cells

Article Depolarization-Associated CircRNA Regulate Neural Expression and in Some Cases May Function as Templates for Translation

Ebrahim Mahmoudi 1,2,3 , Dylan Kiltschewskij 1,2,3, Chantel Fitzsimmons 1,2,3 and Murray J. Cairns 1,2,3,*

1 School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia; [email protected] (E.M.); [email protected] (D.K.); [email protected] (C.F.) 2 Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW 2308, Australia 3 Hunter Medical Research Institute (HMRI), New Lambton, NSW 2305, Australia * Correspondence: [email protected]; Tel.: +61-02-4921-8670; Fax: +61-02-4921-7903

 Received: 2 September 2019; Accepted: 18 December 2019; Published: 20 December 2019 

Abstract: Circular RNAs (circRNAs) are a relatively new class of RNA transcript with high abundance in the mammalian brain. Here, we show that circRNAs expression in differentiated neuroblastoma cells were significantly altered after depolarization with 107 upregulated and 47 downregulated circRNAs. This coincided with a global alteration in the expression of microRNA (miRNA) (n = 269) and mRNA (n = 1511) in depolarized cells, suggesting a regulatory axis of circRNA–miRNA–mRNA is involved in the cellular response to neural activity. In support of this, our in silico analysis revealed that the circular transcripts had the capacity to influence mRNA expression through interaction with common miRNAs. Loss-of-function of a highly expressed circRNA, circ-EXOC6B, resulted in altered expression of numerous mRNAs enriched in processes related to the EXOC6B function, suggesting that circRNAs may specifically regulate the acting in relation to their host genes. We also found that a subset of circRNAs, particularly in depolarized cells, were associated with ribosomes, suggesting they may be translated into . Overall, these data support a role for circRNAs in the modification of gene regulation associated with neuronal activity.

Keywords: circular RNAs (circRNAs), circ-EXOC6B; depolarization; expression; translation

1. Introduction Circular RNAs (circRNAs) are covalently closed-loop RNA molecules that are formed by back- splicing of the 50 and the 30 ends of a primary transcript [1]. Development of high-throughput sequencing technology has helped systematic identification of these in , including and mice, as well as other eukaryotic clades, such as and plants [2–4]. CircRNAs arise from exonic and/or intronic regions and span between 100 to 4000 bp in length [5]. While these molecules are distributed throughout the genome, the majority of circRNAs are produced from coding segments [5]. Emerging evidence suggests that circRNAs are functionally significant for the cell and display high levels of localization at both the tissue level and in respect to different subcellular compartments, including cytoplasm and nucleus [5]. It has been suggested that these transcript isoforms are important for supporting hemostasis as well as asserting a key regulatory role in cellular processes, such as embryonic development, cell signaling, control of cell cycle, and response to cellular stress [6]. Perturbations of their expression are also being reported in association with many diseases including cancer and neurobiological disorders such as schizophrenia [7–10]. CircRNA have been implicated in

Cells 2020, 9, 25; doi:10.3390/cells9010025 www.mdpi.com/journal/cells Cells 2020, 9, 25 2 of 19 several regulatory mechanisms from through to translation but seem to be a particularly active substrate for absorbing small non-coding miRNA and thus represent a significant matrix of competing endogenous RNA (ceRNA) [5]. These molecules are highly stable and capable of encoding multiple antisense sequences that sponge a plethora of small RNAs and prevent them from interacting with their mRNA targets. CircRNA CDR1, for example, was confirmed to contain over 70 conserved target sites for the miR-7, a brain enriched miRNA, and to regulate numerous miR-7 target genes [11–13] through sponging the miRNA and reducing its intracellular bioavailability [1,14]. Interestingly, CDR1-dependent repression of mir-7 can be rescued by another miRNA, miR-671, which is thought to be capable of Argonaute (AGO)-mediated cleavage of CDR1as, resulting in release of its miR-7 cargo [14]. Collectively, circRNA can bind a large proportion of miRNA species and are thought to provide the basis of a very substantial ceRNA network modulating miRNA function. In addition to miRNA sponging, circRNA have been shown to regulate the function of RNA-binding [15] and also can modify the transcription rate of their own host gene [16]. It was recently suggested that circRNAs are not exclusively noncoding RNA; they can serve as templates for the translation of bio-active polypeptides [17–19]. The presence of open reading frames (ORFs) as well as internal ribosome entry sites (IRESs) within endogenous circRNAs enables these molecules to produce functional peptides through a cap-independent mechanism [20,21]. However, the scope of circRNA translation is not yet clear. CircRNAs are expressed in various cell lines and tissues, particularly in the nervous system, where they reach their greatest number and concentration [2,22,23]. These molecules are dynamically regulated during neurodevelopment and are thought to be active in neural differentiation and synaptic plasticity [2,24]. In the current study, we investigated the expression and the regulatory role of circRNA in the response to potassium-induced depolarization in differentiated neuroblast cells. We observed that circRNAs are differentially expressed in response to depolarizing stimulus that is associated with significant changes in miRNA and mRNA expression. Interactions between these circRNAs and their cognate miRNA (and their relationship to target mRNA) suggested that these neural activity-associated circRNAs form an influential ceRNA regulatory axis related to neural function. Through loss-of-function analysis, we show that these circRNAs can specifically regulate , possibly through sponging miRNA. We also found that a subset of circRNAs in depolarized cells associate with ribosomes and may act as templates for translation.

2. Materials and Methods

2.1. Cell Culture and Differentiation

SH-SY5Y human neuroblastoma cells were cultured at 37 ◦C, 5% CO2, 90% humidity in Dulbecco’s Modified Eagle’s Medium (DMEM, Hyclone) supplemented with 10% fetal calf serum (FCS, Sigma-Aldrich, St. Louis, Missouri, MO, USA), 1% L-glutamine, and 2% HEPES. Cells were harvested by washing with phosphate buffered saline (PBS) followed by incubation with trypsin for one minute. To obtain neuronal cells, populations were differentiated as follows. First, 24 h after seeding cells into new culture flasks, new medium supplemented with 10 µM all-trans retinoic acid (ATRA, Sigma) was added, and cultures were subsequently incubated and wrapped in foil for 5 days while media was changed on day 3. After 5 days, ATRA was removed by washing 3 times with DMEM. The neuronal differentiation was confirmed by the high expression of neural marker genes by mRNA sequencing; the average numbers of reads were as follows: TUBB3 (36,121); ENO2 (13,922); MAP2 (6946); MAPT (2033) and SV2A (6410).

2.2. Depolarization Depolarization was induced by 3 min room temperature incubation in stimulating HEPES buffered saline (HBS) (35 mM NaCl, 100 mM KCl, 0.6 mM MgSO4.7H2O, 2.5 mM CaCl2.2H2O, 10 mM HEPES, 6 mM glucose) [25]. The HBS was then replaced with warm complete medium, and cells were Cells 2020, 9, 25 3 of 19 incubated for 10 min under culturing conditions to recover. Additionally, sham-depolarized controls (resting condition) were provided using a non-stimulating HBS from which KCl was removed and NaCl was increased to 140 mM. We used 4 biological replicates per group (depolarized and resting) for mRNA and miRNA and 3 biological replicates per group for circRNA experiments.

2.3. RNA Extraction and Quality Control Total RNA from depolarized and resting differentiated cells was extracted using TRIzol (Invitrogen) with an enhanced 30 C precipitation in isopropanol for at least 2 h using glycogen (Sigma) as a − ◦ co-precipitant. RNA quality was analyzed using an Agilent Bioanalyzer RNA 6000 Nano chip, and small RNA quality was checked by Bioanalyzer Small RNA chip before sequencing library preparation.

2.4. RNA-Seq Library Generation and Sequencing For circRNAs enriched sequencing, 5 µg of total RNA [RNA integrity number (RIN) 8.5] ≥ was treated with DNase I (1U/µg RNA, Thermo Scientific, Waltham, MA, USA) and then depleted for ribosomal RNA using the Ribo-Zero kit (Illumina, San Diego, CA, USA). Next, linear RNAs were degraded via RNase R (3U/µg, Epicenter, Madison, WI, USA)performed at 37 ◦C for 15 min. Sequencing libraries were then constructed using the Illumina TruSeq Stranded Total RNA Library Prep Kit according to the manufacturer’s protocol, after which library quality and concentrations were determined using an Agilent High Sensitivity DNA Bioanalyzer chip. Libraries were all pooled and run on NexSeq500 instrument with 150 cycles of single-end reads. For mRNA sequencing, 1 µg DNase treated total RNA (RIN 8.5) was subjected to mRNA isolation using oligo-dT beads (Illumina ≥ Heat fragmentation, 94 ◦C, 8 min). Library preparation was conducted using the TruSeq Stranded mRNA Library Prep kit (Illumina), and quality control of libraries was performed using Agilent 2100 Bioanalyzer before 150 cycles of single end sequencing on the NexSeq500 sequencer. MiRNA sequencing was conducted using 1 µg of high quality (RIN 8.5) total RNA using the ≥ Illumina TruSeq Small RNA Library Preparation Kit. Briefly, adapter ligation, reverse transcription, and 11 PCR cycles were performed according to the manufacturer’s instructions. The successful production of small RNA amplicons was then confirmed via high sensitivity DNA chip (Agilent), after which all PCR products were run on a 6% native polyacrylamide gel, ~145–160 bp bands corresponding to miRNA were excised and shredded, and cDNA was eluted overnight in molecular grade water with gentle agitation. The following day, cDNA was extracted and quantified via high sensitivity DNA chip. Samples were subsequently pooled in equimolar ratios, denatured, and subjected to 75 cycles of single-end sequencing using the NextSeq 500 sequencing platform (Illumina). All the RNA sequencing experiments were performed in one technical replicate. The data are available for download from the gene expression omnibus (GEO) with accession number GSE139443.

