- Research article
- Open Access
Expansion of ruminant-specific microRNAs shapes target gene expression divergence between ruminant and non-ruminant species
© Bao et al.; licensee BioMed Central Ltd. 2013
- Received: 19 March 2013
- Accepted: 6 September 2013
- Published: 10 September 2013
Understanding how species-specific microRNAs (miRNAs) contribute to species-specific phenotypes is a central topic in biology. This study aimed to elucidate the role of ruminant-specific miRNAs in shaping mRNA expression divergence between ruminant and non-ruminant species.
We analyzed miRNA and mRNA transcriptomes generated by Illumina sequencing from whole blood samples of cattle and a closely related non-ruminant species, pig. We found evidence of expansion of cattle-specific miRNAs by analyzing miRNA conservation among 57 vertebrate species. The emergence of cattle-specific miRNAs was accompanied by accelerated sequence evolution at their target sites. Further, the target genes of cattle-specific miRNAs show markedly reduced expression compared to their pig and human orthologues. We found that target genes with conserved or non-conserved target sites of cattle-specific miRNAs exhibit reduced expression. One of the significantly enriched KEGG pathway terms for the target genes of the cattle-specific miRNAs is the insulin signalling pathway, raising the possibility that some of these miRNAs may modulate insulin resistance in ruminants.
We provide evidence of rapid miRNA-mediated regulatory evolution in the ruminant lineage. Cattle-specific miRNAs play an important role in shaping gene expression divergence between ruminant and non-ruminant species, by influencing the expression of targets genes through both conserved and cattle-specific target sites.
- Target Site
- Insulin Signalling Pathway
- Gene Expression Divergence
- Predict miRNA Target
- miRNA Repertoire
It has long been hypothesized that differences in gene expression contribute extensively to phenotypic differences among species[1, 2]. Numerous studies have investigated the effects of cis-acting elements and trans-acting proteins on gene expression divergence[3–5]. A recently discovered class of regulatory RNA molecules called miRNAs is known to play an important role in gene expression. It is now predicted that nearly 50% of mammalian mRNAs are regulated at the translational level by miRNAs[6, 7]. Many miRNAs exhibiting both broad sequence and expression conservation among animal species have been identified[8, 9]. However, recent high-throughput small RNA sequencing and comparative genomic studies have led to the discovery of a large number of miRNAs with limited species conservation[10–17]. Gene expression regulation by these miRNAs, some of which may be species-specific, may be one of the important mechanisms behind some of the expression and phenotype divergence observed among species.
In this study, we aimed to investigate how species-specific miRNAs drive gene expression divergence by identifying cattle-specific miRNAs and characterizing their contribution to cattle-specific gene expression divergence using Illumina sequencing and comparative genomics. Dramatic physiological and phenotypic differences exist between ruminant and non-ruminant mammalian species. For example, volatile fatty acids produced as by-products of the microbial fermentation in the rumen are used as the major source of energy in ruminants as opposed to glucose absorbed from the small intestine in non-ruminants. Because of this difference in nutrient usage, ruminants are less sensitive to insulin than non-ruminants. Several major genes involved in the insulin pathway, including INSR, GLUT1, GLUT4 and PI3K, show decreased expression in ruminants compared to non-ruminants[19–21].
Profiling of miRNAs from the whole blood of cattle and pigs
Numbers of miRNAs identified from cattle and pigs
Expansion and diversification of cattle-specific miRNAs
Conservation of cattle miRNAs across vertebrate species
Target sites of cattle-specific miRNAs show accelerated sequence evolution
Targets of cattle-specific and non-cattle-specific miRNAs
Predicted target type
Decreased expression of the target genes of cattle-specific miRNAs
We next looked at the magnitude of expression reduction of targets of highly expressed miRNAs compared to genome-wide background. We only looked at the genes with fold change between pig and cattle greater than 1.2. The targets of the highly expressed cattle-specific miRNAs (median logFC = −1.73) showed significantly more reduction (p = 0.0065, Mann–Whitney U test) than the genome-wide background (median logFC = −1.60) (Figure 4C). Thus the target genes of highly expressed cattle-specific miRNAs showed 8% ((1.73-1.60)/1.60) greater expression reduction compared to the genome-wide background. Dividing the targets of the highly expressed cattle-specific miRNAs into those having cattle-specific or conserved target sites, we found that the degree of reduction in expression for both types (median logFC = −1.71 and −1.77 respectively) was significantly greater (p = 0.052 and 0.012 respectively, Mann–Whitney U test) than the genome-wide background (Figure 4D). The reduction in expression of target genes with conserved target sites of the highly expressed cattle-specific miRNAs was not significantly different from the target genes with cattle-specific targets (p > 0.05, Mann–Whitney U test).
