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Differential Expression of Plasma Exo-miRNA in Neurodegenerative Diseases by Next-Generation Sequencing

Nie, Chao, Sun, Yuzhe, Zhen, Hefu, Guo, Mei, Ye, Jingyu, Liu, Zhili, Yang, Yan, Zhang, Xiuqing·2020·Frontiers in Neuroscience
DOI 10.3389/fnins.2020.00438PMC PMC7227778
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FIGURE 1 Clusters of miRNA expression and enriched pathways. (A) Heatmap of differentially expressed miRNAs of six clusters. Down, miRNAs with expression of log2(AD/NC) or log2(PD/NC) < -1; NA, miRNAs with expression of -1 < log2(AD/NC) or log2(PD/NC) < 1; Up, miRNAs with expression of log2(AD/NC) or log2(PD/NC) > 1. (B) KEGG pathway of miRNAs from four clusters. Target genes of miRNAs were predicted by miRanda, TargetScan, and RNAhybrid individually. Joint target genes were selected and KEGG pathway enrichment analysis was carried out. Top three pathways of each cluster were shown. The size of the dots represents the -log10(p-value) of each pathway.
FIGURE 1 Clusters of miRNA expression and enriched pathways. (A) Heatmap of differentially expressed miRNAs of six clusters. Down, miRNAs with expression of log2(AD/NC) or log2(PD/NC) < -1; NA, miRNAs with expression of -1 < log2(AD/NC) or log2(PD/NC) < 1; Up, miRNAs with expression of log2(AD/NC) or log2(PD/NC) > 1. (B) KEGG pathway of miRNAs from four clusters. Target genes of miRNAs were predicted by miRanda, TargetScan, and RNAhybrid individually. Joint target genes were selected and KEGG pathway enrichment analysis was carried out. Top three pathways of each cluster were shown. The size of the dots represents the -log10(p-value) of each pathway.CC BY · PubMed Central
FIGURE 2 Heatmap of differential expression levels of miRNAs in AD and PD patients. The color scale shown on the right illustrates the expression level (log2) of a miRNA across all samples. Each row represents significantly declined miRNAs (p < 0.05) in AD or PD patients including 23 declined miRNAs in AD patients and 13 declined miRNAs in PD patients.
FIGURE 2 Heatmap of differential expression levels of miRNAs in AD and PD patients. The color scale shown on the right illustrates the expression level (log2) of a miRNA across all samples. Each row represents significantly declined miRNAs (p < 0.05) in AD or PD patients including 23 declined miRNAs in AD patients and 13 declined miRNAs in PD patients.CC BY · PubMed Central
FIGURE 3 Using significantly altered expression of miRNAs to distinguish AD from NC. Left, Heatmap of significant altered levels of miRNA expression in AD and PD samples. DESeq2 was used to identify significantly up-/down-expressed levels of miRNAs. Fold change was presented by log2 and p-value was presented by log10. PCA and k-means based clustering was performed to distinguish AD samples from NC samples by using significantly expressed levels of miRNAs.
FIGURE 3 Using significantly altered expression of miRNAs to distinguish AD from NC. Left, Heatmap of significant altered levels of miRNA expression in AD and PD samples. DESeq2 was used to identify significantly up-/down-expressed levels of miRNAs. Fold change was presented by log2 and p-value was presented by log10. PCA and k-means based clustering was performed to distinguish AD samples from NC samples by using significantly expressed levels of miRNAs.CC BY · PubMed Central
FIGURE 4 Validation of candidate miRNAs by SC and PC. (A) Fold change (FC) and p-value of candidate miRNA expression in AD samples, FC and p-value were calculated by DESeq2. (B) Spearman Correlation coefficients of miRNA expression between EQ, SC, and PC. (C) Co-inertia analysis of the relationships between EQ, SC, and PC. Monte-Carlo test was carried out, and the significance of correlation could be observed between EQ and SC (p < 0.05). Each sample is represented with two dots connected by a thin line.