2.5. Prediction and Quantification of CircRNAs Raw sequencing data were demultiplexed based on unique adapter sequences and converted to fastq format using the “bcl2fastq” package (version 2.20, Illumina (https://support.illumina.com/ sequencing/sequencing_software/bcl2fastq-conversion-software.html), ensuring adapter sequences were not masked. Sequencing read quality was then assessed using the FastQC sequencing quality control package (version 0.11.5, (https://www.bioinformatics.babraham.ac.uk/projects/fastqc), after which “cutadapt” (version 1.14) [26] was used to trim adapter sequences and remove low quality 3’ nucleotides. We used CIRCexplorer [27] to predict back-splice junction candidates. Briefly, sequence reads were aligned to human reference genome hg19 using TopHat (v2.0.9) [28] and then unmapped reads were extracted and aligned onto the reference genome using TopHat-Fusion [29] to collect the reads mapped on the same but in a reverse order, which were then considered as candidate back-spliced junctions. Finally, all the back-splice candidates were checked for supporting reads, and those with more than one read were regarded as circRNAs. Estimation of circRNAs expression was performed by quantifying the number of reads spanning back-spliced junctions (circular RNA Cells 2020, 9, 25 4 of 19 reads). Next, we normalized the back-spliced junction read counts by sequencing depth in each sample. CircRNA reads were divided by the total number of mapped reads in each sample to obtain RPM (mapped back-splice junction reads per million mapped reads) values [30]. The relative expression of circRNAs was calculated by comparing RPM values between samples. One sample from the depolarized group was excluded from analysis after quality control (QC) due to a significantly lower number of the circRNAs as well as the lower abundance of back-spliced events (thus considered an outlier), leaving 2 replicates for this group.

2.6. mRNA and miRNA Expression Analysis For mRNA expression analysis, raw reads from individual libraries were demultiplexed, quality checked and trimmed, as mentioned above, and then aligned to the human reference genome hg38 using TopHat (v2.0.9) Reads aligning to features were subsequently counted using htseq-count (v0.7.2) [31], and differential expression analysis was performed using the Bioconductor package EdgeR [32] for differential expression analysis. We also obtained the transcripts per million (TPM) values from normalized read counts in EdgeR through rpkm () function. For miRNA expression analysis, reads were mapped to the reference genome using the Bowtie2 (v2.2.6) short read aligner. Using the miRBase mature miRNA reference annotation, reads mapping to mature miRNAs were then counted using htseq-count, and differential expression was quantified using EdgeR.

2.7. cDNA Synthesis and Quantitative PCR Total RNA treated with DNAse I (1 U/1 µg RNA, Thermo Scientific) was subjected to reverse transcription with Superscript II Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) using random hexamers according to the manufacturer’s instructions. To test the resistance of circRNAs to RNaseR, qRT-PCR was performed on RNA samples with and without RNaseR treatment. Using divergent primers specifically designed to detect back-splice junctions, RT-qPCR reactions were performed on an Applied Biosystems 7500 real-time PCR machine using Power SYBR green master mix (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s instructions. GAPDH served as internal reference to normalize the data using the ∆Ct method. All reactions were performed in triplicate and are presented as mean SEM. The Student’s t-test (one-tailed) was applied to compare expression ± levels. All the primer sequences for RT- PCR and qPCR are listed in Table S6.

2.8. Functional Enrichment Analysis enrichment analysis was carried out for the host genes of the circRNAs and the differentially expressed (DE) mRNAs using GO (http://geneontology.org)[33]. We uploaded the host genes and the DE genes as input list and the Homo sapiens genes (n = 20996) as reference list for the analysis. The Fisher’s exact test was used for the statistical test, and false discovery rate (FDR) was applied for multiple test correction. A p -value < 0.05 and FDR < 0.05 was set to detect the significant clusters. Pathway enrichment analysis was additionally run using Reactome database version 65 [34]. A p-value < 0.05 and FDR < 0.05 was set to identify significant pathways.

2.9. MiRNA Binding Site Prediction and circRNA–miRNA–mRNA Network Construction To predict miRNA binding sites within circRNAs, sequences of the circRNAs were extracted using hg19 annotation. Then, miRNA binding sites were identified using TargetScan_60 and targetscan_ 61_context_scores Perl scripts (http://www.targetscan.org/vert_71/)[35,36]. We used a context score threshold cutoff < 0.2 to obtain reliable interactions. We made the circRNA–miRNA interaction − pairs for the DE circRNAs and the DE miRNA by predicting the miRNA targets, as mentioned earlier. We considered the circRNA–miRNA pairs regardless of the expression patterns between circRNAs and miRNAs with the assumption that the expression of circRNAs may not affect the expression of miRNAs in sponging condition. Then, we generated the miRNA–mRNA interaction pairs applying the same strategy; the DE mRNA and the DE miRNA were included, and the miRNA binding targets Cells 2020, 9, 25 5 of 19 were predicted according to the above criteria. We only considered the pairs with inverse expression directions. The circRNAs–miRNA–mRNA network was generated by combining the circRNA–miRNA pairs and the miRNA–mRNA pairs such that the common miRNAs served as linker between the DE circRNAs and the DE mRNAs. For subnetwork analysis, we ranked the nodes by their network feature using 11 topological analysis methods and then selected the top nodes to make the network. We used CytoHubba plugin [37] in Cytoscape tool for this analysis. All the interaction networks were visualized using Cytoscape tool v.3.5.1 (http://www.cytoscape.org/)[38].

2.10. circRNA Knockdown Silencing of circ-EXOC6B (exocyst complex component 6B) and circ-DROSHA (drosha ribonuclease III) was performed using siRNAs synthesized by GenePharma. The sequences of the siRNAs were 5’-AUCCGUCACUUGAUUUUGCUU-3’ and 5’-UCGCGCAGGUCAGUUAAGUUU-3’ for circ-EXOC6B and circ-DROSHA, respectively. The designed siRNA oligos spanned the back-splice sequence to specifically target the circular transcripts of interest. After the design, we blasted the siRNA oligos and ensured that they only hit the expected targets. SH-SY5Y cells were differentiated as described and transfected by Cell Line Nucleofector Kit (Lonza, Basel, Switzerland) according to the manufacturer’s instruction. Briefly, 1.5 106 cells were resuspended in 100 µL of nucleofector × solution and co-transfected with 30 µM of siRNA and 2 µg pmaxGFP plasmid (Lonza). For the control samples, a scramble siRNA and a GFP plasmid were co-transfected. The G-004 program was used on the Nucleofector II device (Amaxa, Cologne, Germany). Immediately after nucleofection, cells were plated onto six-well plates and incubated under culturing conditions. The knockdown efficiency was tested using specific divergent primers after 24 h, and an 80% reduction in the abundance of circ-EXOC6B as well as a 60% for circ-DROSHA were observed as compared to the scramble siRNAs used as controls. The specificity of the siRNAs was further confirmed by checking the expression of potential off-targets (defined as all the blast hits, including those with the lowest possibility of interaction with the siRNAs), including DPY19L3, TMEM87A, MARCH1, SHC3, and MBNL3, and we found no change in expression in these genes in the RNAseq data. Also, there were no changes in the expression of the linear transcripts of the genes of interest (EXOC6B and DROSHA), ensuring they were not targeted by the siRNAs.

