Novel microRNA revealed by systematic analysis of the microRNA transcriptome in dentate gyrus granule cells

Accepted Manuscript Title: Novel microRNA revealed by systematic analysis of the microRNA transcriptome in dentate gyrus granule cells Author: Brigid Ryan Joanna M. Williams PII: DOI: Reference:

S0304-3940(16)30664-4 http://dx.doi.org/doi:10.1016/j.neulet.2016.09.003 NSL 32280

To appear in:

Neuroscience Letters

Received date: Revised date: Accepted date:

25-5-2016 22-8-2016 2-9-2016

Please cite this article as: Brigid Ryan, Joanna M.Williams, Novel microRNA revealed by systematic analysis of the microRNA transcriptome in dentate gyrus granule cells, Neuroscience Letters http://dx.doi.org/10.1016/j.neulet.2016.09.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Novel microRNA revealed by systematic analysis of the microRNA transcriptome in dentate gyrus granule cells

Brigid Ryana, b,1, Joanna M. Williamsa, b

aDepartment

of Anatomy, University of Otago, PO Box 913, Dunedin 9054, New

Zealand bThe

Brain Health Research Centre, University of Otago, PO Box 56, Dunedin 9054,

New Zealand

1Present

address: Centre for Brain Research, University of Auckland, New

Zealand

Email addresses: Brigid Ryan: [email protected]

Corresponding author: Joanna Williams Lindo Ferguson Building 270 Great King St Dunedin 9016 New Zealand [email protected]

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Highlights 

Nine putative novel microRNAs were discovered in isolated dentate gyrus granule cells



The expression of the most abundant novel microRNA was confirmed using RT-qPCR



Bioinformatic analyses indicated that the novel microRNA functions by regulating synaptic proteins

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ABSTRACT Post-transcriptional control of gene expression by microRNAs provides an important regulatory system within neurons, allowing co-ordinate and finetuned expression of plasticity-related proteins. Indeed, specific microRNAs have been shown to be regulated by synaptic activity in the dentate gyrus, and contribute to the regulated gene expression that underlies the persistence of long-term potentiation (LTP), a model of memory. To fully explore the contribution of microRNAs in synaptic plasticity, it is important to characterize the complete microRNA transcriptome in regions such as the dentate gyrus. Accordingly we used deep sequencing and miRDeep* analysis to search for novel microRNAs expressed in the dentate gyrus granule cell layer. Drawing on combined sequencing and bioinformatics analyses, including hairpin stability and patterns of precursor microRNA processing, we identified nine putative novel microRNAs. We did not find evidence of differential expression of any of these putative microRNAs following LTP at perforant path-granule cell synapses in awake rats (5 h post-tetanus; p > 0.05). Focusing on novel_miR-1, the most abundant novel miRNA, we showed that this sequence could be amplified from RNA extracted from dentate gyrus granule cells by reverse transcriptionquantitative polymerase chain reaction. Further, by computationally predicting mRNA targets of this microRNA, we found that this novel microRNA likely contributes to the regulation of proteins that function at synapses.

Keywords: dentate gyrus; laser microdissection; long-term potentiation; microRNA; miRDeep*; synapse.

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INTRODUCTION MicroRNAs

(miRNAs)

function

primarily

as

post-transcriptional

inhibitors of gene expression, by base-pairing with specific target messenger RNAs (mRNAs) to induce mRNA degradation and/or translational suppression [48]. Converging lines of evidence suggest that miRNAs have a significant impact on protein expression [3, 14, 31, 53]. Comprehensive study of the miRNA transcriptome is therefore vital to our understanding of how gene expression is regulated. It is likely that some miRNAs remain to be discovered, particularly those that are expressed at very low levels, under specific conditions [4, 34], or in a tissue-specific fashion [35]. To date, there have been no reports of comprehensive miRNA expression profiling in isolated dentate gyrus granule cells. Granule cells are the principal cell type of the dentate gyrus, a component of the hippocampus, and receive projections from the entorhinal cortex via the perforant path. The dentate gyrus is an important site of adult-neurogenesis [37] and plays a critical role in memory encoding [29]. Recently, miRNAs have been implicated in memory processes [6, 40] and we and others have reported differential expression of miRNAs in the dentate gyrus in response to the induction of long-term potentiation (LTP), a model of memory formation [23, 41, 46, 47]. The aim of the current study was to search for novel miRNAs in dentate gyrus granule cells. To this end, we performed Illumina deep sequencing on laser-microdissected dentate gyrus granule cell layer tissue, containing granule cell somata, and predicted novel miRNAs using the miRDeep* algorithm. We tested whether the identified putative miRNAs were regulated in response to

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LTP and used bioinformatic analysis to predict target genes and investigate novel miRNA function.