FIGURE 4 Validation of candidate miRNAs by SC and PC. (A) Fold change (FC) and p-value of candidate miRNA expression in AD samples, FC and p-value were calculated by DESeq2. (B) Spearman Correlation coefficients of miRNA expression between EQ, SC, and PC. (C) Co-inertia analysis of the relationships between EQ, SC, and PC. Monte-Carlo test was carried out, and the significance of correlation could be observed between EQ and SC (p < 0.05). Each sample is represented with two dots connected by a thin line.CC BY · PubMed Central
FIGURE 5 Analysis of the differences in exosomal miRNA profiles among three isolation methods. (A) Small RNA yield efficiency by three methods. (B) Identified miRNA species by three methods, average of miRNA species from 46 samples. (C) MiRNA total counts by three methods, average of total miRNA counts from 46 samples. (D) Average read length distribution by three methods. (E) Aligned miRNA reads and unmapped reads of 46 samples by three methods. (F) Venn diagram presents all identified miRNAs that are common or unique in the three method datasets. Most (695) miRNAs were common to the three sets. (G–I), Scatter plots reveal correlations between EQ and PC (D), EQ and SC (E), SC and PC (F). Each scatter plot represents relative expression of miRNA profiles obtained by two methods. * represents p < 0.05 in Student’s T-test. ** represents p < 0.01 in Student’s T-test.
FIGURE 5 Analysis of the differences in exosomal miRNA profiles among three isolation methods. (A) Small RNA yield efficiency by three methods. (B) Identified miRNA species by three methods, average of miRNA species from 46 samples. (C) MiRNA total counts by three methods, average of total miRNA counts from 46 samples. (D) Average read length distribution by three methods. (E) Aligned miRNA reads and unmapped reads of 46 samples by three methods. (F) Venn diagram presents all identified miRNAs that are common or unique in the three method datasets. Most (695) miRNAs were common to the three sets. (G–I), Scatter plots reveal correlations between EQ and PC (D), EQ and SC (E), SC and PC (F). Each scatter plot represents relative expression of miRNA profiles obtained by two methods. * represents p < 0.05 in Student’s T-test. ** represents p < 0.01 in Student’s T-test.CC BY · PubMed Central
FIGURE 6 Correlation of EQ, PC, and SC profiles. (A) Grouping information for each sample by PCA. Colors and shapes represent three exosome isolation methods. Y-axis shows the sample number. X-axis shows the grouping information. (B) Venn diagram represents EQ, PC, and SC samples that are grouped into the same clusters. (C) Co-inertia analysis (CIA) of relationships between the EQ and PC miRNA expressional PCA. (D) CIA of relationships between the PC and SC miRNA expressional PCA. (E) CIA of relationships between the EQ and SC miRNA expressional PCA. (C–E) p-value < 0.01 from 99 Monte-Carlo simulations.
FIGURE 6 Correlation of EQ, PC, and SC profiles. (A) Grouping information for each sample by PCA. Colors and shapes represent three exosome isolation methods. Y-axis shows the sample number. X-axis shows the grouping information. (B) Venn diagram represents EQ, PC, and SC samples that are grouped into the same clusters. (C) Co-inertia analysis (CIA) of relationships between the EQ and PC miRNA expressional PCA. (D) CIA of relationships between the PC and SC miRNA expressional PCA. (E) CIA of relationships between the EQ and SC miRNA expressional PCA. (C–E) p-value < 0.01 from 99 Monte-Carlo simulations.CC BY · PubMed Central
FIGURE 7 miRNA expression under three isolation methods. (A) Spearman correlation coefficient was calculated from miRNA expression data in the pair of EQ-PC and SC-PC in each sample. EQ data exhibited higher correlation with PC data than SC with PC. (B) Heatmap of miRNA expression in 46 samples. The top 100 abundant miRNAs were displayed. Higher expression levels were obviously observed in EQ data.
