Introduction

MicroRNAs (miRNAs) are ubiquitous non-protein coding short RNA molecules (18-26 nucleotides) which play an important role in the post-transcriptional regulation of gene expression [1,2]. They act via binding to the 3’ UTR region of the target mRNAs, resulting in mRNA degradation or translation inhibition [3]. The expression of miRNA is under tight regulation in specific tissues and developmental stages in mammals [4], flies [5], worms [6] and frogs [7]. miRNAs are involved in the control of diverse processes including animal development and growth [8], signal transduction [9], disease [10,11] and virus-induced immune defence [12,13]. The genes encoding miRNA are initially transcribed by RNA polymerase II to generate primary miRNAs (pri-miRNAs). These pri-miRNAs are processed to release miRNA precursors (pre-miRNAs) approximately 70 nt in length with characteristic hairpin structures. Pre-miRNAs are then exported from the nucleus to the cytoplasm. The pre-miRNA hairpin is then cleaved to generate a double-stranded miRNA duplex with a characteristic 3’ two nucleotide overhang. Subsequently, the double-stranded miRNA duplex is separated and one strand is selected as the mature miRNA, whereas most of the other strand, termed mature star-sequence, is degraded [14].

Before the advent of high-throughput sequencing technology the number of known miRNAs was limited to approximately 100, discovered by laborious molecular cloning and Sanger sequencing methods. More recent studies have demonstrated that high-throughput sequencing can be applied to successfully discover even low abundance miRNAs in different species [15,16]. A single mature miRNA can bind to multiple different mRNA transcripts (up to 200 genes) while each mRNA can have recognition sites for more than one miRNA [17]. Over 30% of the human protein-coding genes are believed to be negatively regulated by miRNA [18,19], which highlights the significance of their role in transcriptional and post-transcriptional regulation of gene expression [2,3].

Across all species, in excess of 25,000 mature miRNAs have been reported and deposited in miRBase [20]. The most recent release (release 19, August 2012) contains 2,042 mature miRNAs from Homo sapiens and 368 mature miRNA from Caenorhabditis elegans (nematode). Fish species are under-represented, with data available from Danio rerio (zebrafish; 247 mature miRNAs), Oryzias latipes (medaka; 147 mature miRNAs), Cyprinus carpio (common carp; 146 mature miRNAs), Tetraodon nigroviridis (Tetraodon; 109 mature miRNAs), Fugu rubripes (fugu; 108 mature miRNAs), Paralichthys olivaceus (olive flounder; 38 mature miRNAs) and Hippoglossus hippoglossus (Atlantic halibut; 1 mature miRNA).

Salmo salar (Atlantic salmon; family Salmonidae) is an economically important species for both wild fisheries and aquaculture production. Genomic resources for the species are advanced compared to other farmed fish, but lag behind terrestrial livestock and model species. The S. salar genome is in the process of being sequenced and assembled [21] and a first draft of the genome sequences is available (NCBI Assembly GCA_000233375.1). The salmonid genome is derived from an ancestral whole-genome duplication event that occurred between 25 and 100 million years ago, and is considered to exhibit ‘residual tetraploidy’ [22]. Large regions of chromosome homeology both complicate genomic analyses and provide an interesting model for the study of many facets of re-diploidisation following a whole genome duplication event. One interesting aspect concerns the role that miRNAs may play in the control of duplicate gene expression and subsequent downstream effects on the phenotype. To date experimental exploration of miRNAs in salmonids has been confined to Oncorhynchus mykiss (rainbow trout) where 210 miRNAs were cloned and sequenced from various tissues [23] and more recently, high throughput sequencing has been used [24] to catalogue 496 miRNAs from unfertilised ova. Information for Atlantic salmon is more sparse, with just two recent in silico interrogations of publically available EST sequences being published, reporting computational predictions for 307 [25] and 102 [26] putative miRNAs.

The aim of the current study was to discover S. salar miRNAs empirically and to begin to investigate their functional roles. The primary objectives were to sequence and characterise an extensive miRNA repertoire and to profile a reference expression level in S. salar fry. Identifying miRNA targets is an important step in studying miRNA functions. Gene targets have conserved perfect or near-perfect complementary sites to miRNAs [2,27]. Based on sequence complementarity between miRNAs and mRNAs, computational approaches can be used as a powerful strategy to predict miRNA targets [2,28,29]. Consequently, a secondary objective of this study was the in silico prediction of miRNA targets from the S. salar transcriptome. This miRNA discovery and characterisation phase is a first step to understanding the role of miRNA in regulating gene expression and downstream biological processes in S. salar.

