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Asian-Australas J Anim Sci > Volume 29(10); 2016 > Article
Al-Husseini, Chen, Gondro, Herd, Gibson, and Arthur: Characterization and Profiling of Liver microRNAs by RNA-sequencing in Cattle Divergently Selected for Residual Feed Intake

Abstract

MicroRNAs (miRNAs) are short non-coding RNAs that post-transcriptionally regulate expression of mRNAs in many biological pathways. Liver plays an important role in the feed efficiency of animals and high and low efficient cattle demonstrated different gene expression profiles by microarray. Here we report comprehensive miRNAs profiles by next-gen deep sequencing in Angus cattle divergently selected for residual feed intake (RFI) and identify miRNAs related to feed efficiency in beef cattle. Two microRNA libraries were constructed from pooled RNA extracted from livers of low and high RFI cattle, and sequenced by Illumina genome analyser. In total, 23,628,103 high quality short sequence reads were obtained and more than half of these reads were matched to the bovine genome (UMD 3.1). We identified 305 known bovine miRNAs. Bta-miR-143, bta-miR-30, bta-miR-122, bta-miR-378, and bta-let-7 were the top five most abundant miRNAs families expressed in liver, representing more than 63% of expressed miRNAs. We also identified 52 homologous miRNAs and 10 novel putative bovine-specific miRNAs, based on precursor sequence and the secondary structure and utilizing the miRBase (v. 21). We compared the miRNAs profile between high and low RFI animals and ranked the most differentially expressed bovine known miRNAs. Bovine miR-143 was the most abundant miRNA in the bovine liver and comprised 20% of total expressed mapped miRNAs. The most highly expressed miRNA in liver of mice and humans, miR-122, was the third most abundant in our cattle liver samples. We also identified 10 putative novel bovine-specific miRNA candidates. Differentially expressed miRNAs between high and low RFI cattle were identified with 18 miRNAs being up-regulated and 7 other miRNAs down-regulated in low RFI cattle. Our study has identified comprehensive miRNAs expressed in bovine liver. Some of the expressed miRNAs are novel in cattle. The differentially expressed miRNAs between high and low RFI give some insights into liver miRNAs regulating physiological pathways underlying variation in this measure of feed efficiency in bovines.

INTRODUCTION

MicroRNAs (miRNAs) are small (~22 nucleotides) non-coding RNA that regulate gene expression by target messenger RNA (mRNA) in a sequence-specific manner, leading to either translational repression or degradation of targeted transcript. In animals, miRNAs target the 3′untranslated regions of mRNA by an RNA-induced silencing complex (RISC), and subject to the accuracy of the sequence complementarities, either repression of translation or cleavage of the mRNA target is achieved (Yu et al., 2007). MicroRNAs are now known to repress thousands of target genes and regulate cellular processes, including cellular proliferation, differentiation and apoptosis. The aberrant expression or alteration of miRNAs also contributes to a range of human pathologies, including diabetes and cancer (Lu et al., 2005).
Feed efficiency is an economically important trait in beef production and can be measured as residual feed intake (RFI). This is the difference between an animal’s actual feed intake recorded over a test period and its expected feed intake based on its size and growth rate (Koch et al., 1963). RFI takes into consideration feed required for daily weight gain of the animal as well as for maintenance of its metabolic weight; therefore, understanding of the molecular mechanism regulated RFI will help breeding sustainable and profitable animals in agriculture. Genome wide association studies have been carried out to identify gene markers associated with RFI in beef cattle (Sherman et al., 2009) and more than a hundred single nucleotide polymorphic (SNP) markers have been found associated with variation in RFI in beef cattle. However, a large proportion of SNPs markers are not located in annotated genes in bovine genome. Some of the most significant SNPs for RFI were in miRNAs motifs which suggest that these miRNAs could play an important role in regulation of RFI. Gene expression studies in cattle from high and low RFI divergent selection lines identified more than 160 differentially expressed genes (Chen et al., 2011). The variations of gene expression between high and low RFI cattle ranged from −1.5 to 0.8 fold change, and support the view that the phenotypic differences in RFI may due to level of gene expression instead of genes being switched on or off.
Profiling studies characterizing miRNAs encoded in livestock genomes in the last decade have found a wide and diverse range of conserved and species-specific miRNAs (Liu et al., 2009). In cattle, some initial characterizations of miRNAs have been carried out for various tissues including adipose tissue, mammary tissues (Gu et al., 2007), immune and embryonic tissues, pooled tissue, alveolar macrophages, ovarian, oocyte and testicular tissues (Miles et al., 2012), liver of dairy cows in the early postpartum period (Fatima et al., 2014). Liver is a central controller of metabolism and a major driver of whole animal oxygen consumption in mammals. However, little is known about miRNAs role in regulating key cellular and physiological pathways that may regulate RFI. In the current study, we profiled miRNAs abundance in liver tissue of Angus bulls from high and low RFI-selection lines by next-generation deep sequencing technology. We report liver miRNA-seq profiling study in beef cattle and their known and putative novel bovine miRNAs. We also report the differentially expressed miRNAs between high and low RFI-selection line cattle.

MATERIALS AND METHODS

Animal and liver biopsy sampling

Young Angus bulls resulting from approximately three generations of divergent selection for RFI were used in this study. The selection lines were established in 1993 at the Agricultural Research Centre, Trangie, NSW, Australia (Arthur et al., 2001; Donoghue et al., 2011). The same animals were used for the microarray experiment reported by Chen et al. (2011), and were approved by the University of New England Animal Ethics Committee, certificate no. (AEC 06/123) and followed the University of New England Code of Conduct for Research in meeting the Australian Code of Practice for the Care and Use of Animals. In brief, bulls were born in 2005 and, when approximately one year-of-age, their growth and feed intake were measured. Post-weaning RFI for each animal in the test group was calculated using a linear regression of daily feed intake on mean metabolic mid-test weight and average daily gain (Arthur et al., 2001). Based on the RFI test results, liver biopsies were taken (7 days after the end of the RFI test) from 30 animals with the lowest RFI and 30 animals with the highest RFI (Chen et al., 2011). On average, cattle from the low RFI line consumed about 2 kg less feed per day than cattle from the high RFI line (p<0.001; as shown in Table 1). Total RNA from liver was isolated using TRI reagent (Ambion, Applied Biosystems, Foster City, CA, USA) following the manufacturer’s protocol. The concentration and purity of the extracted RNA was checked by NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). The quality and integrity of RNA was further assessed with the RNA 6000 Nano Lab chip Kit using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). All RNA samples had a RNA integrity number (RIN) of 9.8 to 10) and were stored at −80°C until further analysis.

Small RNA library construction and sequencing

For small RNA library construction and deep sequencing, two pools of total RNA were constructed from 13 top high-RFI animals and 13 bottom low-RFI animals with equal quantities (1 μg of RNA) from each animal. Libraries of small RNAs were prepared using the “Preparing Samples for Small RNA Sequencing Using the Alternative v1.5 Protocol” supplied by the manufacturer and using 10 μg total RNA. The libraries were sequenced on the Illumina Genome Analyzer IIx using Single-Read Cluster Generation Kit v2 (Cat. no. FC-103–2001; Illumina) and 36 Cycle Sequencing Kit v4 (Cat. no. FC-104–4002). All sequencing data sets supporting the results in this study have been deposited in the publicly available NCBI’s Gene Expression Omnibus Database (http://www.ncbi.nlm.nih.gov/geo/). The data are accessible through GEO Series accession number GSE63691 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63691).

