Mar 17, 2025
Screening and validation of key microRNAs regulating muscle development in Hanper Sheep
- yunxia zhi1,
- Boxin Hu1,
- Shujun Tian1,
- Ying Bai2,
- Xiaoyong Chen1
- 1College of Animal Science and Technology, Hebei Agricultural University;
- 2School of Life Science and Food Engineering, Hebei University of Engineering
Protocol Citation: yunxia zhi, Boxin Hu, Shujun Tian, Ying Bai, Xiaoyong Chen 2025. Screening and validation of key microRNAs regulating muscle development in Hanper Sheep. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvoox5xv4o/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 16, 2025
Last Modified: March 17, 2025
Protocol Integer ID: 124464
Keywords: muscle development, functional enrichment, microRNA, Hanper sheep
Funders Acknowledgements:
Talents Introduction Fund of Hebei Agricultural University
Grant ID: YJ201938
Abstract
Background/Objectives: In sheep
farming, the economic significance of meat characteristics is substantial, and
advancing the genetic quality of livestock relies heavily on understanding the
cellular mechanisms behind muscle growth and its regulation. This study examined miRNA expression patterns in the
longissimus dorsi muscle tissue of Hanper sheep of various ages, with
the goal of determining their biological functions and identifying miRNAs and
their target mRNAs that influence muscle development and meat quality. Methods: Using the Image-Pro Plus 6.0 program and HE
and fluorescent staining procedures, we measured the diameter of muscle fibers
in the longissimus dorsi of Hanper sheep at three distinct ages (1, 7,
and 13 months) in order to calculate the average fiber size. For the analysis
of muscle fiber area, one-way ANOVA was conducted using SPSS 25.0, with LSD
tests applied afterward to compare the different groups. Transcriptome
sequencing was conducted to identify miRNAs, and bioinformatics tools were
applied to predict their target genes. GO and KEGG functional annotations were
used to analyze the biological functions of these target genes. RT-qPCR was
performed to validate the expression levels of differentially expressed miRNAs.
Results: Muscle fiber diameter and area increased progressively with age, as
indicated by HE and fluorescence staining. Four novel miRNAs identified for the first time in sheep
were among the 116 differentially expressed miRNAs that were found. These
miRNAs were found to be involved in key pathways such as TGF-β, mTOR, Wnt, and
MAPK, which regulate muscle growth and development. It was determined that
three new miRNA-mRNA pairs—oar-miR-133/MSC, oar-miR-148a/FST, and
oar-miR-410-3p/NIN—may be essential for muscle growth. RT-qPCR results
confirmed the expression trends observed in the transcriptome sequencing data. Conclusions:
Our knowledge of the fundamental molecular mechanisms underpinning muscle
growth and development is improved by the discovery of new miRNAs and the
target genes that correspond to them. These findings may serve as new breeding
targets for improving meat quality in sheep.
Materials
| A | B | C | D | E | |
| Category | Item | Brand / Supplier | Catalog Number | Notes | |
| Sample collection | Hanper rams (1, 7, 13 months old) | Hebei Liansheng Agricultural Development Co., Ltd. | - | Healthy, similar body weight, no genetic kinship | |
| Liquid nitrogen | General Laboratory Supply | - | For rapid freezing of muscle samples | ||
| Ultra-low temperature freezer (-80°C) | - | - | For sample storage | ||
| Slaughter operation standard | NY/T 3469-2019 | - | Humane slaughter of livestock | ||