Results

De-novo identification of miRNAs

Small RNA transcriptomes from 18 S. salar fry were analysed using high-throughput sequencing (data made available through the NCBI BioProject accession number SRP017393). In total, 354,505,167 paired-ended reads (37 nucleotides long) were obtained from the 18 homogenised whole S. salar fry. Low quality reads, reads with ambiguous bases, and non-identical paired-end reads were discarded (6.83%). For the remaining 330,292,464 reads each unique sequence (5-37 nt long) and its count number were generated, with 2,477,426 unique sequences being identified (Figure 1A Table S1). Sequences between 16 and 28 nt long (1,374,027 sequences, 55% of total representing 76% of the read counts) were matched against S. salar genome assembly (NCBI Assembly GCA_000233375.1) using miRanalyzer [30], to identify potential miRNA genes. This produced 11,803 predicted pri-miRNAs with read counts ranging between 1 and 56,087,898 (Figure 1B).

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Figure 1. Small RNA and candidate miRNAs read number distributions.

(A) Size distribution of the captured small RNA. Low quality reads or reads with ambiguous bases were discarded. Each sequence (from 5 to 37 nt long) and its count number were generated. 2,477,426 unique sequences were identified. Only the sequences between 16 and 28 nt were subsequently used. (B) Read number distribution of the 11,803 putative pri-miRNAs predicted my miRanalyzer.

https://doi.org/10.1371/journal.pone.0070136.g001

A total of 946 candidate pri-miRNAs with read counts greater than 10 reads per sample (see criteria from [20]) were filtered using RepeatMasker [31] to identify and remove repeats and low complexity sequences (Tables 1 & S1). Following this, 888 robust pri-miRNAs were retained for further analysis. Collectively these filtered sequences accounted for 72.4% of the read count (sequences between 16 and 28 nt long) and had a median read count of 5,007.

RepeatMasker elements	Number of occurrences
Retro-elements
SINEs	2
LINEs: L2/CR1/Rex	1
LTR elements: Gypsy/DIRS1	5
DNA transposons
hobo-Activator	2
Tc1-IS630-Pogo	2
Tourist/Harbinger	1
Small RNA (tRNA)	39
Simple repeats	6

Table 1. Repeat elements identified using the repeat masker.

The final dataset of putative miRNA was depleted of 58 candidate repetitive elements (details presented Table S1).

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miRNA abundance and genomic distribution

The putative gene copy number for each miRNA was estimated by alignment of candidate miRNA sequences with the S. salar draft reference genome. Of the 888 pri-miRNA genes, there were 122 pairs of identical duplicates, i.e., 16% of the total. The 888 pri-miRNA sequences clustered into 453 families (miRNA with similar mature miRNA, as defined by the Rfam database [32]). The miRNA families exhibited a wide range of gene copy number with let-7 (26 gene copies), miR-30 (18 gene copies), and miR-181 (13 gene copies) having the most copies in the S. salar draft genome assembly (Figure 2A).

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Figure 2. Pie charts of the novel miRNA abundance.

(A) Distribution of the miRNA gene number by family. The top 17 families (over 6 genes) represents 19.5% of all the miRNA genes. All the “others” (6 genes and under) represents overall 80.5% of all the miRNA genes and less than 0.7% individually. (B) Distribution of the miRNA abundance by miRNA family. The top 15 miRNA families (relative expression over 0.5%) represents 94.3% of the expressed miRNA. All the “others” (miRNA representing individually 0.5% of the global abundance and under) represents only 5.7% of the overall expressed miRNA.

https://doi.org/10.1371/journal.pone.0070136.g002

Three miRNAs families predominated: miR-181 (37.9% of total miRNA transcripts), miR-10 (35.0%) and miR-26 (5.9%). Detected gene copy numbers for these miRNAs were 13, 8 and 10 respectively (compared to let-7 at 1.9% of total miRNA transcripts and miR-30 at 1.1%, Figure 2B Table S3). miRNAs with fewer than seven gene copies (n = 715) represented only 15.4% of the miRNA abundance in S. salar. The miRNA library was not normalised, thus providing the opportunity to compare gene copy number with transcript frequency. To assess the correlation between putative gene copy number and miRNA family abundance (Table S3), a Spearman’s rank correlation coefficient was calculated between the two variables. There was a positive correlation between gene copy number and miRNA family abundance (Spearman’s Rho = 0.64, P < 0.001) indicating that miRNA families with multiple gene copies tend to have higher overall expression levels. The distributions of the miRNA family expression among the 18 samples were compared using DESeq [33]; no significant variation of expression was detected among either S. salar families or individuals.