Analysis of small RNA sequencing data

The raw sequence reads were processed with a Perl script to remove low quality reads and adaptor sequences and to count the reads of the Genome Analyzer (Illumina Inc., San Diego, CA, USA) into read-count files (read sequences and counts tab separated). Then the sequencing data were input into miRanalyzer (Hackenberg et al., 2011) to analyse the small RNAs. In brief, known bovine microRNAs were identified by mapping all sequence reads to the known bovine miRNAs database (miRBase v. 21) (Kozomara and Griffiths-Jones, 2011). Those reads matched to known bovine miRNAs were grouped and removed from the dataset so they could not erroneously be predicted as new miRNAs. Then, sequences reads mapped to non-bovine known miRNAs to identify homologous miRNAs for all other species in the miRBase. Genome sequences around the position of those mapped reads were extracted and the energetically best hairpin structures were retained as putative pre-miRNAs if they had: i) at least 19 base pairings in the secondary structure; and ii) at least 11 base pairings located in the read cluster region (number of pairings between putative mature and mature-star). Mapped reads were grouped as novel bovine homologue miRNAs and removed from data set. The remaining reads were further successively aligned to known transcriptome libraries such as RFam 10.1, GtRNAdb, RNAdb, and RefSeq (Gardner et al., 2009). To identify bovine-specific novel miRNAs, the remaining sequence reads were mapped to Bos taurus genome (BosTau6, UMD_3.1) using Bowtie (Langmead et al., 2009). The mapped reads were first clustered into putative mature miRNAs and pre-miRNAs. Putative candidate miRNAs reported based on at least three out of five models from the five different Random Forest models (Hackenberg et al., 2011). The secondary structure of pre-miRNAs was determined by RNAfold using minimum free energy (MFE) algorithm.

Differential expression analysis of miRNAs

The raw read counts of known bovine miRNAs and homologous miRNAs were imported as a count table into the Bioconductor DESeq package (Anders and Huber, 2010), which uses the negative binomial distribution model to test for differential expression in deep sequencing datasets. The normalization and variance estimation were based on the model of two conditions without replicate.

Prediction of putative miRNA targets

Potential gene targets for known miRNAs that differentially expressed between high and low RFI were identified using miRmap (Vejnar and Zdobnov, 2012). Only miRNAs target genes that highly scores (>80%) were selected for investigation as potential target genes. Also, all those genes expressed differentially between high and low RFI-selection line cattle which have been reported by Chen et al. (2011) were investigated for their potential miRNAs targets in the 3′UTR region.

RESULTS AND DISCUSSION

Profiling bovine miRNAs

In order to profile and identify novel and differentially-expressed miRNAs in the bovine liver of low and high RFI cattle, two small RNA libraries were constructed by Solexa sequencing. After trimming the adaptor sequence and filtering low-quality sequences, a total of 23,628,109 sequences of short reads were obtained for low and high RFI libraries. As presented in Table 2, there were 10,820,087 sequences reads for the low RFI library (high feed efficiency cattle) and 12,808,022 sequence reads for the high RFI (low feed efficiency cattle). All sequence reads were aligned against the bovine miRNAs database (miRBase v.21). Importantly, high proportion 74.5% and 83.7% of sequence reads of the two RFI libraries have aligned to the bovine genome (UMD 3.1). Over half of each library reads (56% to 57%) have showed an exact match to the known bovine mature miRNAs, as shown in Table 2. These results demonstrated the high quality of our small RNAs sequencing read libraries. However, the presence of other small RNAs, such as ribosomal fragments, tRNA, snRNA, and mtRNA was approximately 12% and 17% of mapped sequence reads of low and high RFI libraries respectively. Sequence reads that matched to homologous miRNAs of other species reported in the miRBase constituted 1% of mapped reads (Figure 1).

Unique known mature bovine miRNAs

In total 305 known mature miRNAs detected in two libraries and the full profile of unique bovine mature miRNAs expressed in liver tissue was listed in Additional files, Supplementary Table S1. Table 3 lists only the most abundant miRNAs (>100 read count per million mapped read [RCPM]) from 305 known mature miRNAs. About 40% (305 out of 807) of known bovine mature miRNAs were expressed in the liver tissue. The proportion of expressed miRNAs in bovine liver is similar to the level of expressed miRNAs in human and mouse liver (Kornfeld et al., 2013). In contrast, primary bovine mammary epithelial cells expressed only 20% of known bovine miRNA (Lawless et al., 2013).
A very broad range in expression levels of miRNAs was found ranged from 137,000 RCPM to less than one RCPM (Additional files, Supplementary Table S1). Only a small number of miRNAs were expressed at very high levels, such as bta-miR-143, bta-miR-30a, and bta-miR-122, and the majority of miRNAs being expressed at medium and low levels. However, the top 10 most abundantly expressed miRNAs families (bta-miR-143, bta-miR-30, bta-miR-122, bta-miR-378, bta-let-7, bta-miR-148, bta-miR-192, bta-miR-101, bta-miR-140, and bta-miR-21) constituted approximately 78.4% of the total mapped mature known bovine sequence reads. These findings are in agreement with studies of miRNAs profiling which revealed that small number of abundant miRNAs make up a high portion of the total miRNAs pool (Tripurani et al., 2010; Fatima et al., 2014).
Among the highly expressed known miRNAs detected in the bovine liver in our study, miR-143 was the most abundant miRNA, constituting approximately 25% of the total expressed miRNAs. In human and mouse liver the most abundant miRNA is miR-122 (Hu et al., 2012; Rottiers and Näär 2012), which makes up 70% of the total mouse liver miRNA population (Wen and Friedman, 2012). Our results showed that bovine bta-miR-122 was the third most expressed miRNA, comprising approximately 8% of the total known bovine miRNA population.
Previously, tissue-specific miRNAs were reported in dairy and beef cattle (Jin et al., 2009; Tripurani et al., 2010) using cloning and sequencing small-RNA libraries. One of those tissue-specific miRNAs was bta-miR-9. Bovine miR-9 was identified and reported as brain-specific miRNA (Jin et al., 2009). Similarly, bta-miR-1, bta-miR-133a, and bta-miR-206 have been reported as muscle-specific miRNAs in bovine (Jin et al., 2009). In the current study, these four miRNAs have expressed in bovine liver with sensible expression levels in the two high and low RFI libraries. The expression levels of bta-miR-1, bta-miR-206, and bta-miR-133a were 7,081, 1,450, and 689 RCPM respectively. These results suggest that previous reports on tissue specificity of these miRNAs might be limited by the tissues surveyed and/or sensitivity of the techniques that used.

Novel bovine miRNA

Novel isomiRs: riations with respect to the reference miRNA sequence (isomiRs) were named previously as mature-star (miRNA*), and were considered as “minor” products from pre-microRNA sequence. In miRNAs biogenesis, it was often inferred to be degraded and only the dominant arm (sense strand) to be incorporated into the RISC complex. However, there is strong evidence that these isomiRs are heterogeneous variants of canonical miRNAs species and are of functional importance (Yang et al., 2011). We adopted the new nomenclature using −5′p/−3′p for the name of isomiRs miRNA in the bovine library (Figure 2). Fifty novel isomiRs were detected in the present study, which have not been observed previously in the bovine miRNA database. Four isomiRs, bta-mir-143-3p, bta-mir-335-3p, bta-mir-136-3p, and bta-mir-2284w-3p have identified to have higher read counts than the corresponding known mature miRNA reported in the bovine miRBase, as illustrated in Table 4. For instance, bta-mir-143-3p was expressed by approximately 605 RCPM, while mature bta-miR-143-5p was expressed by only about 19.5 RCPM in the average of the two libraries. Therefore, more studies still needed to evaluate which isomiRs is dominant and functional to improve the bovine miRNA annotation.