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METHODS Induction of LTP in freely moving adult rats Adult (22-26 weeks old; n = 12) male Sprague-Dawley rats were chronically implanted with stimulating and recording electrodes bilaterally in the hippocampus, and baseline recordings were made, as previously reported [22]. Immediately following the baseline-recording period, high frequency stimulation (HFS; 50 trains of 400 Hz stimulation: 250 μs pulse duration; 10 pulses/train in sets of five trains, 1 s apart; 60 s between sets) was delivered to the perforant path unilaterally. Eight animals were randomly allocated to receive HFS to the left hemisphere; four received HFS to the right. The unstimulated hemisphere served as a within-animal control. This is an established LTP induction paradigm, known to induce the most persistent form of LTP, LTP3 [5, 22]. Responses were recorded for 20 min after the last train, and for 30 min 4.5 h after the last train. The means of the last 20 responses from both recording periods were calculated. We chose to quantify miRNA expression 5 h after LTP induction because previous work has revealed differential expression of miRNAs at this timepoint [23, 41]. The criteria for LTP were ≥ 10% increase in field extracellular post-synaptic potential (fEPSP) and ≥ 100% increase in population spike (PS), relative to baseline, persisting for 5 h post-HFS. All animals exhibited LTP induction according to these criteria (mean fEPSP increase ± SEM = 33 ± 11 at 20 min, 29 ± 12 at 5 h; mean PS increase ± SEM = 370 ± 227 at 20 min, 400 ± 207 at 5 h). Immediately following the second recording period (i.e., 5 h after HFS), animals were deeply anaesthetized with halothane and decapitated. All surgical protocols were approved by the University of Otago Animal Ethics Committee.

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Isolation of the dentate gyrus granule cell layer Immediately following decapitation, whole brains were flash-frozen in dry ice-cooled isopentane at -90°C for 4 min. Coronal sections of the dorsal hippocampus (20-35 μm) were cut using a Leica CM1950 cryostat (Leica Microsystems). Sections were collected between approximately 2.9 mm and 4.1 mm posterior to Bregma and thaw-mounted onto UV-treated polyethylene naphthalate slides. Cryosections were post-fixed (75% EtOH), stained (0.05% thionin), and dried for 15 min at 40°C immediately prior to laser microdissection. The dentate gyrus granule cell layer was microdissected from control and HFS hemispheres and collected in Lysis Buffer (Norgen Biotek Total RNA Extraction Kit) with added β-mercaptoethanol (0.1%). Samples were vortexed for 5 s to disrupt the tissue then stored at -80°C for up to 10 days. Samples were pooled to give one sample per hemisphere per animal.

RNA extraction The total volume of each pooled sample was adjusted to 600 µL in Lysis Buffer (Norgen Biotek) and total RNA was isolated from matched control and HFS granule cell layer tissue using the Total RNA Purification Kit (Norgen Biotek), including an on-column DNase treatment. Purified total RNA was eluted twice into 50 µL of nuclease-free water. RNA quality and quantity were determined using the NanoDrop™ 1000 Spectrophotometer (Thermo Scientific) and the RNA integrity number (RIN) was determined using a Bioanalyzer 2100 with an RNA 6000 Nano Labchip (Agilent Technologies, USA).

Deep sequencing: Illumina HiSeq™

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Deep sequencing was used to identify novel miRNAs in the granule cell layer and was performed by the Massey Genome service using one lane on the Illumina HiSeq™ 1000 Instrument. Prior to cDNA library preparation, RNA was concentrated in the presence of RNAStable® (Biomatrica, Inc), to achieve the desired concentration of 100 ng/μL for sequencing. For each sample, the two elutions of RNA were combined in 0.6 mL MAXYmum recovery tubes (Axygen), and RNAStable® LD (20 μL) was added. RNA was dehydrated for 60 min using a vacuum concentrator (SpeedVac®) at ambient temperature, stored in a heat-sealed bag with desiccant for transport, and rehydrated in 10 μL nuclease-free water. RNA quality and quantity were determined before cDNA library preparation using both microfluidic electrophoresis analysis (Agilent 2100 Bioanalyzer with RNA 6000 Nano Labchip) and fluorometry (Invitrogen Qubit Fluorometer: Quant-iT RNA Assay, Quant-iT dsDNA HS Assay and Quant-iT Protein Assay). cDNA libraries were prepared using the Illumina TruSeq Small RNA Library Preparation Kit. MiRNA, siRNA, piwiRNA and larger non-coding RNA were included in the cDNA libraries. Prior to reverse transcription, 5’ and 3’ RNA adaptors were ligated to each end of the small RNA (sequence: TGG AAT TCT CGG GTG CCA AGG). The 3’ adaptors are specific to 3’ hydroxyl groups that are generated on mature miRNAs and other small RNAs after cleavage by Dicer or other RNA processing enzymes. cDNA was then PCR amplified and gel purified. One of the two primers used for PCR amplification contains a six-base ‘index’ sequence, allowing identification of different samples in a single sequencing lane. The other primer is common to all amplicons. cDNA quality and quantity were determined before small RNA sequencing using the Agilent 2100 Bioanalyzer

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(High Sensitivity DNA Labchip) and Invitrogen Qubit Fluorometry (Quant-iT RNA Assay, Quant-iT dsDNA Assay, and Quant-iT Protein Assay). Reads (1 x 35 bp) were sequenced using Illumina’s proprietary reversible terminator-based sequencing chemistry.