FIGURE 7 miRNA expression under three isolation methods. (A) Spearman correlation coefficient was calculated from miRNA expression data in the pair of EQ-PC and SC-PC in each sample. EQ data exhibited higher correlation with PC data than SC with PC. (B) Heatmap of miRNA expression in 46 samples. The top 100 abundant miRNAs were displayed. Higher expression levels were obviously observed in EQ data.CC BY · PubMed Central

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Research works printed but not linked: 49

  1. Agarwal, 2015, Predicting effective microRNA target sites in mammalian mRNAs., eLife, 4, 10.7554/eLife.05005 · DOI · Paper page
  2. Alderton, 2015, Diagnosis: fishing for exosomes., Nat. Rev. Cancer, 15, 10.1038/nrc3990 · DOI
  3. Altschul, 1990, Basic local alignment search tool., J. Mol. Biol., 215, 403, 10.1016/S0022-2836(05)80360-2 · DOI · Paper page
  4. Bellingham, 2012, Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells., Nucleic Acids Res., 40, 10937, 10.1093/nar/gks832 · DOI
  5. Betel, 2010, Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites., Genome Biol., 11, 10.1186/gb-2010-11-8-r90 · DOI · Paper page
  6. Burgos, 2014, Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer’s and Parkinson’s diseases correlate with disease status and features of pathology., PLoS One, 9, 10.1371/journal.pone.0094839 · DOI · Paper page
  7. Dawson, 2003, Molecular pathways of neurodegeneration in Parkinson’s disease., Science, 302, 819, 10.1126/science.1087753 · DOI
  8. Derkow, 2018, Distinct expression of the neurotoxic microRNA family let-7 in the cerebrospinal fluid of patients with Alzheimer’s disease., PLoS One, 13, 10.1371/journal.pone.0200602 · DOI
  9. Dong, 2015, Serum MicroRNA profiles serve as novel biomarkers for the diagnosis of Alzheimer’s Disease., Dis Mark.rs, 2015, 10.1155/2015/625659 · DOI
  10. Enderle, 2015, Characterization of RNA from exosomes and other extracellular vesicles isolated by a novel spin column-based method., PLoS One, 10, 10.1371/journal.pone.0136133 · DOI
  11. Fischer, 2015, Unveiling the principle of microRNA-mediated redundancy in cellular pathway regulation., RNA Biol., 12, 238, 10.1080/15476286.2015.1017238 · DOI · Paper page
  12. Gao, 2008, Why neurodegenerative diseases are progressive: uncontrolled inflammation drives disease progression., Trends Immunol., 29, 357, 10.1016/j.it.2008.05.002 · DOI
  13. Gao, 2017, MicroRNA-125b in peripheral blood: a potential biomarker for severity and prognosis of children with viral encephalitis., Neurol. Sci., 38, 1437, 10.1007/s10072-017-2982-x · DOI
  14. Gonzales, 2009, Large-scale proteomics and phosphoproteomics of urinary exosomes., J. Am. Soc. Nephrol., 20, 363, 10.1681/ASN.2008040406 · DOI · Paper page
  15. Hara, 2017, Interaction of reactive astrocytes with type I collagen induces astrocytic scar formation through the integrin-N-cadherin pathway after spinal cord injury., Nat. Med., 23, 818, 10.1038/nm.4354 · DOI
  16. Hewel, 2019, Common miRNA Patterns of Alzheimer’s Disease and Parkinson’s Disease and their putative impact on commensal gut microbiota., Front. Neurosci., 13, 10.3389/fnins.2019.00113 · DOI
  17. Hoss, 2016, microRNA profiles in Parkinson’s Disease prefrontal cortex., Front. Aging Neurosci., 8, 10.3389/fnagi.2016.00036 · DOI · Paper page
  18. Jeppesen, 2019, Reassessment of exosome composition., Cell, 177, 428.e18, 10.1016/j.cell.2019.02.029 · DOI · Paper page
  19. Jia, 2019, Concordance between the assessment of Abeta42, T-tau, and P-T181-tau in peripheral blood neuronal-derived exosomes and cerebrospinal fluid., Alzheimers Dement., 15, 1071, 10.1016/j.jalz.2019.05.002 · DOI
  20. Kanehisa, 2000, KEGG: kyoto encyclopedia of genes and genomes., Nucleic Acids Res., 28, 27, 10.1093/nar/28.1.27 · DOI · Paper page
  21. Ke, 2016, NEAT1 is required for survival of breast cancer cells through FUS and miR-548., Gene Regul. Syst. Bio., 10, 11, 10.4137/GRSB.S29414 · DOI
  22. Keller, 2011, Toward the blood-borne miRNome of human diseases., Nat. Methods, 8, 841, 10.1038/nmeth.1682 · DOI · Paper page
  23. Kim, 2016, Harnessing cerebrospinal fluid biomarkers in clinical trials for treating Alzheimer’s and Parkinson’s Diseases: potential and challenges., J. Clin. Neurol., 12, 381, 10.3988/jcn.2016.12.4.381 · DOI
  24. Konovalova, 2019, Interplay between MicroRNAs and oxidative stress in neurodegenerative diseases., Int. J. Mol. Sci., 20, 10.3390/ijms20236055 · DOI