Functional enrichment of target genes of cattle-specific miRNAs
In order to assess the biological effects of the 33 highly expressed cattle-specific miRNAs, we looked for enriched biological pathways among the genes they target. We examined KEGG pathway enrichment for targets expressed in blood (n = 1708) and for expressed targets with reduced expression in cattle compared to pigs (n = 856) and human (n = 754). Because miRNAs expressed in blood can target genes in other tissues, we also looked at all predicted targets (n = 3255) irrespective of their expression in blood. Although most of the KEGG pathways found to be enriched (p < 0.05 and gene count > =3) did not show an obvious relationship to cattle-specific functions (Additional file6), the insulin signalling pathway, which is known to contribute to metabolic differences between ruminants and non-ruminants, is enriched in targets expressed in blood (p = 0.044) and in expressed targets with reduced expression in cattle compared to human (p = 0.032) but not pig (p = 0.165). The insulin signalling pathway showed a p-value of 0.059 when considering all predicted targets of the 33 highly expressed cattle-specific miRNAs. Notably, cattle-specific miRNAs may target some of the key annotated insulin signalling pathway genes, including AKT3, CBLB, FOXO1 and PIK3R5 (all show reduced expression in cattle compared to human and pig).
In this study, 23% of the miRNAs identified from cattle whole blood are found to have no homologs in 57 other vertebrate species examined. Based on this set of cattle-specific miRNAs, we can provide an estimate of the net gain rate of new miRNAs during cattle evolution. Given the estimated 64.5 Myr (million years) divergence time between cattle and pig and the 71 cattle-specific miRNAs we identified, the net gain rate of miRNAs expressed in blood is estimated as 1.1 miRNAs per Myr. This is about twice the rate of that observed in pigs (0.6 miRNAs per Myr). One of the most interesting cases is the bta-mir-2284 family, which has 24 members. Why does the cattle genome maintain so many members in this family? The abundant miRNA seeds generated by seed shifting and point mutation in this family indicate that the emergence of novel miRNAs may have led to adaptive functional diversification. However, the number of unique seeds is much less than the number of paralogues and many miRNA members share the same seed sequence, suggesting that dosage effect might be also important for the function of mir-2284 family.
It has long been hypothesized that gene expression changes are one of the main underlying causes of phenotypic differences between species[1, 2]. While divergence in cis-acting elements and trans-acting proteins has been shown to underlie evolutionary divergence[3, 4], relatively little is known about the role of miRNAs in shaping gene expression divergence. Here we showed that both the proportion of genes with decreased expression and the degree of expression reduction (relative to their pig and human orthologues) are higher for targets of cattle-specific miRNAs compared to genome-wide background. The target genes of cattle-specific miRNAs might have been under selection for decreased expression, which has been achieved by several means, one of them being cattle-specific miRNAs. However, the fact that the target genes of highly expressed cattle-specific miRNAs show a greater reduction in expression than those of the cattle-specific miRNAs expressed at low levels (Additional file7) further implicates miRNAs as the major player in shaping the expression patterns of these genes, as opposed to other factors. Functionally, cattle-specific miRNAs might be associated with the insulin signalling pathway, and thus potentially have a role in the evolution of insulin resistance in ruminants. It would be worthwhile to analyse how species-specific miRNAs modulate target gene expression divergence across other model animal species for species-specific functions.
In this study, we found more target genes with cattle-specific target sites for cattle-specific miRNAs than for non-cattle-specific miRNAs and we observed accelerated sequence evolution of target sites of cattle-specific miRNAs. This accelerated evolution suggests that selection might have favoured the formation of new target sites. Previous studies have primarily focused on conserved target sites but our findings suggest that the non-conserved targets may represent novel mechanisms of genetic regulation that can contribute to species-specific phenotype. Based on target gene expression analyses, we showed that cattle-specific miRNAs have effects on targets genes of both types: those with conserved targets sites and those with cattle-specific target sites. Thus these miRNAs may fine tune pre-existing regulatory mechanisms as well as contribute to the formation of new regulatory mechanisms.