Putative homologous bovine miRNAs

Bovine putative homologous miRNAs are those miRNAs that have been reported in other species but not in bovine miRNAs database yet. After removing all reads matched to known bovine miRNAs, the remaining reads were then aligned to non-bovine sets of known mature miRNAs from other species (miRBase v. 21). There were 61 detected putative homologous miRNAs which have distinct hairpin loci in the bovine genome (Table 5). These putative novel homologous miRNAs sequences and related genomic locations are presented in Additional files, Supplementary Table S2.

Detection of putative novel bovine microRNAs

More than quarter of the reads (25% from high and 31% from low RFI libraries) that did not matched to known bovine miRNAs matched to the bovine genome. To identify novel miRNAs, we extracted the candidate pre-miRNA structure, based on the location of clusters of mature miRNA on the genome, to select the energetically best candidate if they were having: i) at least 19 base pairings in the secondary structure, and ii) at least 11 base pairings located in the read cluster region (the number of pairings between 5′p and 3′p). Then five different models (Hackenberg et al., 2011) were used to predict whether a given candidate was likely to be a miRNA. We considered a candidate as a novel miRNA only if at least three out of five models were positive. Twelve putative new miRNAs were discovered in the present study. After realigning these putative novel consensus sequences to known mature miRNAs in other species, two candidates (miR-664-3p and miR-802-5p) were found to be homologous to other species. Ten novel bovine-specific candidate miRNAs with their sequence, genomic location, and the number of the models that predicted each novel miRNA candidate are presented in Table 6. These ten novel bovine miRNAs represented by 5,437 and 1,097 reads in the low RFI and high RFI libraries, respectively. The sequences and secondary structure of those 10 novel bovine-specific miRNAs candidates and related pre-miRNAs with the MFE, which have been predicted by RNAfold program, are illustrated in Additional files, Supplementary Figure S1. Novel candidate 1 and 2 have identical mature sequences but resulted from distinct precursor sequences located in different genomic region.

Differentially expressed miRNA between high and low RFI libraries and their target genes

To understand the role of miRNA in regulation of residual intake, we first identified differentially expressed miRNAs from high and low RFI selection lines. As miRNAs play important roles in the regulation of almost every biological process in eukaryotes; these differentially expressed miRNAs could play important roles in regulation of the physiological processes and pathways involved in variation in RFI in beef cattle. To define the differentially expressed miRNAs, we consider only those miRNAs with high to modest expression with at least 100 RCPM and folds changes>2. This approach was adapted in earlier microarray experiment and was less stringent than using p<0.05 due to lack of replicates. A total of 49 unique miRNAs were identified to be differentially expressed between the high RFI line and low RFI line cattle (Table 7). More than two thirds (33 out of 49) of differentially expressed miRNAs were up-regulated in high RFI animals. Specifically, six miRNA (miR-203-3p, bta-miR-32, bta-miR-215, bta-miR-708, and bta-miR-101) that reached p<0.05 were all up-regulated in high RFI animals.
Importantly, many up-regulated miRNAs in high RFI animals played important roles in metabolic homeostasis including glucose and lipid metabolism. These overexpression patterns were similar to the patterns observed in obesity mouse models and human subject. For example bta-miR-143, the most expressed miRNA in bovine liver, was up-regulated in high RFI cattle. It has been reported that hepatic miR-143 were up-regulated in obesity mouse models whether they are genetic or dietary induced. The overexpression of hepatic miR-143 impaired insulin-stimulated AKT activation and glucose homeostasis by targeting insulin signalling and its regulation (Jordan et al., 2011). Bta-mir-122-3p was highly expressed in bovine liver and is up-regulated in high RFI cattle. MiR-122 was the first miRNA to be linked to metabolic control and affect hepatic cholesterol, lipid metabolism and the maintenance of liver cell differentiation (Lewis and Jopling, 2010).
A bovine homologous of miR-802-5p was identified in our study and was up-regulated in high RFI cattle. MiR-802 has been reported up-regulated in the liver of two obese mouse models and obese human subjects (Kornfeld et al., 2013). Overexpression of miR-802 in mice causes impaired glucose tolerance and attenuates insulin sensitivity by suppressing its target gene HNF1 Homeobox B (Hnf1b).
Bta-miR-29b was up-regulated in high RFI. The function of miR-29 has been reported to regulate glucose transport in liver, muscle and adipose (Pandey et al., 2011). Bta-miR-19b, bta-miR-101, bta-miR-106b, and bta-miR-142-3p were up-regulated in high RFI cattle. Recently studies showed their expression in steers liver were highly influenced by energy density of the diet (mainly lipid levels in the diet) (Romao et al., 2012).
To understand miRNAs expression and their target genes, we searched all the potential miRNAs target sites in the 3′UTR region of 161 differentially expressed genes identified by Chen et al. (2011) by using miRmap (Vejnar and Zdobnov, 2012). Then we examined if these potential miRNAs were expressed differentially in liver between high and low RFI cattle.
We found 36 differentially-expressed genes in liver (Chen et al., 2011) containing putative miRNAs target sites (Table 8). Many genes have multiple miRNAs target sites such as helicase with zinc finger (HELZ), espin (ESPN), cytochrome P450 family 2 subfamily C member 18 (CYP2C18), snail family transcriptional repressor 2 (SNAI2), and superoxide dismutase 3 (SOD3). Five down-regulated miRNAs (bta-miR-424-5P, bta-miR-19b, bta-miR-29b, bta-miR-30b, and bta-miR-2285) having sites binding to the mRNA of 12 distinct genes calponin 1 (CNN1), atypical chemokine receptor 3 (CXCR7), endothelin receptor type (BEDNRB), fibrinogen alpha chain (FGA), insulin like growth factor binding protein 3 (IGFBP3), regulator of G-protein signaling 2 (RGS2), periostin, osteoblast specific factor (POSTN), monoamine oxidase (AMAOA), collagen type IV alpha 6 (COL4A6), dehydrogenase/reductase (SDR family) member 3 (DHRS3), solute carrier family 22 (organic anion transporter), member 7 (SLC22A7). These genes were up-regulated in low RFI animals (Chen et al., 2011). Four genes, BEDNRB, IGFBP3, POSTN, and DHRS3 were up-regulated in low-RFI cattle (Chen et al., 2011) and have bta-miR-19b putative target sites in their 3′UTR region. Take together we believe these differentially expressed genes play important roles regulating RFI in beef cattle.
However, we acknowledge the differentially expressed genes detected in this study are based on pools with limited statistic power. Follow up studies are need for validating the differentially expressed miRNAs and their function.
In conclusion, RFI as a measure of feed efficiency is influenced by several physiological systems including basal metabolic rate, energy balance, the regulation of growth and development, regulation of feed intake and homeostatic control of body mass. Our study revealed a comprehensive miRNA population in bovine liver. We identified 305 known bovine miRNAs, 50 novel isomiRs, 52 homologous miRNAs, and 10 novel miRNAs. We further revealed that many up-regulated miRNAs in high RFI cattle showed a similar expression pattern as found in a mouse obesity model and have functions related to glucose and lipid metabolism. We demonstrated the expression of miRNAs have effects on their target genes expression. Combining the patterns of miRNA and mRNA expression will provide further power to understanding the molecular mechanisms that regulate feed efficiency in beef cattle.

Supplementary Information

ACKNOWLEDGMENTS

This work was funded by the former Cooperative Research Centre for Beef Genetic Technologies and the University of New England. Wijdan Al-Husseini was supported by the University of Babylon/Ministry of Higher Education and Scientific Research (MOHESR)/Iraq.

