A Study of Sperm DNA Damage Mechanism Based on miRNA Sequencing

Abstract

To analyze the differential expression profiles of microRNAs (miRNAs) in spermatozoa of patients with sperm DNA damage and to investigate the role of miRNAs in sperm DNA damage. Male infertility patients with sperm DNA damage who attended the First Affiliated Hospital of Henan University of Chinese Medicine from October 2023 to December 2023 were selected and included in this study as a case group. Fertile healthy men who were seen at the health check-up center during the same period and diagnosed by examination were also included as a control group. Sperm miRNA expression was detected in patients with sperm DNA damage (case group, n = 5) and healthy medical check-ups (control group, n = 5) using high-throughput sequencing technology. The differentially expressed miRNAs between the two groups were bioinformatically analyzed to explore the main biological functions of the target genes. We found that 63 miRNAs were significantly changed in the spermatozoa of patients with sperm DNA damage,|log2 (foldchange)| ≥ 1, p < .05. Gene Ontology (GO) enrichment analysis indicated that these differential miRNAs might be involved in developmental process, anatomical structure development, cellular macromolecule metabolic process, multicellular organism development, system development, and so on. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that that they mainly affect the PI3K-AKT signaling pathway. The present study suggests that the altered expression of miR-1255a, miR-921, and miR-3156-5p may play an important role in the sperm DNA damage process, and the mechanism may involve the phosphatidylinositol-3'-kinase-AKT (PI3K-AKT) signaling pathway.

Introduction

Male infertility is an important reproductive health problem, and it is estimated that 8%–12% of couples worldwide suffer from infertility, of which approximately 50% is caused by male factors (Agarwal et al., 2021). In an epidemiologic survey of Chinese couples of reproductive age, the prevalence of infertility was found to be as high as 25% (Zhou et al., 2018). Among the many etiologies of male infertility, sperm DNA fragmentation is considered to be an important contributor to male infertility (Agarwal et al., 2020; Wright et al., 2014), which may affect fertility by preventing fertilization, early embryonic development, implantation, pregnancy or duration of pregnancy, and miscarriage (Lewis et al., 2013). Sperm DNA integrity is critical to the success of both natural and assisted conception.

First discovered in 1993, microRNAs (miRNAs) are a class of small non-coding RNAs approximately 22 nt in length. They are widely found in a variety of organisms and play key roles in a variety of physiopathological processes, including testicular development, spermatogenesis, and early embryonic development in mammals (He et al., 2009; Vashisht & Gahlay, 2020; Wu et al., 2013). miRNAs are closely associated with male infertility, and altered expression of miRNAs has been detected in the seminal fluid, spermatozoa, and testicular biopsy samples of infertile men (Heidary et al., 2019). The miRNAs are also involved in the occurrence of sperm DNA damage, which can induce DNA double-strand breaks during spermatogenesis and is an important molecular mechanism for sperm DNA damage (K. Zhao et al., 2015). Therefore, it is of great significance to study the changes in sperm miRNA profiles in patients with elevated sperm DNA fragmentation and to identify the miRNAs that may play a role in the process of sperm DNA damage, to elucidate the molecular mechanism of sperm DNA damage, as well as the treatment of sperm DNA damage.

Objects and Methods

Objects

Five patients with sperm DNA damage in male infertility who attended The First Affiliated Hospital of Henan University of Chinese Medicine from October 2023 to December 2023 were selected as the case group. At the same time, five cases of fertile healthy men confirmed by examination at the physical examination center consulted in the same period were selected as the control group.

Inclusion criteria: (a) Meet the diagnostic criteria of male infertility and sperm DNA fragmentation index (SDFI) greater than 30% in the second edition of Chinese Medicine and Men’s Science; (b) age between 23 and 45 years; (c) have not used any traditional Chinese medicine or western medicine to improve and enhance semen quality in the 3 months prior to the inclusion of observation.