Novel miRNA prediction: miRDeep* We used miRDeep* to identify putative novel miRNAs in the dentate gyrus granule cell layer. Raw sequences were processed as follows: ligation adaptor sequences were removed, and small RNA sequences (18-23 nucleotides) were mapped to the Rattus norvegicus full genome UCSC version 4 (rn4). Reads that aligned to exonic protein-coding mRNA or known miRNA precursors (miRBase Release 18 [30]) were discarded. For all remaining reads, flanking sequences on either side of the sequence were retrieved to excise potential novel miRNA precursors. Flanking sequences were 22 bp on one end, and 15 bp for the loop region, plus the length of the mature strand, plus 22 bp on the other end. These putative precursors were computationally folded into hairpin structures using RNAfold [19] and evaluated on the basis of negative free energy, structure, and conservation. Reads were then aligned with the putative miRNA precursor fragments (mature, passenger, and hairpin), allowing a 2-nt 5’ overhang and a 5nt 3’ overhang to account for imprecise processing. Putative precursors were discarded if more than 10% of reads failed to align to one of the fragments. Expression and alignment of the three fragments of the putative precursor were evaluated to give the miRDeep* odds ratio score. An average miRDeep* odds ratio of score >1 was used to identify novel miRNA, i.e. there were 10:1 odds that a candidate novel miRNA sequence

9

represented a true miRNA. In addition, candidate novel miRNAs required an average of ≥10 reads across at least six samples/hemispheres. We chose these parameters to ensure that candidate novel miRNAs were consistently expressed across biological replicates, but retained a low cut-off for read number to prevent exclusion of low abundance miRNAs. Putative novel miRNA sequences were searched in miRBase 21 to exclude known miRNAs. All candidate novel miRNAs were then assessed for differential expression in response to LTP induction by comparing the counts per million (CPM) of the HFS hemisphere with the CPM of the matched control hemisphere. We used dual criteria to determine differential expression between the HFS and control hemisphere: fold change ± 15% and p < 0.05 (two-tailed, paired t-test, n = 2-5 animals). We discarded sequences that were not identified in at least two matched pairs. Outliers were identified using Grubb’s test. Normal distribution of the data was confirmed using the D’Agostino and Pearson omnibus normality test (p > 0.05).

RT-qPCR To further verify the most likely putative novel miRNA, we designed and purchased a Custom TaqMan® Small RNA Assay from Applied Biosystems (sequence: UGA UUG GAA GAC ACU CUG CAA C). Individual reverse transcription-quantitative polymerase chain reaction (RT-qPCR) experiments (n = 7 animals) were conducted according to MIQE (minimum information for publication of quantitative real-time PCR experiments) guidelines [7]. Total RNA (10 ng per 15 μL reaction) was converted to cDNA using the TaqMan® MicroRNA Reverse Transcription Kit (Applied Biosystems). TaqMan® MicroRNA

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Assays were performed on the Applied Biosystems 7500 Real-Time PCR System according to manufacturer’s instructions, except that RNA input was increased to 4 ng/μL from the recommended 2 ng/μL and RT product was left undiluted, instead of the recommended 1:15 dilution, because preliminary standard curve data indicated that cycle quantification (Cq) values for the lower concentration were close to the limit of accurate quantification (> 30). Fold change was calculated using the transformation 2-ΔΔCq. All data were normalized to U6 small nuclear RNA (U6). We used dual criteria to determine differential expression between the HFS and control hemisphere: fold change ± 15% and p < 0.05 (twotailed, one-sample t-test with a theoretical mean of 1).

Bioinformatic prediction of novel miRNA function In order to investigate the function of novel miRNAs, we used the TargetScan algorithm (v6.2) [33] to predict mRNA targets. TargetScan requires a minimum 6-mer seed match, does not allow mismatches in the seed sequence, and considers conservation of both the mRNA target site and the miRNA family. TargetScan allocates a context score, based on the extent of seed binding, compensatory binding in the 3’ end of the miRNA, local AU content, and position of the seed in the 3’UTR [16]. A lower context score indicates stronger predicted down-regulation of target expression; we adopted a cut-off of -0.4 to exclude weak predictions. The function of predicted targets was analysed using the Functional Annotation Clustering Tool in DAVID (Database for Annotation, Visualization and Integrated Discovery) [20, 21]. The Functional Annotation Clustering tool uses a modified Fisher Exact test to determine the probability that a group of genes is

11

involved in a particular biological function, and then groups related biological functions together in a ‘cluster’. Each cluster is given an ‘Enrichment Score’, which is the negative log of the geometric mean of all of the p-values in a given cluster. An Enrichment Score > 1.3 was considered statistically significant (equivalent to p < 0.05). DAVID analyses were performed using Homo sapiens as the background and medium stringency settings.

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RESULTS Identification of putative novel miRNAs in the dentate gyrus granule cell layer The dentate gyrus granule cell layer was microdissected (Figure 1) and pooled to give one sample per hemisphere per animal (n = 5 LTP-stimulated and n = 5 matched control hemispheres; mean concentration ± SD = 35 ± 8 ng/µL; range 21-44 ng/µL; A260/280 ratios ≥ 1.9). Microfluidic electrophoresis analysis indicated that the RNA was not degraded (mean RIN ± SD = 8.5 ± 0.7; range 6.89.3). Analysis of the deep sequencing dataset with miRDeep* yielded an average of 3.8 million reads (range: 3.3-4.9 million reads) between 18 and 23 nucleotides in length that mapped to the rat genome. We identified 453 known miRNAs in the dentate gyrus granule cell layer (Table 1). Average abundance varied widely, from < 10 CPM to 85000 CPM. To our knowledge, this is the first comprehensive characterisation of the dentate gyrus granule cell layer miRNome.

