Abstract
MicroRNAs (miRNAs) are mediators of the aging process. The purpose of this work was to analyze the miRNA expression profiles of spermatozoa from men of different ages with normal fertility. Twenty-seven donors were divided into three groups by age (Group A, n = 8, age: 20–30 years; Group B, n = 10, age: 31–40 years; and Group C, n = 9, age: 41–55 years) for high-throughput sequencing analysis. Samples from 65 individuals (22, 22, and 21 in Groups A, B, and C, respectively) were used for validation by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2160 miRNAs were detected: 1223 were known, 937 were newly discovered and unnamed, of which 191 were expressed in all donors. A total of 7, 5, and 17 differentially expressed microRNAs (DEMs) were found in Group A vs B, Group B vs C, and Group A vs C comparisons, respectively. Twenty-two miRNAs were statistically correlated with age. Twelve miRNAs were identified as age-associated miRNAs, including hsa-miR-127-3p, mmu-miR-5100_L+2R-1, efu-miR-9226_L-2_1ss22GA, cgr-miR-1260_L+1, hsa-miR-652-3p_R+1, pal-miR-9993a-3p_L+2R-1, hsa-miR-7977_1ss6AG, hsa-miR-106b-3p_R-1, hsa-miR-186-5p, PC-3p-59611_111, hsa-miR-93-3p_R+1, and aeca-mir-8986a-p5_1ss1GA. There were 9165 target genes of age-associated miRNAs. Gene Ontology (GO) analysis of the target genes identified revealed enrichment of protein binding, membrane, cell cycle, and so on. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of age-related miRNAs for target genes revealed 139 enriched pathways, such as signaling pathways regulating stem cell pluripotency, metabolic pathways, and the Hippo signaling pathway. This suggests that miRNAs play a key role in male fertility changes with increasing age and provides new evidence for the study of the mechanism of age-related male fertility decline.
Keywords: age, fertility, microRNA, spermatozoa
INTRODUCTION
In recent years, there has been a gradual increase in the number of couples who tend to delay having children. The influence of advanced female age on reproduction is well recognized, but the effects of advanced paternal age (APA) on fertility are less well studied, so the issue of fertility in older fathers is gaining widespread attention.1 APA has been considered one of the causes and risk factors for male sterility. The increased incidence of chromosomal aberrations and higher risk of congenital diseases in the offspring conceived by fathers due to their advanced age is known as the paternal age effect (PAE).2 Major complications in the children of older fathers include stillbirth, musculoskeletal syndrome, palatoschisis, acute lymphoblastic leukemia and retinoblastoma, as well as neurodevelopmental disorders and schizophrenia on the autism spectrum.3
Although there is growing evidence that epigenetic variations in sperm chromatin of older fathers are associated with poor offspring outcome, the factors contributing to cross-representational inheritance need more inquiry. The main epigenetic variations include DNA methylation and modifications of histones and microRNA (miRNA) expression.4 It has been proven that miRNAs play a key role in a wide range of biological processes, such as cell development, proliferation, differentiation, metabolism, and apoptosis.5,6 Moreover, miRNAs in various tissues influence gene expression and genomic instability during aging in different ways.7 It has been found that many miRNAs regulate different stages of spermatogenesis.8,9 To some degree, the role of miRNAs in the regulation of spermatogenesis and male fertility is crucial.10 Several studies have investigated miRNAs in human testicular tissues, epididymal tissues, spermatozoa, and seminal plasma of infertile men.11–15 However, there are few studies on the spermatozoa miRNA profiles of fertile men in the context of aging.16,17
In our study, we evaluated miRNA expression profiles of human spermatozoa in normal fertile men of different ages. This will not only help understand the changes in sperm molecular characteristics with age but also provide a series of new sperm biomarkers for male reproductive function during physiological aging. Therefore, research on male reproductive disorders related to aging is of far-reaching significance.
PATIENTS AND METHODS
Patients
All donors with standard semen analysis from May 2019 to April 2021 who attended the Department of Family Planning and Reproductive Medicine at Peking University People’s Hospital (Beijing, China) and Department of Reproduction and Genetics, Maternity and Child Health Care Hospital (Tangshan, China) were aged 18–55 years. These individuals were excluded from the underlying disease and had a normal karyotype. These donors were divided into three groups according to their age: Group A (n = 8) was 20–30 years old, Group B (n = 10) was 31–40 years old, and Group C (n = 9) was 41–55 years old. All donors abstained from sex for 2–7 days before sperm retrieval, and sperm were retrieved by masturbation in a sterile container. Semen samples from donors were routinely analyzed in accordance with the 5th edition World Health Organization (WHO) standards. For later testing, the semen was frozen and kept at −80°C. A protocol for the study was approved by the Ethics Committee of the National Research Institute for Family Planning (NRIFP; Beijing, China; Approval No. 10 of 2018). All participants signed informed consent documentation.
Sperm miRNA extraction
The semen was removed from the −80°C freezer, thawed, and then centrifuged at 500g for 10 min (H1850R, Hunan Xiangyi Instrument Company, Changsha, China). Semen was washed twice to remove seminal plasma with phosphate buffer saline (PBS) buffer. The total RNA of sperm was obtained with miRNeasy Micro Kit 1 (Qiagen, Duesseldorf, Germany) according to the manufacturer’s instructions. The total RNA quantity and purity were determined by the Bioanalyzer 2100 and the RNA 6000 Nano LabChip Kit (Agilent, Palo Alto, CA, USA) with an RNA integrity number (RIN) >7.0.
Sequencing analysis
MiRNAs from 27 different donors (Group A, n = 8; Group B, n = 10; and Group C, n = 9) were used for sequencing analysis. In accordance with the protocol of the TruSeq Small RNA Sample Prep Kit (Illumina, San Diego, CA, USA), approximately 1 µg of total RNA was used from each sample for library preparation. The specific processes were 3´ splice sequence ligation, 5´ splice sequence ligation, reverse transcription to generate complementary DNA (cDNA) strands, polymerase chain reaction (PCR) amplification, electrophoresis to purify the target fragment, and up sequencing. Then, single-end sequencing (1×50 bp) was implemented by Illumina HiSeq 2500 at LC-BIO (LC Biotech, Hangzhou, China) according to the supplier’s instructions.
Data analyses
The raw data were filtered using an internal program, ACGT101-miR (LC Sciences, Houston, TX, USA), to remove adaptor dimers, junk, low complexities, common RNA families, repeats, and sequences <18 nt or >26 nt in length. Subsequently, the distinct sequences of lengths 18–26 nt and 18–25 nt were mapped to miRNA sequences in miRBase 22.0 (www.mirbase.org; last accessed on 20 October 2019). In addition, genomic data from Homo sapiens were mapped against pre-miRNA. We identified known miRNAs based on sequence alignments between miRBase 22.0 and the known miRNA databases. The secondary structure of pre-miRNAs was presented as a hairpin, including 5p- and 3p-derived miRNAs. The distinct sequences mapping to the other arm of pre-miRNA, which were not annotated in miRBase 22.0, were considered to be 5p- or 3p-derived miRNA candidates. Matching Homo sapiens genomic sequences to the remaining unmapped sequences led to the discovery of potential novel miRNA candidates. With RNAfold software (the National Research Council of Canada Ottawa, Ontario, Canada), all putative miRNAs in Homo sapiens were predicted by predicting their secondary structures.
Quantitative real-time polymerase chain reaction (qRT-PCR) for validation of miRNAs
In accordance with the sequencing results, 10 selected miRNAs, mainly hsa-miR-127-3p, hsa-miR-106b-5p, and hsa-miR-1260, were further validated using qRT-PCR. Samples from 65 individuals (22, 22, and 21 in Groups A, B, and C, respectively) were used for qRT-PCR. The cDNAs of the above miRNAs and U6 were synthesized using the PrimeScript II First-Strand cDNA Synthesis Kit (TaKaRa, Dalian, China) with specific stem–loop reverse transcription primers. The cDNAs were synthesized on an Applied Biosystems StepOne real-time PCR system (Applied Biosystems, Foster City, CA, USA) using the SYBR Premix Ex Taq II Kit (TaKaRa) for real-time PCR. To conduct the experiments, Bulge-loop primers and qRT-PCR primers were synthesized and purchased from Shanghai Wcgene biotech Co., Ltd. (Shanghai, China). Initially, 95°C was incubated for 30 s, followed by 5 s at 95°C, and then by 40 cycles of 60°C for 30 s and 95°C for 5 s. The default method was used to analyze melt curves. As the housekeeping gene, U6 was amplified. The PCRs were conducted three times independently in three separate experiments.
The prediction and analysis of miRNA targets
MiRNA binding sites were identified using two computational target prediction algorithms (TargetScan 5.0 and miRanda 3.3a) for differentially expressed miRNAs (DEM). Then, we evaluated the overlap between both algorithms’ prediction results. The enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of these differentially expressed miRNA targets were also annotated by Omicstudio (https://www.omicstudio.cn/index).
Statistical analyses
Statistical analyses were performed using SPSS software version 25.0 (IBM Company, Chicago, IL, USA) and Python 3.8.3 (Zope Cooperation, Fredericksburg, VA, USA). The demographic and semen parameters of the donors were used by the rank sum test. Based on deep-sequencing counts, differential expression of miRNAs was assessed using Student’s t-test or analysis of variance (ANOVA). In qRT-PCR analyses, the comparative cycle threshold (CT) method (2−ΔΔCT) was used to quantify dynamic changes in miRNAs selected by qRT-PCR. The significance threshold was set to 0.05 in the test. GraphPad Prism 9 (GraphPad Software Company, San Diego, CA, USA) was applied to draw histograms.
RESULTS
Demographic and semen basic parameters of donors
Donors were divided into three groups (A, B, and C) according to age: 18–30 years, 31–40 years, and 41–55 years. Comparing the three groups, there were no significant differences (P > 0.05) within semen parameters (Supplementary Table 1).
Supplementary Table 1.
Demographic and semen parameters of the donors
| Parameters | Group A | Group B | Group C | P |
|---|---|---|---|---|
| Age (years) | 21.13±2.59 | 34.50±3.31 | 46.44±3.32 | <0.01 |
| Ejaculate volume (ml) | 3.83±1.50 | 3.61±0.99 | 3.01±1.08 | 0.351 |
| Concentration (millions/ml) | 87.85±50.47 | 51.39±27.01 | 100.20±58.77 | 0.079 |
| Total sperm (millions) | 303.63±168.24 | 181.47±96.54 | 285.22±208.99 | 0.235 |
| Sperm motility (%) | 57.84±11.23 | 64.79±12.08 | 59.24±17.12 | 0.524 |
Group A: 8 individuals, age range ≤30; Group B: 13 individuals, age range >30 and ≤40; Group C: 9 individuals, age range >40
Expression of miRNAs in spermatozoa
A total of 2160 miRNAs were detected in spermatozoa, including 1223 known miRNAs and 937 new unnamed miRNAs. There were DEMs in the three groups; 310 miRNAs were expressed among all donors in Group A, 269 miRNAs were expressed among all donors in Group B, and 200 miRNAs were expressed among all donors in Group C. The expression of 191 miRNAs was consistent among all individuals (Figure 1 and Supplementary Table 2). In contrast, 46, 10, and 2 miRNAs were only expressed in donors in Groups A, B, and C, respectively (Figure 1 and Supplementary Table 3–5). To more precisely represent the accuracy of the DEMs of the sequencing results, we followed LC Biotech’s naming convention, which has been illustrated in the notes in Table 1.