Exclusion criteria: (a) severe oligospermia or azoospermia, the RNA obtained from the sperm precipitate cannot meet the requirements of miRNA sequencing; (b) semen liquefaction abnormalities that make it difficult to separate the seminal plasma from the sperm precipitate; (c) primary kidney disease, cardiovascular and cerebral vascular disease, autoimmune diseases, psychoneurological diseases, tumor history, acute infection, trauma, recent surgery, the use of glucocorticoids and immunosuppressants; (d) those who have taken any drugs to improve semen quality within the past 3 months.

The study was approved by the Hospital Ethics Committee (ethics approval number: 2023HL-050-01), and all subjects agreed to participate in the study and signed an informed consent form.

Collection and Processing of Semen Specimens

Collection of semen specimens was performed according to the standards of The WHO laboratory manual for the examination and processing of human semen (sixth edition). Patients were asked to abstain from sex for 2–7 days for semen collection, and semen was taken by masturbation method and contained in a clean wide-mouth plastic container. The containers with semen samples were placed on a temperature-controlled table and waited for liquefaction at 37°C. The liquefaction of semen was assessed within 30–60 minutes, and semen volume measurements were performed. The total number of spermatozoa, the percentage of forward spermatozoa, and the total sperm viability were calculated using a semiautomated semen analyzer. Finally, the semen samples were centrifuged at 12,000 RPM, 4°C for 10 minutes. The seminal plasma was discarded, the rest was washed with phosphate-buffered saline (PBS) buffer, and then centrifuged again at 12,000 RPM, 4°C for 10 minutes. The final sperm precipitates were frozen in liquid nitrogen and stored in a −80°C cryogenic refrigerator for spare use.

Extraction and Sequencing of Sperm miRNAs

Total sperm RNA was isolated using the RNA Purification and Extraction Kit miRNeasy Mini Kit (50) 217004 (Qiagen, Germany) and analyzed for concentration and purity using a Qubit 3.0 Fluorometer (Life Technologies, CA, USA) and a Nanodrop One spectrophotometer (Thermo Fisher Scientific Inc, USA). The extracted total RNA was assessed for total RNA integrity using an Agilent Bioanalyzer 2100 (Agilent technologies, Santa Clara, CA, USA) and characterized using the RNA integrity number (RIN), and samples with RIN above 7.0 were sequenced.

Library construction was performed according to the QIAseq miRNA Library Kit guidelines, and paired-end libraries were synthesized using the QIAseq miRNA Library Kit (Qiagen, Germany). Specific steps include purification of extracted RNA fragments, ligation of 3′ and 5′ junctions (Ligate 3′ and 5′ Adapters), reverse transcription to complementary DNA (cDNA), followed by PCR purification and enrichment of the products to create the final cDNA library. Purified libraries were quantified using a Qubit 3.0 fluorometer (Life Technologies, CA, USA) and verified using an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) to confirm the insert fragment size and calculate the molar concentration. Clusters were generated by diluting the library to 10 pM by cBot and then sequenced on an Illumina Hiseq Xten (Illumina, USA). The paired-end program was selected for double-end (PE) sequencing. The sequencing process was fully controlled by the data-collection software provided by Illumina, and the sequencing result data were analyzed in real time. The raw data obtained from sequencing were stored in fastq format files, and for each individual fastq file, the quality of the sequencing results was assessed using the FastQC software.

Differential Expression Analysis of Sperm miRNAs

The sequences within the original fastq file are first filtered: removing the connector sequence fragments at both ends of the reads, filtering reads with lengths less than 14bp and more than 41bp, as well as low-quality reads and so on, to complete the initial filtration of the data and to obtain filtered reads that can be used for data analysis.

The filtered reads were compared to the corresponding species in the miRBase database using Bowtie software, which in turn annotated the known miRNAs. Unique reads were obtained by de-weighting the Unique Molecular Identifier(UMI) sequence tags added in the experiment using UMI-tools. miRNA counts were calculated using the miRDeep2 software.