| Measurement of muscle fiber area | 4% Neutral paraformaldehyde solution | Servicebio or equivalent | G1101 | Tissue fixation | |
| Paraffin wax | Leica Biosystems or equivalent | - | Embedding fixed tissue | ||
| Microtome (5 μm slices) | Leica or equivalent | - | Tissue sectioning | ||
| Hematoxylin and Eosin (HE) staining kit | Servicebio or equivalent | G1120 | Includes all HE staining reagents | ||
| Neutral resin mounting medium | Servicebio or equivalent | G8590 | For slide sealing | ||
| Microscope (15×40 magnification) | Olympus / Nikon / Leica | - | Observation and image capture | ||
| Eyepiece micrometer | - | - | Fiber diameter measurement | ||
| Image-Pro Plus software (Version 6.0) | Media Cybernetics, USA | - | Image analysis | ||
| SPSS statistical software (Version 25.0) | IBM | - | Statistical analysis | ||
| Total RNA extraction and miRNA sequencing | TRIzol Reagent Kit | Invitrogen, Carlsbad, CA, USA | 15596026 | Total RNA extraction | |
| 1% Agarose gel electrophoresis reagents | Sangon Biotech / Thermo Fisher | - | RNA integrity check | ||
| NanoPhotometer‱ spectrophotometer | IMPLEN, CA, USA | - | RNA purity detection | ||
| Qubit‱ RNA Assay Kit | Life Technologies, CA, USA | Q32852 | RNA concentration measurement | ||
| Qubit‱ 2.0 Fluorometer | Life Technologies, CA, USA | Q32866 | For RNA quantification | ||
| RNA Nano 6000 Assay Kit | Agilent Technologies, CA, USA | 5067-1511 | RNA integrity evaluation | ||
| Agilent Bioanalyzer 2100 system | Agilent Technologies, CA, USA | G2939BA | RNA quality assessment | ||
| Small RNA Library Preparation & Sequencing | NEBNext‱ Multiplex Small RNA Library Prep Set for Illumina‱ | NEB, USA | E7560S | Small RNA library construction | |
| High Sensitivity DNA Chips | Agilent Technologies, CA, USA | 5067-4626 | Library quality check | ||
| cBot Cluster Generation System | Illumina | - | Library clustering | ||
| TruSeq SR Cluster Kit v3-cBot-HS | Illumina | GD-401-3001 | Cluster generation | ||
| Illumina HiSeq 2500 Sequencing Platform | Illumina | - | 50 bp single-end sequencing | ||
| Data processing | Bowtie (v1.2.3) | SourceForge | - | Small RNA sequence alignment | |
| mirdeep2 | GitHub | - | Known/novel miRNA prediction | ||
| miREvo | GitHub | - | Novel miRNA prediction | ||
| srna-tools-cli | GitHub | - | Small RNA annotation | ||
| MiRBase (v20.0) | - | - | Known miRNA database | ||
| RepeatMasker & Rfam databases | - | - | Non-miRNA filtering | ||
| miRanda | - | - | Target gene prediction | ||
| TargetScan | - | - | Target gene prediction (cross-validation) | ||
| Differential expression miRNA analyses and functional pathway enrichment | DESeq (R package v1.8.3) | Bioconductor | - | Differential expression analysis | |
| GOseq | Bioconductor | - | GO enrichment analysis | ||
| KOBAS | http://kobas.cbi.pku.edu.cn/ | - | KEGG pathway enrichment analysis | ||
| Cytoscape | https://cytoscape.org/ | - | miRNA-mRNA interaction network | ||
| RT-qPCR Validation | M5 miRNA cDNA Synthesis Kit | Mei5 Biotechnology Co., Ltd. | M51103-500 | miRNA reverse transcription | |
| M5 miRNA qPCR Assay Kit | Mei5 Biotechnology Co., Ltd. | M52002-500 | qPCR detection for miRNA | ||
| ABI Prism 7500 Real-Time PCR System | Thermo Fisher Scientific, USA | 4351104 | qPCR amplification | ||
| RNase-free water | Thermo Fisher Scientific, USA | AM9937 | PCR-grade water | ||
| Primers (Forward & Reverse) | - | See Table 1 | Specific to each miRNA and U6 (reference) |
Table 1. Primer sequences utilized in quantitative fluorescence PCR.