Figure 1.
The Venn diagram shows the overlapping miRNAs of the three groups (A, B, and C). miRNA: microRNA.
Supplementary Table 2.
The microRNAs were consistently expressed for all individuals in all groups
| miR_name | miR_seq |
|---|---|
| hsa-let-7f-5p | TGAGGTAGTAGATTGTATAGTT |
| hsa-let-7d-5p | AGAGGTAGTAGGTTGCATAGTT |
| hsa-let-7d-3p | CTATACGACCTGCTGCCTTTCT |
| hsa-let-7e-5p | TGAGGTAGGAGGTTGTATAGTT |
| hsa-let-7a-5p | TGAGGTAGTAGGTTGTATAGTT |
| hsa-let-7i-5p | TGAGGTAGTAGTTTGTGCTGTT |
| hsa-let-7c-5p | TGAGGTAGTAGGTTGTATGGTT |
| hsa-let-7g-5p | TGAGGTAGTAGTTTGTACAGTT |
| hsa-let-7b-5p | TGAGGTAGTAGGTTGTGTGGTT |
| hsa-let-7b-3p_1ss22CT | CTATACAACCTACTGCCTTCCT |
| hsa-miR-10a-5p_R-1 | TACCCTGTAGATCCGAATTTGT |
| hsa-miR-10b-5p_R-1 | TACCCTGTAGAACCGAATTTGT |
| hsa-miR-17-5p | CAAAGTGCTTACAGTGCAGGTAG |
| hsa-miR-21-5p | TAGCTTATCAGACTGATGTTGA |
| hsa-miR-22-3p | AAGCTGCCAGTTGAAGAACTGT |
| hsa-miR-23a-3p_R+1 | ATCACATTGCCAGGGATTTCCA |
| hsa-miR-23b-3p_R+1 | ATCACATTGCCAGGGATTACCACT |
| hsa-miR-24-3p_R-2 | TGGCTCAGTTCAGCAGGAAC |
| hsa-miR-25-3p | CATTGCACTTGTCTCGGTCTGA |
| hsa-miR-26b-5p_R+1 | TTCAAGTAATTCAGGATAGGTT |
| hsa-miR-26a-5p | TTCAAGTAATCCAGGATAGGCT |
| hsa-miR-27a-3p_R-1 | TTCACAGTGGCTAAGTTCCG |
| hsa-miR-27b-3p | TTCACAGTGGCTAAGTTCTGC |
| hsa-miR-28-3p | CACTAGATTGTGAGCTCCTGGA |
| hsa-miR-29a-3p_R-1 | TAGCACCATCTGAAATCGGTT |
| hsa-miR-29c-5p_R-1 | TGACCGATTTCTCCTGGTGTT |
| hsa-miR-29c-3p_R-1 | TAGCACCATTTGAAATCGGTT |
| hsa-miR-29b-2-5p_R+1 | CTGGTTTCACATGGTGGCTTAGA |
| hsa-miR-30b-5p | TGTAAACATCCTACACTCAGCT |
| hsa-miR-30a-5p_R+2 | TGTAAACATCCTCGACTGGAAGCT |
| hsa-miR-30a-3p | CTTTCAGTCGGATGTTTGCAGC |
| hsa-miR-30c-5p_R+1 | TGTAAACATCCTACACTCTCAGCT |
| hsa-miR-30c-2-3p | CTGGGAGAAGGCTGTTTACTCT |
| hsa-miR-30d-5p_R+2 | TGTAAACATCCCCGACTGGAAGCT |
| hsa-miR-30e-5p_R+2 | TGTAAACATCCTTGACTGGAAGCT |
| hsa-miR-30e-3p_1ss22CT | CTTTCAGTCGGATGTTTACAGT |
| hsa-miR-34c-5p_R-2 | AGGCAGTGTAGTTAGCTGATT |
| hsa-miR-92a-3p | TATTGCACTTGTCCCGGCCTGT |
| hsa-miR-93-5p | CAAAGTGCTGTTCGTGCAGGTAG |
| hsa-miR-96-5p_R-2 | TTTGGCACTAGCACATTTTTG |
| hsa-miR-99a-5p_R-1 | AACCCGTAGATCCGATCTTGT |
| hsa-miR-99a-3p_R+1 | CAAGCTCGCTTCTATGGGTCTGT |
| hsa-miR-99b-5p | CACCCGTAGAACCGACCTTGCG |
| hsa-miR-100-5p | AACCCGTAGATCCGAACTTGTG |
| hsa-miR-103a-3p | AGCAGCATTGTACAGGGCTATGA |
| hsa-miR-106b-3p_R-1 | CCGCACTGTGGGTACTTGCTG |
| hsa-miR-106a-5p | AAAAGTGCTTACAGTGCAGGTAG |
| hsa-miR-107_R-4 | AGCAGCATTGTACAGGGCT |
| hsa-miR-125b-5p_R-1 | TCCCTGAGACCCTAACTTGTG |
| hsa-miR-125b-2-3p_L-2R+2 | ACAAGTCAGGCTCTTGGGACCT |
| hsa-miR-125a-5p_R-1 | TCCCTGAGACCCTTTAACCTGTG |
| hsa-miR-127-3p | TCGGATCCGTCTGAGCTTGGCT |
| hsa-miR-132-3p | TAACAGTCTACAGCCATGGTCG |
| hsa-miR-135a-5p | TATGGCTTTTTATTCCTATGTGA |
| hsa-miR-140-3p_L-1R+2 | ACCACAGGGTAGAACCACGGAC |
| hsa-miR-146a-5p | TGAGAACTGAATTCCATGGGTT |
| hsa-miR-148b-3p | TCAGTGCATCACAGAACTTTGT |
| hsa-miR-148a-5p | AAAGTTCTGAGACACTCCGACT |
| hsa-miR-148a-3p | TCAGTGCACTACAGAACTTTGT |
| hsa-miR-149-5p | TCTGGCTCCGTGTCTTCACTCCC |
| hsa-miR-151b_R+2 | TCGAGGAGCTCACAGTCTAG |
| hsa-miR-151b_R+3 | TCGAGGAGCTCACAGTCTAGT |
| hsa-miR-151a-3p | CTAGACTGAAGCTCCTTGAGG |
| hsa-miR-181a-5p | AACATTCAACGCTGTCGGTGAGT |
| hsa-miR-181b-5p | AACATTCATTGCTGTCGGTGGGT |
| hsa-miR-182-5p | TTTGGCAATGGTAGAACTCACACT |
| hsa-miR-183-5p | TATGGCACTGGTAGAATTCACT |
| hsa-miR-186-5p | CAAAGAATTCTCCTTTTGGGCT |
| hsa-miR-187-3p_R+1 | TCGTGTCTTGTGTTGCAGCCGGT |
| hsa-miR-191-5p | CAACGGAATCCCAAAAGCAGCTG |
| hsa-miR-193a-5p | TGGGTCTTTGCGGGCGAGATGA |
| hsa-miR-193b-3p | AACTGGCCCTCAAAGTCCCGCT |
| hsa-miR-196b-5p | TAGGTAGTTTCCTGTTGTTGGG |
| hsa-miR-200c-3p | TAATACTGCCGGGTAATGATGGA |
| hsa-miR-200b-5p | CATCTTACTGGGCAGCATTGGA |
| hsa-miR-200b-3p_R-1 | TAATACTGCCTGGTAATGATG |
| hsa-miR-200a-3p_R+1 | TAACACTGTCTGGTAACGATGTT |
| hsa-miR-205-5p | TCCTTCATTCCACCGGAGTCTG |
| hsa-miR-210-3p | CTGTGCGTGTGACAGCGGCTGA |
| hsa-miR-221-3p_R-1 | AGCTACATTGTCTGCTGGGTTT |
| hsa-miR-222-3p | AGCTACATCTGGCTACTGGGT |
| hsa-miR-320b_R-2 | AAAAGCTGGGTTGAGAGGGC |
| hsa-miR-320c_R-1 | AAAAGCTGGGTTGAGAGGG |
| hsa-miR-320a-3p | AAAAGCTGGGTTGAGAGGGCGA |
| hsa-miR-324-5p_R+1 | CGCATCCCCTAGGGCATTGGTGT |
| hsa-miR-324-3p_L-3R+1 | ACTGCCCCAGGTGCTGCTGGT |
| hsa-miR-326_R+1 | CCTCTGGGCCCTTCCTCCAGT |
| hsa-miR-328-3p | CTGGCCCTCTCTGCCCTTCCGT |
| hsa-miR-339-5p | TCCCTGTCCTCCAGGAGCTCACG |
| hsa-miR-339-3p | TGAGCGCCTCGACGACAGAGCCG |
| hsa-miR-342-3p | TCTCACACAGAAATCGCACCCGT |
| hsa-miR-345-5p_R+1 | GCTGACTCCTAGTCCAGGGCTCT |
| hsa-miR-361-5p | TTATCAGAATCTCCAGGGGTAC |
| hsa-miR-361-3p_R-1 | TCCCCCAGGTGTGATTCTGATT |
| hsa-miR-363-3p_R-1 | AATTGCACGGTATCCATCTGT |
| hsa-miR-365a-3p | TAATGCCCCTAAAAATCCTTAT |
| hsa-miR-375-3p | TTTGTTCGTTCGGCTCGCGTGA |
| hsa-miR-378a-3p | ACTGGACTTGGAGTCAGAAGGC |
| hsa-miR-378c_R-5 | ACTGGACTTGGAGTCAGAAG |
| hsa-miR-423-5p | TGAGGGGCAGAGAGCGAGACTTT |
| hsa-miR-423-3p | AGCTCGGTCTGAGGCCCCTCAGT |
| hsa-miR-425-5p | AATGACACGATCACTCCCGTTGA |
| hsa-miR-425-3p_L+1R-1 | CATCGGGAATGTCGTGTCCGCC |
| hsa-miR-429 | TAATACTGTCTGGTAAAACCGT |
| hsa-miR-484 | TCAGGCTCAGTCCCCTCCCGAT |
| hsa-miR-486-5p_R-1 | TCCTGTACTGAGCTGCCCCGA |
| hsa-miR-500a-3p_L+1R-1_1ss22TA | AATGCACCTGGGCAAGGATTCA |
| hsa-miR-501-3p | AATGCACCCGGGCAAGGATTCT |
| hsa-miR-502-3p | AATGCACCTGGGCAAGGATTCA |
| hsa-miR-504-5p_R-1 | AGACCCTGGTCTGCACTCTAT |
| hsa-miR-532-5p | CATGCCTTGAGTGTAGGACCGT |
| hsa-miR-532-3p | CCTCCCACACCCAAGGCTTGCA |
| hsa-miR-574-5p_R+1 | TGAGTGTGTGTGTGTGAGTGTGTG |
| hsa-miR-574-3p | CACGCTCATGCACACACCCACA |
| hsa-miR-629-5p_R+1_1ss10GC | TGGGTTTACCTTGGGAGAACTT |
| hsa-miR-652-3p_R+1 | AATGGCGCCACTAGGGTTGTGT |
| hsa-miR-744-5p | TGCGGGGCTAGGGCTAACAGCA |
| hsa-miR-769-5p | TGAGACCTCTGGGTTCTGAGCT |
| hsa-miR-891a-5p | TGCAACGAACCTGAGCCACTGA |
| hsa-miR-892a_R+1 | CACTGTGTCCTTTCTGCGTAGA |
| hsa-miR-892b | CACTGGCTCCTTTCTGGGTAGA |
| hsa-miR-1180-3p | TTTCCGGCTCGCGTGGGTGTGT |
| hsa-miR-1260b_1ss9AG | ATCCCACCGCTGCCACCAT |
| hsa-miR-1268a | CGGGCGTGGTGGTGGGGG |
| hsa-miR-1296-5p_R-2 | TTAGGGCCCTGGCTCCATCT |
| hsa-miR-1307-5p | TCGACCGGACCTCGACCGGCT |
| hsa-miR-1307-3p_R+1 | ACTCGGCGTGGCGTCGGTCGTGG |
| hsa-miR-2110_R-1 | TTGGGGAAACGGCCGCTGAGT |
| hsa-miR-3195_L+4R-1 | CCGGCGCGCCGGGCCCGGGT |
| hsa-mir-3196-p5_1ss4GT | GGGTCGGGGCGGCAGGGGCC |