  25. Kruger, 2006, RNAhybrid: microRNA target prediction easy, fast and flexible., Nucleic Acids Res., 34, W451, 10.1093/nar/gkl243 · DOI · Paper page
  26. Lehmann, 2012, An unconventional role for miRNA: let-7 activates Toll-like receptor 7 and causes neurodegeneration., Nat. Neurosci., 15, 827, 10.1038/nn.3113 · DOI · Paper page
  27. Leidinger, 2013, A blood based 12-miRNA signature of Alzheimer disease patients., Genome Biol., 14, 10.1186/gb-2013-14-7-r78 · DOI
  28. Long, 2013, Tumor suppressive microRNA-424 inhibits osteosarcoma cell migration and invasion via targeting fatty acid synthase., Exp. Ther. Med., 5, 1048, 10.3892/etm.2013.959 · DOI
  29. Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2., Genome Biol., 15, 10.1186/s13059-014-0550-8 · DOI · Paper page
  30. Mak, 2017, Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing., Gigascience, 6, 1, 10.1093/gigascience/gix049 · DOI · Paper page
  31. Mashek, 2007, Long-chain acyl-CoA synthetases and fatty acid channeling., Future Lipidol., 2, 465, 10.2217/17460875.2.4.465 · DOI
  32. McKhann, 2011, The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease., Alzheimers Dement., 7, 263, 10.1016/j.jalz.2011.03.005 · DOI · Paper page
  33. Melo, 2014, Cancer exosomes perform cell-independent microRNA biogenesis and promote tumorigenesis., Cancer Cell, 26, 707, 10.1016/j.ccell.2014.09.005 · DOI
  34. Nelson, 2008, MicroRNAs (miRNAs) in neurodegenerative diseases., Brain Pathol., 18, 130, 10.1111/j.1750-3639.2007.00120.x · DOI
  35. Rekker, 2014, Comparison of serum exosome isolation methods for microRNA profiling., Clin. Biochem., 47, 135, 10.1016/j.clinbiochem.2013.10.020 · DOI
  36. Ritchie, 2015, limma powers differential expression analyses for RNA-sequencing and microarray studies., Nucleic Acids Res., 43, 10.1093/nar/gkv007 · DOI · Paper page
  37. Robinson, 2010, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data., Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616 · DOI · Paper page
  38. Sadlon, 2019, miRNAs identify shared pathways in Alzheimer’s and Parkinson’s Diseases., Trends Mol. Med., 25, 662, 10.1016/j.molmed.2019.05.006 · DOI
  39. Satoh, 2015, MicroRNA-seq data analysis pipeline to identify blood biomarkers for Alzheimer’s Disease from public data., Biomark Insights, 10, 21, 10.4137/BMI.S25132 · DOI
  40. Schwarzenbach, 2014, Clinical relevance of circulating cell-free microRNAs in cancer., Nat. Rev. Clin. Oncol., 11, 145, 10.1038/nrclinonc.2014.5 · DOI · Paper page
  41. Sun, 2018, MiR-423-5p in brain metastasis: potential role in diagnostics and molecular biology., Cell Death Dis., 9, 10.1038/s41419-018-0955-5 · DOI
  42. Tan, 2015, Causes and consequences of MicroRNA dysregulation in neurodegenerative diseases., Mol. Neurobiol., 51, 1249, 10.1007/s12035-014-8803-9 · DOI
  43. Tang, 2017, Comparison of isolation methods of exosomes and exosomal RNA from cell culture medium and serum., Int. J. Mol. Med., 40, 834, 10.3892/ijmm.2017.3080 · DOI
  44. Taylor, 2011, Exosome isolation for proteomic analyses and RNA profiling., Methods Mol. Biol., 728, 235, 10.1007/978-1-61779-068-3_15 · DOI
  45. Thery, 2006, Isolation and characterization of exosomes from cell culture supernatants and biological fluids., Curr. Protoc. Cell Biol. Chapter, 3, 10.1002/0471143030.cb0322s30 · DOI · Paper page
  46. Valadi, 2007, Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells., Nat. Cell Biol., 9, 654, 10.1038/ncb1596 · DOI · Paper page
  47. Vlachos, 2015, DIANA-miRPath v3.0: deciphering microRNA function with experimental support., Nucleic Acids Res., 43, W460, 10.1093/nar/gkv403 · DOI
  48. Witwer, 2013, Standardization of sample collection, isolation and analysis methods in extracellular vesicle research., J. Extracell Vesicles, 2012, 10.3402/jev.v2i0.20360 · DOI
  49. Wood, 2011, Exosomes and the blood-brain barrier: implications for neurological diseases., Ther. Deliv., 2, 1095, 10.4155/tde.11.83 · DOI

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Metadata

Authors
Nie, C., Sun, Y., Zhen, H., Guo, M., Ye, J., Liu, Z., Yang, Y., & Zhang, X.
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2020-05-07
Volume
14
ISSN
1662-453X
DOI
10.3389/fnins.2020.00438
PMC
PMC7227778
Cited by
89
References
1
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20170412153100794, JSGG20170824152728492)

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