The reads of each sample were compared to those in the existing miRNA database (miRBase) to calculate the miRNA expression. miRNA expression was calculated using the CPM function in the edgeR package to calculate the metrics (counts per million). The edgeR software was used to analyze the differential expression between the sperm DNA damage group and the healthy control group, to screen the differentially expressed miRNAs (DEM), to calculate the expression amount of each sample and the mean value within the group, and to calculate the inter-group difference foldchange, as well as to calculate log2 (foldchange) for the subsequent screening of the differential genes, which was used to screen the differential genes according to the significance criterion of the difference,|log2 (foldchange)| ≥ 1 and p < .05, to screen the test results and count the DEM.

Prediction and Enrichment Analysis of Differential miRNA Target Genes

The prediction of miRNA target genes was performed using the R language-based miRanda package to screen the target genes with binding energy less than −20 for output. Subsequently, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the target genes of the differential miRNAs using the R-based Enrich package, and the cellular localization associated with the target genes was inferred, along with molecular functions, biological processes, and biological pathways associated with target genes.

Results

Screening for DEM

In this experiment, a total of 63 DEM were screened between samples, including 11 upregulated miRNAs and 52 downregulated miRNAs, and the top 10 most significantly upregulated differential miRNAs and the top 10 most significantly downregulated differential miRNAs are provided in Tables 1 and 2, respectively. The differential miRNAs were selected for clustering, and the clustering heat map of differential miRNAs was drawn (Figure 1).

Table 1.

Top 10 miRNA Molecules With the Most Significant Differences in Upregulated Expression

miRNA_id	HSDF group (n = 5)	Control group (n = 5)	log2 (FC)	p value
hsa-miR-1255a	4.377707841	0.965967259	2.18012948	.000870504
hsa-miR-1471	0.981123666	0	Inf	.006272308
hsa-miR-2114-3p	3.077277222	0.623507963	2.303174528	.005923149
hsa-miR-1269a	3.310182533	1.210189986	1.451677221	.029887647
hsa-miR-196a-1-3p	0.93784735	0.079888954	3.553285169	.02508436
hsa-miR-3144-5p	0.598351781	0	Inf	.031409869
hsa-miR-4632-5p	13.68977223	3.578916124	1.935503805	.03263217
hsa-miR-5696	0.889816639	0.079888954	3.477440125	.02228308
hsa-miR-6790-3p	1.963149264	0.85029692	1.207131252	.035341102
hsa-miR-6842-3p	2.128754317	0.828299938	1.36178427	.017224611

Note. Expression data for both samples have been homogenized by the CPM function built into edgeR. Inf = positive infinity; HSDFI = high sperm DNA fragmentation.

Table 2.

Top 10 miRNA Molecules With the Most Significant Differences in Downregulated Expression

miRNA_id	HSDF group (n = 5)	Control group (n = 5)	log2 (FC)	p value
hsa-miR-7154-3p	0.296737496	3.71812506	−3.647316155	.0000708
hsa-miR-7154-5p	34.59357144	145.5161185	−2.072603096	.001369957
hsa-miR-488-3p	5.075086637	17.74259451	−1.805712635	.001483697
hsa-miR-3180	0.267730807	2.13833867	−2.997635303	.001918455
hsa-miR-3180-3p	0.267730807	2.13833867	−2.997635303	.001919865
hsa-miR-1911-3p	0.086430237	8.681977828	−6.650343815	.002261539
hsa-miR-1269b	0.133865404	2.324542791	−4.118091921	.002342642
hsa-miR-1233-3p	0	1.616962031	−Inf	.002396185
hsa-miR-7843-5p	0	2.221739597	−Inf	.002423984
hsa-miR-7159-5p	9.699677314	31.73904928	−1.710250256	.003544562

Note. Expression data for both samples have been homogenized by the CPM function built into edgeR; −Inf = negative infinity.