2.11. Ribosome Profiling Ribosome profiling was performed using the Illumina TruSeq Ribo Profile kit (H/M/R) according to Ingolia et al. [39]. The differentiated SH-SY5Y cells (15 samples for depolarized and four for resting condition) were treated with cycloheximide-supplemented culture medium (0.1 mg/mL) for 1 min and subsequently washed with cycloheximide-supplemented ice-cold PBS (0.1 mg/mL). Cell lysates were extracted using Mammalian Lysis Buffer (1x Mammalian Polysome Buffer, 1% Triton X-100, 1 mM DTT, 1 U/µL DNase I, 0.1 mg/mL cycloheximide, 0.1% NP-40) and subsequently triturated (22-gauge needle) and clarified by centrifugation (17,000 g, 4 C, 10 min). The unprotected RNA was × ◦ degraded by TruSeq Ribo Profile Nuclease (RNase I; 90 units, 45 min at 23 ◦C) followed by adding 15 µL SUPERase-In RNase Inhibitor (Life Technologies, Carlsbad, CA, USA) to inhibit RNase activity. The protected RNA fragments in addition to small RNAs were subsequently fractionated by size exclusion chromatography using Illustra MicroSpin S-400 columns (GE Healthcare, Pittsburge, PA,USA). The resultant RNA was purified using RNA Clean and Concentrator-25 kit (Zymo Research) according to the manufacturer’s instructions, with minor amendments made by Illumina. After ribosomal RNAs removal, the 27–33 nt RNA fragments were cut, eluted from the gel, and used to construct sequencing libraries. The sequencing was run on NexSeq500 instrument (Illumina) with 75 cycles of single-end reads. Cells 2020, 9, 25 6 of 19

2.12. Analysis of Ribosome Profiling Data Data quality was assessed using the FastQC, after which cutadapt (http://journal.embnet.org/ index.php/embnetjournal/article/view/200) was used to trim adapter sequences, retain reads between 25 nt and 40 nt, remove low quality 3’ nucleotides, and trim singular 5’ nucleotides. Over-represented sequences derived from rRNA, miRNA, snoRNA, and snRNA were discarded, and the remaining reads were used to identify circRNAs. The reads were first aligned to the reference genome hg19 with Tophat 2.0.10 (tophat2 -a 6 –microexon-search –m 2), and unmapped reads were extracted. Theses reads were then aligned to an index of back-splice junctions (over 80,000 unique back-splice sites of 36 nt in length) created from our previous datasets using Bowtie 1.0.0 (parameters: bowtie -y –chunkmbs 128 -a –best –strata), and all the mapped reads were collected and considered as circRNAs.

2.13. Annotation of ribo-circRNAs and Prediction of Coding Sequence and Domains

To annotate the ribo-circRNAs we used the genomic features including 50-UTR, 30-UTR, and CDS coordinates downloaded from UCSC table browser [40]. The ribo-circRNAs coordinates were then intersected to these features using the bedtools intersect function [41]. All the circRNAs that overlapped with any of the features were reported. To predict the coding sequence and the domains within the ribo-circRNAs, the exonic sequence of each circRNA was retrieved, and then each circRNA sequence was multiplied four times to allow rolling circle translation. To predict circRNA with potential protein coding, we used the coding potential calculator (CPC2) tool [42] and selected those circRNAs with “coding” label. Predictions of conserved domains within the coding regions of ribo-circRNAs were performed using the Conserved Domain Database (CDD) [43].

3. Results

3.1. Annotation of circRNAs in Differentiated Neuroblastoma To determine genome-wide profiles of circRNAs in differentiated human SH-SY5Y neuroblast cultures depolarized with KCl, we performed RNA-sequencing using total RNA depleted of linear RNA using RNase R treatment. A total of over 300 million reads were generated for all samples with an average of 62 million per sample, with mappable reads ranging from 82.8 to 91.2%. CircRNA candidates were detected using the CIRCexplorer method, and to improve the confidence, we removed reads with fewer than two back-spliced junctions in at least one sample. In total, we identified 28,117 unique circRNA species ranging from two to 500 reads (Figure1A and Table S1). After comparing these to the circRNA database circBase [44], comprising over 92,000 human circRNAs, we discovered 11,453 were already known. Surprisingly, an additional 16,664 circRNAs were novel with no previous annotation in circBase (Figure1B and Table S1). Analysis of genomic features showed that, while circRNAs emerge from a variety of genomic segments, including intronic and untranslated regions, the vast majority (~90%) originate from protein coding (Figure1C). Although circRNAs host genes are distributed across with the genome, they reach their greatest abundance in 1, 2, 3, and 7 (not corrected for chromosome length). By contrast, chromosomes X, Y, 21, and 22 had the lowest number of circRNAs and lowest level of circRNAs expression (Figure1D). While approximately 45% (1987) of parental genes produced only one circRNA, the remainder could yield two or more circularized transcripts including 11% with over ten distinct circRNAs, such as BIRC6 and ASH1L, which generated 97 and 40 distinct molecules, respectively (Figure1E). Analysis of the relationship between the number of circRNAs per gene and the circRNA expression level (average) showed a positive relationship (Spearman’s rank correlation test: p < 2.2 10 16; Figure S1). The length of circRNAs varied from one × − exon to over 30 exons; however, 2–5 exon isoforms were dominant, accounting for 61%, and a lower fraction (10%) contained over 10 exons, such as circ-PTK2 and circ-ACACA (Figure1F). We verified the authenticity of identified circRNAs by quantifying their resistance to RNase R using qPCR with specific divergent primers. All tested 10 circRNAs that were selected from very lowly to highly expressed Cells 2020, 9, 25 7 of 19 circRNAs were found to be over 20-fold more resistant compared to the linear transcripts following the

RNaseCells R treatment2019, 8, x (Figure S2). 7 of 19

A B

This study circBase 16,664 80,916 11,453

C D 3’ UTR

Intronic 347 218 163

CDS exon

23,214 3,642 23 497 Intergenic 5’ UTR

Others 23

E F

FigureFigure 1. Annotation 1. Annotation of theof the neuroblastoma neuroblastoma circular RNAs RNAs (circRNAs). (circRNAs). (A) The (A )number The number of circRNAs of circRNAs and back-splicedand back-spliced reads reads identified identified inin five five samples samples of differentiated of differentiated neuroblastoma. neuroblastoma. (B) Comparison (B) Comparison of of thethe identified identified circRNAs circRNAs withwith the circRNA circRNA database database circBase. circBase. (C) Genomic (C) Genomic origin originof the identified of the identified circRNAs.circRNAs. (D) Distribution(D) Distribution and and expression expression level level of of circRNAs circRNAs per perchromosome. chromosome. The outer The circle outer is circle is circRNA host gene, the middle circle represents chromosome name, and the inner circle indicates bar circRNA host gene, the middle circle represents chromosome name, and the inner circle indicates graph of expression level of individual circRNAs. (E) Number of circRNAs produced per gene. (F) bar graphHistogram of expression of the number level of ofexons individual in the exonic circRNAs. circRNAs. (E ) Number of circRNAs produced per gene. (F) Histogram of the number of exons in the exonic circRNAs. 3.2. CircRNAs are Differentially Expressed in Neuronal Depolarization 3.2. CircRNAs are Differentially Expressed in Neuronal Depolarization Analysis of circRNA expression revealed a significant alteration in response to depolarization Analysisinvolvingof 152 circRNA differentially expression expressed revealed (DE) circRNAs a significant (p-value < alteration 0.05 and fold in change response ≥ 2), to including depolarization involving107 upregulated 152 differentially and 45 downregulated expressed (DE) (Figure circRNAs 2A). The ( pdetails-value of< the0.05 altered and circRNAs fold change are provided2), including ≥ 107 upregulated and 45 downregulated (Figure2A). The details of the altered circRNAs are provided in

TableS2. Hierarchical clustering showed that the circRNA expression pattern was clearly distinguishable between resting and the depolarized conditions (Figure2B). Gene ontology (GO) analysis of host Cells 2019, 8, x 8 of 19

Cells 2020, 9, 25 8 of 19 in Table S2. Hierarchical clustering showed that the circRNA expression pattern was clearly distinguishable between resting and the depolarized conditions (Figure 2B). Gene ontology (GO) analysis of host genes from differentially expressed circRNA revealed enrichment of cellular process, genes from differentially expressed circRNA revealed enrichment of cellular process, such as cellular such as cellular macromolecule metabolic process, cellular component organization/biogenesis, and macromolecule metabolic process, cellular component organization/biogenesis, and cell cycle process cell cycle process (Figure 2C). To verify the circRNAs differential expression, we randomly selected (Figureeight2C). altered To verify circRNAs the circRNAs and performed di fferential qPCR expression,with specific we divergent randomly primers. selected In addition, eight altered we tested circRNAs and performedthe expression qPCR of CDR1as, with specific a brain divergent enriched circRNA primers. with In addition, a key regulatory we tested role the in neuronal expression function of CDR1as, a brain[14]. enriched The results circRNA showed with a significant a key regulatory change of role allin the neuronal tested circRNAs function in [14 depolarized]. The results cells showed as a significantcompared change to resting of all cells the tested (Figure circRNAs 2D). in depolarized cells as compared to resting cells (Figure2D).