Notes

CONFLICT OF INTEREST

We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Figure 1
Distribution of high quality reads that mapped to the bovine genome in the low RFI line (A) and high RFI line (B) cattle. RFI, residual feed intake.
ajas-29-10-1371f1.gif
Figure 2
Precursor miRNA structures, the 5′p (mature sequence) is highlighted in red and 3′p (miRNA* sequence) is highlighted in black bold fonts.
ajas-29-10-1371f2.gif
Table 1
Feed efficiency performance of young Angus bulls selected for and against residual feed intake (RFI)
Traits Mean±SE

Low RFI High RFI
Residual feed intake (kg/d) −1.34 ±0.51 0.56 ±0.28
Feed intake (kg/d) 10.45 ±1.19 12.57 ±0.97
Average daily gain (kg/d) 2.09 ±0.30 1.94 ±0.21
Start body weight (kg) 324 ±29 357 ±29
End body weight (kg) 471 ±41 493 ±40

SE, standard error.

Table 2
Summary of miRNA sequences present in low and high RFI libraries
Parameter Low RFI High RFI
Initial high quality read count 10,820,087 12,808,022
Match to known bovine mature miRNAs 4,493,321 6,151,626
Matched to mature Homolog 114,603 103,465
Matched to known transcript libraries1 987,892 1,855,305
Matched to bovine genome 2,461,668 2,614,666
Not matched to bovine genome 2,762,603 2,082,960

RFI, residual feed intake.

1 rRNA, tRNA, snRNA, and mtRNA.

Table 3
The most abundant bovine mature miRNAs in the high and low RFI sequence libraries from liver tissue
miRNA ID Low RFI library High RFI library Average of two libraries miRNA ID Low RFI library High RFI library Average of two libraries






RC RCPM RC RCPM RCPM RC RCPM RC RCPM RCPM
bta-miR-143 692,736 85,974.2 1,883,457 175,612.7 137,158.9 bta-let-7e 7,633 947.3 3,207 299 577.1
bta-miR-30a-5p 539,136 66,911.2 859,054 80,097.8 74,440.9 bta-miR-99b 7,465 926.5 3,224 300.6 569.1
bta-miR-122 535,909 66,510.7 287,465 26,803.1 43,837.2 bta-miR-181a 7,101 881.3 3,423 319.2 560.3
bta-miR-378 341,772 42,416.7 30,5081 28,445.6 34,439 bta-miR-27a-3p 5,065 628.6 5,040 469.9 538
bta-let-7f 277,021 34,380.6 285,630 26,632 29,956.1 bta-miR-151-5p 5,738 712.1 4,270 398.1 532.8
bta-miR-148a 159,891 19,843.8 346,010 32,261.8 26,934.6 bta-miR-379 4,125 511.9 4,922 458.9 481.7
bta-let-7a-5p 259,842 32,248.5 172,859 16,117.3 23,037.4 bta-miR-22-3p 4,695 582.7 3,987 371.7 462.2
bta-miR-192 106,160 13,175.3 309,352 28,843.8 22,122.2 bta-miR-125a 4,910 609.4 2,808 261.8 410.9
bta-miR-101 55,479 6,885.4 244,427 22,790.3 15,967.3 bta-miR-146a 2,299 285.3 4,752 443.1 375.4
bta-miR-140 98,925 12,277.4 81,434 7,592.9 9,602.5 bta-miR-16a 3,520 436.9 3,478 324.3 372.6
bta-miR-21-5p 39,273 4,874.1 139,116 12,971.1 9,497.6 bta-miR-2904 4,655 577.7 2,305 214.9 370.6
bta-miR-30d 97,949 12,156.3 63,107 5,884.1 8,574.8 bta-miR-99a-5p 3,502 434.6 2,995 279.3 345.9
bta-miR-2340 80,052 9,935.1 66,378 6,189.1 7,796.1 bta-miR-331 3,284 407.6 2,952 275.2 332
bta-let-7b 98,033 12,166.7 47,944 4,470.3 7,771.9 bta-miR-2284x 4,192 520.3 1,937 180.6 326.3
bta-miR-30e-5p 45,947 5,702.4 99,728 9,298.6 7,755.9 bta-miR-455-3p 5,401 670.3 417 38.9 309.8
bta-miR-24-3p 55,651 6,906.7 48,356 4,508.7 5,537.4 bta-miR-199a-3p 2,350 291.7 3,078 287 289
bta-let-7g 40,814 5,065.4 37,961 3,539.5 4,194.1 bta-miR-28 2,505 310.9 2,673 249.2 275.7
bta-miR-26a 34,314 4,258.6 38,649 3,603.6 3,884.6 bta-miR-885 3,531 438.2 1,513 141.1 268.5
bta-miR-103 40,051 4,970.7 31,093 2,899.1 3,787.8 bta-miR-423-3p 3,054 379 1,666 155.3 251.3
bta-miR-27b 44,864 5,568 26,279 2,450.2 3,787.7 bta-miR-2285t 1,275 158.2 3,252 303.2 241
bta-miR-127 39,439 4,894.7 23,104 2,154.2 3,329.8 bta-miR-342 2,666 330.9 1,764 164.5 235.9
bta-miR-423-5p 46,473 5,767.7 14,950 1,393.9 3,270.2 bta-miR-2285i 1,923 238.7 2,471 230.4 233.9
bta-miR-30c 31,144 3,865.2 30,057 2,802.5 3,258.4 bta-miR-199a-5p 2,595 322.1 1,623 151.3 224.6
bta-miR-92a 35,708 4,431.7 22,295 2,078.8 3,088.1 bta-miR-10a 2,006 249 1,869 174.3 206.3
bta-miR-152 24,291 3,014.7 30,852 2,876.6 2,935.9 bta-miR-185 1,727 214.3 2,007 187.1 198.8
bta-miR-145 30,560 3,792.7 22,396 2,088.2 2,819.4 bta-miR-486 2,406 298.6 1,280 119.3 196.2
bta-miR-191 30,755 3,816.9 20,972 1,955.4 2,754 bta-miR-411a 1,470 182.4 2,154 200.8 192.9
bta-let-7c 29,533 3,665.3 15,760 1,469.5 2,411.4 bta-miR-139 2,244 278.5 1,376 128.3 192.7
bta-miR-194 24,257 3,010.5 17,788 1,658.5 2,238.5 bta-miR-505 2,090 259.4 1,415 131.9 186.6
bta-miR-23b-3p 20,639 2,561.5 19,048 1,776 2,113 bta-miR-17-5p 1,630 202.3 1,864 173.8 186
bta-miR-186 20,776 2,578.5 17,689 1,649.3 2,047.9 bta-miR-106b 1,125 139.6 2,036 189.8 168.3
bta-miR-151-3p 20,077 2,491.7 16,933 1,578.8 1,970.4 bta-miR-15a 1,358 168.5 1,800 167.8 168.1
bta-miR-451 8,510 1,056.2 22,749 2,121.1 1,664.3 bta-miR-32 431 53.5 2,664 248.4 164.8
bta-miR-193b 17,868 2,217.6 11,363 1,059.5 1,556.3 bta-miR-146b 1,628 202 1,356 126.4 158.9
bta-miR-29a 14,498 1,799.3 14,405 1,343.1 1,538.8 bta-miR-206 974 120.9 1,926 179.6 154.4
bta-miR-30f 16,308 2,024 12,138 1,131.7 1,514.5 bta-miR-200c 1,623 201.4 1,168 108.9 148.6
bta-miR-125b 13,918 1,727.3 13,383 1,247.8 1,453.5 bta-miR-708 472 58.6 2,109 196.6 137.4
bta-miR-26b 11,314 1,404.2 9,777 911.6 1,122.9 bta-miR-98 960 119.1 1,533 142.9 132.7
bta-miR-100 10,433 1,294.8 9,971 929.7 1,086.3 bta-miR-215 426 52.9 2,015 187.9 130
bta-miR-30b-5p 6,914 858.1 13,348 1,244.6 1,078.8 bta-miR-3432 1,591 197.5 821 76.5 128.4
bta-miR-365-3p 9,859 1,223.6 10,292 959.6 1,072.9 bta-miR-361 1,539 191 854 79.6 127.4
bta-miR-148b 14,899 1,849.1 5,231 487.7 1,071.7 bta-miR-1940 943 117 1,394 130 124.4
bta-miR-93 11,603 1,440 7,282 679 1,005.5 bta-miR-150 1,435 178.1 830 77.4 120.6
bta-let-7d 9,114 1,131.1 8,230 767.4 923.4 bta-miR-2484 1,580 196.1 620 57.8 117.1
bta-miR-23a 7,554 937.5 8,017 747.5 829 bta-miR-200b 1,438 178.5 740 69 116
bta-let-7i 8,951 1,110.9 6,467 603 820.9 bta-miR-195 839 104.1 1,306 121.8 114.2
bta-miR-126-5p 3,028 375.8 10,647 992.7 728.1 bta-miR-193a-3p 405 50.3 1,725 160.8 113.4
bta-miR-25 6,956 863.3 6,179 576.1 699.3 bta-miR-29b 418 51.9 1,588 148.1 106.8
bta-miR-126-3p 7,816 970 5,250 489.5 695.6 bta-miR-374b 1,085 134.7 913 85.1 106.4
bta-miR-339a 6,292 780.9 5,246 489.1 614.3 bta-miR-22-5p 609 75.6 1314 122.5 102.4
bta-miR-320a 7,320 908.5 4,090 381.3 607.5