Figure 1.

Heatmap of Differentially Expressed miRNAs

Note. A3, A4, A5, A7, and A9 are the serial numbers of samples from the spermatozoa with elevated DFI group, and B1, B2, B3, B4, and B5 are the serial numbers of samples from the healthy control group; miRNAs are microRNAs. miRNAs sequenced by high-throughput sequencing of a total of 63 miRNAs met the criterion of significance for the sequence differences,|log2 (foldchange)| ≥ 1 and p < .05.

Target Gene Prediction of DEM

The miRanda package based on R language was used for the prediction of target genes of differential miRNAs. The target genes with binding energy less than −20 were screened for the output. A total of 19,122 target genes were predicted, and some of the target gene prediction results are shown in Table 3.

Table 3.

Selected Results of Differential Expression miRNA Target Gene Prediction

miRNA_id	Gene name	Tot energy
hsa-miR-7154-3p	B4GALT3	−23.13
hsa-miR-7154-3p	BICRA	−68.6
hsa-miR-7154-3p	MAPK10	−20.86
hsa-miR-7154-3p	DAG1	−20.3
hsa-miR-7154-3p	NDEL1	−20.82
hsa-miR-7154-3p	RABGGTA	−26.23
hsa-miR-7154-3p	VPS35L	−20.71
hsa-miR-7154-3p	FGGY	−20.69
hsa-miR-7154-3p	ZKSCAN2	−23.46
hsa-miR-7154-3p	TMEM63C	−22.78

Note. The data volume of target gene prediction results is large, and only part of the results are shown. Tot Energy is the total value of sequence binding free energy.

Target Gene Enrichment Analysis of DEM

GO enrichment analysis of target genes of differential miRNAs showed that a total of 1,992 GO entries were enriched, involving 1,506 biological processes, 239 cellular components, and 247 molecular functions. The top 30 GO entries with the most significant enrichment were plotted as bubble plots using the ggplot2 package (Figure 2). Among the top 30 GO entries with the most significant differences, 23 were involved in biological processes, which mainly included developmental process, anatomical structure development, cellular macromolecule metabolic process, multicellular organism development, system development, organonitrogen compound metabolic process, regulation of primary metabolic process, regulation of nitrogen compound metabolic process, biosynthetic process, organic substance biosynthetic process, and other biological processes. Two cellular components are involved, mainly cytoplasm, and five molecular functions are involved, mainly cation binding, protein binding, and catalytic activity.

Figure 2.

TOP30 GO Entries of the GO Enrichment Analysis

Note. The figure shows the results of the top 30 items sorted by richness significance (p value). The horizontal coordinate is the Rich factor, and the vertical coordinate is the name of the specific GO entry. The color of the dots on the graph indicates the degree of GO significance (p value), the shape of the dots indicates which of the three major categories of the GO database the corresponding GO entry belongs to, and the size of the dots characterizes the number of genes mapped to this GO entry.

KEGG enrichment analysis of the target genes of differentially miRNAs was performed, and a total of 159 meaningful pathways were enriched. The top 30 significantly enriched pathways were plotted as bubble plots using the ggplot2 package (Figure 3), which showed that the target genes of DEM were mainly involved in endocytosis, tight junctions, regulation of the actin cytoskeleton, metabolic pathways, lipids and atherosclerosis, amyotrophic lateral sclerosis, and neuroactive ligand-receptor interactions through the PI3K-AKT signaling pathway, MAPK signaling pathway, Rap1 signaling pathway, Ras signaling pathway, cAMP signaling pathway, and Wnt signaling pathway that affects the onset and development of sperm DNA damage.

Figure 3.

TOP30 Pathway of the KEGG Enriches

Note. The figure shows the results of the top 30 items sorted by p value. The horizontal coordinate is the Rich factor, and the vertical coordinate is the specific KEGG pathway name. The color of the dots on the graph indicates the significance of the pathway (p value), and the size of the dots characterizes the number of genes mapped to the pathway.