| hsa-mir-3665-p5_1ss2CA | GAGGGCGGCGGCGGCGGC |
| hsa-miR-3960_L-2_1ss12AC | CGGCGGCGGCGGCGGGGG |
| hsa-miR-4286_R+1 | ACCCCACTCCTGGTACCA |
| hsa-miR-4448_R-2_1ss6CG | GGCTCGTTGGTCTAGGGG |
| hsa-miR-4488_L+2R-1 | GGAGGGGGCGGGCTCCGGC |
| hsa-miR-4492_L+1 | CGGGGCTGGGCGCGCGCC |
| hsa-miR-4508_L+1R+1 | AGCGGGGCTGGGCGCGCGC |
| hsa-miR-4516_R+2 | GGGAGAAGGGTCGGGGCGG |
| hsa-miR-7704_R-1 | CGGGGTCGGCGGCGACGT |
| hsa-miR-7977_1ss6AG | TTCCCGGCCAACGCACCA |
| hsa-miR-10400-5p_R-3_1ss12GA | CGGCGGCGGCGACTCTGG |
| hsa-mir-12136-p5 | AGTCATGGAGGCCATGGGGTTGG |
| ssc-mir-1285-p5 | CAGTAGTGGGATCGCGCCTGTGAA |
| ssc-mir-1285-p3 | ATAGCGAGACCCCGTCTCT |
| mmu-mir-1983-p3_1ss2GA | TAATGCCGAGGTTGTGAGTTCG |
| bta-miR-2904_L-1R+4 | GGAGCCTCGGTTGGCCTCGGAT |
| bta-mir-2904-2-p3 | CCTCGGATAGCCGGTCCCCCGC |
| efu-miR-9277_L-3R+1 | TCGAATCCTGCCGACTACGCC |
| pal-miR-10001-5p_R+1_1ss2AT | GTGGGCGGGCGGGGCCGGGG |
| mdo-miR-200a-3p_R+2 | TAACACTGTCTGGTAACGATGTTT |
| bta-mir-2887-1-p5_1ss5CA | GGGAAACGGGGCGCGGCC |
| ssc-mir-4332-p3_1ss16TG | CGGGGGTGGGCGGGCGGGG |
| mmu-mir-6240-p5_1 | ATTTCTGCCCAGTGCTCTG |
| efu-miR-9226_2ss4AG22GA | TCAGGTCCCTGTTCGGGCGCCA |
| cgr-miR-1260 | ATCCCACCGCTGCCACCA |
| cgr-miR-1260_L+1 | AATCCCACCGCTGCCACCA |
| mmu-miR-2137_L-1R-1_1ss16AG | CCGGCGGGAGCCCCGGGGA |
| mmu-miR-2137_L-2R-1_1ss16AG | CGGCGGGAGCCCCGGGGA |
| mmu-miR-2137_L-2_1ss16AG | CGGCGGGAGCCCCGGGGAG |
| bta-miR-2478_L+2 | TCGTATCCCACTTCTGACACCA |
| bta-miR-2887_L-1R+4 | GGGACCGGGGTCCGGTGCGGAGT |
| bta-mir-2887-1-p5_1ss23AT | GGGACCGGGGTCCGGTGCGGAGT |
| bta-miR-2887_L-3R+4 | GACCGGGGTCCGGTGCGGAGT |
| mmu-miR-3963_L+1R+2_1ss2TC | TCGTATCCCACTTCTGACACCA |
| mmu-miR-3968_L-3_1ss14AT | ATCCCACTCCTGACACCA |
| mmu-miR-5126_L-1_1ss18CT | CGGGCGGGGCCGGGGGTGGGG |
| mmu-miR-5126_L-3_1ss18CT | GGCGGGGCCGGGGGTGGGG |
| mmu-miR-5100_L+2R-1 | GTTCGAATCCCAGCGGTGCCTC |
| mmu-mir-6236-p5 | TCAACTAGCCCTGAAAAT |
| mmu-mir-6236-p3_1ss19GC | TGGATGGCGCTGGAGCGTCGGG |
| mmu-mir-6236-p3_1 | GAATCAACTAGCCCTGAAA |
| mmu-mir-6236-p3_2 | GAATCAACTAGCCCTGAAAAT |
| mmu-mir-6236-p3_1ss15GC | TGGCGCTGGAGCGTCGGG |
| mmu-mir-6240-p5_2 | TGTGATTTCTGCCCAGTGCTCTGAA |
| mmu-mir-6240-p5_1ss10AG | CGAAGGCCCGCGGCGGGT |
| mmu-mir-6240-p3 | TGTGATTTCTGCCCAGTGCTCTGAA |
| mmu-mir-6240-p5_1ss14AG | ATCGCGAAGGCCCGCGGCGGGT |
| mmu-mir-6240-p3_1ss2TG | CGCGATGTGATTTCTGCCCAG |
| mmu-mir-6240-p5_1ss19GT | TCTGCCCAGTGCTCTGAATGTCA |
| bta-miR-12034_1ss19TG | CCCCGGGGAGCCCGGCGGG |
| pal-miR-9993a-3p_1ss11GA | ATCTCGGTGGAACCTCCA |
| pal-miR-9993b-3p | ATCTCGCTGGGGCCTCCA |
| pal-miR-9993a-3p | ATCTCGGTGGGACCTCCA |
| pal-miR-9995-3p | ATCTCGGTGGAACCTCCA |
| bta-miR-11980_L-1R-1_1ss4CG | GGGAACGGGCTTGGCGGA |
| bta-miR-11987_L-1_1ss8TA | GAGGAAACTCTGGTGGAGGT |
| bta-miR-11987_L-2_1ss8TA | AGGAAACTCTGGTGGAGGT |
| bta-miR-12034_L-1_1ss19TG | CCCGGGGAGCCCGGCGGG |
| bta-miR-2478_L-2 | ATCCCACTTCTGACACCA |
| cja-miR-9984_1ss4CG | CGCGGCGGCGGCGGCGGC |
| mmu-let-7j_1ss8TG | TGAGGTAGTAGTTTGTGCTGTTAT |
The miRNA name: L-n means the miRNA sequence (detected) is n base less than known rep_miRSeq in the left side; R-n means the miRNA sequence (detected) is n base less than known rep_miRSeq in the right side; L+n means the miRNA sequence (detected) is n base more than known rep_miRSeq in the left side; R+n means the miRNA sequence (detected) is n base more than known rep_miRSeq in the right side; 2ss5TC13TA means 2 substitution (ss), which are T->C at position 5 and T->A at position 13. If there is no matching annotation, the miRNA sequence (detected) is exactly same as known rep_miRSeq. New discovered 5p/3p sequence has been annotated as p3/p5: Which is directly differentiate with the reported sequences. MiRNAs beginning with PC indicate newly discovered sequences, which were given name by computer randomly. miRNA sequence is a read at 5' or 3' of maximum mapped score in each cluster. miRNAs: microRNA
Supplementary Table 3.
The microRNAs were only expressed in donors in Group A
| miR_name | miR_seq |
|---|---|
| hsa-miR-7-5p | TGGAAGACTAGTGATTTTGTTGTT |
| hsa-miR-9-5p_R-2 | TCTTTGGTTATCTAGCTGTAT |
| hsa-miR-16-2-3p_L+1R-2 | ACCAATATTACTGTGCTGCTT |
| hsa-miR-19b-3p_R-1 | TGTGCAAATCCATGCAAAACTG |
| hsa-miR-25-5p_R+2 | AGGCGGAGACTTGGGCAATTGCT |
| ptr-mir-92b-p5 | CCGGGCCCCGGGCGGGCGGG |
| hsa-miR-92a-1-5p | AGGTTGGGATCGGTTGCAATGCT |
| hsa-miR-95-3p | TTCAACGGGTATTTATTGAGCA |
| hsa-miR-101-3p_L+1R-1 | GTACAGTACTGTGATAACTGA |
| hsa-miR-106b-5p | TAAAGTGCTGACAGTGCAGAT |
| hsa-miR-125a-3p_R-1 | ACAGGTGAGGTTCTTGGGAGC |
| hsa-miR-132-5p | ACCGTGGCTTTCGATTGTTACT |
| hsa-miR-140-5p | CAGTGGTTTTACCCTATGGTAG |
| hsa-miR-185-3p_R-1 | AGGGGCTGGCTTTCCTCTGGT |
| hsa-miR-196b-3p | TCGACAGCACGACACTGCCTTC |
| hsa-miR-200a-5p | CATCTTACCGGACAGTGCTGGA |
| hsa-miR-202-3p | AGAGGTATAGGGCATGGGAA |
| hsa-miR-210-5p | AGCCCCTGCCCACCGCACACTG |
| hsa-miR-224-5p_L-1R-2 | CAAGTCACTAGTGGTTCCGTTT |
| hsa-miR-338-5p_R-1 | AACAATATCCTGGTGCTGAGT |
| hsa-miR-500a-5p_R-1 | TAATCCTTGCTACCTGGGTGAG |
| hsa-miR-501-5p | AATCCTTTGTCCCTGGGTGAGA |
| hsa-miR-506-3p_L+1 | GTAAGGCACCCTTCTGAGTAGA |
| hsa-miR-508-5p_R-1 | TACTCCAGAGGGCGTCACTCAT |
| hsa-miR-508-3p_R-1 | TGATTGTAGCCTTTTGGAGTAG |
| hsa-miR-513c-5p | TTCTCAAGGAGGTGTCGTTTAT |
| hsa-miR-598-3p | TACGTCATCGTTGTCATCGTCA |
| hsa-miR-660-5p_R+1 | TACCCATTGCATATCGGAGTTGT |
| hsa-miR-760_R+2 | CGGCTCTGGGTCTGTGGGGAGT |
| hsa-miR-888-5p | TACTCAAAAAGCTGTCAGTCA |
| hsa-miR-888-3p_L+1R-1 | TGACTGACACCTCTTTGGGTGA |
| hsa-miR-890 | TACTTGGAAAGGCATCAGTTG |
| hsa-miR-892c-3p | CACTGTTTCCTTTCTGAGTGGA |
| hsa-miR-1287-5p_R+1 | TGCTGGATCAGTGGTTCGAGTCT |
| hsa-miR-3620-5p_R-2_1ss1GC | CTGGGCTGGGCTGGGCTGGG |
| hsa-miR-4507_1ss6TC | CTGGGCTGGGCTGGGCTGGG |
| hsa-mir-5100-p3_1ss17TC | ATCCCAGCGGTGCCTCCA |
| hsa-mir-6089-2-p3_1ss6TC | CGGGGCGGGGCGGGGCGGG |
| hsa-miR-6821-5p_R-1 | GTGCGTGGTGGCTCGAGGCGGG |
| mmu-miR-146a-5p_R+1 | TGAGAACTGAATTCCATGGGTTT |
| bta-miR-2478_L-1_1ss2TA | AATCCCACTTCTGACACCA |
| mmu-miR-3968_L-2_1ss14AT | AATCCCACTCCTGACACCA |
| ssc-mir-4332-p5_1ss17TG_2 | GCGGGGGTGGGCGGGCGGGG |
| ssc-mir-4332-p3_1ss17TG | GCGGGGGTGGGCGGGCGGGG |
| efu-miR-9226_L-3_1ss22GA | AGTCCCTGTTCGGGCGCCA |
| pal-miR-9993b-3p_1ss1AT | TTCTCGCTGGGGCCTCCA |
The instruction for this table is same as Supplementary Table 2
Supplementary Table 5.
