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  • Altered miRNA profile in testis of post-cryptorchidopexy patients with non-obstructive azoospermia

    Contributed equally
    Reproductive Biology and Endocrinology201816:78

    https://doi.org/10.1186/s12958-018-0393-3

    • Received: 13 May 2018
    • Accepted: 31 July 2018
    • Published:

    Abstract

    Background

    Cryptorchidism is one of the most common causes of non-obstructive azoospermia (NOA) leading to male infertility. Despite various medical approaches been utilised, many patients still suffer from infertility. MicroRNAs (miRNAs) play vital roles in the progress of spermatogenesis; however, little is known about the miRNA expression profile in the testes. Therefore, the miRNA profile was assessed in the testis of post-cryptorchidopexy patients.

    Methods

    Three post-cryptorchidopexy testicular tissue samples from patients aged 23, 26 and 28 years old and three testis tissues from patients with obstructive azoospermia (controls) aged 24, 25 and 36 years old were used in this study. Next-generation sequencing (NGS) was used to perform the miRNA expression profiling. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) assays were subsequently used to confirm the results of several randomly-selected and annotated miRNAs.

    Results

    A series of miRNAs were found to be altered between post-cryptorchidopexy testicular tissues and control tissues, including 297 downregulated and 152 upregulated miRNAs. In the subsequent qRT-PCR assays, the expression levels of most of the selected miRNAs (9/12, P < 0.05) were consistent with the results of NGS technology. Furthermore, signal transduction, adaptive immune response and biological regulation were associated with the putative target genes of the differentially-expressed miRNAs via GO analysis. In addition, oxidative phosphorylation, Parkinson’s disease and ribosomal pathways were shown to be enriched using KEGG pathway analysis of the differentially-expressed genes.

    Conclusions

    This study provides a global view of the miRNAs involved in post-cryptorchidopexy testicular tissues as well as the altered expression of miRNAs compared to control tissues, thus confirming the vital role of miRNAs in cryptorchidism.

    Keywords

    • miRNA
    • Cryptorchidism
    • Cryptorchidopexy
    • Spermatogenesis
    • Next-generation small RNA sequencing

    Background

    Male factors account for approximately 50% of infertility cases, which affect 10–15% of couples around the world [1]. Although most cases of male infertility are idiopathic with no known etiological factor, some causes (i.e. varicocele, sexual dysfunction etc.) are known [2]. Among these causes, cryptorchidism is a relatively common anomaly in the male genitalia that affects approximately 2–4% of male infants. Despite various medical approaches (i.e. surgical operations and hormone administration) being applied for years, many patients still suffer from infertility [3, 4], and little is known about the clear mechanism of spermatogenesis arrest in these patients.

    Spermatogenesis is a complex process consisted of three phases including mitotic, meiotic and haploid processes [5]. These cellular events require highly regulated spatiotemporal expression of specific protein-coding genes, especailly at the post-transcriptional levels [6]. MicroRNAs (miRNAs) are a series of small noncoding RNAs that negatively regulate gene expression after transcription [7]. Research has shown that miRNAs play crucial roles in spermatogenesis [5, 6, 813]; for example, Lian et al. identified a series of altered miRNAs in patients with non-obstructive azoospermia (NOA) using microarray technology. These identified 154 significantly downregulated and 19 upregulated miRNAs indicated the important role of miRNAs in spermatogenesis [10]. It was reported that during mouse testicular development, up-regulation of miR-449 coincided with initiation of meiotic, and miR-449 was predominantly expressed in spermatocytes and spermatids during adult spermatogenesis. Furthermore, Cdc20b/miR-449 cluster activity was documented to be cooperatively mediated by CREMT and SOX5 during postnatal testes development [5]. Later on, Comazzetto et al. have identified the miR-34 family consisted of miR-34b/c and miR-449a/b/c as upregulated from late meiosis to sperm stage. miR-34b/c and miR-449 deletion led to sterility due to abnormal spermatozoa production with reduced motility [11]. With regards to the effects of miRNAs in cryptorchidism, Duan et al. found that miR-210, a significantly upregulated miRNA in patients with NOA, was also highly expressed in patients with cryptorchidism [12]. In addition, Moritoki et al. demonstrated that miR-135a was downregulated in unilateral undescended testes in a rat model of cryptorchidism [13].

    Although some miRNAs were shown to be involved in the regulation of spermatogenesis in patients with cryptorchidism, no studies have yet investigated miRNA expression in the testis of post-cryptorchidopexy patients with NOA. Therefore this study investigated the miRNA profile in the testis of post-cryptorchidopexy patients and aimed to provide a platform to expound the mechanism of spermatogenesis arrest in post-cryptorchidopexy patients with NOA.