Discussion

miRNA are conserved, endogenous short-stranded RNA molecules that bind to the 3′UTR region of mRNA and play important roles in cellular physiopathology by degrading mRNA or inhibiting mRNA translation (Galimov et al., 2022; Makeyev & Maniatis, 2008). In addition, miRNAs can be used as prospective disease markers and contribute to the development of precision medicine, such as the study of Li et al. in which it was found that miR-374b and miR-26b can be used as auxiliary biomarkers for diagnosing idiopathic infertility in men (Li et al., 2020).

miRNAs can be involved in the onset and development of sperm DNA damage in patients or animal models. It has been shown that miR-424/322 can lead to sperm DNA damage by causing breaks in the sperm DNA double strand during spermatogenesis (K. Zhao et al., 2015). miR-125a-5p regulates mitochondrial function by targeting RNA-binding motif protein 38 (Rbm38) and activating the p53 damage response pathway, which significantly increases the levels of reactive oxygen species and DNA damage, leading to poorer sperm DNA integrity (Liang et al., 2021). Therefore, the search for DEM in spermatozoa from patients with sperm DNA damage and normal population is crucial for the study of the pathogenesis of sperm DNA damage, as well as for the exploration of new therapeutic approaches for sperm DNA damage.

Current research suggests that there are three main sources of RNA in spermatozoa: residual RNA produced by the testes, formation of RNA carried by epididymal vesicles that are transported into sperm cells when the sperm cells are in the epididymis, and formation of RNA from mature spermatozoa that undergo de novo transcription by the mature spermatozoa themselves (Santiago et al., 2022). The study by Ostermeier et al. (2002) affirmed the potential of ejaculated spermatozoa RNA in monitoring past events of gene expression during spermatogenesis and advocated that ejaculated spermatozoa RNA could be used as a sample for the study of testis-specific infertility (Miller & Ostermeier, 2006). Therefore, ejaculated spermatozoa were used in this study for high-throughput sequencing to observe changes in miRNA profiles during spermatogenesis in patients with sperm DNA damage.

In this study, miRNA profiles in ejaculated spermatozoa from patients with elevated SDFI and healthy medical examiners were detected by high-throughput sequencing, and with the help of bioinformatics analysis, the differential expression of sperm miRNAs was observed to support the study of molecular mechanisms of sperm DNA damage. The results showed that 63 DEM, including 52 downregulated miRNAs and 11 upregulated miRNAs, were found in the spermatozoa of patients with elevated SDFI compared with those of healthy subjects, suggesting that the expression profiles of miRNAs in spermatozoa cells of patients with sperm DNA damage were significantly changed compared with those of healthy subjects, and that these DEM might be involved in the occurrence and development of sperm DNA damage.

Regarding the mechanism of origin of sperm DNA damage, current studies have focused on three aspects: chromatin abnormalities, oxidative stress damage, and apoptosis abnormalities during sperm formation (Muratori et al., 2019), of which apoptosis is the main pathway of sperm DNA breaks (Muratori et al., 2015). Apoptosis is a type of programmed cell death associated with characteristic morphological and biochemical changes in cells, and this programmed cell death plays an important role in many physiological and pathological processes. In cells undergoing apoptosis, DNA is broken down into 180–200 base DNA fragments by nucleic acid endonucleases (Elmore, 2007). Because of this, DNA fragmentation is also often used as one of the detection indicators to determine whether apoptosis has occurred (Banfalvi, 2017; Majtnerová & Roušar, 2018). Cysteine aspartate protease-2 (caspase 2, CASP-2) has protein hydrolase activity and can act as an inducer of apoptosis, playing a central role in apoptosis and inflammation (Bronner et al., 2015; Kumar et al., 1994; Machado et al., 2016). In this study, we found that miR-3156-5p expression was significantly reduced in patients with elevated sperm DNA fragmentation, and target gene prediction showed that CASP-2 might be a target gene of miR-3156-5p, which was verified in the study by Huang et al. (2021). miR-3156-5p expression was reduced, which might lead to elevated sperm DNA fragmentation and elevated expression of CASP-2 in germ cells during spermatogenesis, which in turn causes abnormal apoptosis during spermatogenesis by promoting CASP-2-mediated apoptosis pathway, resulting in sperm DNA damage and increased DNA fragmentation.