The microRNAs were only expressed in donors in Group C
| miR_name | miR_seq |
|---|---|
| efu-miR-9226_L-2_1ss22GA | AAGTCCCTGTTCGGGCGCCA |
| bta-miR-12034_R-1 | CCCCGGGGAGCCCGGCGG |
The instruction for this table is same as Supplementary Table 2
Table 1.
Differentially expressed microRNAs between each pair of groups
| Group comparison | miRNAs | Sequence (5’–3’) | Up/down |
|---|---|---|---|
| Group A vs B | hsa-miR-493-5p | TTGTACATGGTAGGCTTTCATT | Up |
| hsa-miR-362-5p_R-1 | AATCCTTGGAACCTAGGTGTGAG | Down | |
| hsa-miR-338-5p_R-1 | AACAATATCCTGGTGCTGAGT | Down | |
| hsa-miR-888-3p_L+1R-1 | TGACTGACACCTCTTTGGGTGA | Down | |
| PC-3p-59611_111 | CTGGGTGGGAATGTGGGGA | Down | |
| hsa-miR-501-5p | AATCCTTTGTCCCTGGGTGAGA | Down | |
| hsa-miR-17-3p | ACTGCAGTGAAGGCACTTGTAG | Down | |
| Group B vs C | efu-miR-9226_L-2_1ss22GA | AAGTCCCTGTTCGGGCGCCA | Up |
| hsa-miR-941_R-1 | CACCCGGCTGTGTGCACATGTG | Down | |
| ssc-miR-339_R+1 | TCCCTGTCCTCCAGGAGCTCAT | Down | |
| hsa-miR-181a-2-3p | ACCACTGACCGTTGACTGTACC | Down | |
| PC-3p-4383_2805 | CGAGTCCCATCAGCCACCCCA | Down | |
| Group A vs C | hsa-miR-127-3p | TCGGATCCGTCTGAGCTTGGCT | Up |
| pal-miR-9993a-3p_L+2R-1 | AAATCTCGGTGGGACCTCC | Up | |
| cgr-miR-1260_L+1 | AATCCCACCGCTGCCACCA | Up | |
| efu-miR-9226_L-2_1ss22GA | AAGTCCCTGTTCGGGCGCCA | Up | |
| bta-miR-12034_R-1 | CCCCGGGGAGCCCGGCGG | Up | |
| mmu-miR-5100_L+2R-1 | GTTCGAATCCCAGCGGTGCCTC | Up | |
| hsa-miR-199b-3p_R-1 | ACAGTAGTCTGCACATTGGTT | Down | |
| PC-3p-59611_111 | CTGGGTGGGAATGTGGGGA | Down | |
| hsa-miR-93-3p_R+1 | ACTGCTGAGCTAGCACTTCCCGA | Down | |
| eca-mir-8991-p3_1ss1CG_2 | GGAGGACATCGTCAGGCTG | Down | |
| eca-mir-8991-p3_1ss1CG_1 | GGAGGACATCGTCAGGCT | Down | |
| hsa-miR-212-5p | ACCTTGGCTCTAGACTGCTTACT | Down | |
| eca-mir-8986a-p5_1ss1GA | ATCGAGGCTAGAGTCACGCTTGG | Down | |
| hsa-miR-362-5p_R-1 | AATCCTTGGAACCTAGGTGTGAG | Down | |
| hsa-miR-16-2-3p_L+1R-2 | ACCAATATTACTGTGCTGCTT | Down | |
| ssc-mir-4332-p5_1ss17TG_2 | GCGGGGGTGGGCGGGCGGGG | Down | |
| ssc-mir-4332-p3_1ss17TG | GCGGGGGTGGGCGGGCGGGG | Down |
The miRNA name: L-n means the miRNA sequence (detected) is n base less than known rep_miRSeq in the left side; R-n means the miRNA sequence (detected) is n base less than known rep_miRSeq in the right side; L+n means the miRNA sequence (detected) is n base more than known rep_miRSeq in the left side; R+n means the miRNA sequence (detected) is n base more than known rep_miRSeq in the right side; 2 ss5TC13TA means 2 substitution (ss), which are T->C at position 5 and T->A at position 13. If there was no matching annotation, the miRNA sequence (detected) was exactly the same as the known rep_miRSeq. The newly discovered 5p/3p sequence has been annotated as p3/p5, which is directly differentiated from the reported sequences. MiRNAs beginning with PC indicate newly discovered sequences, randomly given names by the computer. The miRNA sequence is a read at 5’ or 3’ of the maximum mapped score in each cluster. The definitions of Groups A, B, and C are shown in Figure 1. miRNAs: microRNAs
Supplementary Table 4.
The microRNAs were only expressed in donors in Group B
| miR_name | miR_seq |
|---|---|
| hsa-miR-98-5p | TGAGGTAGTAAGTTGTATTGTT |
| hsa-miR-146b-5p | TGAGAACTGAATTCCATAGGCTG |
| hsa-miR-378i_R+1_1ss9AT | ACTGGACTTGGAGTCAGAAGGT |
| hsa-miR-499a-5p | TTAAGACTTGCAGTGATGTTT |
| hsa-miR-766-3p | ACTCCAGCCCCACAGCCTCAGC |
| hsa-miR-941_R-1 | CACCCGGCTGTGTGCACATGTG |
| hsa-miR-1260a_R+3_1ss9TG | ATCCCACCGCTGCCACCAAAA |
| hsa-miR-3615_R+1 | TCTCTCGGCTCCTCGCGGCTCG |
| bta-miR-2478_1ss2TA | GAATCCCACTTCTGACACCA |
| PC-5p-36951_272 | GGTCGGTGGGTGGCGGGGC |
The instruction for this table is same as Supplementary Table 2
Based on the mean and coefficient of variation (standard deviation/mean) of miRNAs expressed in the spermatozoa of all donors, the miRNAs with the highest average expression level and most stable expression in the spermatozoa of donors with normal semen results were determined. The top 10 miRNAs with the highest average expression and most stable expression are shown in Supplementary Table 6.
Supplementary Table 6.
Top 10 most expressed and top 10 most stable microRNAs
| Most expressed | Most stable | ||
|---|---|---|---|
|
|
|
||
| miRNA | Mean | miRNA | CV |
| hsa-miR-375-3p | 46336.071 | hsa-miR-99b-5p | 0.225 |
| hsa-let-7b-5p | 41561.516 | hsa-miR-345-5p_R+1 | 0.244 |
| hsa-mir-12136-p5 | 37889.989 | hsa-miR-30d-5p_R+2 | 0.256 |
| bta-miR-2904_L-1R+4 | 13840.396 | hsa-miR-425-3p_L+1R-1 | 0.270 |
| hsa-let-7a-5p | 11786.991 | hsa-miR-106b-3p_R-1 | 0.291 |
| hsa-miR-200c-3p | 11675.130 | hsa-miR-27b-3p | 0.316 |
| hsa-miR-99a-5p_R-1 | 10583.005 | hsa-miR-25-3p | 0.317 |
| hsa-miR-320a-3p | 10313.702 | hsa-miR-1180-3p | 0.340 |
| hsa-miR-891a-5p | 9358.208 | hsa-miR-135a-5p | 0.350 |
| hsa-miR-148a-3p | 7869.291 | hsa-miR-1307-3p_R+1 | 0.355 |
The instruction for this table is same as Supplementary Table 2. miRNA: microRNA; CV: coefficient of variation
In the hierarchical clustering analysis, we generated a heatmap by normalizing miRNAs from all sequenced samples (Supplementary Figure 1 (331.5KB, tif) ). In addition, heatmaps were generated by comparing two of these groups. The samples self-segregated into Groups A, B, and C. Eleven miRNAs were included in the heatmap between Groups A and B (Figure 2a). The heatmap between Groups B and C included 13 miRNAs (Figure 2b), and 24 miRNAs were included in the heatmap between Groups A and C (Figure 2c).
Figure 2.
The differentially expressed microRNAs (DEMs) of all volunteers were analyzed in three groups (A, B, and C). Hierarchical clustering analysis of DEMs in each donor between (a) Group A and B, (b) Group B and C, and (c) Group A and C. The definitions of Groups A, B, and C are shown in Figure 1.
DEMs
According to the DESeq analysis, there were 7, 5, and 17 DEMs among the three groups (Group A vs B, Group B vs C, and Group A vs C, respectively). Between Groups A and B, 6 miRNAs were upregulated, and 1 miRNA was downregulated. Between Groups B and C, 4 miRNAs were significantly upregulated, and 1 miRNA was downregulated. Comparing Group A with Group C, 11 miRNAs were significantly upregulated, and 6 miRNAs were significantly downregulated (Table 1). The log2(fold change) for DEMs ranged from −1.0 to 1.0, with P < 0.05.