A B

C D

FigureFigure 2. Di 2.ff erentialDifferential expression expression of circRNAs of circRNAs in neuronal in neuronal depolarization. depolarization. (A) Volcano (A) Volcano plot constructed plot usingconstructed fold change using values fold and changep-values values to compare and p-values the circRNA to compare expression the circRNA changes expression between changes depolarized andbetween resting cells.depolarized Red dots and representresting cells. significantly Red dots represent upregulated significantly circRNAs, upregulated and green circRNAs, dots representand green dots represent downregulated circRNAs, with the vertical lines corresponding to two-fold downregulated circRNAs, with the vertical lines corresponding to two-fold change and the horizontal change and the horizontal line representing a p-value < 0.05 cut off. (B) Hierarchical clustering line representing a p-value < 0.05 cut off.(B) Hierarchical clustering analysis of circRNA expression analysis of circRNA expression between depolarized and resting cells. Depolarized group contains betweentwo and depolarized resting group and contains resting three cells. samples. Depolarized Expression group values contains are represented two and resting in different group colors contains threewhere samples. red shows Expression upregulation values and are green represented shows downregulation. in different colors (C) Functional where red enrichment shows upregulationanalysis andof green the differentially shows downregulation. expressed (DE) (C circRNAs) Functional host genes. enrichment The top analysis 10 significantly of the di enrichedfferentially biological expressed (DE)process circRNAs terms host by genes.gene ontology The top (GO) 10 significantly analysis. (D) enrichedValidation biological of circRNA process differential terms expression by gene ontologyfor (GO)nine analysis. circRNAs (D using) Validation q-PCR. Data of circRNA are shown di asff erentialmean ± SEM. expression Reactions for were nine performed circRNAs in triplicate using q-PCR. Datafor are each shown biological as mean treatmentSEM. group, Reactions and circRNAswere performed expression in triplicate was normalized for each using biological GAPDH treatment as ± group,control. and circRNAsStatistical significance expression was was assessed normalized by Student’s using GAPDH t-test (one-tailed). as control. *p Statistical< 0.05; *** p significance < 0.001. was assessed by Student’s t-test (one-tailed). *p < 0.05; *** p < 0.001. 3.3. CircRNA Regulation Coincides with Global Alteration of miRNA and mRNA 3.3. CircRNA Regulation Coincides with Global Alteration of miRNA and mRNA

CircRNA transcripts are known for their capacity to fine-tune miRNA function through their activity as ceRNA [45]. To observe this influence, we analyzed the expression profiles of miRNA and mRNA in response to depolarization using RNA sequencing. After mapping and aligning read counts, we further identified 269 miRNAs with differential expression after depolarization (FDR < 0.05, FC > 1.5) compared to the resting state cells. This consisted of 130 upregulated and 139 downregulated Cells 2019, 8, x 9 of 19

CircRNA transcripts are known for their capacity to fine-tune miRNA function through their activity as ceRNA [45]. To observe this influence, we analyzed the expression profiles of miRNA and mRNA in response to depolarization using RNA sequencing. After mapping and aligning read counts,Cells 2020we, 9further, 25 identified 269 miRNAs with differential expression after depolarization9 of(FDR 19 < 0.05, FC > 1.5) compared to the resting state cells. This consisted of 130 upregulated and 139 downregulated miRNAs (Figure 3A and Table S3). Also, we observed 1511 mRNA transcripts to be miRNAs (Figure3A and Table S3). Also, we observed 1511 mRNA transcripts to be di fferentially differentially expressed (FDR < 0.05, FC > 2), including 557 displaying upregulation and 954 with expressed (FDR < 0.05, FC > 2), including 557 displaying upregulation and 954 with downregulation downregulation (Figure 3B and Table S3). When considered in respect to the observed change in (Figure3B and Table S3). When considered in respect to the observed change in circRNA expression, circRNAthis suggested expression, there this is a suggested regulatory relationshipthere is a regulatory between circRNA, relationship miRNA, between and mRNA circRNA, in the molecularmiRNA, and mRNAresponse in the to molecular depolarization. response to depolarization.

A B

FigureFigure 3. Differential 3. Differential expression expression of of miRNA miRNA and and mRNA inin neuronalneuronal depolarization. depolarization (A. )(A Volcano) Volcano plot plot generated using fold change values and adjusted p-values [false discovery rate (FDR)] to compare generated using fold change values and adjusted p-values [false discovery rate (FDR)] to compare the the expression changes of miRNA and (B) mRNA between depolarized and resting cells. Red dots expression changes of miRNA and (B) mRNA between depolarized and resting cells. Red dots represent significant upregulation and green dots represent downregulation, with the vertical lines represent significant upregulation and green dots represent downregulation, with the vertical lines corresponding to 1.5-fold (miRNA) and two-fold (mRNA) change and the horizontal line representing correspondingan FDR cut otoff <1.5-fold0.05. (miRNA) and two-fold (mRNA) change and the horizontal line representing an FDR cut off < 0.05. 3.4. CircRNA–miRNA–mRNA Association Network 3.4. CircRNA–miRNA–mRNATo glean further insight Association into the DE Network circRNA molecular mechanisms and the involved regulatory pathwaysTo glean in further neuronal insight depolarization, into the we DE constructed circRNA the molecular circRNAs–miRNA–mRNA mechanisms and association the involved regulatorynetwork pathways mediated by in common neuronal miRNAs depolarization, that bind to we the constructed DE circRNAs the and circRNAs–miRNA–mRNA DE mRNAs. We used TargetScan algorithm with stringent context score threshold (< 0.2) settings to predict high-confidence association network mediated by common miRNAs that bind− to the DE circRNAs and DE mRNAs. binding sites. A total of 237 and 13,281 interactions were detected for circRNA–miRNA and circRNA– We used TargetScan algorithm with stringent context score threshold (< −0.2) settings to predict high- miRNA–mRNA respectively, in which 99 circRNAs, 65 miRNAs, and 802 mRNAs were associated. confidenceThe data binding for each circRNAsites. A andtotal its of potential 237 and binding 13,281 miRNAsinteractions and mRNAwere detected targets are for provided circRNA–miRNA in Table andS4, circRNA–miRNA–mRNA and an entire network of circRNA–miRNA respectively, in andwhich circRNA–miRNA–mRNA 99 circRNAs, 65 miRNAs, interactions and 802 is mRNAs illustrated were associated.in Figures The4A anddata4 B. for One each significant circRNA example and its of potential the interaction binding that miRNAs was previously and mRNA established targets is are providedthe CDR1as in Table/miR-7 S4, axis and [14 an]. We entire found network miR-7-5p of to circRNA–miRNA be the third most significant and circRNA–miRNA–mRNA miRNA, which was interactionsenhanced is in illustrated depolarized in cells, Figure along 4A with and CDR1as Figure that 4B. displayed One significant a significant example upregulation of the interaction trend in this that wasstudy. previously Also, miR-7 established gene targets is the such CDR1as/miR-7 as PAX6, TET2, axis and [14]. XIAP We were found found miR-7-5p to be significantly to be the decreased. third most significantThis suggests miRNA, that thewhich CDR1as was/miR-7 enhanced/target in genes depolarized axis is a critical cells, pathway along with in neuronal CDR1as activation. that displayed Some a significantother examples upregulation of interactions trend in of this the study. differentially Also, miR-7 expressed gene RNAs targets are circ-PUM1such as PAX6,/miR-145-5p TET2, /andSOX9, XIAP werecirc-PPEF1 found to/ miR197-3pbe significantly/PDGFC, decreased. and circ-CACUL1 This suggests/miR-146a-5p that the/APPL1 CDR1as/miR-7/target (Table S4). genes axis is a critical pathway in neuronal activation. Some other examples of interactions of the differentially expressed RNAs are circ-PUM1/miR-145-5p/SOX9, circ-PPEF1/miR197-3p/PDGFC, and circ- CACUL1/miR-146a-5p/APPL1 (Table S4).

Cells 2020, 9, 25 10 of 19 Cells 2019, 8, x 10 of 19

A

B

Figure 4. The circRNA regulatory network in neuronal activation. (A) The circRNA–miRNA interaction networkFigure that 4. consistsThe circRNA of 99 circRNAs regulatory and networ 65 miRNAs.k in neuronal The circle activation. represents (A) circRNAThe circRNA–miRNA and the inverted interaction network that consists of 99 circRNAs and 65 miRNAs. The circle represents circRNA and triangle represents miRNA. The colors red and goldenrod indicate upregulation, and blue and the inverted triangle represents miRNA. The colors red and goldenrod indicate upregulation, and green indicate downregulation. (B) The circRNA–miRNA–mRNA interaction network that contains blue and green indicate downregulation. (B) The circRNA–miRNA–mRNA interaction network that 99 circRNAs, 65 miRNAs, and 802 mRNAs. The circle represents circRNA, the inverted triangle contains 99 circRNAs, 65 miRNAs, and 802 mRNAs. The circle represents circRNA, the inverted representstriangle miRNA, represents and miRNA, the rectangle and the representsrectangle represents mRNA. ThemRNA. red The color red denotes color denotes upregulation upregulation and blue denotesand downregulation.blue denotes downregulation.