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Table 1 The dentate gyrus granule cell layer miRNome: all known microRNAs Name

Name

Name

Name

Name

Name

Name

Name

Name

rno-let-7a-13p rno-let-7a-23p rno-let-7a-5p

rno-miR130a-3p rno-miR130b-3p rno-miR130b-5p rno-miR-1323p rno-miR-1325p rno-miR133a-3p rno-miR-1343p rno-miR-1345p rno-miR135a-3p rno-miR135a-5p rno-miR135b-5p rno-miR-1363p

rno-miR181b-2-3p rno-miR181b-5p rno-miR181c-3p rno-miR181c-5p rno-miR181d-5p rno-miR-182

rno-miR219-5p rno-miR221-3p rno-miR221-5p rno-miR222-3p rno-miR-223p rno-miR-225p rno-miR23a-3p rno-miR23b-3p rno-miR-241-5p rno-miR-242-5p rno-miR-243p rno-miR-253p

rno-miR-30c1-3p rno-miR-30c2-3p rno-miR-30c5p rno-miR-30d3p rno-miR-30d5p rno-miR-30e3p rno-miR-30e5p rno-miR3102 rno-miR-31a3p rno-miR-31a5p rno-miR-3203p rno-miR-3223p

rno-miR3473 rno-miR34a-5p rno-miR34b-3p rno-miR34b-5p rno-miR34c-3p rno-miR34c-5p rno-miR350 rno-miR351-5p rno-miR352 rno-miR3543 rno-miR3546 rno-miR3547

rno-miR376a-3p rno-miR376a-5p rno-miR376b-3p rno-miR376b-5p rno-miR376c-3p rno-miR377-3p rno-miR377-5p rno-miR378a-3p rno-miR378a-5p rno-miR379-3p rno-miR379-5p rno-miR380-3p

rno-miR490-3p rno-miR493-3p rno-miR493-5p rno-miR494-3p rno-miR-495

rno-miR-671

rno-let-7b-3p rno-let-7b-5p rno-let-7c-13p rno-let-7c-23p rno-let-7c-5p rno-let-7d-3p rno-let-7d-5p rno-let-7e-3p rno-let-7e-5p

rno-miR-1835p rno-miR1839-3p rno-miR1839-5p rno-miR-184 rno-miR1843-3p rno-miR1843-5p

14

rno-miR496-3p rno-miR497-3p rno-miR497-5p rno-miR499-5p rno-miR500-3p rno-miR501-3p rno-miR503-3p

rno-miR672-3p rno-miR672-5p rno-miR673-3p rno-miR673-5p rno-miR674-3p rno-miR674-5p rno-miR702-3p rno-miR702-5p rno-miR708-3p rno-miR708-5p rno-miR758-3p

rno-let-7f-13p rno-let-7f-23p rno-let-7f-5p rno-let-7i-3p rno-let-7i-5p rno-miR-1005p rno-miR101a-3p rno-miR101a-5p rno-miR101b-3p rno-miR-1031-5p rno-miR-1033p rno-miR106b-3p rno-miR106b-5p rno-miR-107-

rno-miR-1365p rno-miR-1373p rno-miR-1375p rno-miR-1381-3p rno-miR-1382-3p rno-miR-1385p rno-miR-1393p rno-miR-1395p rno-miR-1403p rno-miR-1405p rno-miR-1413p rno-miR-1423p rno-miR-1425p rno-miR-143-

rno-miR-1855p rno-miR-1865p rno-miR-1873p rno-miR-18a5p rno-miR190a-3p rno-miR190a-5p rno-miR191a-3p rno-miR191a-5p rno-miR191b rno-miR-1925p rno-miR-1933p rno-miR-1945p rno-miR1949 rno-miR-195-

rno-miR-255p rno-miR26a-3p rno-miR26a-5p rno-miR26b-3p rno-miR26b-5p rno-miR27a-3p rno-miR27a-5p rno-miR27b-3p rno-miR27b-5p rno-miR-283p rno-miR-285p rno-miR296-3p rno-miR2964 rno-miR-

rno-miR-3225p rno-miR-3233p rno-miR-3235p rno-miR-3243p rno-miR-3245p rno-miR-3253p rno-miR-3255p rno-miR-325p rno-miR-3263p rno-miR328a-3p rno-miR328b-3p rno-miR-3293p rno-miR-3295p rno-miR-330-

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rno-miR3553 rno-miR3556a rno-miR3556b rno-miR3557-3p rno-miR3557-5p rno-miR3559-3p rno-miR3559-5p rno-miR3560 rno-miR3570 rno-miR3572 rno-miR3574 rno-miR3576 rno-miR3577 rno-miR-

rno-miR380-5p rno-miR381-3p rno-miR382-3p rno-miR382-5p rno-miR383-3p rno-miR383-5p rno-miR384-3p rno-miR384-5p rno-miR409a-3p rno-miR409a-5p rno-miR409b rno-miR410-3p rno-miR410-5p rno-miR-

rno-miR-504 rno-miR505-3p rno-miR505-5p rno-miR532-3p rno-miR532-5p rno-miR539-3p rno-miR539-5p rno-miR540-3p rno-miR540-5p rno-miR541-5p rno-miR542-3p rno-miR542-5p rno-miR543-3p rno-miR-

rno-miR760-3p rno-miR764-3p rno-miR770-5p rno-miR-7a1-3p rno-miR-7a2-3p rno-miR-7a5p rno-miR-7b rno-miR872-3p rno-miR872-5p rno-miR873-3p rno-miR873-5p rno-miR874-3p rno-miR874-5p rno-miR-877

3p rno-miR-1075p rno-miR-10a5p rno-miR1188-5p rno-miR1193-3p rno-miR1224 rno-miR-1243p rno-miR-1245p rno-miR1249 rno-miR125a-3p rno-miR125a-5p rno-miR125b-1-3p rno-miR125b-2-3p rno-miR125b-5p

