Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 May 10:14:1174145.
doi: 10.3389/fgene.2023.1174145. eCollection 2023.

miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential

Affiliations

miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential

Priyanka Banerjee et al. Front Genet. .

Abstract

Reproductive performance is the most critical factor affecting production efficiency in the cow-calf industry. Heifers with low reproductive efficiency may fail to become pregnant during the breeding season or maintain a pregnancy. The cause of reproductive failure often remains unknown, and the non-pregnant heifers are not identified until several weeks after the breeding season. Therefore, improving heifer fertility utilizing genomic information has become increasingly important. One approach is using microRNAs (miRNA) in the maternal blood that play an important role in regulating the target genes underlying pregnancy success and thereby in selecting reproductively efficient heifers. Therefore, the current study hypothesized that miRNA expression profiles from peripheral white blood cells (PWBC) at weaning could predict the future reproductive outcome of beef heifers. To this end, we measured the miRNA profiles using small RNA-sequencing in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 7) or subfertile (SFH, n = 7). In addition to differentially expressed miRNAs (DEMIs), their target genes were predicted from TargetScan. The PWBC gene expression from the same heifers were retrieved and co-expression networks were constructed between DEMIs and their target genes. We identified 16 differentially expressed miRNAs between the groups (p-value ≤0.05 and absolute (log2 fold change ≥0.05)). Interestingly, based on a strong negative correlation identified from miRNA-gene network analysis with PCIT (partial correlation and information theory), we identified miRNA-target genes in the SFH group. Additionally, TargetScan predictions and differential expression analysis identified bta-miR-1839 with ESR1 , bta-miR-92b with KLF4 and KAT2B, bta-miR-2419-5p with LILRA4, bta-miR-1260b with UBE2E1, SKAP2 and CLEC4D, and bta-let-7a-5p with GATM, MXD1 as miRNA-gene targets. The miRNA-target gene pairs in the FH group are over-represented for MAPK, ErbB, HIF-1, FoxO, p53, mTOR, T-cell receptor, insulin and GnRH signaling pathways, while those in the SFH group include cell cycle, p53 signaling pathway and apoptosis. Some miRNAs, miRNA-target genes and regulated pathways identified in this study have a potential role in fertility; other targets are identified as novel and need to be validated in a bigger cohort that could help to predict the future reproductive outcomes of beef heifers.

Keywords: beef heifer; miRNA; pathways; reproductive potential; small-RNA sequencing.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic representation of the study design and analysis steps.
FIGURE 2
FIGURE 2
Volcano plot of differentially expressed miRNAs between FH and SFH groups. Each dot corresponds to a miRNA. The log2fold change is represented in the x-axis represents, while the negative log (base 10) of the p-value in the y-axis. The horizontal dashed line represents the threshold with a p-value cutoff <0.05, while the vertical bars represent the absolute log2fold change >0.5. The 16 DEMIs are labeled in the plot. The left panel (0 to −3 of log2fold change) is downregulated, while the right panel (0–4 of log2fold change) is upregulated miRNAs.
FIGURE 3
FIGURE 3
Number of genes targeted by 16 DEMIs (ranked in descending order).
FIGURE 4
FIGURE 4
Regulatory networks of co-expressed genes with 16 DEMIs in (A) FH and (B) SFH groups. Nodes are genes significantly correlated with miRNAs, while edges are positive or negative interactions (correlations) between the miRNA and target genes. The blue diamond represents the DEMIs; green strokes represent positive correlations, while red strokes represent negative correlations.
FIGURE 5
FIGURE 5
Central reference network constructed using DyNet. (A) Network comparison based on the rewired node in the FH and SFH group. The network comprises of 650 nodes and 1547 edges. The blue diamond represents the DEMIs. Unique nodes are shown in red (FH) and green (SFH). Shared nodes are shown in white. (B) Central reference network showing the miRNA-gene correlated pair as identified from TargetScan (Supplementary Table S7). Unique nodes are shown in red (FH) and green (SFH). Shared nodes are shown in white. For ease of visualization, each miRNA-target gene pair is labeled with the same color.

References

    1. Abane R., Mezger V. (2010). Roles of heat shock factors in gametogenesis and development. FEBS J. 277, 4150–4172. 10.1111/j.1742-4658.2010.07830.x - DOI - PubMed
    1. Agarwal V., Bell G. W., Nam J.-W., Bartel D. P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005. 10.7554/eLife.05005 - DOI - PMC - PubMed
    1. Ali A., Hadlich F., Abbas M. W., Iqbal M. A., Tesfaye D., Bouma G. J., et al. (2021). MicroRNA–mRNA networks in pregnancy complications: A comprehensive downstream analysis of potential biomarkers. Int. J. Mol. Sci. 22, 2313. 10.3390/ijms22052313 - DOI - PMC - PubMed
    1. Andrew S. (2010). FastQC: A quality control tool for high throughput sequence data. Available at: https://www.bioinformatics.babraham.ac.uk/index.html .
    1. Assenov Y., Ramírez F., Schelhorn S.-E., Lengauer T., Albrecht M. (2008). Computing topological parameters of biological networks. Bioinformatics 24, 282–284. 10.1093/bioinformatics/btm554 - DOI - PubMed