| A | B | C | |
| Primer | Primer sequence（5'to3'） | Length, nt | |
| oar-miR-133 | TTGGTCCCCTTCAACCAGCTGT | 22 | |
| oar-miR-148a | TCAGTGCACTACAGAACTTTGT | 22 | |
| oar-miR-191 | CAACGGAATCCCAAAAGCAGCT | 22 | |
| oar-miR-27a | TTCACAGTGGCTAAGTTCCGC | 21 | |
| oar-miR-30a-5p | TGTAAACATCCTCGACTGGAAGC | 23 | |
| oar-miR-3957-3p | ACGCACAGCACCTCACTGAGCT | 22 | |
| oar-miR-410-3p | AATATAACACAGATGGCCTGT | 21 | |
| oar-miR-541-5p | AAAGGATTCTGCTGTCGGTCCCACT | 25 | |
| U6 | AGTGCAGGGTCCGAGGTATT | 20 |
Sample collection
Sample collection
In this study, we selected three uncastrated Hanper rams at three different
developmental stages (1 month, 7 months, and 13 months old “M1, M7, and M13”)
from the Hebei Liansheng Agricultural Development Co., Ltd. sheep farm. At each
stage, three rams with similar body weights, good health status, and no genetic
kinship were chosen. To ensure animal welfare during transport, the rams underwent a pre-shipment
quarantine in a separate area. Careful handling practices were implemented
throughout the journey, avoiding excessive confinement and unnecessary force to
mitigate both stress responses and injuries. After arriving at the slaughterhouse,
the sheep were humanely slaughtered according to the “Livestock and Poultry
Slaughtering Operation Regulations for Sheep” (NY/T 3469-2019). Following the
butchering process, longissimus dorsi muscle specimens were promptly excised
and subjected to rapid freezing using liquid nitrogen. To maintain sample
integrity, they were later transferred to an ultra-low temperature environment
of -80°C for preservation.
Measurement of muscle fiber area
Measurement of muscle fiber area
The methods of fluorescence staining and HE staining were applied. The
longissimus dorsi muscle tissue was maintained in a 4% neutral paraformaldehyde
solution prior to being stained with HE staining, following Guo's protocol [1].
Briefly, 5μm-thick paraffin slices were created by removing the fixed muscle
tissue. Fix a small piece of tissue with wax, cut it into thin slices with a
slicer, then per-form HE staining, staining steps are dewaxing, covering water,
hematoxylin staining, 5% acetic acid differentiation, returning blue, eosin
staining, dehydration, dropping neutral resin. The stained samples were viewed
under a microscope with a 15×40 zoom, using an eyepiece micrometer for accurate
measurements. Fifteen images were captured from diverse, brightly lit areas
across different focal points. Measuring their diameter and calculating the average
using Image-Pro Plus 6.0 software. Data related to the muscle fiber areas were
processed with SPSS 25.0 software, employing a one-way analysis to assess
variance. Multiple comparisons were conducted using the LSD method to pinpoint
any notable differences between the groups.
Total RNA extraction and miRNA sequencing
Total RNA extraction and miRNA sequencing
Following the comprehensive directions for the process, the Trizol reagent
Kit (Invitrogen, Carlsbad, CA, USA) was used to extract RNA from longissimus
dorsi muscle tissue. RNA degradation and contamination were assessed using 1%
agarose gel electrophoresis. RNA purity was determined with a NanoPhotometer‱
spectrophotometer (IMPLEN, CA, USA). RNA concentration
was measured using the Qubit‱ RNA Assay Kit on a Qubit‱ 2.0 Fluorometer (Life
Technologies, CA, USA). RNA integrity was evaluated with the RNA Nano 6000
Assay Kit on the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA,
USA). For each sample, 3 μg of total RNA was used as the starting material for
small RNA library construction. Libraries were prepared using the NEBNext‱
Multiplex Small RNA Library Prep Set for Illumina‱ (NEB, USA.),
following the manufacturer’s recommended protocol, with index sequences added
to distinguish between different samples. Finally, library quality was assessed
on the Agilent Bioanalyzer 2100 system using High Sensitivity DNA Chips.
Clustering of the index-coded samples was performed on a cBot Cluster
Generation System using the TruSeq SR Cluster Kit v3-cBot-HS (Illumina),
according to the manufacturer’s instructions. After cluster generation,
sequencing of the prepared libraries was carried out on the Illumina HiSeq 2500
platform, generating 50 bp single-end reads.