There is also a close relationship between oxidative stress and sperm DNA damage, and many factors such as varicocele (Zhang, Zhang et al., 2022) and infections of male accessory gonads (Berg et al., 2021) can cause elevated levels of oxidative stress, which ultimately leads to sperm DNA fragmentation (Walke et al., 2023). Under normal circumstances, the intracellular oxidative and antioxidant systems are in dynamic balance, and a certain level of reactive oxygen species is necessary for cells to exercise normal physiological functions (Chen & Zhang, 2023). Once the cells are subjected to endogenous or exogenous stimuli, resulting in the production of excessive reactive oxygen species, which breaks this balance, oxidative stress will occur (Pisoschi & Pop, 2015). Excessive reactive oxygen species, if not scavenged in a timely manner, can interact with lipids, proteins, and DNA causing DNA damage (Fraczek et al., 2022; Sharma et al., 2016). The effect of oxidative stress on sperm cells is even greater in spermatozoa due to the lack of antioxidant enzyme system (Bisht et al., 2017), and these reactive oxygen species can induce peroxidative damage to the sperm plasma membrane and DNA fragmentation in the sperm nuclear/mitochondrial genome and lead to dysregulation of the mRNA/transcript levels, which is an important cause of DNA damage in spermatozoa (Bisht & Dada, 2017). Therefore, the antioxidant system is crucial in maintaining normal spermatogenesis and normal sperm function (Yu & Huang, 2015), and antioxidant supplementation can improve the antioxidant activity of the body, inhibit superoxide production, reduce sperm DNA fragmentation caused by oxidative stress, and effectively alleviate oxidative stress damage (Romano et al., 2023).

The main antioxidant enzymes involved in spermatogenesis are catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST), nitric oxide synthase (NOS), nuclear transcription factor red lineage 2–related factor 2 (NRF2), and superoxide dismutase (SOD; Yu & Huang, 2015). Among them, glutathione peroxidase 3 (GPX3) is an important component of the antioxidant system in vivo, is present in the cytoplasm of male germ cells, and is highly expressed in human spermatogonial stem cell (SSC) lines (S. Wu et al., 2023). It can prevent oxidative damage to spermatozoa throughout sperm maturation. It is essential for redox homeostasis in sperm cells. miR-921 showed differential expression in this study, and miR-921 can regulate the expression of GPx3 (Choi et al., 2019), which is consistent with the predicted results of target genes in this study.

In addition, miRNAs may mediate sperm DNA damage by influencing the DNA damage response (DDR). DDR is critical to maintaining genomic stability, and it equips cells with surveillance systems that recognize DNA damage, allowing the cell to respond appropriately depending on the type of lesion it is experiencing. Upon detection of DNA damage, information is transferred to mechanisms that manage cell cycle progression to pause or slow cell division. During this time, the cell can repair the DNA damage using mechanisms appropriate for repairing the lesion (Carusillo & Mussolino, 2020). Sperms have a DNA damage repair system similar to that of somatic cells before maturation. It is generally recognized that there are five main pathways in the germ cell damage repair system of male mammals (Gunes et al., 2015): (a) nucleotide excision repair (NER), (b) base excision repair (BER), (c) DNA mismatch repair (MMR), (d) postreplication repair (PRR), and (e) double-stranded DNA break repair (DSBR). Cells after DNA damage face three regressions (Cadet & Davies, 2017): (a) activation of apoptotic pathways; (b) tolerance to damage, but inheritance of such damage to offspring; and (c) activation of damage repair systems. When the DDR is dysregulated, it may result in damaged DNA not being repaired in a timely manner, resulting in the accumulation of DNA damage and even more severe DNA damage, such as DNA double-strand breaks.

