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Small RNA-Seq reveals novel miRNAs shaping the transcriptomic identity of rat brain structures

Anaïs Soula, Mélissa Valere, María-José López-González, Vicky Ury-Thiery, Alexis Groppi, View ORCID ProfileMarc Landry, Macha Nikolski, View ORCID ProfileAlexandre Favereaux  Correspondence email
Anaïs Soula
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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Mélissa Valere
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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María-José López-González
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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Vicky Ury-Thiery
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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Alexis Groppi
3Centre de Bioinformatique de Bordeaux, University of Bordeaux, Bordeaux, France
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Marc Landry
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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  • ORCID record for Marc Landry
Macha Nikolski
3Centre de Bioinformatique de Bordeaux, University of Bordeaux, Bordeaux, France
4CNRS/Laboratoire Bordelais de Recherche en Informatique, University of Bordeaux, Talence, France
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Alexandre Favereaux
1University of Bordeaux, Bordeaux, France
2Centre Nationale de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 5297, Interdisciplinary Institute of Neuroscience, Bordeaux, France
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  • ORCID record for Alexandre Favereaux
  • For correspondence: alexandre.favereaux@u-bordeaux.fr
Published 30 October 2018. DOI: 10.26508/lsa.201800018
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  • Figure 1.
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    Figure 1. Pre-miRNAs expressed in the CNS structures as revealed by RNA-Seq.

    (A) Pie chart of miRBase-referenced pre-miRNAs, 365 of them are expressed in the CNS. (B) Predictive structures of novel miRNAs were calculated with the RNAfold program. Colors represent the probability of matching between the bases. The predictive structures of novel miRNAs are similar to those of known miRNAs. (C) Histogram of the number of known and novel precursors of miRNAs expressed in each structure. (D) Venn diagramm of the repartition of pre-miRNAs in the CNS structures considering known and novel pre-miRNAs.

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    Figure S1. Predictive structure of rno-miR-124-1.

    Predictive structure of rno-miR-124-1 has been performed with the RNAfold program. Colors represent the probability of matching between the bases.

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    Figure 2. Chromosomic repartition of miRNAs.

    (A) Pie charts of the repartition of pre-miRNAs per DNA strand. There are more novel miRNAs on the positive strand than for miRBase-referenced miRNAs, Fisher exact test, P < 0.05. (B) Histogram of the repartition of known and novel pre-miRNAs along the 22 chromosomes of the rat genome. (C) Correlative analysis of the number of miRNAs in function of the length of chromosomes, R2 = 0.3814, P = 0.0022. (D) Pie chart represents genome mapping of the novel pre-miRNAs: most of the new miRNAs are intergenic (57.94%) or intronic (28.04%). Genome browser view of an intergenic pre-miRNA (novel-miR-42, E) and an intronic pre-miRNA (novel-miR-13, F).

  • Figure S2. Chromosomal repartition of miRBase-referenced miRNAs expressed in the five CNS structures.
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    Figure S2. Chromosomal repartition of miRBase-referenced miRNAs expressed in the five CNS structures.

    Considering rat miRNAs already referenced in miRBase, chromosomes 1, 6, and X contain the highest number of miRNAs (64, 73, and 59, respectively). Analysis of the expression of the miRBase-referenced miRNAs in the five CNS structures studied shows that chromosomes 1, 6, and X host the highest number of miRNAs (37, 61, and 45, respectively).

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    Figure 3. Analysis of the sequence of novel miRNAs.

    (A) The nucleotidic composition of all novel miRNAs was compiled, and the probability of each nucleotide at each position is plotted; U is the predominant nucleotide at the 5′ position. (B) Proof of concept for the “functional” ortholog miRNA hypothesis, mmu-miR-676-3p, and novel-rno-miR-21-5p share the same seed region on the LCE2D 3′UTR. Luciferase experiment proves that both miRNAs are able to interact with LCE2D 3′UTR and mediate luciferase translation inhibition. Cel-miR-67, a miRNA from C. elegans known to have no target in mammals, is used as control. Data shown are mean ± SD; numbers within the bars indicate biological replicates, one-way ANOVA, followed by the Dunnett post hoc test, ***P < 0.001, *P < 0.05.