RFI, residual feed intake; RC, read counts; RCPM, read count per million mapped reads.

Table 4
Novel isomiRs detected in low and high RFI libraries and their related expression (read counts)
miRNA Read sequence Low RFI library High RFI library Average of two libraries



RC RCPM RC RCPM RCPM
bta-mir-30a-3p CTTTCAGTCGGATGTTTGCAGC 22,799 2,829.54 23,959 2,233.93 2,489.44
bta-mir-30e-3p CTTTCAGTCGGATGTTTACAGC 11,837 1,469.07 17,288 1,611.93 1,550.64
bta-mir-122-3p AACGCCATTATCACACTAAATA 2,707 335.96 10,549 983.58 705.76
bta-mir-142-3p AGTGTTTCCTACTTTATGGA 2,743 340.43 8,622 803.91 605.08
bta-mir-30f-3p CTGGGAGAAGGCTGTTTACTCT 1,067 132.42 639 59.58 90.83
bta-let-7d-3p CTATACGACCTGCTGCCTTTCT 1,096 136.02 527 49.14 86.41
bta-mir-148a-5p AAAGTTCTGAGACACTCCGACT 703 87.25 796 74.22 79.81
bta-mir-125b-2-3p ACAAGTCAGGCTCTTGGGACCT 823 102.14 661 61.63 79.01
bta-mir-140-5p CAGTGGTTTTACCCTATGGTAGG 1,373 170.40 0 0.00 73.10
bta-mir-885-3p AGGCAGCGGGGTGTAGTGGATA 851 105.62 312 29.09 61.92
bta-mir-194-2-3p CAGTGGGGCTGCTGTTATCTG 476 59.08 210 19.58 36.52
bta-mir-374a-3p CTTATCAGGTTGTATTGTAATT 93 11.54 582 54.27 35.94
bta-mir-106b-3p CCGCACTGTGGGTACTTGCTG 351 43.56 255 23.78 32.26
bta-mir-361-3p CCCCCAGGTGTGATTCTGATTTGC 319 39.59 182 16.97 26.67
bta-mir-148b-5p GAAGTTCTGTTATACACTCAGGCT 0 0.00 369 34.41 19.65
bta-mir-139-3p TGGAGACGCGGCCCTGTTGGAGT 346 42.94 0 0.00 18.42
bta-mir-335-3p GTTTTTCATTATTGCTCCTGACC 126 15.64 116 10.82 12.88
bta-mir-532-3p CCTCCCACACCCAAGGCTTGCA 172 21.35 62 5.78 12.46
bta-mir-379-3p TATGTAACATGGTCCACTAAC 157 19.48 62 5.78 11.66
bta-mir-145-3p GGATTCCTGGAAATACTGTTCTT 191 23.70 0 0.00 10.17
bta-mir-411a-3p TATGTAACACGGTCCACTAACC 174 21.59 0 0.00 9.26
bta-mir-26b-3p CCTGTTCTCCATTACTTGGCT 97 12.04 58 5.41 8.25
bta-mir-129-2-5p CTTTTTGCGGTCTGGGCTTGC 61 7.57 46 4.29 5.70
bta-mir-15b-3p CGAATCATTATTTGCTGCTCTA 55 6.83 47 4.38 5.43
bta-mir-3432-2-3p CAGCAACTAAAGATCCCTCAGG 71 8.81 27 2.52 5.22
bta-mir-7-2-3p CAACAAATCACAGTCTGCCATA 28 3.48 63 5.87 4.84
bta-mir-185-3p AGGGGCTGGCTTTCCTCCGGC 58 7.20 23 2.14 4.31
bta-mir-27b-5p AGAGCTTAGCTGATTGGTGAACA 39 4.84 38 3.54 4.10
bta-mir-136-3p CATCATCGTCTCAAATGAGTCT 0 0.00 74 6.90 3.94
bta-mir-493-5p TTGTACATGGTAGGCTTTCATT 57 7.07 16 1.49 3.89
bta-mir-3613-3p ACAAAAAAAAAAGCCCAACCCT 30 3.72 24 2.24 2.88
bta-let-7e-3p TATACGGCCTCCTAGCTTTCC 50 6.21 0 0.00 2.66
bta-mir-195-3p CCAATATTGGCTGTGCTGCTCCA 30 3.72 18 1.68 2.56
bta-mir-338-5p AACAATATCCTGGTGCTGAGT 34 4.22 11 1.03 2.40
bta-mir-33a-3p CAATGTTTCCACAGTGCATCA 0 0.00 40 3.73 2.13
bta-mir-2284w-3p AAAACCTCAATGAACTCTTTGG 0 0.00 39 3.64 2.08
bta-mir-125b-1-3p ACGGGTTAGGCTCTTGGGAGC 24 2.98 13 1.21 1.97
bta-mir-26a-2-3p CCTATTCTTGATTACTTGTTTC 0 0.00 36 3.36 1.92
bta-mir-30d-3p CTTTCAGTCAGATGTTTGCTGC 6 0.74 27 2.52 1.76
bta-mir-21-3p CAACAGCAGTCGATGGGCTGTC 0 0.00 29 2.70 1.54
bta-mir-16a-3p CCAGTATTAACTGTGCTGCTGAA 0 0.00 25 2.33 1.33
bta-mir-374b-3p CTTATCAGGTTGTATTATCATT 7 0.87 18 1.68 1.33
bta-mir-19b-5p AGTTTTGCAGGTTTGCATCCAGC 10 1.24 14 1.31 1.28
bta-mir-210-5p AGCCACTGCCCACCGCACACTGC 15 1.86 8 0.75 1.22
bta-mir-365-2-5p GAGGGACTTTCAGGGGCAGCTGT 19 2.36 0 0.00 1.01
bta-mir-130b-5p ACTCTTTCCCTGTTGCACTACT 12 1.49 0 0.00 0.64
bta-mir-25-5p AGGCGGAGACTTGGGCAATTGCT 11 1.37 0 0.00 0.59
bta-mir-204-3p GCTGGGAAGGCAAAGGGACGT 8 0.99 0 0.00 0.43
bta-mir-380-5p ATGGTTGACCATAGAACATGCG 8 0.99 0 0.00 0.43
bta-mir-369-5p AGATCGACCGTGTTATATTCG 6 0.74 0 0.00 0.32

RFI, residual feed intake; RC, read counts; RCPM, read count per million mapped read.