In this study, we found that the expression of miR-1255a was significantly upregulated in sperm cells from patients with sperm DNA damage, and the results of target gene prediction indicated that it might act on SMAD4 to cause dysregulation of sperm DDR, and the targeting relationship of miRNA-1255a on SMAD4 was also confirmed in one study (Xin et al., 2020). SMAD4 is a transforming growth factor beta (TGF-β) signaling central mediator that can play an important role in maintaining DDR and DNA damage repair by regulating the transcription and activity of some key genes involved in the DDR and DNA damage repair aspects (M. Zhao et al., 2018). Deficiency of SMAD4 inhibits the downregulation of expression and function of genes related to DNA damage repair in head and neck epithelial cells, resulting in functional defects in DNA repair pathways and genomic instability (Bornstein et al., 2009). Studies have shown that SMAD4 knockout mice have significantly reduced expression of the ERCC1 gene, which can directly affect the accumulation of DNA damage in keratinocytes, leading to an increase in DNA single-stranded damage versus DNA double-stranded damage (Mitra et al., 2013). In addition, SMAD4 can be involved in the expression of checkpoint kinase 1 (CHK1) and Rad51 recombinase in cells (Citro et al., 2022) and affect the repair of DNA damage, especially for DNA double-strand breaks. Therefore, upregulation of miRNA-1255a can lead to dysregulation of DNA damage repair during spermatogenesis by affecting the miRNA-1255a/SMAD4/DDR pathway, resulting in single- and double-stranded sperm DNA breaks, causing elevated SDFI, and even male infertility.

Among the top five KEGG-enriched pathways with significant KEGG enrichment, we focused on the PI3K/AKT signaling pathway, which is one of the important transduction pathways regulating cell proliferation and plays an important role in cell growth, proliferation, differentiation, and survival (Tassinari et al., 2015) and exerts a positive effect in spermatogonial stem cell proliferation and differentiation, spermatogonial meiosis, spermatogenesis, spermatogonial capacitation, and acrosome reaction (Manfredi et al., 2015). Activated AKT can act on different substrates to regulate different cellular functions and on Bcl-2 to regulate apoptosis (Hird & Tron, 2019). The PI3K/AKT signaling pathway has been associated with bis(2-ethylhexyl) phthalate–induced DNA damage in mouse spermatozoa (Zhang, Wang et al., 2022), and the PI3K-AKT signaling pathway may also play an important role in spermatozoa DNA damage repair (Zhang et al., 2023). Granulocyte-macrophage colony-stimulating factor (GM-CSF) treatment also increased sperm viability and improved SDFI through the PI3K/AKT pathway (Tanhaye Kalate Sabz et al., 2022). Therefore, these differential miRNAs may contribute to sperm DNA damage by inhibiting the PI3K-AKT signaling pathway and promoting apoptosis.

Shortcomings and Prospects

First, this study used bioinformatics methods to predict miRNA target genes and their functions, but the specificity and sensitivity of the current bioinformatics analyses are yet to be improved, and their putative possible cellular signaling pathways need to be confirmed by further animal or cellular experiments; second, miRNAs were detected from spermatocytes, whereas ideally the genes would be evaluated in testicular biopsies, which is not ethically feasible; third, the data are somewhat regional, which may affect the representativeness of the experimental results. Fourth, due to the high cost of sequencing and the small sample size included in this study, there may be some bias in the results.

The cost of sequencing is costly, resulting in a small sample size in this study, and the author subsequently intends to increase the sample size to verify the expression of the DEM identified so far. The correspondence between miRNAs and target genes will be determined by detecting changes in sperm mRNA levels and protein levels after transfection or knockdown of miRNAs.