-2105-9-559
- https://doi.org//10.1002/0471250953.bi1107s32
- https://doi.org//10.1093/bib/bbw042
- https://doi.org//10.1016/j.biomaterials.2018.03.044
- https://doi.org//10.1093/bioinformatics/btt656
- https://doi.org//10.5713/ajas.2012.12375
- https://doi.org//10.1155/2017/7831251
- https://doi.org//10.1016/j.bbi.2019.05.020
- https://doi.org//10.2337/db16-1182
- https://doi.org//10.1159/000494174
- https://doi.org//10.1681/ASN.2009070773
- https://doi.org//10.1039/c8fo00387d
- https://doi.org//10.1016/j.jsbmb.2014.02.015
- https://doi.org//10.1007/s004010050783
- https://doi.org//10.1186/s13059-019-1758-4
- https://doi.org//10.2337/db10-1541
- https://doi.org//10.1038/nbt.3122
- https://doi.org//10.1126/science.1073374
- https://doi.org//10.1016/j.jpain.2019.04.010
- https://doi.org//10.1016/j.cell.2011.07.014
- https://doi.org//10.1111/j.1471-4159.2006.03885.x
- https://doi.org//10.1038/nm.3978
- https://doi.org//10.3389/fnmol.2018.00381
- https://doi.org//10.2337/db11-1329
- https://doi.org//10.1196/annals.1338.067
- https://doi.org//10.1002/glia.22570
- https://doi.org//10.1155/2019/4628962
- https://doi.org//10.1016/j.mayocp.2015.05.004
- https://doi.org//10.2337/diab.41.1.47
- https://doi.org//10.1007/s11064-018-2466-2
- https://doi.org//10.1093/nar/gks750
- https://doi.org//10.1111/jphp.12244
- https://doi.org//10.2202/1544-6115.1128
- https://doi.org//10.1097/WNR.0000000000001058
- https://doi.org//10.3389/fbioe.2019.00222
- https://doi.org//10.1186/s12881-019-0832-9
- https://doi.org//10.1002/dmrr.716
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