Table 5
Unique putative homolog miRNAs identified in bovine liver tissue
miRNA Homolog mature miRNAs name Sequence Align length Low RFI library High RFI library Average of two libraries



RC CPM RC CPM CPM
miR-4448 hsa-miR-4448 GGCTCGTTGGTCTAGGGGTATGATTC 20 2,405 298.48 1,464 136.50 217.49
miR-203-3p mmu-miR-203-3p; hsa-miR-203a; rno-miR-203a-3p; mml-miR-203; ptr-miR-203; cfa-miR-203; ppy-miR-203 TGAAATGTTTAGGACCACTAGTATCT 21 512 63.54 3,342 311.61 187.57
miR-6243 mmu-miR-6243 ACCATCTGTGGGATTATGACTGAACG 26 1,612 200.06 1,883 175.57 187.82
miR-574-3p hsa-miR-574-3p; mmu-miR-574-3p; cfa-miR-574; ssc-miR-574; ggo-miR-574 CACGCTCATGCACACACCCACATCTC 22 1,875 232.70 1,218 113.57 173.13
miR-802-5p hsa-miR-802-5p; mmu-miR-802-5p TCAGTAACAAAGATTCATCCTTGT 21 624 77.44 1,782 166.15 121.80
miR-1285 cgr-miR-1285 CTCCAGCCTGGGCAACATAGCGAGAC 20 1,485 184.30 603 56.22 120.26
miR-4532 hsa-miR-4532 CCCCGGGGAGCCCGGCGGGCATCTCG 17 1,034 128.33 574 53.52 90.92
miR-3535 gga-miR-3535 GGATATGATGACTGATTATCTGAGAA 23 1,023 126.96 268 24.99 75.98
miR-664-3p ssc-miR-664-3p TATTCATTTATCTCCCAGCCTACAAA 20 1,285 159.48 1 0.09 79.79
miR-5100 mmu-miR-5100 TCGAATCCCAGCGGTGCCTCCAATCT 20 737 91.47 375 34.96 63.22
miR-716b sha-miR-716b TCTTGGTGGTAGTAGCAAATATTCAA 22 367 45.55 124 11.56 28.55
miR-5115 mmu-miR-5115 CTGGACGCGAGCCGGGCCCTTCCCGT 19 191 23.70 126 11.75 17.73
miR-6238 mmu-miR-6238 TATTAGTCAGCGGAGGAAAAGAAACT 19 170 21.10 86 8.02 14.56
miR6173 hbr-miR6173 CGTAAACGATGAATACTAGGTGTCGG 17 247 30.65 0 0.00 15.33
miR-6239 mmu-miR-6239 AGCGGTGGATCACTCGGCTCGTGCGT 17 114 14.15 44 4.10 9.13
miR-6240 mmu-miR-6240 CAAAGCATCGCGAAGGCCCGCGATCT 19 120 14.89 23 2.14 8.52
miR-6412 mmu-miR-6412 TCGAAACCATCCTCTGCTACCAATCT 20 90 11.17 21 1.96 6.56
miR-1895 mmu-miR-1895 AGAGGAGGACGAGGAGGAAGAGGAGG 18 60 7.45 33 3.08 5.26
miR-320d hsa-miR-320d; ppy-miR-320d AAAAGCTGGGTTGAGAGGATCTCGTA 19 56 6.95 30 2.80 4.87
miR-6129 ptr-miR-6129; hsa-miR-6129 TGAGGGAGTAGGGTGTATAGTATCTC 19 48 5.96 31 2.89 4.42
miR-2779 bmo-miR-2779 TTTCCGGCTCGAAGGACCAATCTCGT 19 49 6.08 29 2.70 4.39
miR-124c-3p gga-miR-124c-3p TCAAGGTCCGCTGTGAACACGGATCT 0 25 3.10 50 4.66 3.88
miR-5109 mmu-miR-5109 TGGTGCGGACCAGGGGAATCCGACAT 23 41 5.09 30 2.80 3.94
miR-1230 mml-miR-1230 TGGGTCGGGGCATCTCGTATGCCGTC 17 1 0.12 67 6.25 3.19
miR-6236 mmu-miR-6236 GCCGTCGCCGGCAGTCGGAGAGATCT 18 45 5.58 8 0.75 3.17
miR-5097 mmu-miR-5097 TCATGTCCCTGTTCGGGCGCCAATCT 22 22 2.73 19 1.77 2.25
miR-300-3p mmu-miR-300-3p; rno-miR-300-3p; cgr-miR-300 TATGCAGGGGCAAGCTCTCTGTATCT 20 18 2.23 21 1.96 2.10
miR-24b xtr-miR-24b; xla-miR-24b TGGCTCAGTTCAGCAGGAGATCTCGT 18 17 2.11 19 1.77 1.94
miR-4485 hsa-miR-4485 AACGGCCGCGGTATCCTGACCGTGCA 17 7 0.87 23 2.14 1.51
miR-4497 hsa-miR-4497 CTCCGGGACGGCTGGGAAGGCCGGCA 23 23 2.85 7 0.65 1.75
miR6300 gma-miR6300 GTCGTTGTAGTATAGTGGTGAGTATT 18 28 3.48 0 0.00 1.74
miR-5108 mmu-miR-5108 GTAGAGCACTGGATGGATCTCGTATG 18 27 3.35 1 0.09 1.72
miR-5106 mmu-miR-5106 GGGTCTGTAGCTCAGTTGGTTAGAGC 19 25 3.10 1 0.09 1.60
miR-323c oar-miR-323c CACAATACACGGTCGGCCTCTATCTC 21 16 1.99 8 0.75 1.37
miR-5119 mmu-miR-5119 CATCACATCCTGGGGCTGTAGCCGGA 18 18 2.23 0 0.00 1.12
miR-3168 hsa-miR-3168 GAGTTCTACAGTCCGACGATCGTATG 18 0 0.00 18 1.68 0.84
miR-4492 hsa-miR-4492 GGGGCTGGGCGCGCGCCGCGGCATCT 17 12 1.49 6 0.56 1.02
miR-27e dre-miR-27e; fru-miR-27e; tni-miR-27e TTCACAGTGGCTAAGTAGAATCTCGT 20 8 0.99 10 0.93 0.96
miR-6089 hsa-miR-6089 CGGGGTGGGTCGGGGCGGGGCGGACT 18 16 1.99 0 0.00 0.99
miR-6327 rno-miR-6327 AGGACTGTAGATCCATCTCGTATGCC 18 0 0.00 12 1.12 0.56
miR-1957a mmu-miR-1957a AGTGGTAGAGCATTTGACTGATCTCG 18 1 0.12 11 1.03 0.57
miR159c-3p ath-miR159c; aly-miR159c-3p TTTGGATTGAAGGGAGCATCTCGTAT 17 10 1.24 0 0.00 0.62
miR-3607-3p hsa-miR-3607-3p ACTGTAAACGCTTTCTGATGATCTCG 20 10 1.24 0 0.00 0.62
miR-5124a mmu-miR-5124a GTCAAGTGACTAAGAGCATATGGTGG 19 7 0.87 1 0.09 0.48
miR-3591-5p hsa-miR-3591-5p TTTAGTGTGATAATGGCGTTTATCTC 21 1 0.12 7 0.65 0.39
miR-378g hsa-miR-378g ACTGGGCTTGGAGTCGGAAGGCATCT 20 1 0.12 7 0.65 0.39
miR-4792 hsa-miR-4792 CGGTGAGCTCTCGCTGGCATCTCGGA 18 1 0.12 7 0.65 0.39
miR-1261 hsa-miR-1261 ATGGATAAGGCATTGGCTTCCTAAGC 19 6 0.74 0 0.00 0.37
miR-535d mdm-miR535d TGACGACGAGAGAGAGCACGCATCTC 21 6 0.74 0 0.00 0.37
miR-1949 mmu-miR-1949 CTATACCAGGATGCCAGCATAGTTAT 24 6 0.74 0 0.00 0.37
miR-378a ppy-miR-378a ACTGGACTTGGGTCAGAAGGCATCTC 21 6 0.74 0 0.00 0.37
miR-6516-3p gga-miR-6516-3p CATGTATGATACTGCAAACAGAAATC 20 0 0.00 6 0.56 0.28

RFI, residual feed intake; RC, read counts; RCPM, read count per million mapped reads.