We provide evidence of rapid miRNA-mediated regulatory evolution in the ruminant lineage. Cattle-specific miRNAs play an important role in shaping gene expression divergence between ruminant and non-ruminant species, by influencing the expression of target genes with either conserved or cattle-specific target sites. One interesting potential role for these miRNAs is to increase insulin resistance in ruminants by targeting insulin signalling.
Sample collection and RNA preparation
Three cattle and three pig blood samples were used for miRNA sequencing. One pooled sample from seven cattle blood samples and three separate pig blood samples were used for mRNA sequencing. Peripheral whole blood (approximately 2.5 mL) was collected from the jugular vein of cattle and pigs, into PAXgene Blood RNA tubes (BD, Cat. No. 762165) and processed according to the manufacturer’s instructions. Total RNA was extracted from 4.5 – 9.0 ml of solution from the PAXgene Blood RNA tubes using the PreAnalytiX kit (Qiagen, Cat. No. 763134). The quality and quantity of the RNA were determined using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA). The animal study was approved by the Animal Care and Use Committee of the University of Alberta (Guan 019).
Library construction and Illumina sequencing
Total RNA (1.5 μg for each sample) was used to construct miRNA and mRNA libraries using the TruSeq Small RNA and mRNA Sample Preparation Kit (Illumina, San Diego, CA) according to the manufacturer’s instructions. PCR amplification was performed for 12 cycles. Library quality for miRNA and mRNA libraries was determined using the High Sensitivity DNA Chip and an Agilent 2100 Bioanalyzer (Agilent Technologies). qRT-PCR was then performed for library quantification using the StepOneTM Real-Time PCR System (Applied Biosystems, Carlsbad, CA) with the KAPA SYBR ® Fast ABI Prism qPCR kit (Kapa Biosystems, Woburn, MA).
The individual libraries were adjusted to 2 nM concentrations and pooled before denaturation and dilution according to Illumina’s instructions. The diluted libraries (8 – 10 pM) were loaded on a cBot (Illumina) for cluster generation using the TruSeq™ SR Cluster Kit v3 (Illumina). Sequencing was performed on the HiScan SQ system (Illumina) using the TruSeq™ SBS Kit v3 (50 cycels, Illumina). Real-time analysis and base calling was performed using the HiSeq Control Software version 1.4.8 (Illumina).
Identification and quantification of known and novel miRNAs
Sequence reads with base quality scores were produced by the Illumina sequencer. Reads flagged as low-quality by CASAVA 1.8 were removed. After trimming the 3’ adaptor sequence, all sequences ranging in length from 18–26 nt were recorded in a non-redundant file along with copy number. Further analyses were performed using miRDeep2[22, 28] and custom Perl scripts. All tags were compared to the Rfam database of RNA families[29, 30] to filter out non-miRNA sequences. To identify known miRNAs, the miRNA tags of the six samples were aligned against miRNA precursor sequences reported in the miRNA database ‘miRBase’ (release 18)[31–34] using the ‘quantifier.pl’ script (with the default parameters) within miRDeep2. Candidates for novel miRNAs were identified by miRNA precursor prediction within miRDeep2. For novel miRNA prediction, the following criteria as recommended by the authors of miRDeep2 were chosen: miRDeep score cutoff of 5, which is estimated by miRDeep2 to yield a true positive rate > 90% and a signal-to-noise ratio >10. Each sample was processed separately and the results for each species were combined by genomic location. We considered only those miRNAs with read counts greater than five in all three samples as being expressed. The read counts per miRNA in each sample were normalized to counts per million mapped reads (cpm).
Identification of miRNA homologs
A three-step procedure was used to find homologs of the known and novel cattle miRNAs identified. First, the mature sequences of the cattle miRNAs were aligned against the genomes of 57 vertebrate species with good quality assembled genomes available in the Ensembl database version 71 (http://www.ensembl.org/index.html) using the short read aligner software Bowtie. The hairpin (precursor) sequences of the cattle miRNAs with mature sequences that showed no more than two mismatches in the previous alignment step were then selected for a second round of alignments against the 57 vertebrate genomes. The nucleotide blast program (blastn) of NCBI’s ‘Basic Local Alignment Search Tool’ (BLAST) was used to perform this alignment, with cutoffs for expected score set at below 0.1 and percent identity set at above 60% of the length of the hairpin sequence. Finally, the stabilities of the secondary structures of the predicted homologous hairpin sequences were tested based on their minimum free energy of folding (below −25 kcal/mol) using the RNA secondary structure prediction program RNAfold within the Vienna RNA package (version 2.0). The cattle miRNAs with no homolog in any other species were classified as “cattle-specific” and those with a homolog in any other species were classified as “non-cattle-specific”.