3p rno-miR-1443p rno-miR-1445p rno-miR-1453p rno-miR-1455p rno-miR146a-5p rno-miR146b-5p rno-miR148b-3p rno-miR148b-5p rno-miR-1505p rno-miR-1513p rno-miR-1515p rno-miR-1523p rno-miR-1533p

3p rno-miR-1955p rno-miR199a-3p rno-miR199a-5p rno-miR-19a3p rno-miR-19b3p rno-miR203a-3p rno-miR203b-5p rno-miR-2043p rno-miR-2045p rno-miR-205 rno-miR-20a5p rno-miR-2103p rno-miR-2105p

296-5p rno-miR298-5p rno-miR299a-3p rno-miR299a-5p rno-miR29a-3p rno-miR29a-5p rno-miR29b-2-5p rno-miR29b-3p rno-miR29c-3p rno-miR29c-5p rno-miR300-3p rno-miR301a-3p rno-miR301a-5p rno-miR301b-3p

3p rno-miR-3305p rno-miR-3313p rno-miR-3315p rno-miR-333p rno-miR-335 rno-miR-335p rno-miR-3373p rno-miR-3375p rno-miR-3383p rno-miR-3385p rno-miR-3393p rno-miR-3395p rno-miR-3403p

16

3578 rno-miR3583-3p rno-miR3586-3p rno-miR3587 rno-miR3588 rno-miR3589 rno-miR3590-5p rno-miR3592 rno-miR3594-3p rno-miR3594-5p rno-miR3595 rno-miR3596a rno-miR3596b rno-miR3596c

411-3p rno-miR411-5p rno-miR412-3p rno-miR412-5p rno-miR421-5p rno-miR423-3p rno-miR423-5p rno-miR425-3p rno-miR425-5p rno-miR431 rno-miR433-3p rno-miR433-5p rno-miR434-3p rno-miR434-5p

543-5p rno-miR547-5p rno-miR551b-3p rno-miR551b-5p rno-miR582-3p rno-miR582-5p rno-miR-592 rno-miR598-3p rno-miR6215 rno-miR6315 rno-miR6318 rno-miR6319 rno-miR6321 rno-miR6324

rno-miR879-3p rno-miR879-5p rno-miR92a-1-5p rno-miR92a-3p rno-miR92b-3p rno-miR92b-5p rno-miR-933p rno-miR-935 rno-miR-935p rno-miR-983p rno-miR-985p rno-miR99a-3p rno-miR99a-5p

rno-miR126a-3p rno-miR126a-5p rno-miR-1273p rno-miR-1275p rno-miR-1281-5p rno-miR-1282-5p rno-miR-1283p rno-miR-1291-3p rno-miR-1292-3p rno-miR-1295p rno-miR1298 rno-miR1306-5p

rno-miR-1535p rno-miR-1545p rno-miR-15b3p rno-miR-15b5p rno-miR-163p rno-miR-165p rno-miR-171-3p rno-miR-175p rno-miR181a-1-3p rno-miR181a-2-3p rno-miR181a-5p rno-miR181b-1-3p

rno-miR-2115p rno-miR-2123p rno-miR-2125p rno-miR-213p rno-miR-215 rno-miR-215p rno-miR218a-1-3p rno-miR218a-2-3p rno-miR218a-5p rno-miR218b rno-miR-2191-3p rno-miR-2192-3p

rno-miR3065-3p rno-miR3065-5p rno-miR3068-3p rno-miR3068-5p rno-miR3072 rno-miR3074 rno-miR3085 rno-miR3099 rno-miR30a-3p rno-miR30a-5p rno-miR30b-3p rno-miR30b-5p

rno-miR-3405p rno-miR-341 rno-miR-3423p rno-miR-3425p rno-miR344a-3p rno-miR344b-1-3p rno-miR344b-2-3p rno-miR344b-5p rno-miR-344i rno-miR-3453p rno-miR-3455p rno-miR-346

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rno-miR3596d rno-miR361-3p rno-miR361-5p rno-miR362-3p rno-miR365-3p rno-miR369-3p rno-miR369-5p rno-miR370-3p rno-miR370-5p rno-miR374-3p rno-miR374-5p rno-miR375-3p

rno-miR448-3p rno-miR449a-5p rno-miR450a-5p rno-miR451-5p rno-miR455-3p rno-miR455-5p rno-miR484 rno-miR485-3p rno-miR485-5p rno-miR487b-3p rno-miR487b-5p rno-miR488-3p

rno-miR6325 rno-miR6329 rno-miR6331 rno-miR652-3p rno-miR652-5p rno-miR664-2-5p rno-miR664-3p rno-miR-665 rno-miR666-3p rno-miR667-3p rno-miR667-5p rno-miR-668

rno-miR99b-3p rno-miR99b-5p rno-miR-9a3p rno-miR-9a5p rno-miR-9b3p rno-miR-9b5p

Preliminary, low-stringency analysis revealed an average of 484 ± 30 putative novel miRNAs in each hemisphere (range: 420-532). When these lists were filtered (miRDeep* scores ≥ 1 and ≥ 10 reads) this number was reduced to 46 ± 12 (mean ± SD; range: 32-63) putative novel miRNAs. The majority of these were predicted in only one of the 10 hemispheres and none of the putative miRNAs were expressed exclusively in one group (LTP or control). By excluding those that were not identified in at least 6 of the 10 hemispheres, 18 putative miRNAs were identified. A search for the mature sequences in the miRBase database (Release 21) revealed that nine of these putative miRNAs had the same sequence as known mature miRNAs. The miRDeep* algorithm uses an earlier version of miRBase (Release 18), which explains this discrepancy. Thus, miRDeep* analysis identified nine putative miRNAs that have not been reported in the literature (Table 2). While our data are derived from microdissection of the dentate gyrus granule cell layer, we cannot rule out the possibility that the observed miRNA expression is occurring occurring outside of granule cells. There are other types of neuron in the granule cell layer, most of which are inhibitory interneurons, including pyramidal basket cells, axo-axonic cells, and hilar commissuralassociated pathway-related cells. A small population of non-neural cells is also present [36]. However, granule cells dominate the RNA makeup of the granule cell layer, outnumbering inhibitory interneurons by 300 to 1 [43].