Table 6
Novel bovine miRNAs detected in the bovine liver tissue, with sequence, genomic location and their related expression (read counts)
Predicted miRNA ID Chr. Position Strand No.mod. Pred. Read cluster sequence (5′ - 3′) RC in two pools1

Start End
Candidate_1 chr11 76,942,835 76,942,919 + 3 GAGAGAACGTAATCTGAGTGGTTTC 2,102
Candidate_2 chr19 8,794,560 8,794,644 4 GAGAGAACGTAATCTGAGTGGTTTC 2,102
Candidate_3 chr21 65,541,217 65,541,301 4 TTCACTGGGCATCCTCTGCTTTAT 1,022
Candidate_4 chr27 7,419,033 7,419,126 + 5 GTTCCGGGGGGAGTATGGTTGCAAAG 416
Candidate_5 chr28 32,693,899 32,693,979 3 GTCTGGTGGGAAGGAAGGGACACATC 217
Candidate_6 chr29 49,019,853 49,019,926 3 GGAATACCGGGTGCTGCAGGCTTT 63
Candidate_7 chr21 173,761 173,839 + 4 TTGTCCTACTTCTCAGCTGTCTT 53
Candidate_8 chr27 7,419,033 7,419,119 + 4 TTATTCCCATGACCCGCCTGGCAGC 22
Candidate_9 chr3 67,781,210 67,781,312 3 CTGCGGGATGAACCGAACGCCGGGTTAAG 339
Candidate_10 chr12 36,358,265 36,358,343 5 TCCACATCCCTCACAGTTTGGTG 198

Chr., chromosome number; No.mod. Pred., the number of models that predict the specific novel microRNA; RFI, residual feed intake.

1 RC in two pools, total reads count in low and high RFI libraries.

Table 7
miRNAs differentially expressed between the high and low RFI libraries
miRNA Base-mean Fold-change p-value

Low RFI High RFI
bta-miR-32 392.5 2,925.6 7.45 0.02
bta-miR-215 387.9 2,212.8 5.7 0.04
bta-miR-708 429.8 2,316.1 5.39 0.05
bta-miR-101 50,519 268,425.1 5.31 0.03
bta-miR-193a-3p 368.8 1,894.4 5.14 0.06
bta-miR-29b 380.6 1,743.9 4.58 0.08
bta-miR-1 2,820.1 12,150.3 4.31 0.06
bta-miR-21-5p 35,761.9 152,774.6 4.27 0.06
bta-miR-126-5p 2,757.3 11,692.3 4.24 0.06
bta-miR-424-5p 220.4 932.4 4.23 0.12
bta-miR-192 96,668.9 339,724.6 3.51 0.1
bta-miR-6119-5p 176.7 600.7 3.4 0.21
bta-miR-143 630,803 2,068,377.1 3.28 0.12
bta-miR-451 7,749.2 24,982.5 3.22 0.13
bta-miR-19b 364.2 1,166.3 3.2 0.19
bta-miR-7 123.8 394.2 3.18 0.29
bta-miR-2285t 1,161 3,571.3 3.08 0.16
bta-miR-29c 117.5 355.8 3.03 0.32
bta-miR-374a 359.7 1,089.4 3.03 0.21
bta-miR-30e-5p 41,839.2 109,519.4 2.62 0.2
bta-miR-148a 145,596.2 379,981.7 2.61 0.2
bta-miR-22-5p 554.6 1,443 2.6 0.26
bta-miR-146a 2,093.5 5,218.6 2.49 0.24
bta-miR-6120-3p 269.5 649 2.41 0.35
bta-miR-206 886.9 2,115.1 2.38 0.28
bta-miR-30b-5p 6,295.9 1,4658.5 2.33 0.27
bta-miR-106b 1,024.4 2,235.9 2.18 0.33
bta-miR-2285f 592.8 1,278.3 2.16 0.36
bta-miR-130a 621 1,247.5 2.01 0.41
bta-miR-31 584.6 289.9 0.5 0.46
bta-miR-874 642.9 318.5 0.5 0.45
bta-miR-1307 1,068.1 519.4 0.49 0.4
bta-miR-15b 1,160.1 551.3 0.48 0.38
bta-miR-2484 1,438.7 680.9 0.47 0.37
bta-miR-148b 13,567 5,744.6 0.42 0.26
miR-716b 334.2 136.2 0.41 0.42
bta-miR-380-3p 284.1 113.1 0.4 0.43
bta-miR-197 1,249.3 488.7 0.39 0.27
bta-miR-423-5p 42,318.2 16,417.8 0.39 0.21
bta-miR-375 312.3 114.2 0.37 0.38
bta-miR-6529 356 124.1 0.35 0.34
bta-miR-455-3p 4,918.1 457.9 0.09 0.01
isomiRs differentially expressed between high and low RFI
 bta-mir-122-3p 2,465 11,584.7 4.7 0.05
 bta-mir-142-3p 2,497.8 9,468.5 3.79 0.09
 bta-mir-885-3p 774.9 342.6 0.44 0.37
Homologous differentially expressed between high and low RFI
 miR-203-3p 466.2 3,670.1 7.87 0.01
 miR-802-5p 568.2 1,957 3.44 0.14
 miR-1285 1,352.2 662.2 0.49 0.39
 miR-3535 931.5 294.3 0.32 0.2

RFI, residual feed intake.