Correlation of miRNA expression in spermatozoa with age and semen parameters
This study included correlations among miRNA expression in spermatozoa and age, sperm concentration, sperm motility, and total sperm count. A total of 22 miRNAs were statistically correlated with age (Table 2); a total of 83 miRNAs were correlated with sperm concentration, and 2 miRNAs (hsa-miR-892c-3p and hsa-miR-888-3p_L+1R-1) were strongly correlated; 12 miRNAs were correlated with sperm motility; 59 miRNAs were correlated with total sperm count, and 2 miRNAs (PC-5p-52502_144 and hsa-miR-890-p3) were strongly correlated; and 18 miRNAs were correlated with semen volume (Supplementary Table 7). Lowly expressed miRNAs within spermatozoa were eliminated. |r| indicates the correlation coefficient, |r| > 0.4 indicated a correlation, |r| > 0.7 indicated a strong correlation, and P < 0.05 was applied as the screening condition.
Supplementary Table 7.
Statistical correlation between microRNAs and semen parameters
| Semen parameters | miRNAs | r | P |
|---|---|---|---|
| Sperm concentration | hsa-miR-888-3p_L+1R-1 | 0.7710 | 0.0000 |
| hsa-miR-892c-3p | 0.7439 | 0.0000 | |
| hsa-mir-12121-p3_1ss1CG | 0.6692 | 0.0001 | |
| hsa-miR-892a_R+1 | 0.6661 | 0.0001 | |
| PC-5p-52502_144 | 0.6597 | 0.0002 | |
| hsa-miR-3195_L+4R-1 | 0.6426 | 0.0003 | |
| hsa-mir-892a-p5 | 0.6378 | 0.0003 | |
| hsa-miR-769-5p | 0.6283 | 0.0004 | |
| PC-5p-64398_95 | 0.6118 | 0.0007 | |
| bta-miR-11980_L-1R-1_1ss4CG | 0.5909 | 0.0012 | |
| hsa-miR-892b | 0.5811 | 0.0015 | |
| bta-mir-2887-1-p5_1ss5CA | 0.5671 | 0.0020 | |
| hsa-mir-890-p3 | 0.5635 | 0.0022 | |
| hsa-miR-501-3p | 0.5518 | 0.0028 | |
| mmu-miR-5126_L-1_1ss18CT | 0.5387 | 0.0037 | |
| hsa-miR-383-5p | 0.5167 | 0.0058 | |
| ssc-mir-4332-p3_1ss16TG | 0.5105 | 0.0065 | |
| bta-miR-2887_L-3R+4 | 0.5094 | 0.0066 | |
| PC-5p-45334_191 | 0.5084 | 0.0068 | |
| hsa-miR-221-3p_R-1 | 0.5010 | 0.0078 | |
| hsa-miR-514a-3p | 0.4998 | 0.0079 | |
| eca-mir-8969-p3_1ss4GT | 0.4960 | 0.0085 | |
| hsa-miR-506-3p_L+1 | 0.4922 | 0.0091 | |
| hsa-miR-532-5p | 0.4877 | 0.0099 | |
| hsa-miR-891a-5p | 0.4695 | 0.0135 | |
| hsa-miR-222-3p | 0.4692 | 0.0135 | |
| bta-miR-2887_L-1R+4 | 0.4528 | 0.0177 | |
| bta-mir-2887-1-p5_1ss23AT | 0.4528 | 0.0177 | |
| hsa-miR-34c-5p_R-2 | 0.4521 | 0.0179 | |
| hsa-miR-6715b-3p | 0.4504 | 0.0184 | |
| eca-mir-8969-p3_1ss5AT | 0.4444 | 0.0202 | |
| mdo-miR-34b-5p | 0.4413 | 0.0212 | |
| hsa-miR-4485-5p_L+4R+2 | 0.4370 | 0.0226 | |
| mmu-miR-5126_L-3_1ss18CT | 0.4283 | 0.0258 | |
| hsa-miR-30a-3p | 0.4219 | 0.0284 | |
| hsa-miR-4488_L+2R-1 | 0.4163 | 0.0308 | |
| hsa-miR-149-3p_L+1 | 0.4150 | 0.0314 | |
| hsa-miR-501-5p | 0.4099 | 0.0337 | |
| hsa-miR-187-3p_R+1 | 0.4049 | 0.0362 | |
| hsa-miR-3620-5p_R-2_1ss1GC | 0.4019 | 0.0377 | |
| hsa-miR-4507_1ss6TC | 0.4019 | 0.0377 | |
| hsa-miR-34a-5p | −0.4040 | 0.0366 | |
| PC-3p-7941_1612 | −0.4043 | 0.0365 | |
| hsa-miR-22-5p_R-1 | −0.4091 | 0.0341 | |
| rno-miR-1843b-3p_R+1 | −0.4096 | 0.0339 | |
| hsa-miR-125b-5p_R-1 | −0.4129 | 0.0323 | |
| hsa-miR-140-5p | −0.4194 | 0.0294 | |
| mmu-let-7c-5p_R+2 | −0.4219 | 0.0284 | |
| hsa-mir-2110-p3 | −0.4250 | 0.0271 | |
| hsa-miR-149-5p | −0.4272 | 0.0263 | |
| hsa-miR-99b-3p | −0.4358 | 0.0231 | |
| hsa-miR-423-5p | −0.4359 | 0.0230 | |
| hsa-miR-21-3p | −0.4392 | 0.0219 | |
| hsa-miR-941_R-1 | −0.4405 | 0.0215 | |
| pal-miR-9993b-3p | −0.4463 | 0.0196 | |
| hsa-miR-99b-5p | −0.4480 | 0.0191 | |
| hsa-miR-193b-5p | −0.4512 | 0.0182 | |
| hsa-miR-320a-3p | −0.4537 | 0.0175 | |
| hsa-miR-106b-5p | −0.4616 | 0.0154 | |
| hsa-miR-940_R+1 | −0.4623 | 0.0152 | |
| hsa-miR-1260a_R+3_1ss9TG | −0.4707 | 0.0132 | |
| hsa-let-7b-5p | −0.4861 | 0.0102 | |
| pal-miR-9993b-3p_1ss1AT | −0.4868 | 0.0100 | |
| hsa-miR-141-3p_R-1 | −0.4905 | 0.0094 | |
| hsa-miR-28-3p | −0.4950 | 0.0087 | |
| hsa-miR-505-3p_L-1R+2 | −0.4950 | 0.0087 | |
| hsa-mir-5100-p3_1ss17TC | −0.4974 | 0.0083 | |
| hsa-miR-1468-5p_R+1 | −0.5097 | 0.0066 | |
| hsa-miR-197-3p | −0.5145 | 0.0060 | |
| hsa-miR-1296-5p_R-2 | −0.5170 | 0.0058 | |
| hsa-miR-19b-3p_R-1 | −0.5239 | 0.0050 | |
| hsa-miR-339-5p | −0.5260 | 0.0048 | |
| hsa-miR-1260b_1ss9AG | −0.5360 | 0.0040 | |
| hsa-miR-26b-3p | −0.5460 | 0.0032 | |
| hsa-miR-3615_R+1 | −0.5716 | 0.0018 | |
| hsa-miR-365a-3p | −0.5826 | 0.0014 | |
| hsa-miR-331-3p_R-1 | −0.5856 | 0.0013 | |
| hsa-miR-484 | −0.5884 | 0.0012 | |
| hsa-miR-671-3p | −0.5938 | 0.0011 | |
| hsa-miR-766-3p | −0.6052 | 0.0008 | |
| hsa-miR-92a-3p | −0.6079 | 0.0008 | |
| hsa-miR-125a-5p_R-1 | −0.6266 | 0.0005 | |
| hsa-miR-423-3p | −0.6562 | 0.0002 | |
| Sperm motility | efu-mir-9277-p5 | 0.6188 | 0.0006 |
| pal-miR-9995-3p_L+1_1 | 0.5001 | 0.0079 | |
| pal-miR-9995-3p_L+1_2 | 0.5001 | 0.0079 | |
| eca-mir-8969-p3_1ss14GT | 0.4526 | 0.0178 | |
| hsa-miR-766-3p | 0.4088 | 0.0343 | |
| hsa-miR-7162-3p_1ss3TC | −0.4005 | 0.0385 | |
| hsa-miR-25-5p_R+2 | −0.4054 | 0.0359 | |
| hsa-miR-409-3p | −0.4217 | 0.0285 | |
| hsa-miR-125a-3p_R-1 | −0.4294 | 0.0254 | |
| hsa-miR-134-5p | −0.4417 | 0.0211 | |
| cja-mir-3135-p5_1ss6CG | −0.4435 | 0.0205 | |
| hsa-miR-181b-5p | −0.4603 | 0.0157 | |
| Total sperm count | PC-5p-52502_144 | 0.7960 | 0.0000 |
| hsa-mir-890-p3 | 0.7232 | 0.0000 | |
| hsa-mir-12121-p3_1ss1CG | 0.6993 | 0.0000 | |
| eca-mir-8969-p3_1ss4GT | 0.6551 | 0.0002 | |
| eca-mir-8969-p3_1ss5AT | 0.6348 | 0.0004 | |
| eca-mir-8969-p5_1ss5AG | 0.6282 | 0.0004 | |
| hsa-mir-892a-p5 | 0.6268 | 0.0005 | |
| hsa-miR-888-3p_L+1R-1 | 0.6175 | 0.0006 | |
| hsa-miR-892c-3p | 0.6079 | 0.0008 | |
| bta-miR-11980_L-1R-1_1ss4CG | 0.5910 | 0.0012 | |
| hsa-miR-892a_R+1 | 0.5854 | 0.0013 | |
| hsa-miR-3195_L+4R-1 | 0.5655 | 0.0021 | |
| hsa-miR-501-3p | 0.5270 | 0.0047 | |
| hsa-miR-769-5p | 0.5258 | 0.0048 | |
| hsa-miR-506-3p_L+1 | 0.5244 | 0.0050 | |
| hsa-miR-514a-3p | 0.5079 | 0.0068 | |
| mmu-miR-5126_L-1_1ss18CT | 0.4976 | 0.0083 | |
| PC-5p-64398_95 | 0.4960 | 0.0085 | |
| hsa-miR-891a-5p | 0.4808 | 0.0111 | |
| hsa-miR-892b | 0.4653 | 0.0145 | |
| hsa-miR-149-3p_L+1 | 0.4621 | 0.0152 | |
| hsa-miR-34c-5p_R-2 | 0.4358 | 0.0231 | |
| hsa-miR-30a-3p | 0.4227 | 0.0281 | |
| hsa-miR-509-3-5p | 0.4212 | 0.0286 | |
| hsa-miR-30e-3p_1ss22CT | 0.4153 | 0.0312 | |
| bta-mir-2887-1-p5_1ss5CA | 0.4125 | 0.0325 | |
| hsa-miR-10a-5p_R-1 | 0.4084 | 0.0344 | |
| PC-5p-54039_137 | 0.4025 | 0.0374 | |
| PC-5p-45334_191 | 0.4006 | 0.0384 | |
| hsa-miR-141-3p_R-1 | −0.4049 | 0.0362 | |
| hsa-miR-22-5p_R-1 | −0.4084 | 0.0344 | |
| hsa-miR-1468-5p_R+1 | −0.4135 | 0.0320 | |
| hsa-miR-941_R-1 | −0.4216 | 0.0285 | |
| hsa-miR-1296-5p_R-2 | −0.4247 | 0.0272 | |
| hsa-miR-505-3p_L-1R+2 | −0.4337 | 0.0238 | |
| hsa-miR-19b-3p_R-1 | −0.4378 | 0.0224 | |
| bta-miR-4286_R+2 | −0.4422 | 0.0209 | |
| hsa-miR-766-3p | −0.4478 | 0.0192 | |
| hsa-miR-193b-5p | −0.4508 | 0.0183 | |
| hsa-miR-1260b_1ss9AG | −0.4560 | 0.0168 | |
| hsa-miR-34a-5p | −0.4563 | 0.0168 | |
| hsa-miR-339-5p | −0.4571 | 0.0165 | |
| hsa-let-7b-5p | −0.4623 | 0.0152 | |
| hsa-miR-99b-3p | −0.4647 | 0.0146 | |