Sequence conservation of target seed sites
To evaluate the conservation of target seed sites of miRNAs across species, we used the predicted miRNA target sites with context + scores above 95th percentile and determined their positions on the 23-way UTR alignments available in TargetScan (release 6.2, June 2012)[40–42]. The target predictions in TargetScan are made only for the major sequence and not the minor (star) sequence of the miRNAs. We used the Perl scripts provided on the TargetScan website (http://targetscan.org) for predicting and calculating the context scores for the targets of novel miRNAs.
For each cattle miRNA target seed site the aligned sequences from five other species available in the 23-way UTR alignments were examined: human, dog, mouse, rat and chicken (the pig genome was not available in this multi-species alignment). If the target site was not perfectly conserved with any of the five species considered (both nucleotide substitutions and indels in the other species were considered as divergence), then the target site was classified as cattle-specific.
We used the method described by Zheng et al. to calculate the normalized divergence rates of target sites between cattle and human. The normalized divergence rate is defined as the divergence rate of the target site minus the divergence rate of flanking region. The divergence rate of the target site is defined as the number of nucleotide substitutions in the target sequence divided by its length (7-mer or 8-mer) and the divergence rate of the flanking regions is defined as the number of nucleotide substitutions in the upstream and downstream regions, divided by the corresponding flanking region lengths (84 and 96 respectively for flanking regions of 7-mer and 8-mer seed sequences).
Comparison of expression levels of cattle mRNAs with their orthologues in pig and human
Sequence reads with base quality scores were produced by the Illumina sequencer. RNA-seq reads flagged as low-quality by CASAVA 1.8 were removed. Cattle and pig reads were aligned to the cattle (UMD 3.1) and pig (Sus 10.2) reference genome sequences respectively using Tophat 1.4.0 with default parameters. The number of reads mapped to each gene was determined using htseq-count (v0.5.3.p3). The read counts per gene were normalized to cpm.
The pig and human orthologues of cattle mRNAs were determined using the homology mappings provided in the BioMart biological database (version 0.7) (http://ensembl.org/biomart/martview/). A total of 8680 orthologous pairs between cattle and pig showed cpm > 0.5 in both species and were used for downstream analyses. Similarly, we identified 9442 orthologous pairs between cattle and human. The RNA-seq-determined expression levels (expressed in cpm) of the cattle mRNA targets were then compared to those of their porcine and human orthologues for various subsets of targets, using cumulative proportion plots and Kolmogorov-Smirnov and Mann–Whitney U statistical tests.
Test for biological pathway enrichment among target genes of highly expressed cattle-specific miRNAs
The target genes (predicted by TargetScan with context + scores above 95th percentile) of 33 highly expressed cattle-specific miRNAs were subjected to KEGG pathway enrichment analysis using the ‘GOstats’ tool within the Bioconductor package (version 2.12) of the R statistical programming language (version 3.0.1). Of the 390 KEGG terms from the ‘KEGG.db’ annotation map, we used 375 for the enrichment testing after excluding 15 cancer-related terms. Conversions between bovine Ensembl IDs, Entrez gene IDs and gene symbols, when needed, were done using the genome wide annotation map for bovine available from the Bioconductor package ‘org.Bt.eg.db’.
The miRNA and mRNA sequence data sets described in this article are available from the NCBI Sequence Read Archive under accession ID SRP018740 at http://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP018740.
Co-first authors; Hua Bao and Arun Kommadath.
This work is supported by the Applied Livestock Genomics Program (ALGP13) funded by Genome Alberta and Alberta Livestock and Meat Agency, and the Large-Scale Applied Research Project, CanadaCow, funded by Genome Canada. Graham Plastow, Leluo Guan, and Paul Stothard are grateful for the financial support of the Alberta Livestock and Meat Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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