Table 8
Differentially expressed genes and their potential miRNA expression in liver; up and down- regulations were based on contrast of low RFI1
Target gene miRNA Seed match Expression of miRNA Expression of mRNA Network ID Top functions
AVPR1A bta-miR-885 8mer UP Down 1 Cellular Growth and Proliferation, Cancer, Cardiovascular System Development and Function
CNN1 bta-miR-424-5P 7mer-m8 Down Up 1
CXCR7 bta-miR-29b 7mer-m8 Down Up 1
EDNRB bta-miR-19b 8mer Down Up 1
FGA bta-miR-29b 7mer Down Up 1
GHR bta-miR-101 8mer Down Down 1
IGFBP3 bta-miR-19b 8mer Down Up 1
NKIRAS1 bta-miR-19b 7mer-m8 Down Down 1
RGS2 bta-miR-30b 7mer-m9 Down Up 1
AHR bta-miR-29b 7mer-m8 Down Down 2 Hepatic System Disease, Dermatological Disease and Conditions, Cellular Growth and Proliferation
CD4 bta-miR-143 7mer Down Up 2
GSTM1 bta-miR-30b-3p 7mer-m8 Down Down 2
S100A10 bta-miR-21 7mer-m8 Down Down 2
HELZ bta-miR-15 8mer Up Down 2
bta-miR-424 8mer Down Down 2
HLA-DRB1 bta-miR-197 7mer-m8 Up Up 2
POSTN bta-miR-19b 8mer Down Up 2
AP3B2 bta-miR-30b 7mer-m8 Up Down 3 Cellular Assembly and Organization, Cancer, Cellular Movement
ESPN bta-miR-424 7mer-m8 Down Down 3
bta-miR-15b 7mer-m8 Up Down 3
MAOA bta-miR-2285 8mer Down Up 3
CPEB1 bta-miR-19b 7mer-m8 Down Down 4 Protein Synthesis, Development Disorder, Neurological disease
AHSG bta-miR-31 8mer Up Up 5 Drug Metabolism, Endocrine System Development, Lipid Metabolism
COL3A1 bta-miR-122 8mer Down Up 5
CYP2C18 bta-miR-424 7mer-m8 Down Down 5
bta-miR-143 7mer Down Down 5
MEP1B bta-miR-32 7mer Down Down 5
SLC27A6 bta-miR-424 7mer-m8 Down Down 5
ABCC4 bta-miR-19b 7mer-m8 Down Down 6 Carbohydrate Metabolism, Drug Metabolism, Small Molecular Biochemistry
ABHD5 bta-miR-19b 7mer-m8 Down Down 6
COL4A6 bta-miR-29b 8mer Down Up 6
MAP2K6 bta-miR-29b 7mer Down Down 6
SNAI2 bta-miR-30b 7mer-m8 Down Down 6
bta-miR-30d 7mer-m8 Down Down 6
AVPR1A bta-miR-885 8mer Up Down 7 Cell Death, Cell Signaling, Molecular Transport
DDC bta-miR-708 7mer-m8 Down Down 7
DHRS3 bta-miR-19b 8mer Down Up 7
SLC22A7 bta-miR-29b 8mer Down Up 7
SOD3 bta-miR-1 7mer-m8 Down Down 7
bta-miR-423-5p 7mer Up Down 7
bta-miR-708 7mer-m8 Down Down 7
bta-miR-19a 7mer Down Down 7
bta-miR-19b 7mer Down Down 7

RFI, residual feed intake.

1 The differentially expressed genes and gene networks were from previous study (Chen et al., 2011).

REFERENCES

Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol 11:R106
crossref pmid pmc
Arthur PF, Archer JA, Johnston DJ, Herd RM, Richardson EC, Parnell PF. 2001. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J Anim Sci 79:2805–2811.
crossref pmid
Chen Y, Gondro C, Quinn K, Herd RM, Parnell PF, Vanselow B. 2011. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim Genet 42:475–490.
crossref pmid
Donoghue KA, Arthur PF, Wilkins JF, Herd RM. 2011. Onset of puberty and early-life reproduction in Angus females divergently selected for post-weaning residual feed intake. Anim Prod Sci 51:183–190.
crossref
Fatima A, Lynn DJ, O’Boyle P, Seoighe C, Morris D. 2014. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics 15:279
crossref pmid pmc
Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, Bateman A. 2009. Rfam: updates to the RNA families database. Nucl Acids Res 37:D136–D140.
crossref pmid pmc
Kozomara A, Griffiths-Jones S. 2014. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucl Acids Res 42:D68–D73.
crossref pmid pmc
Gu Z, Eleswarapu S, Jiang H. 2007. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Lett 581:981–988.
crossref pmid
Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM. 2011. miRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucl Acids Res 39:W132–W138.
crossref pmid pmc
Hu J, Xu Y, Hao J, Wang S, Li C, Meng S. 2012. MiR-122 in hepatic function and liver diseases. Protein Cell 3:364–371.
crossref pmid pmc pdf
Jin W, Grant JR, Stothard P, Moore SS, Guan LL. 2009. Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Mol Biol 10:90
crossref pmid pmc
Jordan SD, Kruger M, Willmes DM, Redemann N, Wunderlich FT, Bronneke HS, Merkwirth C, Kashkar H, Olkkonen VM, Bottger T, Braun T, Seibler J, Bruning JC. 2011. Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nat Cell Biol 13:434–446.
crossref pmid
Koch RM, Swiger LA, Chambers D, Gregory KE. 1963. Efficiency of feed use in beef cattle. J Anim Sci 22:486–494.
crossref
Kornfeld JW, Baitzel C, Konner AC, Nicholls HT, Vogt MC, Herrmanns K, Scheja L, Haumaitre C, Wolf AM, Knippschild U, Seibler J, Cereghini S, Heeren J, Stoffel M, Brüning JC. 2013. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature 494:111–115.
crossref pmid
Kozomara A, Griffiths-Jones S. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucl Acids Res 39:D152–D157.
crossref pmid pmc
Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25
crossref pmid pmc
Lawless N, Foroushani AB, McCabe MS, O’Farrelly C, Lynn DJ. 2013. Next Generation sequencing reveals the expression of a unique miRNA profile in response to a gram-positive bacterial infection. PLoS One 8:e57543
crossref pmid pmc
Lewis AP, Jopling CL. 2010. Regulation and biological function of the liver-specific miR-122. Biochem Soc Trans 38:1553–1557.
crossref pmid
Liu HC, Hicks JA, Trakooljul N, Zhao SH. 2010. Current knowledge of microRNA characterization in agricultural animals. Anim Genet 41:225–231.
crossref pmid
Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR. 2005. MicroRNA expression profiles classify human cancers. Nature 435:834–838.
crossref pmid
Miles JR, McDaneld TG, Wiedmann RT, Cushman RA, Echternkamp SE, Vallet JL, Smith TPL. 2012. MicroRNA expression profile in bovine cumulus-oocyte complexes: Possible role of let-7 and miR-106a in the development of bovine oocytes. Anim Reprod Sci 130:16–26.
crossref pmid
Pandey AK, Verma G, Vig S, Srivastava S, Srivastava AK, Datta M. 2011. miR-29a levels are elevated in the db/db mice liver and its overexpression leads to attenuation of insulin action on PEPCK gene expression in HepG2 cells. Mol Cell Endocrinol 332:125–133.
crossref pmid
Romao JM, Jin W, He M, McAllister T, Guan LL. 2012. Altered MicroRNA expression in bovine subcutaneous and visceral adipose tissues from cattle under different diet. PLoS One 7:e40605
crossref pmid pmc
Rottiers V, Näär AM. 2012. MicroRNAs in metabolism and metabolic disorders. Nat Rev Mol Cell Biol 13:239–250.
crossref pmid pmc
Sherman EL, Nkrumah JD, Li C, Bartusiak R, Murdoch B, Moore SS. 2009. Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle. J Anim Sci 87:37–45.
crossref pmid
Tripurani SK, Xiao C, Salem M, Yao J. 2010. Cloning and analysis of fetal ovary microRNAs in cattle. Anim Reprod Sci 120:16–22.
crossref pmid
Vejnar CE, Zdobnov EM. 2012. miRmap: Comprehensive prediction of microRNA target repression strength. Nucl Acids Res 40:11673–11683.
crossref pmid pmc
Wen J, Friedman JR. 2012. miR-122 regulates hepatic lipid metabolism and tumor suppression. J Clin Invest 122:2773–2776.
crossref pmid pmc
Yang JS, Phillips MD, Betel D, Mu P, Ventura A, Siepel AC, Chen KC, Lai EC. 2011. Widespread regulatory activity of vertebrate microRNA* species. RNA 17:312–326.
crossref pmid pmc
Yu Z, Jian Z, Shen SH, Purisima E, Wang E. 2007. Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos. Nucl Acids Res 35:152–164.
crossref pmid pmc


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