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    Figure 4. Hierarchical clustering of the miRNome of the different CNS structures.

    (A, B) Heatmaps representing the expression of each miRNA from all the studied structures. miRNAs are represented at the bottom, biological replicates of the different CNS structures are represented on the right, and statistical dendrogram of clusterization of the samples is represented on the left. Colors represent the level of miRNA expression (log2 of count per million); red: high expression; green: low expression. (A) Considering miRBase-referenced miRNAs, all biological replicates of the same structure are grouped together, except cortex replicate #3. (B) Hierarchical clustering of novel miRNAs shows a perfect clustering of the biological replicates from the same structure. Black arrowheads indicate miRNAs those expression are strongly different in a structure compared with all other samples.

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    Figure 5. Differentially expressed miRNAs between the structures.

    All the depicted miRNAs are statistically dysregulated in the structure considered compared with all other structures as assessed with the DESeq2 algorithm and Wald test (P < 0.05). The log2 of the fold change of over- (green) and down-regulated (red) miRNAs is indicated; miRNAs written in bold correspond to novel miRNAs. (A) The olfactory bulb shows the highest number of differentially regulated miRNAs: 44 are up-regulated and 34 are down-regulated. In the cortex (B), four miRNAs are significantly up-regulated and seven are down-regulated, whereas in the hippocampus (C), only one miRNA is overexpressed and one is underexpressed. (D) In the striatum, 20 and 2 miRNAs are up- and down-regulated, respectively. (E) In the spinal cord, 32 and 34 miRNAs are up- and down-regulated, respectively.

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    Figure 6. Enrichment/depletion of miRNAs in specific structures.

    To focus on the miRNAs that could have the most relevant role in transcriptome regulation, we defined as enriched miRNAs those with a fold change >16, and depleted miRNAs those with a fold change <−16. Except for the striatum, all structures express specifically enriched or depleted miRNAs. The olfactory bulb shows the highest number of specific miRNAs (13 miRNAs), the spinal cord expresses 11 specific miRNAs, the cortex and the hippocampus exhibit only one specifically depleted miRNA, and interestingly, the cortex-specific miRNA is a novel miRNA (novel miRNAs are written in bold).

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    Figure 7. Olfactory bulb–specific miRNAs and their regulated targets.

    (A) Concerning specifically enriched miRNAs, cumulative frequency distribution of expression changes of all predicted targets is significantly different from nontarget genes (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows a down-regulation compared with nontarget genes. (B) Individual gene statistical analysis revealed that 12 target genes of the enriched miRNAs are statistically down-regulated (DESeq2 algorithm and Wald test, P < 0.05). Interestingly, some regulated genes are predicted to be the target of multiple miRNAs (gene names written in italics). Data shown are mean ± SD form three biological replicates. (C) Concerning specifically depleted miRNAs, cumulative frequency distribution of expression changes of all predicted targets is significantly different from nontarget genes (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows an up-regulation compared with nontarget genes. (D) Individual gene statistical analysis revealed that 40 target genes of miR-544-3p are up-regulated (DESeq2 algorithm and Wald test, P < 0.05); only the 20 most regulated are depicted. Data shown are mean ± SD form three biological replicates. (E) GO term enrichment analysis of the selected targets.

  • Figure 8.
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    Figure 8. Cortex-specific miRNA and its regulated targets.

    (A) Cumulative frequency distribution of expression changes of all predicted targets is significantly shifted to the right compared with nontarget genes, revealing a global up-regulation (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows an up-regulation compared with nontarget genes. (B) Individual gene statistical analysis revealed that 20 target genes of novel-miR-28-3p are up-regulated (DESeq2 algorithm and Wald test, P < 0.05). Data shown are mean ± SD form three biological replicates. (C) GO term enrichment analysis of the selected targets.

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    Figure 9. Hippocampus-specific miRNA and its regulated targets.