Discussion

In the current study, a detailed characterisation of the miRNA profile of S. salar fry was performed and their potential target genes assessed. Unlike most miRNA sequencing studies where single pass reads have been generated, paired-end sequencing was used to improve read accuracy, aiding reliable discrimination between miRNAs differing by only one or two nucleotides. Guidelines for miRNA discrimination derived from RNA deep-sequencing data were established in 2011 [20]. All 888 pri-miRNAs identified in this study can be classed as high-confidence miRNAs according to their criteria.

From a comparison with miRBase it would appear that only 51.5% of the identified mature miRNAs have been previously reported. This is likely to be a reflection of the comparative under-representation of fishes in the current database and stage / state specific expression rather than an indication that the remaining sequences are necessarily unique to S. salar. Lack of detectable differential miRNA expression among individuals and families, reared in different tanks, suggests either that the detected miRNA profile is constitutively expressed in a conserved manner or that responses are relatively uniform between individuals in this study.

While 453 miRNA families were identified in the current experiment, just three of these accounted for approximately 78.8% of the miRNA abundance; namely miR-181 (37.9%), miR-10 (35.0%) and miR-26 (5.9%). This likely reflects the nature of the biological samples, sequences being derived from whole fry homogenates leading to overrepresentation of miRNAs from the more abundant tissues. Given the young age of the salmon fry in this experiment (approximately 4 months post-hatching), the fry are fast growing with the predominant tissue being skeletal muscle. In O. mykiss eggs, Ma et al. [24] identified let-7, miR-21 and miR-24 as the most abundant miRNA with 24.06%, 18.71% and 6.59% respectively. Whereas in O. mykiss pooled tissues [23], miR-21, miR-125 and miR 204 were the most abundant miRNA with 35%, 14% and 10% respectively. In the current study, let-7 and miR-21 are also among the most highly expressed miRNAs with 1.9% and 1.1% of the relative abundance respectively.

All of the three most abundant S. salar miRNAs have been found to be involved in regulation of developmental processes, these observations being conserved across several species. miR-181 regulates haematopoiesis development and plays a role in T-lymphocyte maturation and the sensitivity of lymphocytes to T-cell receptor stimulation [37]. miR-10 regulates the highly-conserved transcription factor Hox genes which have highly conserved roles in early development [38]. In most vertebrates studied to date, the miR-10 genes co-locate with the Hox cluster and, in D. rerio, loss of function of miR-10 points to a role in anterior–posterior patterning [39]. Finally, miR-26 contributes to myogenesis [40] and neurogenesis [41]. miR-26 expression has been suggested to be involved in O. mykiss embryonic development and Ramachandra et al. [42] suggested a possible link between the miR-26 response to hypoxia and the short supply of dissolved oxygen in the aquatic environment where salmonid eggs develop [42].

The S. salar miRNAs identified in this study may have applications for both fundamental and applied research. For example, they can be assessed as potential markers for specific functional and diagnostic applications in S. salar aquaculture production. Polymorphism in the miRNA genes or their target sites may underlie phenotypic variation in quantitative traits (e.g., fillet characteristics or disease resistance) and could be assessed for use in selective breeding programs to improve the efficiency of salmon production Where the precise function of a miRNA is known, artificial miRNAs may be used to suppress gene expression for the knockdown of targeted genes [2] as a potentially useful tool in salmonid research. Therefore, these miRNAs provide a platform for the elucidation of gene function via a series of hypothesis-driven studies in Atlantic salmon for aquaculture and biomedical research. This functional approach has already proven productive in the study of the role of hepatic miRNAs in insulin regulation pathways in O. mykiss [43].

Conclusion

We have discovered an extensive repertoire of putative miRNA in Atlantic salmon, quantified their expression levels in basal conditions in fry and identified the location and putative copy number of the miRNA genes in the draft Atlantic salmon reference genome sequence. These S. salar miRNAs provide a novel resource to advance functional genome research in salmonid species.

Materials and Methods

Supporting Information

Acknowledgments

We thank Dr Michael Hackenberg for his help with miRanalyzer web-server, Dr David Verner-Jeffreys, Dr Richard Paley, Dr Georgina Rimmer and Mr Ian Tew for assistance with fish maintenance and sampling, and Mr Richard Talbot and Mrs Karen Troup for assistance with the miRNA library preparation and sequencing at ARK-Genomics.