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Table 2 Summary of putative novel microRNAs in the dentate gyrus granule cell layer

Name

Mature sequence 5’-3’

N

novel_miR-1

TGATTGGAAGACAC TCTGCAAC

10

Mean no. mature reads 1021

Pre-miRNA loci

Mature miRNA loci

55765741-55765762 Chr10 55765694-55765772 + strand 10 1021 Chr4 151944237-151944315 15194424-151944268 - strand 10 1021 Chr6 145183788-145183866 14518379-145183819 - strand novel_miR-2 TAGTCTGGTACAGG 10 46 Chr1 116152600-116152678 11615261-116152631 ATCCTTCC + strand novel_miR-3 CTGGAGGACCAAGA 9 23 Chr2 180815226-180815306 18081523-180815258 AGGCGGAGT + strand novel_miR-4 AAGCCAACCTTGGA 8 85 Chr5 137468914-137468994 13746896-137468984 GAGCTGAGC - strand novel_miR-5 ATGGTAATGGTGGT 7 15 64686732-64686753 Chr8 64686685-64686763 GGTGATGG + strand novel_miR-6 CAGTGACAGACACC 8 16 7988850-7988871 Chr4 7988840-7988918 TAACGGCC + strand novel_miR-7 CCTCCTCTTCTCTCT 7 25 77148117-77148138 Chr1 77148070-77148148 CTCTAGT + strand novel_miR-8 CACAGCGGAGCTGG 7 20 Chr5 156425084-156425164 15642509-156425116 GCACTGGCG + strand novel_miR-9 GGGGGTGTAGCTCA 6 123 45953400-45953422 Chr12 45953352-45953432 GTGGTAGAG - strand Chr: chromosome; N: number of hemispheres that mature sequence was identified in; nt: nucleotides. 19

PremiRNA length (nt)

Mean miRDeep* score

79

546

79

555

79

555

79

23

81

13

81

1.4

79

1.4

79

11

79

15

81

15

81

1.4

No evidence for differential expression of putative novel microRNAs in the granule cell layer 5 h after LTP induction in vivo The nine putative novel miRNAs were investigated to determine if they were regulated in the granule cell layer in response to LTP induction at perforant path-granule cell synapses. Robust LTP was induced unilaterally at perforant path-granule cell synapses in awake freely moving animals (n = 5 animals) using our established tetanization protocol [5]. In cases where a single mature miRNA mapped to multiple precursors, one pre-miRNA was retained and all others were excluded from the analysis. Differential expression analysis of CPM revealed no significant difference in the expression of the nine putative miRNAs between HFS and control hemispheres (p > 0.05; n = 5; Figure 2). This indicates that these putative miRNAs are not regulated at a level that is detectable by deep sequencing in granule cell somata 5 h after LTP induction in vivo. Novel_miR-1 is robustly expressed in dentate gyrus granule cells Novel_miR-1 was the most abundant novel miRNA (1021 mature reads); the other eight miRNAs were expressed at much lower levels (15-123 reads). A similar pattern was observed for miRDeep* scores, consistent with expression level as one of the parameters considered in the score. Interestingly, the predicted

mature

novel_miR-1

sequence

mapped

to

three

separate

chromosomes in the rat genome (rn4). This resulted in multiple predicted precursor sequences and concomitantly, multiple varying miRDeep* scores. Figure 3 shows the three predicted precursors, at three separate loci, for novel_miR_1, along with representative read counts for the mature sequence and various precursor fragments. Two of the predicted precursor sequences are identical; the third differs by one nucleotide in the 3’ end. This analysis cannot

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determine which loci contributed to the sequenced reads. Read counts of the predicted 5’ precursor fragment indicate that novel_miR-1 exhibits 5’ end homogeneity (Figure 3). This finding supports the authenticity of novel_miR-1: in keeping with the importance of the 5’ seed region to miRNA:mRNA binding, the 5’ ends of genuine miRNAs are rarely subject to RNA editing [18]. No known rat miRNAs (miRBase 21) were found to share a seed sequence with novel_miR-1; however, novel_miR_1 was found to be a close paralogue of efu-miR-2638: it differs only at positions 1 and 11 and has an identical seed. The function of efumiR-2638 is unknown: it has been predicted by miRNA discovery analysis in testes tissue from Eptesicus fuscus but has not been experimentally validated [38]. Furthermore, we found that novel_miR-1 also has similarity with gma-miR4387b: it differs by two nucleotides in the crucial seed region, indicating that it will likely target different mRNAs. The authenticity of gma-miR-4387b is questionable: it has been computationally identified as a putative novel miRNA in soybean (Glycine max) sequencing data, but has not been experimentally validated [24]. As novel_miR-1 was expressed in all 10 hemispheres examined and exhibited substantially higher expression and miRDeep* scores than the other putative novel miRNAs (Table 2), we identified this as a candidate authentic miRNA. To test whether novel_miR-1 is a genuine miRNA, we performed RTqPCR analysis with a Custom TaqMan® Small RNA Assay using an additional set of animals (n = 14 hemispheres; mean total RNA concentration ± SD = 30.5 ± 9 ng/µL; range 18-44 ng/µL; A260/280 ratios ≥ 1.9). Robust expression of novel_miR-1 was observed in all 14 hemispheres, across both HFS and control samples (Figure 4A). As expected from the deep sequencing result (Figure 2),