ConsideringConsidering that that sponging sponging of miRNA of miRNA may may not necessarilynot necessarily result result in a detectablein a detectable change change in miRNA in levels,miRNA we also levels, conducted we also conducted the analysis the with analysis only with DE only circRNA DE circRNA and DE and mRNA DE mRNA using using predicted predicted miRNA mediatorsmiRNA expressed mediators in expressed the cells in and the found cells and that found most ofthat the most modulated of the modulated genes (84%) genes were (84%) potentially were targetedpotentially by the targeted circRNAs by throughthe circRNAs binding through to 324 binding miRNAs. to 324 miRNAs.

3.5. Subnetwork3.5. Subnetwork Analysis Analysis and and Functional Functional Regulatory Regulatory ModulesModules To identifyTo identify the the central central regulatory regulatory elements elements inin thethe neuronal activation activation process, process, we we ranked ranked the the networknetwork nodes nodes using using 11 11 topological topological analysis analysis methodsmethods consisting of of both both local- local- and and global-based global-based algorithms from cytoHubba plugin of Cytoscape. We found 15 high ranking circRNAs with high level overlap across all the analysis methods, for which the regulatory subnetwork was constructed. Cells 2020, 9, 25 11 of 19 Cells 2019, 8, x 11 of 19

As shownalgorithms in Figure from 5cytoHubbaA, this was plugin composed of Cytoscape. of 74 circRNA–miRNA We found 15 high andranking 1357 circRNAs miRNA–mRNA with highpairs, level overlap across all the analysis methods, for which the regulatory subnetwork was constructed. in which 39 DE miRNA bound to 15 DE circRNAs and DE 572 mRNA. Three circRNAs including As shown in Figure 5A, this was composed of 74 circRNA–miRNA and 1357 miRNA–mRNA pairs, circ-RANBP17, circ-POGZ, and circ-BARD1–2 were found to be top regulatory hubs, suggesting in which 39 DE miRNA bound to 15 DE circRNAs and DE 572 mRNA. Three circRNAs including substantialcirc-RANBP17, roles for circ-POGZ, these circRNAs and circ-BARD1–2 in the biology were of found neuronal to be excitation. top regulatory To identify hubs, suggesting the associated biologicalsubstantial processes roles for and these pathways, circRNAs we in performedthe biology theof neuronal GO and excitation. pathways To analysis identify forthe theassociated subnetwork genes.biological As shown processes in Figure and pathways5B, the,mRNA we performed targets the were GO and significantly pathways analysis enriched forwith the subnetwork several highly relevantgenes. biological As shown processes,in Figure 5B, including the mRNA transcription targets were andsignificantly metabolic enriched process, with plasmaseveral highly membrane, andrelevant cell junction. biological We alsoprocesses, found including a significant transcription enrichment and metabolic of the genes process, in pathways, plasma membrane, such as early and rapid depolarizationcell junction. and We cell–cellalso found communication. a significant enrichment of the genes in pathways, such as early rapid depolarization and cell–cell communication.

LAMP3 IFITM3 S100A16 PDLIM4 CACNG6 KCNG1 circ-UBA2 IL15 TFPI2 A GYLTL1B CSRNP1 ATAD3B UBTD1 GDF10 FAM65B WNT7B IL1B IFITM2 SLCO2B1 BCL3 GPR3 MT2A VEGFC CGNL1 CLCF1 G0S2 GMPR WNK4 AMOTL2 circ-RANBP17 CARD10 CD44 HLA-E IL1A EBI3 PTGES CD68 IKBKE C4orf26 RIPK4 JSRP1 ZNF385D FAM89A GPC3 TNFRSF12ASECTM1 TRIM55 PTHLH miR-877-5p CMTM7 PVRL2 CBR3 TM4SF1 MMP9 circ-NUP98 miR-497-5pPLAC9 IFFO2 ANKRD29 S100A2 miR-296-5p ITGA3 NUPR1 PTPRK NPC2 TMEM158 PROSER2 ATP2B1 EGR1 AGER miR-149-5p BTN1A1 IRAK2 CDKN2A BMPR2 EVA1A PIEZO1 TNRC6B MT1E TRNP1 ITGB2 MAP4K3 circ-CACUL1 KCNIP1 HLA-DPA1 ZNF557 ZNF14 HMGA2 GADD45G ZFPM2 FHL2 PRR15 ZBED6CL FCHO2 SRPX MICB FOSL1 STXBP5 BCAM PAWR GLIPR1 NLRC5 CLDN18 SIRPA FZD8 MSX1 ASB2 miR-641 LATS2 KCNQ3 ZNF492 TGFBI IGFN1 CEP135 FNIP1 ZNF443 STEAP1B miR-103a-3p miR-132-3p P2RY2 EPM2AIP1 circ-POGZ RBM24 HMGA1 DIRAS3 miR-1307-3p HOOK3 YTHDF3 ANGPT1 TPM2 SCN1B PRKAA2 SIK1 ERBB3 FGFRL1 MDGA1 UBASH3B SERPINA3 AJUBA MSRB3 NOTUM ITGB3 ABHD14B KIAA2022 MR1 PLLP C10orf11 FXYD5 KIF3A ZNF225 DRAM1 MGLL NPR1 ATF3 TGM2 YOD1 miR-107 GTSF1 circ-FAM13B BHLHE40 HES7 MLKL BCL6 let-7g-5p let-7i-5p miR-361-3p A4GALT miR-345-5p TPBGL TIE1 CTGF PPARA MGST1 KLF6 RAB38 GBX2 PLEKHA8 MRPL23 RNF144B FOSB KBTBD6 FBXO30 PHYHIP C3orf52ITGA5 IER2 SOCS4 ZNF493 ZNF286B S100A10 RAMP1 ZNF879 TWIST2 CNN1LRRC4 CLDN1 NCOA2 miR-184 CNTRL KIF27 GPX8 ALOXE3 JRKL LSMEM1 FIGN circ-SLC4A7 IVL miR-767-3p MYCT1 EEA1 MGA CDH13 PLEKHA7 CHI3L2 ZNF254 KCTD16 LCOR TGFBR2 TMBIM1CABP1 TAGLN STC2 CRLF1 ZNF772 ZNF451 miR-1304-5p XRN1KCNJ3 RICTORCSRNP3 MAP3K7CL ARC IMPG2 TRMT13 miR-22-3p TMEM229B MAP3K1 FSD2 CALB2 CD2AP HTR1E SELL miR-636 CEBPA THAP2 CREB1 EFCAB6 let-7f-5p GPATCH2 ZNF765 ZFP36L1 ARID2 ZNF148 CHST2 ADAMTSL5NIPAL4 MICU3 PRLHR PTPN4 THAP9 TMEM65 circ-IQGAP1 PDGFC SREK1IP1 SOX9 RGS10 CEBPD SKIL let-7c-5p PDGFRL LYRM7 let-7e-5pPARD6BZNF391 miR-197-3p CD82 MECOM RIN1 CASD1 SWT1 DNER RILP miR-885-3p ZNF529 HELZ HMOX1 BOD1L1 ZNF24 SLC16A12 IGF2BP2 CYR61 TAP1 OSR1 MICA DOCK4 MKL2 miR-98-5pUTRNTBCEL PKN2 GABPA PDCD1LG2 DICER1 GXYLT1miR-10a-5p circ-DOCK1 SNTB1 GJA1 FLNC TRIM2 miR-10b-5p ZNF449 FAM160B1 GPRC5A ATOH8 ZNF790 ZNF280B FAM135A PCDH17 ARHGAP10 KPNA5 BPTF PRRG4 KRT15 GPNMB PCNX IGDCC3 ZC3HAV1L CXorf23 SCN11A ZC2HC1A RHOB PLEK2 CITED2 BRSK2 LAMB3 SLC25A46 CADM2FAM73A SPRED1 ATP5E MBLAC2 USP6RGS17 LRIG2 MIB1 SPOCD1 KCNK6 CCL2 PRKACB ANKRD44 DDI2 NHLRC2miR-424-5p COL4A3BP circ-ETFA ZNF10 BRWD3 CCNT2SCN3A MX1 miR-182-5p USP47MAP3K2 RAB20 SECISBP2LSTOX2CACNB4 CACNA2D1 PTRF ZBTB6 HOOK1 CPEB4 GSC PXDC1 ULBP1 RASA2 ZNF224THUMPD1 IL4R FGF5 TMEM170B STARD4CUL5 ETV3 TNFSF13B LRP6 CSF1 RBPMS ZNF660 RORA FAT4 KIRREL GADD45B CHDH ZBTB41 miR-1249-3p miR-145-5p C21orf91 PIK3CA C8orf4CDC14A ZFC3H1 RAB39A PEX12 CNOT6Lcirc-PUM1 ATF7IP THAP6TM6SF1 GCC2 IL31RA CCDC80 PAN3TET2 miR-129-2-3p KCNS1 CITED4 ARHGAP24 GPR22 SPAST KCNH3 OAF SERPINE1 CLOCK KLF7 ESRRG SCN2AmiR-16-5p TMEM215 RORB BTAF1 ZC3H6 NKTR CEP85L LGR5 PLAU PTPRT CCDC146 UBE2W JMJD1C TNRC6C DMXL1RNF219PIK3R1 GFRA1 PXN BAZ2B BCL2 CCDC88A FRYL PDIK1L SMAD5 FLI1 MOB1BPRKAA1 RUNDC3BRASSF8 ASH1L circ-KIF4A FBXL3 ARL6 PCDHA6 TTLL7 FJX1 TNRC6A CREBRF RSBN1 PIK3C2A miR-148a-3pFBXO28 miR-148b-3pARHGAP12 RBM41 CPEB3 PTCH1 SULT1B1 SULF2 SYT12 STEAP2 NEB SNX16 MSC BCLAF1 GMFBB4GALT6 MIER1 DCUN1D4 ATHL1 CRISPLD2 NIPBL FAM111B PCMTD1 PURA GPR4 ZDHHC17 PCDHA2 RFX3 ARL5B HOXA5 ZNF382 FOXN2 MANEA PCDHA3 PHLDA1 GPR137C ABCA1 JMY ACVR2B circ-PPEF1 GRB14 HBEGF PUS7L PPP1CB ZSCAN31 CHRM2 ZNF569 CRYBG3 CD93 ICAM5 miR-1248 ATP8A1 HMBOX1 THSD7A TTC14 ESCO1DCP2 ZNF267 MMP16 ZNF302KIF14 CHD1PPP1R12A SLITRK6miR-221-3pmiR-222-3p UNC80 ZDBF2 G2E3 AGTR1ZNF281 CXADR FAM214ATTBK2 SCAF11 LNPEP SCN9AALCAM LRCH2ZNF594 ZNF181 ZNF506 HMGN5 DPP8 circ-BARD1-2 ZMAT1 ZNF322ARFGEF1 DST miR-146a-5p ZNF33A AKAP5 MIER3 ZNF737 ROCK1 ATAD2B ZNF813 DENND4C DUSP19 ZNF624 KMT2C RAB11FIP2FAM179B PI15 SEC62 ZFYVE16 MYCBP2 miR-362-5p SMC5 TMED7 ZNF273 ZNF486 RFX7 PANK3 PGGT1B circ-BARD1 SBSPON KBTBD8 ZNF93 RAB8B ZNF91 ANKRD12 REV3L ZNF253 ZNF572 ZNF208PPIP5K2 ZNF257 ZNF540 THAP5 CARF FAM169A ZNF470 ZNF550 NAT8 ZNF136 SLC7A14 ZNF354B LMBRD2 ZNF677ZNF626TMTC3 APPL1 OCLM USP51 SUCNR1 RMI1 DMXL2 ZNF605 INTS2 SPOPL NRXN3 C18orf54 TUBB1 VAMP4 KBTBD3 B