    (A) Cumulative frequency distribution of expression changes of all predicted targets is significantly shifted to the right compared with nontarget genes, revealing a global up-regulation (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows an up-regulation compared with nontarget genes. (B) Individual gene statistical analysis revealed that 70 target genes of novel-miR-28-3p are up-regulated (DESeq2 algorithm and Wald test, P < 0.05); only the 20 most regulated are depicted. Data shown are mean ± SD form three biological replicates. (C) GO term enrichment analysis of the selected targets.

  • Figure 10.
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    Figure 10. Spinal cord–specific miRNAs and their regulated targets.

    (A) Concerning specifically enriched miRNAs, cumulative frequency distribution of expression changes of all predicted targets is significantly different from nontarget genes (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows a down-regulation compared with nontarget genes. (B) Individual gene statistical analysis revealed that 71 target genes of the enriched miRNAs are statistically down-regulated (DESeq2 algorithm and Wald test, P < 0.05); only the 20 most regulated are depicted. Interestingly, some regulated genes are predicted to be the target of multiple miRNAs (gene names written in italics). Data shown are mean ± SD form three biological replicates. (C) Concerning specifically depleted miRNAs, cumulative frequency distribution of expression changes of all predicted targets is significantly different from nontarget genes (blue line versus black line, Kolmogorov–Smirnov test). Distribution of expression changes of statistically regulated target genes (red) shows an up-regulation compared with nontarget genes. (D) Individual gene statistical analysis revealed that 91 target genes of the depleted miRNAs are up-regulated (DESeq2 algorithm and Wald test, P < 0.05); only the 20 most regulated are depicted. Interestingly, some regulated genes are predicted to be the target of multiple miRNAs (gene names written in italics). Data shown are mean ± SD form three biological replicates. (E) GO term enrichment analysis of the selected targets.

Tables

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    Table 1.

    Chromosomic organization of the novel miRNAs.

    Name of the novel miRNANumber of copiesChromosomic locationNumber of chromosomes
    rno-novel-miR-2-5p62 x chr 1; chr 7; chr 12; chr 15; chr 185
    rno-novel-miR-8-3p33 x chr 11
    rno-novel-miR-12-5p3chr 2; 2 x chr 42
    rno-novel-miR-19-5p2chr 3; chr 202
    rno-novel-miR-22-5p5chr 3; 2 x chr 5, chr 6; chr 74
    rno-novel-miR-26-3p2chr 18; chr 52
    rno-novel-miR-52-3p2chr 10; chr 112
    rno-novel-miR-66-5p2chr 12; chr 142
    • Duplicated miRNAs are localized either on the same chromosome or on different chromosomes.

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    Table 2.

    Some novel miRNAs have orthologous sequences in human and/or mouse genomes.