| hsa-miR-21-3p | −0.4650 | 0.0145 | |
| hsa-miR-197-3p | −0.4664 | 0.0142 | |
| hsa-miR-125a-5p_R-1 | −0.4740 | 0.0125 | |
| cgr-miR-1260_L+2 | −0.4750 | 0.0123 | |
| hsa-miR-92a-3p | −0.4827 | 0.0108 | |
| hsa-miR-1260a_R+3_1ss9TG | −0.4836 | 0.0106 | |
| hsa-miR-3615_R+1 | −0.4917 | 0.0092 | |
| hsa-miR-671-3p | −0.4991 | 0.0081 | |
| hsa-miR-28-3p | −0.5079 | 0.0068 | |
| hsa-miR-365a-3p | −0.5152 | 0.0060 | |
| hsa-miR-26b-3p | −0.5215 | 0.0053 | |
| hsa-mir-5100-p3_1ss17TC | −0.5237 | 0.0051 | |
| hsa-miR-484 | −0.5265 | 0.0048 | |
| hsa-miR-331-3p_R-1 | −0.5696 | 0.0019 | |
| hsa-miR-423-3p | −0.5724 | 0.0018 | |
| Semen volume | hsa-miR-125a-5p_R-1 | 0.4663 | 0.0142 |
| hsa-miR-125b-5p_R-1 | 0.4578 | 0.0163 | |
| efu-miR-9226_2ss4AG22GA | 0.4539 | 0.0174 | |
| hsa-miR-99a-3p_R+1 | 0.4358 | 0.0231 | |
| hsa-miR-99a-5p_R-1 | 0.4289 | 0.0256 | |
| hsa-miR-4326_R+4 | 0.4206 | 0.0289 | |
| hsa-miR-4433a-5p_R+1 | 0.4159 | 0.0310 | |
| hsa-miR-1468-5p_R+1 | 0.4082 | 0.0345 | |
| hsa-miR-342-5p_R+1 | 0.4037 | 0.0368 | |
| mmu-mir-6236-p5_1ss1GT | 0.3972 | 0.0402 | |
| hsa-mir-8485-p5_1ss20AG | 0.3867 | 0.0463 | |
| hsa-mir-2110-p3 | 0.3836 | 0.0482 | |
| bta-miR-2478_L-1_1ss2TA | −0.3984 | 0.0396 | |
| mmu-miR-6412_R-3_1ss15AT | −0.4199 | 0.0292 | |
| mmu-miR-3968_R-3_1ss14AT | −0.4440 | 0.0203 | |
| bta-miR-378_R+1 | −0.4450 | 0.0200 | |
| bta-mir-2887-1-p3_1ss11AT | −0.4712 | 0.0131 |
The instruction for this table is same as Supplementary Table 2. miRNAs: microRNAs
Table 2.
Statistical correlation of microRNAs with different age
| miRNAs | r | P |
|---|---|---|
| hsa-miR-127-3p | 0.6170 | 0.0006 |
| mmu-miR-5100_L+2R-1 | 0.5357 | 0.0040 |
| efu-miR-9226_L-2_1ss22GA | 0.5345 | 0.0041 |
| cgr-miR-1260_L+1 | 0.5168 | 0.0058 |
| hsa-miR-652-3p_R+1 | 0.5146 | 0.0060 |
| mmu-mir-5100-p3_1ss17TG | 0.5025 | 0.0076 |
| cgr-miR-1260_L+1R-1 | 0.4838 | 0.0106 |
| pal-miR-9993a-3p_L+2R-1 | 0.4568 | 0.0166 |
| hsa-miR-378d_R-2 | 0.4558 | 0.0169 |
| pal-miR-9993b-3p_L+1R-1 | 0.4495 | 0.0187 |
| mmu-miR-5100_L+2R-1_1ss17TG | 0.4426 | 0.0208 |
| mmu-mir-1983-p3_1ss2GA | 0.4408 | 0.0214 |
| hsa-miR-7977_1ss6AG | 0.4373 | 0.0226 |
| efu-miR-9226_L-3_1ss22GA | 0.4096 | 0.0338 |
| hsa-miR-378a-3p | 0.3969 | 0.0404 |
| efu-miR-9226_R-1 | 0.3907 | 0.0439 |
| hsa-miR-874-3p | −0.3886 | 0.0451 |
| hsa-miR-27b-3p | −0.4002 | 0.0386 |
| hsa-miR-106b-3p_R-1 | −0.4060 | 0.0356 |
| hsa-miR-186-5p | −0.4073 | 0.0350 |
| hsa-miR-935_L-1R-1 | −0.4307 | 0.0249 |
| hsa-miR-513c-5p | −0.4403 | 0.0215 |
| hsa-miR-93-3p_R+1 | −0.5263 | 0.0048 |
| PC-3p-59611_111 | −0.5396 | 0.0037 |
| eca-mir-8986a-p5_1ss1GA | −0.5404 | 0.0036 |
The instruction for this table is same as that in Table 1. miRNAs: microRNAs
Age-associated miRNAs
Heat map results, DEMs and statistical correlation analysis results were integrated to derive age-associated miRNAs. Among the total 12 miRNAs, 7 miRNAs were expressed upregulated and 5 miRNAs were expressed downregulated (Table 3).
Table 3.
Age-associated microRNAs
| miRNAs | Sequence (5’–3’) | Up/down |
|---|---|---|
| hsa-miR-127-3p | TCGGATCCGTCTGAGCTTGGCT | Up |
| mmu-miR-5100_L+2R-1 | GTTCGAATCCCAGCGGTGCCTC | Up |
| efu-miR-9226_L-2_1ss22GA | AAGTCCCTGTTCGGGCGCCA | Up |
| cgr-miR-1260_L+1 | AATCCCACCGCTGCCACCA | Up |
| hsa-miR-652-3p_R+1 | AATGGCGCCACTAGGGTTGTGT | Up |
| pal-miR-9993a-3p_L+2R-1 | AAATCTCGGTGGGACCTCC | Up |
| hsa-miR-7977_1ss6AG | TTCCCGGCCAACGCACCA | Up |
| hsa-miR-106b-3p_R-1 | CCGCACTGTGGGTACTTGCTG | Down |
| hsa-miR-186-5p | CAAAGAATTCTCCTTTTGGGCT | Down |
| hsa-miR-93-3p_R+1 | ACTGCTGAGCTAGCACTTCCCGA | Down |
| PC-3p-59611_111 | CTGGGTGGGAATGTGGGGA | Down |
| eca-mir-8986a-p5_1ss1GA | ATCGAGGCTAGAGTCACGCTTGG | Down |
The instruction for this table is same as that in Table 1. miRNAs: microRNAs
Validation of the sequencing results
As a validation step, we performed qRT-PCR on the DEMs above to verify their expression levels. Three miRNAs out of 12, namely, hsa-miR-127-3p, hsa-106b-5p, and hsa-miR-1260, were determined for their expression profiles among the three groups. These miRNA expression trends generally matched those found in the sequencing data analysis (Figure 3). Demographic and semen parameters of donors in the validation group, as well as additional miRNAs screened for validation, are shown in Supplementary Table 8 and Supplementary Figure 2 (188.7KB, tif) and 3 (116.1KB, tif) .
Figure 3.
The expression levels of 3 miRNAs (hsa-miR-127-3p, hsa-miR-106b-5p and hsa-miR-1260) were measured by sequencing and qRT-PCR. The qRT-PCR results are shown as (a) box plots and the sequencing results are shown as (b) bar charts. The definitions of Groups A, B, and C are shown in Figure 1. miRNA: microRNA; qRT-PCR: quantitative real-time polymerase chain reaction; RPKM: Reads Per Kilobase per Million.
Supplementary Table 8.
Demographic and semen parameters of donors in the validation group
| Parameters | Group A | Group B | Group C | P |
|---|---|---|---|---|
| Age (years) | 23.0±1.07 | 36.18±2.30 | 46.67±3.76 | <0.01 |
| Ejaculate volume (ml) | 3.19±1.14 | 3.25±0.89 | 2.74±0.89 | 0.186 |
| Concentration (millions/ml) | 52.30±26.16 | 51.83±23.93 | 51.67±32.90 | 0.997 |
| Total sperm (millions) | 168.15±116.86 | 160.52±63.19 | 134.69±83.77 | 0.001 |
| Sperm motility (%) | 56.18±10.20 | 45.24±9.67 | 46.60±8.80 | 0.455 |
Group A: 8 individuals, age range ≤30; Group B: 13 individuals, age range >30 and ≤40; Group C: 9 individuals, age range >40
Target and functional prediction of aging-associated miRNAs
Based on TargetScan and MiRanda data, aging-associated miRNA target genes were predicted. The number of target genes was 9165, including mitogen-activated protein kinase 8 (MAPK8), cyclin-dependent kinase inhibitor 1A (CDKN1A), MAPK10, apoptosis-inducing factor mitochondria associated 1 (AIFM1), and other genes that have been predicted. GO and KEGG functional annotation were performed for the above target genes. Analysis of the GO terms associated with age-related miRNA target genes revealed that changes in biological process (BP) are primarily influenced by phosphorylation, the cell cycle, and so on. There was an enrichment of cellular components (CCs) in the membrane, nucleus, and other areas. The molecular function (MF) of the GO term analysis was significantly enriched in transferase activity, protein binding, and other functions (Supplementary Figure 4a (293.1KB, tif) ). According to KEGG enrichment analysis, 139 pathways were enriched for the target genes, such as signaling pathways regulating pluripotency of stem cells, metabolic pathways, the Hippo signaling pathway, and the phosphatidylinositol 3-kinase and protein kinase B (PI3K-Akt) signaling pathway (Supplementary Figure 4b (293.1KB, tif) and Supplementary Table 9). The network relationship among these age-associated miRNAs and their partial target genes and pathways was established (Supplementary Figure 5 (211.2KB, tif) ). The majority of the target genes are related to metabolism, growth and development and apoptosis.
Supplementary Table 9.