FigureFigure 5. The 5. The subnetwork subnetwork analysis analysis andand functional enrichment. enrichment. (A) ( AThe) The top top15 highly 15 highly ranked ranked circRNA circRNA nodes from cytoHubba were used to construct the subnetwork consisting of 74 circRNA–miRNA and nodes from cytoHubba were used to construct the subnetwork consisting of 74 circRNA–miRNA and 1357 miRNA–mRNA pairs with 39 miRNAs and 572 mRNAs associated. The circle represents 1357 miRNA–mRNA pairs with 39 miRNAs and 572 mRNAs associated. The circle represents circRNA,

the inverted triangle represents miRNA, and the rectangle represents mRNA. The red color denotes upregulation and blue denotes downregulation. (B) Functional enrichment analysis of the interacting mRNAs. The top significantly enriched GO terms and pathways are shown. Cells 2019, 8, x 12 of 19

circRNA, the inverted triangle represents miRNA, and the rectangle represents mRNA. The red color Cells 2020denotes, 9, 25 upregulation and blue denotes downregulation. (B) Functional enrichment analysis of the12 of 19 interacting mRNAs. The top significantly enriched GO terms and pathways are shown.

3.6.3.6. Circular Circular and and Linear Linear Transcripts Transcripts Expre Expressionssion are Associated in Neuronal Biology WeWe explored potential association between the expression levels of circular circular and linear linear transcripts transcripts inin all all samples. Circular Circular and and linear linear expression expression levels levels were were estimated estimated in in linear linear depleted depleted and and mRNA mRNA sequencingsequencing libraries, libraries, respectively, respectively, which which prevented prevented cross-estimation cross-estimation of of the the transcripts. transcripts. We We found found a significanta significant positive positive relationship relationship between between the expres the expressionsion of circRNAs of circRNAs and their and corresponding their corresponding linear 4 linear transcripts (Spearman’s rank correlation test:× p =−4 5 10 ; Figure6A), suggesting that transcripts (Spearman’s rank correlation test: p = 5 10 ; Figure× − 6A), suggesting that circRNA expressioncircRNA expression is co-regulated is co-regulated with the withlevel theof their level linear of their isoforms. linear isoforms. Notably, Notably, host genes host with genes higher with expressionhigher expression tended to tended produce to produce a higher a number higher numberof circRNAs, of circRNAs, which resulted which resultedin increased in increased average expressionaverage expression of the circRNAs of the circRNAsper gene (Figure per gene 6B). (Figure Also, 6inB). order Also, to intest order the relationship to test the relationshipbetween the magnitudebetween the change magnitude of circRNA change and of the circRNA linear andisoforms the linear in response isoforms to indepolarization, response todepolarization, we conducted correlationwe conducted analysis correlation after filtering analysis for after highly filtering expressed for highly circular expressed (RPM > 0.2) circular and (RPMlinear >transcripts0.2) and (TPMlinear transcripts> 0.2) accounting (TPM > for0.2 )2707 accounting genes. The for 2707 findings genes. interestingly The findings showed interestingly a positive showed relationship a positive betweenrelationship changes between in these changes transcripts in these transcripts (Spearman’s (Spearman’s rank correlation rank correlation test: p = test: 0.001;p = Figure0.001; FigureS3). This S3). suggestsThis suggests changes changes in circRNAs in circRNAs expression expression are asso areciated associated with corresponding with corresponding changes changes in their in linear their counterparts.linear counterparts.

A B

FigureFigure 6. 6. RelationshipRelationship between between the the expression expression of of circRNAs circRNAs and and their their linear linear isoforms. isoforms. ( (AA)) Comparison Comparison ofof the expression level of circular transcripts (RPM) with the linear transcripts (TPM) categorized categorized by by expressionexpression level. level. ( (BB)) Comparison Comparison of of the the expression expression level level of of host host genes genes with with the the expression and and the numbernumber of circRNAs per gene.

3.7.3.7. CircRNA CircRNA Loss-of-Function Loss-of-Function Re Regulatesgulates Specific Specific Gene E Expressionxpression ToTo further explore the molecular function of circRNAs, we performed functional analysis on circ-EXOC6Bcirc-EXOC6B andand circ-DROSHA circ-DROSHA by by siRNA siRNA knockdown knockdown in differentiated in differentiated SH-SY5Y SH-SY5Y cell culture. cell culture. The criteria The criteriafor selecting for selecting these two these circRNAs two are circRNAs explained are as follows.explained Circ-EXOC6B, as follows. spanningCirc-EXOC6B, three exons spanning (Figure three7A), exonswas among (Figure the 7A), top was five among most abundant the top five circRNAs most abundant (average circRNAs RPM > 48) (average with a stableRPM > expression 48) with a acrossstable expressionall the studied across samples all the asstudied well assamples in the as human well as brain in the [10 human]. Furthermore, brain [10]. the Furthermore, host gene hasthe beenhost geneimplicated has been in brain implicated disorders in brain [46]. Also,disorders circ-DROSHA [46]. Also, back-spliced circ-DROSHA from back-spliced exons 27–29 from (NM_001100412) exons 27–29 (NM_001100412)was found the most was abundant found circRNAthe most withinabundant genes circRNA involved within with miRNA genes biogenesisinvolved with and thusmiRNA may biogenesisaffect transcriptome and thus may by modulatingaffect transcriptome miRNA by processing. modulating SH-SY5Y miRNA cellsprocessi wereng. electroporated SH-SY5Y cells in were the electroporatedpresence of circ-EXOC6B in the presence and circ-DROSHA of circ-EXOC6 siRNAs,B and respectively, circ-DROSHA before siRNAs, transcriptome respectively, analysis before using transcriptomemRNA-sequencing. analysis While using the mRNA-sequencing. lowly expressed circ-DROSHA While the knocklowly downexpressed did not circ-DROSHA appear to modulate knock downhost gene did nornot anyappear other to genemodulate (FDR host< 0.05) gene (Figure nor an S4),y other circ-EXOC6B gene (FDR was < 0.05) shown (Figure to significantly S4), circ-EXOC6B alter the wasexpression shown levelsto significantly of 60 genes alter (FDR the< expression0.05 and FC levels> 1.5), of 60 as showngenes (FDR in Figure < 0.057B and (Table FC S5).> 1.5), There as shown were in27 Figure genes upregulated7B (Table S5). and There 33 downregulated. were 27 genes Weupregulated crossed these and to33 thedownregulated. depolarization We associated crossed genesthese and found 14 genes were also differentially expressed in depolarization. To explore the potential function Cells 2020Cells ,20199, 25, 8, x 13 of 1913 of 19