21

there was no evidence of differential expression of novel_miR-1 in response to LTP induction (p < 0.05, two-tailed one-sample t-test; Figure 4B). Together these data provide strong evidence for the authenticity of novel_miR-1.

Bioinformatic analysis of predicted novel_miR-1 targets Having found that novel_miR-1 was robustly expressed in dentate gyrus granule cell somata, we set out to explore the biological significance of this by identifying potential targets of novel_miR-1. TargetScan analysis predicted 248 targets of novel_miR-1. To identify targets within this list that may be involved in learning mechanisms, we cross-checked the list of predicted targets with LTPresponsive mRNAs [41]. This analysis indicated that none of the predicted targets were regulated at the level of mRNA in whole dentate gyrus lysates 5 h or 24 h after LTP induction in vivo [42]. This is in keeping with our finding that novel_miR-1 was not regulated 5 h after LTP induction (Figure 4B). Next, to identify genes that were co-expressed with novel_miR-1, and therefore more likely to be genuine targets, we filtered the list of predicted targets for genes that are expressed in primary hippocampal cell somata [8]. This analysis resulted in 46 predicted targets, with context scores ranging from -0.4 to -0.001. Exclusion of transcript variants left 24 unique genes that were predicted targets of novel_miR-1 and expressed in primary hippocampal cell somata (Table 3).

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Table 3 All genes predicted to be targeted by novel_miR-1 and expressed in rat hippocampal primary cell somata. Official gene symbol ARNT ASPHD2 CHTF8 DCLK1 DEDD EIF4E GPM6B GRID1 GTF3C2 HNMT KCNMA1 KLHL7 MET MEX3C PCDHGA10 PHLPP2 RGS7BP SLC37A3 SLC44A1 SNAP91 SSBP3 SYT4 TLK2 USP42

Context score (TargetScan)

Gene name Aryl hydrocarbon receptor nuclear translocator Aspartate beta-hydroxylase domain containing 2 Chromosome transmission fidelity factor 8 homolog Doublecortin-like kinase 1 Death effector domain-containing Hypothetical LOC630527; eukaryotic translation initiation factor 4E; similar to eukaryotic translation initiation factor 4E Glycoprotein m6b Glutamate receptor, ionotropic, delta 1 General transcription factor IIIC, polypeptide 2, beta; Mpv17 transgene, kidney disease mutant Histamine N-methyltransferase Potassium large conductance calciumactivated channel, subfamily M, alpha member 1 Kelch-like 7 (Drosophila) Met proto-oncogene Mex3 homolog C (C. elegans) Protocadherin gamma subfamily A, 10 PH domain and leucine rich repeat protein phosphatase 2 Regulator of G-protein signaling 7 binding protein Solute carrier family 37 (glycerol-3phosphate transporter), member 3 Solute carrier family 44, member 1 Synaptosomal-associated protein 91 Single-stranded DNA binding protein 3 Synaptotagmin IV Predicted gene 13161; tousled-like kinase 2 (Arabidopsis) Ubiquitin specific peptidase 42

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-0.001 -0.223 -0.149 -0.14 -0.115 -0.166 -0.106 -0.163 -0.133 -0.273 -0.127 -0.15 -0.098 -0.248 -0.145 -0.095 -0.256 -0.442 -0.28 -0.219 -0.109 -0.101 -0.273 -0.092

Analysis of this set using the functional annotation clustering tool in DAVID, revealed a cluster that was significantly enriched for the ontological terms ‘synapse and plasma membrane’ (enrichment score = 1.38). This enrichment was due to 10 genes: ASPHD2 (aspartate beta-hydroxylase domain containing 2), DCLK1 (doublecortin-like kinase 1), GRID1 (glutamate receptor, ionotropic, delta 1), KCNMA1 (potassium large conductance calcium-activated channel, subfamily M, alpha member 1), MET (met proto-oncogene), PCDHGA10 (protocadherin gamma subfamily A, 10), RGS7BP (regulator of G-protein signaling 7 binding protein), SLC44A1 (solute carrier family 44, member 1), SNAP91 (synaptosomal-associated protein 91), and SYT4 (synaptotagmin IV). This analysis suggests that novel_miR-1 contributes to synaptic activity and neurotransmission.