The pathways were enriched according to Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the age-related gene
| Pathway description | P value of Fisher’s exact test |
|---|---|
| Pathways in cancer | 8.65E-12 |
| Proteoglycans in cancer | 3.91E-11 |
| Metabolic pathways | 1.82E-08 |
| Signaling pathways regulating pluripotency of stem cells | 2.79E-08 |
| Axon guidance | 1.17E-07 |
| Inositol phosphate metabolism | 2.38E-07 |
| EGFR tyrosine kinase inhibitor resistance | 2.75E-07 |
| Transcriptional misregulation in cancer | 3.85E-07 |
| Phosphatidylinositol signaling system | 4.22E-07 |
| Neurotrophin signaling pathway | 4.68E-07 |
| Rap1 signaling pathway | 1.82E-06 |
| PI3K-Akt signaling pathway | 2.30E-06 |
| ErbB signaling pathway | 2.75E-06 |
| HIF-1 signaling pathway | 3.01E-06 |
| Regulation of actin cytoskeleton | 5.27E-06 |
| FoxO signaling pathway | 5.48E-06 |
| Pancreatic cancer | 7.04E-06 |
| Colorectal cancer | 7.69E-06 |
| Hippo signaling pathway | 1.05E-05 |
| Hepatocellular carcinoma | 1.12E-05 |
| Prostate cancer | 1.22E-05 |
| Autophagy - animal | 1.54E-05 |
| Ras signaling pathway | 1.54E-05 |
| Thyroid hormone signaling pathway | 1.77E-05 |
| Progesterone-mediated oocyte maturation | 1.82E-05 |
| Oxytocin signaling pathway | 2.28E-05 |
| Focal adhesion | 2.40E-05 |
| MAPK signaling pathway | 2.53E-05 |
| Yersinia infection | 3.04E-05 |
| Long-term potentiation | 3.07E-05 |
| Choline metabolism in cancer | 4.08E-05 |
| Calcium signaling pathway | 4.21E-05 |
| Renal cell carcinoma | 5.04E-05 |
| Oocyte meiosis | 5.41E-05 |
| Nonsmall cell lung cancer | 8.09E-05 |
| Arrhythmogenic right ventricular cardiomyopathy | 8.17E-05 |
| Wnt signaling pathway | 1.13E-04 |
| Glutamatergic synapse | 1.58E-04 |
| Ubiquitin mediated proteolysis | 1.76E-04 |
| VEGF signaling pathway | 2.13E-04 |
| Endometrial cancer | 2.13E-04 |
| Long-term depression | 2.14E-04 |
| AGE-RAGE signaling pathway in diabetic complications | 2.39E-04 |
| Tight junction | 2.91E-04 |
| Circadian entrainment | 3.16E-04 |
| Prolactin signaling pathway | 3.19E-04 |
| cAMP signaling pathway | 3.40E-04 |
| GnRH signaling pathway | 3.41E-04 |
| Hepatitis B | 3.41E-04 |
| Type II diabetes mellitus | 3.43E-04 |
| AMPK signaling pathway | 3.45E-04 |
| Apoptosis | 3.57E-04 |
| Platelet activation | 4.01E-04 |
| Chronic myeloid leukemia | 4.57E-04 |
| Glioma | 4.57E-04 |
| Lysine degradation | 5.15E-04 |
| Acute myeloid leukemia | 5.15E-04 |
| Insulin resistance | 5.36E-04 |
| Phospholipase D signaling pathway | 5.46E-04 |
| Endocrine and other factor-regulated calcium reabsorption | 5.56E-04 |
| mTOR signaling pathway | 5.88E-04 |
| Sphingolipid signaling pathway | 5.89E-04 |
| Inflammatory mediator regulation of TRP channels | 6.48E-04 |
| Gastric cancer | 6.68E-04 |
| Cholinergic synapse | 7.54E-04 |
| Insulin signaling pathway | 8.25E-04 |
| Small cell lung cancer | 9.36E-04 |
| Fc epsilon RI signaling pathway | 1.00E-03 |
| Hepatitis C | 1.11E-03 |
| T cell receptor signaling pathway | 1.22E-03 |
| SNARE interactions in vesicular transport | 1.27E-03 |
| PD-L1 expression and PD-1 checkpoint pathway in cancer | 1.28E-03 |
| Lysosome | 1.38E-03 |
| cGMP-PKG signaling pathway | 1.39E-03 |
| Breast cancer | 1.45E-03 |
| Other types of O-glycan biosynthesis | 1.45E-03 |
| Central carbon metabolism in cancer | 1.61E-03 |
| Growth hormone synthesis, secretion and action | 1.64E-03 |
| Cushing syndrome | 2.15E-03 |
| Adrenergic signaling in cardiomyocytes | 2.54E-03 |
| C-type lectin receptor signaling pathway | 2.91E-03 |
| TGF-beta signaling pathway | 3.06E-03 |
| Purine metabolism | 3.10E-03 |
| Melanoma | 3.19E-03 |
| Pyrimidine metabolism | 3.20E-03 |
| Shigellosis | 3.47E-03 |
| Leukocyte transendothelial migration | 3.56E-03 |
| RNA transport | 3.59E-03 |
| Homologous recombination | 3.80E-03 |
| Aldosterone-regulated sodium reabsorption | 3.89E-03 |
| Bacterial invasion of epithelial cells | 3.99E-03 |
| p53 signaling pathway | 4.61E-03 |
| Hippo signaling pathway - multiple species | 6.37E-03 |
| Melanogenesis | 6.68E-03 |
| N-Glycan biosynthesis | 7.53E-03 |
| Relaxin signaling pathway | 7.56E-03 |
| Salmonella infection | 7.82E-03 |
| Morphine addiction | 7.87E-03 |
| Fc gamma R-mediated phagocytosis | 7.87E-03 |
| Adherens junction | 7.97E-03 |
| Fanconi anemia pathway | 1.02E-02 |
| Gastric acid secretion | 1.05E-02 |
| Butanoate metabolism | 1.06E-02 |
| Valine, leucine and isoleucine degradation | 1.17E-02 |
| GnRH secretion | 1.24E-02 |
| Dopaminergic synapse | 1.30E-02 |
| Various types of N-glycan biosynthesis | 1.38E-02 |
| Hypertrophic cardiomyopathy | 1.42E-02 |
| Basal cell carcinoma | 1.54E-02 |
| Propanoate metabolism | 1.54E-02 |
| Hedgehog signaling pathway | 1.56E-02 |
| Fructose and mannose metabolism | 1.59E-02 |
| Pathogenic Escherichia coli infection | 1.62E-02 |
| Cholesterol metabolism | 1.64E-02 |
| Parathyroid hormone synthesis, secretion and action | 1.72E-02 |
| Biosynthesis of unsaturated fatty acids | 1.72E-02 |
| GPI-anchor biosynthesis | 1.75E-02 |
| Mannose type O-glycan biosynthesis | 1.76E-02 |
| Mismatch repair | 1.76E-02 |
| Thyroid cancer | 1.98E-02 |
| Insulin secretion | 2.24E-02 |
| Gap junction | 2.34E-02 |
| Serotonergic synapse | 2.40E-02 |
| Citrate cycle (TCA cycle) | 2.57E-02 |
| Spinocerebellar ataxia | 2.79E-02 |
| Thyroid hormone synthesis | 2.82E-02 |
| Nicotine addiction | 2.92E-02 |
| mRNA surveillance pathway | 2.95E-02 |
| Glycosaminoglycan biosynthesis - chondroitin sulfate/dermatan sulfate | 2.95E-02 |
| JAK-STAT signaling pathway | 3.25E-02 |
| Protein processing in endoplasmic reticulum | 3.29E-02 |
| Adipocytokine signaling pathway | 3.30E-02 |
| Mucin type O-glycan biosynthesis | 3.58E-02 |
| Chemokine signaling pathway | 3.60E-02 |
| Apelin signaling pathway | 3.86E-02 |
| Fatty acid degradation | 4.00E-02 |
| Maturity onset diabetes of the young | 4.12E-02 |
| Human papillomavirus infection | 4.19E-02 |
| Carbohydrate digestion and absorption | 4.20E-02 |
GPI: glycosylphosphatidylinositol; PI3K-Akt: phosphatidylinositol 3-kinase and protein kinase B; MAPK: mitogen-activated protein kinase; mTOR: mammalian target of rapamycin; EGFR: epidermal growth factor receptor; VEGF: vascular endothelial growth factor; cAMP: Cyclic adenosine monophosphate; GnRH: Gonadotropin-releasing hormone; AMPK: Adenosine 5`-monophosphate-activated protein kinase; TRP: transient receptor potential; PD: programmed death; cGMP-PKG: cyclic guanosine monophosphate-protein kinase G; TCA: tricarboxylic acid; JAK-STAT: janus kinase-signal transducer and activator of transcription
DISCUSSION
This study demonstrated the characteristics of miRNAs in the spermatozoa of men with normal semen parameters and assessed their correlations with age. This study revealed the expression profiles of DEMs within the spermatozoa of donors in different age groups. Moreover, some miRNAs were found to correlate with age and semen parameters. The GO and KEGG analyses and target gene prediction of DEMs further validated that intrasperm miRNAs are products of spermatogenesis rather than the result of random processes and are likely to play a role in regulating early embryonic development.18–21
In this study, the diversity among these miRNAs was greater than that among the miRNAs reported in other studies. This is mainly attributed to the use of high-throughput sequencing in this study. Next-generation sequencing (NGS) is a relatively new technology for profiling miRNAs because it detects both known and novel miRNAs and can identify miRNA sequences precisely (e.g., with NGS, single-nucleotide variants can easily be differentiated from isomiR variants).22 More specifically, the miRNA identification method used in our analysis not only identified miRNAs with the same sequences as those included in the miRBase database but also identified sequences that were not identical to those in the miRBase database. Meanwhile, the name approach is mainly to identify more miRNAs, and there are some isoforms of miRNAs whose sequences are slightly different, but it does not necessarily affect the function of miRNAs since the seed sequences are the same.
MiRNAs and aging
In the present study, some miRNAs were identified to be associated with aging, which was derived from comparisons among different age groups and obtained from analysis of age correlation. Twelve age-associated miRNAs were derived using heatmaps, DEMs, and statistical correlation analysis. Among them, hsa-miR-127-3p deserves more attention. hsa-miR-127-3p showed differences in the comparison between Groups A and C and proved to be significantly correlated with age in the age-related analysis. Moreover, validation of hsa-miR-127-3p expression by qRT-PCR was performed with a large sample size. Its expression in spermatozoa was generally consistent with the trend of the sequencing results: has-miR-127-3p expression increases with age or is relatively stable until 40 years of age and increases after 40 years of age. Several studies have confirmed that hsa-miR-127-3p inhibits biological processes involving proliferation and differentiation or promotes the aging process through certain gene targets. Auler et al.23 found that hsa-miR-127-3p, which induced a prolonged arrest in the cell cycle and the appearance of senescent molecules, is an epigenetic activator of myofibroblast senescence. Ji et al.24 reported that hsa-miR-127-3p suppresses the proliferation, migration, and invasion osf oral squamous cell carcinomas (OSCCs) by targeting kinesin family member 3B (KIF3B). Yuan et al.25 confirmed that hsa-miR-127-3p targets lysine methyltransferase 5A (KMT5a) to inhibit myocyte proliferation. We searched the MEDLINE-PubMed and EMBASE databases for relevant literature and did not identify any reports on hsa-miR-127-3p in relation to male or androgenic fertility. Therefore, further study is needed to determine whether hsa-miR-127-3p expression in the sperm of elderly men has an effect on sperm function.