to the depolarization associated genes and found 14 genes were also differentially expressed in of thedepolarization. circ-EXOC6B, To weexplore predicted the potential its interaction function with of the miRNA circ-EXOC6B, that potentially we predicted targets its interaction the DE genes andwith identified miRNA ten that genes potentially that are targets candidate the DE targets genes and for identified the EXOC6B ten genes binding that miRNA. are candidate To interpret targets the biologicalfor the significance EXOC6B binding of thedifferentially miRNA. To expressed interpret the genes, biological we performed significance GO annotation. of the differentially Interestingly, expressed genes, we performed GO annotation. Interestingly, we observed significant enrichment of we observed significant enrichment of terms related to the host gene function, including extracellular terms related to the host gene function, including extracellular matrix organization, cell surface matrix organization, cell surface receptor signaling pathway, regulation of cell adhesion, cell migration, receptor signaling pathway, regulation of cell adhesion, cell migration, plasma membrane region, plasmaand membraneplasma membrane region, part and (Figure plasma 7C). membrane Pathway partenrichment (Figure analysis7C). Pathway also indicated enrichment two categories analysis also indicatedincluding two extracellular categories including matrix organization extracellular and matrix intergenic organization surface interactions. and intergenic These surface observations interactions. Thesesuggest observations that at least suggest some that circRNAs at least act some to modulate circRNAs biological act to modulate processes biological associated processes with their associated host withgenes. their host genes.

A B

Chr2: 72945232 Chr2: 72960247

circ-EXOC6B 390 nt

C

FigureFigure 7. CircRNA 7. CircRNA circ-EXOC6B circ-EXOC6B loss-of-function loss-of-function analysis. analysis. (A (A) The) The genomic genomic features features of circ-EXOC6B. of circ-EXOC6B. (B) Volcano(B) Volcano plot plot constructed constructed using using fold fold change change values values and and adjusted adjustedp -valuesp-values (FDR) (FDR) to to compare compare thethe gene expressiongene expression changes between changes thebetween control the and control the knockdown and the knockdown conditions. conditions. Red dots Red represent dots represent significantly upregulatedsignificantly genes upregulated and green genes dots and represent green dots downregulated represent downregulated genes, with thegenes, vertical with the lines vertical corresponding lines corresponding to 1.5-fold change and the horizontal line representing an FDR cut off < 0.05. (C) to 1.5-fold change and the horizontal line representing an FDR cut off < 0.05. (C) Functional enrichment Functional enrichment analysis of the DE genes. The top significantly enriched GO terms are shown. analysis of the DE genes. The top significantly enriched GO terms are shown.

3.8.3.8. Detection Detection of circRNAsof circRNAs with with Protein-Coding Protein-Coding Potential To investigate whether circRNAs have the potential for translation in the depolarized cells, we To investigate whether circRNAs have the potential for translation in the depolarized cells, we sequenced ribosome protected RNA fragments (RPF) by Ribo-Seq and quantified the back-splice sequenced ribosome protected RNA fragments (RPF) by Ribo-Seq and quantified the back-splice junction spanning reads. Interestingly, we found 173 unique circRNAs, suggesting there was some junctionevidence spanning of active reads. circRNA Interestingly, translation we(Figure found 8A; 173 Table unique S7). The circRNAs, majority suggestingof the RPF back-spliced there was some evidencereads oforiginated active circRNA from circRNAs translation with medium (Figure8 orA; low Table abundance, S7). The not majority highly ofexpressed the RPF circRNAs, back-spliced readsindicating originated that fromthe detected circRNAs circRNA with reads medium were or not low due abundance, to random binding. not highly In order expressed to get insight circRNAs, indicatinginto the that genomic the detected composition circRNA of the readsribosome-associated were not due circRNAs to random (ribo-circRNAs), binding. In order we performed to get insight intoannotation the genomic and composition interestingly of found the ribosome-associated a significant difference circRNAs compared (ribo-circRNAs), to non-translated we circRNAs performed annotationfrom RNAseq and interestingly (p = 1  10−15 found); 43% aof significant ribo-circRNAs diff erencewere spliced compared from tothe non-translated 5-UTR region circRNAsof the host from RNAseqgenes, (p whereas= 1 10 the15 non-translated); 43% of ribo-circRNAs circRNAs were were mainly spliced derived from thefrom 5 CDS,-UTR with region only of 14% the spliced host genes, × − 0 whereas the non-translated circRNAs were mainly derived from CDS, with only 14% spliced from the 50-UTR region of the host genes (Figure8B). Next, we examined whether ribo-circRNAs could potentially encode proteins by predicting highly reliable coding sequence within the exon regions of Cells 2019, 8, x 14 of 19

′ fromCells 2020 the, 9 5, 25-UTR region of the host genes (Figure 8B). Next, we examined whether ribo-circRNAs14 of 19 could potentially encode proteins by predicting highly reliable coding sequence within the exon regions of the circRNAs. The results showed that 47 ribo-circRNAs had potential to be translated into athe protein circRNAs. (Table The S7), results the majority showed of thatwhich 47 ribo-circRNAswere in frame and had partially potential overlapped to be translated with intothe host a protein gene protein.(Table S7), Approximately the majority of62% which of thes weree putative in frame circRNAs and partially derived overlapped proteins withhad at the least host one gene conserved protein. domain,Approximately including 62% circ-FOXO3, of these putative circ-ARNT2, circRNAs circ-PTP derivedRG, proteins and several had others, at least whose one conserved genes have domain, been implicatedincluding circ-FOXO3, in synaptic circ-ARNT2, function (Table circ-PTPRG, S7), sugg andesting several they others, could whose support genes some have neural been implicated function. Interestingly,in synaptic function nine ribo-circRNAs (Table S7), suggesting were derived they could from support the genes some neuralwhose function.circRNAs Interestingly, were also differentiallynine ribo-circRNAs expressed were after derived depolarization. from the genes We co whosempared circRNAs the ribo-circRNA were also profiles differentially (normalized expressed by theafter total depolarization. mapped reads) We with compared depolarized the ribo-circRNA and resting profiles samples (normalized and found bythere the were total more mapped circRNA reads) readswith depolarizedin depolarized and cells resting (p = 7 samples × 10−10). and The foundtop three there circRNAs were more displaying circRNA active reads translation in depolarized included cells (p = 7 10 10). The top three circRNAs displaying active translation included circ-MTOR, circ-MRPL38, circ-MTOR,× − circ-MRPL38, and circ-KSR2, which were relatively enriched in ribose reads observed in depolarizedand circ-KSR2, cells. which were relatively enriched in ribose reads observed in depolarized cells.

A B

FigureFigure 8.8. AnalysisAnalysis of of ribosome-associated ribosome-associated circRNAs. circRNAs (A). The (A) number The number of ribosome-associated of ribosome-associated circRNAs circRNAs(ribo-circRNAs) (ribo-circRNAs) and their and back-spliced their back-spliced reads detected reads detected in ribosome in ribosome profiling. profiling. (B) Annotation (B) Annotation of the ofribo-circRNAs the ribo-circRNAs and their and comparison their comparison with nonribo with nonribo (non-translated) (non-translated circRNAs) circRNAs from RNAseq. from RNAseq.