Data processing
Data processing
After quality control, sequences
within a specific length range were selected from the clean reads for
subsequent analyses. Bowtie (version 1.2.3) [2]was used to align small RNA tags to
the reference genome (Ovis aries Oar_v4.0) with no mismatches allowed. The
small RNA sequences mapped to the genome were used for the identification of
known miRNAs. MiRBase 20.0 was used as the reference database, and miRNA
prediction along with secondary structure visualization was performed using
mirdeep2 [3] and srna-tools-cli. The first
nucleotide bias of miRNAs with specific lengths and the base preference at each
position of all miRNAs were analyzed. To eliminate small RNA tags originating
from protein-coding genes, repetitive sequences, rRNA, tRNA, snRNA, and snoRNA,
the small RNA tags were aligned to the RepeatMasker database, Rfam database, or
species-specific datasets. The hairpin structure of miRNA precursors was used
to predict novel miRNAs. In this study, a combination of miREvo [4] and mirdeep2 [3] was used to predict unannotated small
RNA tags by analyzing secondary structures, Dicer cleavage sites, and minimum
free energy (MFE). Custom scripts were used to calculate the counts of the
identified miRNAs, and both the first nucleotide bias for miRNAs of specific
lengths and the nucleotide preference at each position for all miRNAs were
analyzed separately. Target gene prediction was conducted using miRanda [5]. The expression levels of miRNAs were
normalized and estimated using TPM (Transcripts Per Million), according to the
following formula [6]: Normalized expression = mapped
readcount/Total reads*1, 000, 000
Differential expression miRNA analyses and functional pathway enrichment
Differential expression miRNA analyses and functional pathway enrichment
The expression levels of miRNAs were
normalized using TPM (Transcripts Per Million). Differentially expressed miRNAs
(DEMs) between the longissimus dorsi muscle tissues of Hanper sheep at 1 month,
7 months, and 13 months were identified using the DESeq R package (version
1.8.3). P-values were adjusted for multiple hypothesis testing using the
Benjamini & Hochberg method to control the false discovery rate (FDR). By
default, an adjusted P-value (FDR) < 0.05 was considered the threshold for
significant differential expression. The criteria for selecting DEMs were as
follows: TPM > 1, |log2(FC)| ≥ 1, P-value or FDR < 0.05, and at
least three biological replicates [7].
Gene Ontology (GO) enrichment analysis
was performed on the predicted target genes of the DEMs. GO enrichment was
analyzed using the GOseq software, based on the Wallenius non-central
hypergeometric distribution [8]. The significance thresholds for GO
enrichment were set at P < 0.05 or FDR < 0.05. Kyoto Encyclopedia of
Genes and Genomes (KEGG) enrichment analysis was conducted using the KOBAS
software [9] [10] to evaluate the statistical
enrichment of candidate target genes in KEGG pathways and identify important
biological pathways potentially regulated by miRNAs. The significance
thresholds for KEGG enrichment were P < 0.05 or FDR < 0.05. Target gene
prediction was performed through cross-validation using multiple databases,
including miRanda and TargetScan, and further filtered by RNA-seq data to
remove genes with low expression. Additionally, to explore the interactions
between miRNAs and muscle growth-related genes, an interaction network diagram
was constructed using Cytoscape software, revealing regulatory relationships at
the molecular level.
RT-qPCR verification
RT-qPCR verification
RNA isolated from the longissimus dorsi muscle was processed for reverse
transcription with the M5 miRNA cDNA Synthesis Kit, a tool from Mei5
Biotechnology Co., Ltd, specializing in small RNA first strand synthesis. Using
U6 (Small Nuclear RNA) as an internal reference. Table 1 presents the primer
specifications. The quantitative PCR process was conducted following the
protocols of the M5 miRNA qPCR Assay Kit, a system designed for miRNA detection
using fluorescent methods (Mei5 Biotechnology Co., Ltd). The reaction system
was as follows: 2 × M5 miRNA qPCR Mix10μL, 7.2uL of RNase-free water, 2μL of
cDNA template, 0.4μL of forward primer, and 0.4μL of reverse primer. An ABI
Prism 7500 real-time PCR machine (Thermo Fisher Scientific, Waltham, MA, USA)
was used for all qPCR reactions. To determine the target gene's expression, the
relative quantification approach of 2-∆∆Ct was used.
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Acknowledgements
This research was supported by the Talents Introduction Fund of Hebei Agricultural University, grant number
"YJ201938".