Among the age-associated miRNAs that have been identified, in addition to miR-127-3p, several other miRNAs have been shown to be potentially associated with aging. In a study of age-related changes in vascular function, it was found that miR-106b-3p expression levels in human umbilical vein endothelial cells decreased with age at certain ages.26 This is consistent with the trend of miR-106b-3p expression changes in the results of the present study. Huang et al.27 have confirmed that miR-652-3p exhibits an inhibitory effect on proliferation by inhibiting cyclin D2 (CCND2) expression. In our study, eight miRNAs were negatively correlated with age. Hsa-miR-93-3p_R+1, as one of them, has progressively lower expression within sperm with aging. Salas-Huetos et al.17 reported that miR-93-3p with significantly lower expression paired with miR-34b-3p was significantly associated with unexplained male infertility. The results suggest that miR-93-3p might be useful as a biomarker to determine sperm aging as one gets older. In this study, some miRNAs were obtained in a two-by-two comparison of the three groups. Ssc-miR-339_R+1 was differentially expressed in the comparison of Group B with Group C. It was reported that intestinal epithelial cells are inhibited by miR-339 after exposure to lipopolysaccharide (LPS) by targeting toll-like receptor 4 (TLR4).28 A downregulation of miR-339 expression was observed in Group C in this study. However, there is still a lack of understanding regarding how miRNAs regulate sperm decline with age. Meanwhile, most of our other identified miRNAs were not reported accordingly and need to be confirmed by further studies.
Target and functional prediction of aging-associated miRNAs
In our study, target gene prediction was conducted for all age-related miRNAs. Among these predicted genes, MAPK10 was identified as a target in all group comparisons, whose main function is to encode MAPK10, belonging to the MAP kinase family. Meanwhile, MAPK8 was acquired from the age correlation analysis. MAP kinases respond to multiple biochemical signals and act as integration points, including a range of biological processes, such as proliferation, differentiation, transcription regulation, and development.29–31 CDKN1A and other genes have been predicted as age-related targets more frequently than MAPK10. There is a potent inhibitor of cyclin-dependent kinases encoded by CDKN1A. Proteins encoded by this gene bind to and inhibit complexes containing cyclin/cyclin-dependent kinase 2 or 4; thus, it regulates cell cycle progression at the G1 stage.32,33 Among other target genes, AIFM1, which is detected in the mitochondrial intermembrane space, enciphers a flavoprotein indispensable for nuclear disassembly in apoptotic cells. Translocation of this protein into the nucleus causes chromosomes to condense and fragment under apoptosis.34,35
The results of the GO analysis of age-related miRNA target genes demonstrated the relationship between these miRNAs and age. Meanwhile, many of the pathways obtained by KEGG enrichment analysis were age-related. Some genes of the MAK kinase family were predicted; it is reasonable that the MAKP pathway was also identified, and MAPK signaling pathways are widely linked with male reproductive capacity.36–38 The Hippo signaling pathway was enriched in the comparisons. Hippo signaling is critical for embryonic development; in contrast to low Hippo activity, high Hippo activity allows for the differentiation of trophoblasts into inner cell masses.39 Furthermore, early embryonic development as influenced by sperm miRNAs has reached consensus.40,41 However, the mechanism of the effect of DEMs on embryonic development requires further investigation. The mammalian target of rapamycin (mTOR) pathway is an important signaling pathway that regulates the pluripotency of stem cells. It has been reported that age and age-related pathologies are likely to be affected by mTOR through a broad range of mechanisms in cell growth, proliferation, metabolism, immune functions, and proteostasis.42,43 Additionally, one study showed an association between the mTOR pathway and the effect of age on spermatozoa quality.44 However, the exact mechanism needs to be confirmed.
MiRNAs and semen parameters
In the analysis of the correlation between intrasperm miRNAs and semen parameters, there were 12 miRNAs associated with sperm motility in total. A variety of genetic factors may affect sperm motility, including chromosomal abnormalities and mitochondrial and nuclear mutations. However, there has been an increase in investigation into the pathophysiology of asthenozoospermia (AZS) based on epigenetic factors, altered miRNA expression signatures, and proteomics.45 MiRNAs have been implicated in sperm motility defects in a study.46 It has been reported that Sertoli cell proliferation and apoptosis are regulated by miR-125a-5p through its target ras-related protein Rab-3D (RAB3D) and its regulation of the PI3K/AKT pathway.47 Interestingly, it is associated with inferior DNA integrity of the sperm of aging males and is upregulated in their sperm. Moreover, the authors of this study proved that miR-125a-5p inhibited mitochondrial function, which led to DNA injury in GC2 cells.16 A strong correlation was observed between miR-629-3p expression levels and sperm motility in human spermatozoa, as previously mentioned by Salas-Huetos et al.17 Another study with 39 patients with unexplained asthenozoospermia demonstrated that miR-888-3p expression levels were significantly higher in those patients.48 In addition, several other studies have reported miRNAs associated with sperm motility,49,50 but the miRNAs differ from those found in this study. Since the semen specimens selected for this study exhibited normal parameters, the differences in testing methods, and other reasons, it is likely that the conclusions drawn will be different from those of other studies.
A total of 83 miRNAs were found to correlate with sperm concentration in this study. Among them, hsa-miR-888-3p_L+1R-1 (r = 0.771) and miR-892c-3p (r = 0.744) showed a strong correlation. MiR-888-3p was reported to be correlated with sperm motility.48 However, neither hsa-miR-888-3p_L+1R-1 nor miR-892c-3p has been found to correlate with sperm concentration. Among all other miRNAs statistically associated with sperm concentration, miR-34c-5p and miR-34b-5p have attracted the most widespread attention. Several studies have shown that miR-34c-5p and miR-34b-5p play an important role in spermatogenesis and are expected to be diagnostic markers of male infertility.51,52 Salas-Huetos et al.17 found a negative correlation between miR-335-5p, miR-885-5p, and miR-152-3p and sperm concentration.
Based on this study, a total of 18 miRNAs were found to correlate with semen volume. Although miR-125a-5p and miR-125b-5p were hypothesized to play important roles in the course of male fertility disorders in studies on the effects of coronavirus disease 2019 (COVID-19) on male fertility,53 neither of the aforementioned miRNAs has been reported in almost any studies related to sperm parameters or other reproductive parameters. In general, the correlations between miRNAs and semen parameters are inconsistent in different studies, mainly because of the differences in the tested populations, the quality of the extracted RNA, the influence of the replication process, and the detection methods. Therefore, many of the miRNAs reported in this study need to be assessed further in in-depth validation studies.
CONCLUSION
This study demonstrates the characteristics of the miRNA expression profiles of fertile individuals at different ages and provides good reference value for future in-depth study on the changes in reproductive function, genetic risk, and related mechanisms of male aging, which will improve the understanding of male fertility at advanced ages and facilitate improvements in fertility treatment, reproductive health, and reproductive safety.
AUTHOR CONTRIBUTIONS
MJZ and YNZ were in charge of sperm RNA extraction, data analysis and organization, and writing of the manuscript. YPZ is responsible for clinical data analysis. XBC is responsible for collecting clinical samples, and BSH is responsible for collecting validation samples. ND provided experimental work assistance. SSW, JM, and YQG gave guidance and suggested modifications to the experimental design and paper revision. MLL is the corresponding author in charge of experimental design, quality control, data analysis, and writing of the manuscript. All authors read and approved the final manuscript.
COMPETING INTERESTS
All authors declare no competing interests.
Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.
Hierarchical clustering analysis of the sequencing results of all miRNAs in each volunteer; green and purple indicate low-frequency and high-frequency miRNAs sequenced in the libraries, respectively. The different colors represent the different fold changes (log2). MiRNAs: microRNAs.
The expression levels of 4 miRNAs (hsa-miR-1-3p, hsa-miR-34b-3p, hsa-miR-93-39 and hsa-miR-126-3p) were measured by sequencing and shown with a box plot, which were validated by qRT-PCR and shown with a column chart. MiRNAs: microRNAs; qRT-PCR: quantitative real-time polymerase chain reaction.
The expression levels of 3 miRNAs (hsa-miR-196b-3p, hsa-miR-671-3p and hsa-miR-1468-5p) were measured by sequencing and shown with a box plot, which were validated by qRT-PCR and shown with a column chart. MiRNAs: microRNAs; qRT-PCR: quantitative real-time polymerase chain reaction.
The GO and KEGG pathway enrichment analysis of partial target genes of age-associated miRNAs. (a) Partial common upregulated and downregulated target genes of age-associated miRNAs. (b) Partial common upregulated and downregulated pathways of age-associated miRNAs. MiRNAs: microRNAs; GO: gene ontology; KEGG: the Kyoto Encyclopedia of Genes and Genomes.
MiRNAs associated with aging and their partial targets and pathways were identified in a network relationship. MiRNAs: microRNAs.
ACKNOWLEDGMENTS
This work was supported by the Central Public Interest Scientific Institution Basal Research Fund of National Research Institute for Family Planning (No. 2021GJZ09) and the CAMS Innovation Fund for Medical Sciences (CIFMS; No. 2018-I2M-1-004).
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Associated Data
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Supplementary Materials
Hierarchical clustering analysis of the sequencing results of all miRNAs in each volunteer; green and purple indicate low-frequency and high-frequency miRNAs sequenced in the libraries, respectively. The different colors represent the different fold changes (log2). MiRNAs: microRNAs.
The expression levels of 4 miRNAs (hsa-miR-1-3p, hsa-miR-34b-3p, hsa-miR-93-39 and hsa-miR-126-3p) were measured by sequencing and shown with a box plot, which were validated by qRT-PCR and shown with a column chart. MiRNAs: microRNAs; qRT-PCR: quantitative real-time polymerase chain reaction.
The expression levels of 3 miRNAs (hsa-miR-196b-3p, hsa-miR-671-3p and hsa-miR-1468-5p) were measured by sequencing and shown with a box plot, which were validated by qRT-PCR and shown with a column chart. MiRNAs: microRNAs; qRT-PCR: quantitative real-time polymerase chain reaction.
The GO and KEGG pathway enrichment analysis of partial target genes of age-associated miRNAs. (a) Partial common upregulated and downregulated target genes of age-associated miRNAs. (b) Partial common upregulated and downregulated pathways of age-associated miRNAs. MiRNAs: microRNAs; GO: gene ontology; KEGG: the Kyoto Encyclopedia of Genes and Genomes.
MiRNAs associated with aging and their partial targets and pathways were identified in a network relationship. MiRNAs: microRNAs.