    Novel rat miRNAsMouse orthologsHuman orthologs
    NameSequenceNameSequenceNameSequence
    rno-novel-miR-7-5pAGUCAGGCUACUGGUUAUAUUCCAmmu-miR-344-5pAGUCAGGCUCCUGGCUAGAUUCCAGGN/AN/A
    rno-novel-miR-8-3pGGUAUAACCAAAGCCCGACUGAmmu-miR-344h-3pGGUAUAACCAAAGCCCGACUGUN/AN/A
    rno-novel-miR-14-3pUCUCUGGGCCUGUGUCUUmmu-miR-330-5pUCUCUGGGCCUGUGUCUUAGGChsa-miR-330-5pUCUCUGGGCCUGUGUCUUAGGC
    rno-novel-miR-24-5pUCAAGAGCAAUAACGAAAAmmu-miR-335-5pUCAAGAGCAAUAACGAAAAAUGUhsa-miR-335-5pUCAAGAGCAAUAACGAAAAAUGU
    rno-novel-miR-35-5pUUUCCUCUCUGCCCCAUAGGGUmmu-miR-3059-5pUUUCCUCUCUGCCCCAUAGGGUN/AN/A
    rno-novel-miR-37-3pGCAGGAACUUGUGAGUCUmmu-miR-873a-5pGCAGGAACUUGUGAGUCUCCUhsa-miR-873-5pGCAGGAACUUGUGAGUCUCCU
    rno-novel-miR-38-5pACUCUAGCUGCCAAAGGCGCUmmu-miR-1251-5pACUCUAGCUGCCAAAGGCGCUhsa-miR-1251-5pACUCUAGCUGCCAAAGGCGCU
    rno-novel-miR-56-5pACUGGACUUGGAGUCAGAAGmmu-miR-378cACUGGACUUGGAGUCAGAAGChsa-miR-378a-3pACUGGACUUGGAGUCAGAAGGC
    rno-novel-miR-64-5pCUAAGGCAGGCAGACUUCAGUGUmmu-miR-6540-5pCUAAGGCAGGCAGACUUCAGUGN/AN/A
    rno-novel-miR-72-3pCCAGUAUUGACUGUGCUGCUGAAmmu-miR-16-1-3pCCAGUAUUGACUGUGCUGCUGAhsa-miR-16-1-3pCCAGUAUUAACUGUGCUGCUGA
    rno-novel-miR-87-5pGUUCCUGCUGAACUGAGCCAGUmmu-miR-3074-5pGUUCCUGCUGAACUGAGCCAGUhsa-miR-3074-5pGUUCCUGCUGAACUGAGCCAG
    rno-novel-miR-90-5pAGGUCCUCAGUAAGUAUUUGUUmmu-miR-1264-5pAGGUCCUCAGUAAGUAUUUGUUN/AN/A
    • Novel-miRNAs with their corresponding orthologous sequences in the mouse and/or human. We defined as ortholog an miRNA sequence with a minimum of 95% similarities with an miRNA from another species.

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    Table 3.

    Novel miRNAs with their corresponding functional orthologous sequences in the mouse and/or human.