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DISCUSSION Here we report the discovery of a novel miRNA, novel_miR-1, that is expressed in dentate gyrus granule cell somata. That novel_miR-1 is a genuine novel miRNA is supported by our findings that it is abundant and consistently measurable in biological replicates using two different assays (deep sequencing and RT-qPCR). This is reinforced by a high miRDeep* score and the observation that novel_miR-1 exhibits 5’ end homogeneity (Figure 3), in keeping with the importance of the 5’ seed region to miRNA:mRNA binding: the 5’ end of genuine miRNAs is under strong selective pressure [18]. Confident identification of novel miRNAs is problematic, as they can easily be mistaken for degradation products: one study of miRNAs catalogued in miRBase has reported that nearly one-third lack robust experimental evidence [9]. To overcome this problem in the current study, we used a combination of deep sequencing, bioinformatic analysis, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation. The high sensitivity of deep sequencing technology makes it well suited for miRNA discovery. A number of different miRNA discovery algorithms are available to analyze deep sequencing datasets and determine the likelihood that unannotated sequences are novel miRNAs. MiRDeep* [12, 13] is the most widely used and stringent miRNA discovery program [52]. MiRDeep* uses stability, alignment, and expression data to determine the likelihood that a particular sequence encodes a genuine novel miRNA. Activity-dependent synaptic plasticity is necessary and sufficient for memory formation and storage in the mammalian brain. At perforant pathgranule cell synapses, persistent LTP requires translation of extant and newly

25

transcribed mRNA [25, 26]. Newly translated synaptic proteins include glutamate receptor subunits [17, 27, 28, 49-51], and proteins that are involved in actin polymerization [15]. Bioinformatics analysis revealed that novel_miR-1 may regulate the expression of proteins important to synaptic plasticity, learning and memory. Although we did not find that novel_miR-1 was differentially expressed 5 h after LTP induction in vivo, it is possible that novel_miR-1 is LTPresponsive, but at a time other than we investigated. Interestingly, the predicted targets of novel_miR-1 include plasticityrelated molecules, including the glutamate receptor subunit, Grid1, alpha subunit of large conductance calcium- and voltage-activated potassium channels (BKCa), (Kcnma1; aka Slo1), Synaptotagmin 4 (SYT4) and Synaptosomal-Associated Protein, 91kDa (SNAP91). Grid1 knockout mice exhibit impaired reversal learning in a spatial memory task and dysregulation of the glutamate receptor subunits GluA1 and GluA2 [54]. BKCa mediate fast after-hyperpolarization and regulate neurotransmitter release at presynaptic terminals. Mice deficient in Kcnma1 display impaired acquisition of spatial working memory, but the exact role of BKCa channels in learning and memory is not yet understood [45]. SYT4 is a membrane trafficking protein that regulates the trans-synaptic action of brainderived neurotrophic factor (BDNF), thereby controlling pre-synaptic vesicle release [11]. Consequently, syt4 knockout mice demonstrate enhanced LTP [11]. DCLK1, which is itself upregulated by BDNF in the hippocampus, is associated with memory deficits in humans [32]. Finally, SNAP91 regulates synaptic vesicle endocytosis, which contributes to synaptic strength [44]. While it is tempting to speculate that novel_miR-1 contributes to memory processing in the dentate gyrus via regulation of these predicted proteins, target prediction algorithms are

26

inherently unreliable [1, 2, 10, 39]. An important next step is to validate these putative interactions either in vitro using luciferase assays or miRNA pull-down assays, or in vivo by quantifying target gene expression in response to novel_miR-1 overexpression or knockdown. In addition further research is required to experimentally validate the authenticity of the eight additional putative miRNAs identified in this study.

CONCLUSIONS In this study, nine putative novel miRNAs were identified in dentate gyrus granule cell somata using deep sequencing and miRDeep*. The authenticity of one of them, novel_miR-1, was confirmed by RT-qPCR. Enrichment analysis of the predicted targets of novel_miR-1 indicated that they function at the synapse. This intriguing result suggests that novel_miR-1 may regulate the gene expression changes that underlie learning and memory.

ACKNOWLEDGEMENTS This research was conducted during the tenure of a Postgraduate Scholarship of the New Zealand Neurological Foundation awarded to Brigid Ryan. We thank Barbara Logan for performing the LTP induction and surgical protocols, and Professor Cliff Abraham for providing expert guidance that greatly assisted the research.

CONTRIBUTORS JMW and BR designed the study. BR collected and analyzed data. BR and JMW prepared and approved the final article.

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CONFLICTS OF INTEREST The authors declare no actual or potential conflicts of interest.

FIGURE LEGENDS Figure 1 Laser microdissection of the dentate gyrus granule cell layer from a thionin-stained 35 µm section of rat brain: intact section (left) and section after microdissection of the granule cell layer (right). GCL: granule cell layer; H: hilus; CA1: cornu ammonis 1.

Figure 2 Mean and individual fold changes 5 h after LTP induction in vivo for each of the nine putative novel microRNAs revealed by miRDeep* analysis. Fold change: counts per million HFS/control; dotted lines indicate fold change cut-off (15%). None of the nine putative novel miRNAs were differentially expressed in response to LTP induction (p > 0.05 two-tailed paired t-test).

Figure 3 Representative miRDeep* result for novel_miR-1. Three predicted precursor sequences for novel_miR-1 identified in sample 1L; mature sequence reads highlighted in red. Numbers indicate frequency of reads for various fragments of the precursor. All three precursors map to distinct chromosomes.

Figure 4 Novel_ miR-1 is robustly expressed in the dentate gyrus granule cell layer. A) Amplification curves from RT-qPCR analysis of novel_miR-1 indicate consistent expression across all samples (14 hemispheres: 7 HFS, 7

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control). B) Mean and individual fold changes 5 h after LTP induction in vivo show that novel_miR-1 was not differentially expressed in HFS versus control hemispheres (p < 0.05 two-tailed one-sample t-test). Dotted lines indicate fold change cut-off (15%). Data normalized to U6.

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