4.4. Discussion Discussion InIn this this study, we investigated genome-wide circRNA expression in in differentiated differentiated human neuroblastsneuroblasts and and found found a a remarkable diversity diversity of of species species with with more more than than 28,000 28,000 distinct distinct molecules, molecules, moremore than than half half of of which which had had not not previously previously been been re reportedported in in the the circBase circBase [44]. [44 ].Notably, Notably, there there was was a significanta significant positive positive correlation correlation between between the the number number of ofcircRNA circRNA spliced spliced per per gene gene and and thethe level level of circRNAof circRNA expression. expression. Our Our differenti differentialal analysis analysis of of these these molecules molecules after after neuronal neuronal depolarization suggestedsuggested there there is is significant significant alteration alteration of of circRNA circRNA expression expression in in depolarized depolarized cells cells compared compared to to their their restingresting state state and and perhaps perhaps a a role fo forr circRNA in activity-associated intracellular intracellular signaling signaling processes. processes. These observationsobservations build build on ouron previousour previous investigation investigation of miRNA–mRNA of miRNA–mRNA interaction ininteraction differentiated in differentiatedhuman neuroblast human cells, neuroblast where significant cells, where changes sign wereificant observed changes after were single observed and multiple after depolarizing single and multiplestimuli [ 47depolarizing]. stimuli [47]. RecentRecent evidenceevidence suggests suggests that that circRNAs circRNAs can can modulate modulate transcription transcription and fine and tune fine miRNA tune functionmiRNA functionthrough spongingthrough sponging [5]. Therefore, [5]. Therefore, to understand to un thederstand mechanism the ofmechanism circRNA action of circRNA in the depolarization action in the depolarizationprocess, we profiled process, the expressionwe profiled of the miRNA expressi andon mRNA of miRNA in our and samples mRNA alongside in our samples circRNAand alongside found circRNAandthese molecules found were substantiallythese molecules regulated were insubstantially response to neuronalregulated depolarization. in response Analysis to neuronal of the depolarization.circRNAs–miRNA–mRNA Analysis of associationthe circRNAs–miRNA–mRNA network revealed the association potential for network a large numberrevealed of the interactions potential forbetween a large the number differentially of interactions expressed RNAs.between Prediction the differentially of the central expressed regulatory RNAs. elements Prediction suggested of thatthe central15 circRNAs regulatory were potentialelements hubs suggested in the network.that 15 circ ThreeRNAs circRNAs were potential including hubs circ-RANBP17, in the network. circ-POGZ, Three circRNAsand circ-BARD1 including were circ-RANBP17, found to be top regulatorycirc-POGZ, hubs, and suggestingcirc-BARD1 substantial were found roles to for be these top circRNAsregulatory in hubs,the intracellular suggesting signaling substantial associated roles for with these neuronal circRNAs excitation. in the Inintracellular addition to signaling these circRNAs, associated we found with neuronalCDR1as toexcitation. be a potential In addition regulator, to these as functional circRNAs, studies we found have robustlyCDR1as reportedto be a potential this circRNA regulator, to bind as miR-7-5, through which many target genes are regulated [14,45]. CDR1as-deficient brains showed Cells 2020, 9, 25 15 of 19 downregulated miR-7, suggesting an autoregulatory mechanism. This resulted in a dysfunction of synaptic transmission and behavior of mice model by dysregulating miR-7 target genes [14]. Here, we found miR-7-5p to be the third most significant miRNA with increased expression along with CDR1as, which showed a significant upregulation trend in depolarized cells. Also, miR-7 validated gene targets such as PAX6, TET2, and XIAP in addition to many predicted targets, such as ESRRG, RSBN1, and STOX2, were significantly decreased. This finding suggests that the CDR1as/miR-7/target gene is a key regulatory axis in neuronal depolarization. Activity-associated changes in the CDR1as network observed in our study are now well established in the literature [11–13] and provide a useful reference or positive control to support the existence of novel circRNA-based ceRNA networks that were also apparent in our data. To further explore the functional significance of one of these molecules, we profiled the molecular consequences after siRNA knockdown. Circ-EXOC6B loss-of-function resulted in a significant change in the expression levels of many genes. Bioinformatics analysis showed this expression change could be partly mediated by miRNAs targeted by the circ-EXOC6B, and supported a causal role for circRNA in the regulation of this network. Gene enrichment analysis showed these genes are responsible for cellular processes related to the function of the EXOC68 gene and its family. This gene encodes a homolog in Saccharomyces cerevisiae known as SEC15, which serves as a component of the exocyst, an octameric protein complex involved in directing exocytic vesicles to specific sites on the plasma membrane and mediating tethering to the membrane prior to fusion. It is involved in numerous cell processes, including cell migration, cell growth, morphogenesis, cytokinesis, cell polarity, and primary ciliogenesis [48]. SEC15 is essential for localization of specific cell adhesion and signaling molecules required for establishment of synaptic specificity in Drosophila [49]. This gene promotes notch signaling and is associated with proper neuronal fate specification by mediating specific vesicle trafficking [50]. Disruption of EXOC6B was reported in a patient with intellectual disability, epilepsy, and behavioral features resembling autism [46]. It has been shown that circRNAs with retained can induce expression of their target genes by RNA polymerase II to the region of the target genes [16]. CircRNAs can also regulate the expression of the linear counterparts by serving as competitors for translation, leading their cognate linear isoforms to escape translation, as in the case of circ-HIPK3, circ-DMD, and circ-FMN [5]. The level of circ-MBL modulates splicing of the linear transcript in favor of the circular by forming an autoregulatory loop [18]. These evidence together with our findings suggest that the circRNA regulatory role is likely related to the cognate mRNA function. We also provided evidence of potential translation of circRNAs by ribosome profiling. Analysis of Ribo-Seq reads identified a number of circRNAs, many of which contained strong ORF sequence, enabling them to potentially produce polypeptides of various lengths. Interestingly, we observed a strong enrichment of the 50-UTR region in the ribo-circRNAs, which is reasonable in the view of the significant role of this region in initiation and regulation of cellular translation through various mechanisms, such as ribosome complex binding motifs, IRES regions, and interacting trans-acting factors [51]. Moreover, we found many putative ribo-circRNAs proteins had at least one conserved domain, suggesting these may generate proteins with biological functions. Comparison of the ribo- circRNA profiles between depolarized and resting samples established that there were more circRNA reads in depolarized cells, suggesting that, if these circRNA are translated, they may serve some purpose in synaptic plasticity. In support of this hypothesis, several polypeptides were predicted to be generated from circRNA templates, such as MTOR, which is a critical component for synaptic plasticity, learning, and memory [52] as well as for translation of potassium channels in dendrites and neuronal membrane potential [53]. Translation of endogenous circRNAs was reported previously; for example, Pamudurti et al. indicated that a group of circRNAs encode proteins in fly heads that contain specific protein domains [19]. They revealed that circ-Mbl encodes a protein that was detected by mass spectrometry and was found to be enriched in synaptosomes of fly heads. A novel protein derived from circ-SHPRH was also found to be abundantly expressed in human brains and dysregulated in glioblastoma [21]. The circ-SHPRH protected the host protein from degradation, thus increasing Cells 2020, 9, 25 16 of 19 the tumor suppressive activity of the gene. Similarly, circ-ZNF609 was shown to be translated in murine and human myoblasts, and the protein was suggested to modulate myoblast proliferation in response to stress conditions [20]. Other circRNAs such as circ-FBXW7 and circ-LINC-PINT were reported to be translated in brain tumors, suggesting that additional coding circRNAs have yet to be discovered. However, the study of circRNA translation is difficult due to the limitations of circRNA identification that exclusively focus on back-spliced events, which cover only a small proportion of circRNA transcripts. This results in detecting a low number of spliced RFP reads, preventing comprehensive analysis of these molecules in translation. In summary, we provided evidence of circRNAs association with neuronal activation by showing their differential expression in depolarization of human neuroblasts. This was combined with a global change in the abundance of miRNA and mRNA in depolarized cells. In many cases, these circRNAs were altered after depolarization in accordance with their cognate mRNAs and bioinformatically associated by miRNA, supporting their action as miRNA sponges. This kind of interaction was exemplified for circ-EXOC6B by siRNA-induced loss-of-function, which demonstrated a causal relationship between circRNA specific regulation of its gene interaction network with functional significance to neural activity. Finally, we found evidence that suggests some circRNAs may serve as templates for active translation, particularly in depolarized neuroblasts.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4409/9/1/25/s1, Figure S1: Correlation between the number of circRNAs per gene and their average expression in the neuroblastoma., Figure S2: Validation of the identified circRNAs in neuroblastoma using q-PCR., Figure S3: Correlation between the change of circRNA and linear isoforms in response to depolarization, Figure S4: CircRNA circ-DROSHA loss-of-function analysis, Table S1: List of all the identified circRNAs in neuroblastoma, Table S2: List of differentially expressed circRNAs, Table S3: List of differentially expressed miRNAs and mRNA, Table S4: All the circRNA interactions with miRNA and mRNA., Table S5: List of differentially expressed genes for the EXOC6B KD experiment, Table S6: List of RT-qPCR divergent primers for circRNA validation, Table S7: List of ribo-circRNAs. Author Contributions: Conceptualization, formal analysis, visualization, validation and first draft preparation, E.M.; methodology and software, E.M. and D.K.; investigation, C.F.; data curation, E.M. and D.K.; writing—review and editing, M.J.C., D.K. and C.F.; project conceptualization, supervision, and funding, M.J.C. All authors read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by The National Health and Medical Research Council (NHMRC) project grants (1067137 and 1147644). This study was also supported by a University of Newcastle RHD scholarship provided for E.M. and D.K. Murray Cairns is supported by an NHMRC Senior Research Fellowship (APP1121474). Acknowledgments: We thank the University of Newcastle and HMRI staff for providing the laboratory facilities and equipment. Conflicts of Interest: The authors declare no conflict of interest.

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