    NameSequenceNameSequenceNameSequence
    rno-novel-miR-78-5pUGCCCCUGCUGAGGCUGUGUCUmmu-miR-3070-5pAGCCCCUGACCUUGAACCUGGGAAGCCCCUGACCUUGAACCUGGGAN/AN/A
    rno-novel-miR-79-3pUUAGGACUCUGGUCAUCUUUGGN/AN/Ahsa-miR-3117-3pAUAGGACUCAUAUAGUGCCAGAUAGGACUCAUAUAGUGCCAG
    rno-novel-miR-60-5pCAGGGAGAGAACUAGUACAGN/AN/Ahsa-miR-6760-5pCAGGGAGAAGGUGGAAGUGCAGACAGGGAGAAGGUGGAAGUGCAGA
    rno-novel-miR-86-3pACAGACUGGUGCCUGGGUGUGGN/AN/Ahsa-miR-4494CCAGACUGUGGCUGACCAGAGGCCAGACUGUGGCUGACCAGAGG
    rno-novel-miR-63-5pGCCUGAGAGCUGGGGGUAN/AN/Ahsa-miR-4324CCCUGAGACCCUAACCUUAACCCUGAGACCCUAACCUUAA
    rno-novel-miR-21-5pCCGUCCUGUACUGCAGCAmmu-miR-676-3pCCGUCCUGAGGUUGUUGAGCUCCGUCCUGAGGUUGUUGAGCUhsa-miR-676-3pCCGUCCUGAGGUUGUUGAGCUCCGUCCUGAGGUUGUUGAGCU
    rno-novel-miR-54-3pUGUGUCUUUCUCCCUCUUGACN/AN/Ahsa-miR-4711-3pCGUGUCUUCUGGCUUGAUCGUGUCUUCUGGCUUGAU
    rno-novel-miR-57-3pCUCUCACCUCCCGCUCUCCACAN/AN/Ahsa-miR-1229-3pCUCUCACCACUGCCCUCCCACAGCUCUCACCACUGCCCUCCCACAG
    rno-novel-miR-48-5pAUGGCGGCUGUGAGUCUGN/AN/Ahsa-miR-6770-3pCUGGCGGCUGUGUCUUCACAGCUGGCGGCUGUGUCUUCACAG
    rno-novel-miR-84-5pCUGUGGGCUGCAGGAGGACmmu-miR-134-3pCUGUGGGCCACCUAGUCACCCUGUGGGCCACCUAGUCACChsa-miR-134-3pCUGUGGGCCACCUAGUCACCCUGUGGGCCACCUAGUCACC
    rno-novel-miR-80-3pCCCAGGGAGCUGUAAGAGCCGmmu-miR-7226-5pGCCAGGGAAGUUGAUUGUGUGAAGGGGCCAGGGAAGUUGAUUGUGUGAAGGGN/AN/A
    rno-novel-miR-59-3pUCCCCUGGGUCUGUGCUCUGCAmmu-miR-331-3pGCCCCUGGGCCUAUCCUAGAAGCCCCUGGGCCUAUCCUAGAAhsa-miR-331-3pGCCCCUGGGCCUAUCCUAGAAGCCCCUGGGCCUAUCCUAGAA
    rno-novel-miR-25-3pGCGGGCGGGCGGGAGGCGmmu-miR-5126GCGGGCGGGGCCGGGGGCGGGGGCGGGCGGGGCCGGGGGCGGGGN/AN/A
    rno-novel-miR-3-3pAUAAGUGUAGAGAGUCUGUAGUmmu-miR-668-5pGUAAGUGUGCCUCGGGUGAGCAUGGUAAGUGUGCCUCGGGUGAGCAUGN/AN/A
    rno-novel-miR-71-5pCUCCCUCUAGUCCUCUUGGUUGUN/AN/Ahsa-miR-642a-5pGUCCCUCUCCAAAUGUGUCUUGGUCCCUCUCCAAAUGUGUCUUG
    rno-novel-miR-9-3pCUGGGCGGGAUGGGAGGUGGN/AN/Ahsa-miR-1228-5pGUGGGCGGGGGCAGGUGUGUG
    rno-novel-miR-17-5pGCAAGGCCCAGCGAGUGACUN/AN/Ahsa-miR-3922-5pUCAAGGCCAGAGGUCCCACAGCA
    rno-novel-miR-41-5pUCCGGGGCUGCGGGAUGAmmu-miR-673-3pUCCGGGGCUGAGUUCUGUGCACCN/AN/A
    rno-novel-miR-32-5pACUCGGAUCAAGCUGAGAGCCAmmu-miR-6336UCUCGGAUUUAGUAAGAGAUCN/AN/A
    rno-novel-miR-49-5pACUCUCUCACUCUGCAUGGUUAN/AN/Ahsa-miR-7110-3pUCUCUCUCCCACUUCCCUGCAG
    rno-novel-miR-73-3pUGGAGGAGAGAAAAAGAGAN/AN/Ahsa-miR-765UGGAGGAGAAGGAAGGUGAUG
    rno-novel-miR-77-5pAGUUUUCUGCCUUUCGCUCUGUGGmmu-miR-7214-5pUGUUUUCUGGGUUGGAAUGAGAAN/AN/A
    rno-novel-miR-10-3pAUUCUUCUCAGUGGGCUUAGAmmu-miR-6946-3pUUUCUUCUCUUCCCUUUCAGN/AN/A
    • Functional orthologous sequences were determined with a perfect homology into the seed region (italicized sequence).

    • View popup
    Table 4.

    Relevance of the correlation between miRNA and mRNA expression.

    StructureSpecific miRNARegulated target mRNAmRNA function associated with the structure
    Olfactory bulbmiR-544-3pSp8Transcription factor known to regulate olfactory bulb interneuron development (Li et al, 2018)
    Barhl2Transcription factor involved in the development of region-specific differences in the forebrain (Parish et al, 2016) and the diencephalon (Ding et al, 2017)
    DcxMandatory for proper migration and development of olfactory bulb neurons (Belvindrah et al, 2011)
    Wnt5aNecessary for olfactory axon connections (Zaghetto et al, 2007; Paina et al, 2011; Pino et al, 2011)
    Cortexnovel-miR-28-3pVipVip-expressing interneurons are crucial for cortical circuits development (Batista-Brito et al, 2017)
    Cbln2Expressed in the subpopulation of excitatory cortical neurons (Seigneur & Südhof, 2017)
    Lynx1Mandatory for cortical network stability (Morishita et al, 2010)
    HippocampusmiR-3065-5pLhx9Implicated in the development of the hippocampal subdivisions (Abellán et al, 2014)
    Il16Modulation of Kv4.2K+ currents and thus neuronal intrinsic properties (Fenster et al, 2007)
    GnrhrInvolved in the hippocampus-specific neuronal plasticity mechanism (Schang et al, 2011)
    PrkcgSupports hippocampal long-term potentiation (Gärtner et al, 2006)
    Pcdh19Regulated in plasticity paradigm (Kim et al, 2010) and mutations linked to epilepsy and mental retardation (Dibbens et al, 2008)
    Gria1Involved in neuronal homeostatic plasticity (Sutton et al, 2006; Letellier et al, 2014)
    Spinal CordmiR-10b-5pBcl11bMandatory for the development of axonal projections (Arlotta et al, 2005)
    miR-615Slc17a7Defines a subpopulation of neurons involved in pain processing (Landry et al, 2004; Brumovsky et al, 2007)
    FosBIncreased expression during spinal cord development and nociception (Herdegen et al, 1991; Redemann-Fibi et al, 1991)
    ArcEnhanced expression in response to pain (Hossaini et al, 2010)
    SynpoRegulated in bone cancer pain conditions (Elramah et al, 2017)
    miR-344gPax2Involved in spinal cord development (Larsson, 2017)
    Lmx1bInvolved in spinal cord development (Ding et al, 2004; Hilinski et al, 2016)
    Lbx1Involved in pain mechanisms (Gross et al, 2002; Cheng et al, 2017)
    AplnrInvolved in pain mechanisms (Xiong et al, 2017)
    miR-551b-5pHoxb3Involved in spinal cord development (Yau et al, 2002)
    Pax8Involved in spinal cord development (Batista & Lewis, 2008)
    Nkx6-1Involved in spinal cord development (Sander et al, 2000; Vallstedt et al, 2001)
    Glp1rInvolved in pain mechanisms (Djenoune et al, 2014)
    Pkd2l1Defines a subpopulation of neurons regulating locomotion (Böhm et al, 2016)
    • For each structure, specifically enriched or depleted miRNAs (in bold or italic, respectively) were associated with oppositely regulated mRNAs (up-regulated in bold and down-regulated in italic) known for their role in the given CNS structure.

Supplementary Materials

  • Figures
  • Tables
  • Table S1 Small-RNA Seq reads quality assessment with FastQC software.

  • Table S2 miRPro prediction of novel pre-miRNAs from the rat central nervous system.

  • Table 3 Final list of novel pre-miRNAs from the rat central nervous system.

  • Table S4 List of novel mature miRNAs from the rat central nervous system.

  • Table S5 Chromosomal repartition of pre-miRNAs.

  • Table S6 miRNA cluster analysis.

  • Table S7 Expression data for all miRNAs for all biological replicates (count per million).

  • Table S8 Differential expression analysis of miRNAs in each structure determined with the DESeq2 algorithm.

  • Table S9 Differential expression analysis of mRNAs in each structure determined with the DESeq2 algorithm.

  • Table S10 Correlation of miRNA and mRNA expression data according to miRNA target prediction from Tagetscan and miRDB.

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miRNAs shape brain transcriptome
Anaïs Soula, Mélissa Valere, María-José López-González, Vicky Ury-Thiery, Alexis Groppi, Marc Landry, Macha Nikolski, Alexandre Favereaux
Life Science Alliance Oct 2018, 1 (5) e201800018; DOI: 10.26508/lsa.201800018

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miRNAs shape brain transcriptome
Anaïs Soula, Mélissa Valere, María-José López-González, Vicky Ury-Thiery, Alexis Groppi, Marc Landry, Macha Nikolski, Alexandre Favereaux
Life Science Alliance Oct 2018, 1 (5) e201800018; DOI: 10.26508/lsa.201800018
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Volume 1, No. 5
October 2018
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