    Methods

    Ethics statement

    Three patients (23, 26 and 28 years old) who underwent cryptorchidopexy but were still experiencing NOA, as well as three patients (24, 25 and 36 years old) suffering from obstructive azoospermia (OA) signed informed consent and approved the use of their tissues for research purposes. The local medical ethics committee approved this study.

    Clinical specimen collection

    Testes tissues were collected by testicular biopsy from all six subjects between July 2017 and January 2018 at the Reproductive Medicine Center, First Affiliated Hospital of Anhui Medical University (Hefei, Anhui, China). For post-cryptorchidopexy patients, all cases were bilateral. Case one was 23 years old and underwent the operation 1 year ago, case two was 26 years old and underwent the operation 18 years ago and case three was 28 years old and underwent the operation 12 years ago. Testes samples were frozen at − 80 °C in RNAlater (Ambion, USA) immediately after surgery. Haematoxylin and eosin (HE) staining and the Johnson score system were used to assess testicular spermatogenic function.

    Construction of a smRNA library and next-generation sequencing (NGS)

    Total RNA was extracted from the six samples using TRIzol (Life Technologies, USA) and was used to construct miRNA libraries using the NEBNext® Multiplex Small RNA Library Prep Set (Illumina®) according to the manufacturer’s instructions. Sequencing was performed on a Hiseq X (Illumina) using the HiSeq X Reagent Kit v2.

    Data analyses and novel miRNA exploration

    Data were analysed according to previously-reported methods. Known miRNAs were identified by mapping reads to miRBase (version 21.0) in Homo sapiens, whilst nonmatched reads were subsequently aligned against other noncoding RNAs within the Ensembl database [14]. The remaining nonannotated sequences were selected for alignment with the integrated human transcriptome to explore novel miRNAs. All hairpin-like structures containing unclassified smRNA reads (no less than 45 reads) were predicted using miRDeep2 [15] following the criteria described previously [16].

    Bioinformatic analyses for miRNAs with differential expression patterns

    The target genes of the differentially-expressed miRNAs in the two groups were predicted using TargetScan [17] and miRanda [18]. Enriched GO terms and KEGG pathway analysis was subsequently applied to predict the target genes of miRNAs with differential expression patterns in the two groups of specimens.

    QRT-PCR verification for altered miRNA expression

    cDNA synthesis was performed using a PrimeScript RT reagent kit following the manufacturer’s instructions (Takara, Japan). The abundance of individual miRNAs was subsequently assessed via an Applied Biosystems 7500 PCR System (Applied Biosystems) using SYBR Premix Ex Taq II (Tli RNaseH Plus, Takara) under optimised reaction conditions. The specific reverse transcription and qPCR primers for all miRNAs are listed in Additional file 1. The processes were performed in accordance with the protocols supplied by the manufacturers. Briefly, for qPCR, triplicate reactions were performed at 95 °C for 10 min, and the subsequent 40 amplification cycles were conducted at 95 °C for 15 s and 60 °C for 60 s. Meanwhile, 18S rRNA was used as an internal normalised control. Relative miRNA abundances were calculated using 2Ct (threshold cycle) formula, where Ct = CtmiRNA − Ct18S rRNA and Ct = (Ctpost-cryptorchidopexy − Ctobstructive azoospermia). The miRNA concentration differences between post-cryptorchidopexy and control tissues were analysed using unpaired t-tests. P < 0.05 indicated a statistically significant difference.

    Results

    Histopathological characteristics of post-cryptorchidopexy testicular tissue and control tissue

    To clarify the histopathological characteristics of the post-cryptorchidopexy testicular tissue (hereafter referred to as ‘cryptorchid tissue’) and control tissue (hereafter referred to as ‘normal tissue’), HE staining and the Johnson scoring system were used to assess the function of spermatogenesis (Fig. 1). The Johnson scores were 3, 3 and 3 in cryptorchid tissues, which indicated maturation arrest, and 9, 9 and 10 in normal control tissues, which indicated normal spermatogenesis.
    Fig. 1
    Fig. 1

    HE-staining of cryptorchid tissue and control tissue, which clarify the histopathological characteristics of cryptorchid tissues (a) and control tissues (b).

    Comprehensive overview of whole genome smRNAs in cryptorchid and normal tissues

    All smRNAs [18–32 nucleotides (nt)] acquired from cryptorchid and normal tissues were deep sequenced by NGS. A total of 19,931,698 (out of 21,212,215) and 20,243,124 (out of 21,524,351) sequence reads that aligned to the human genome sequence dataset were obtained in the cryptorchid and normal tissues, respectively. MiRNAs accounted for 85.5% and 71.19% in cryptorchid and normal tissues, respectively (Fig. 2).
    Fig. 2
    Fig. 2

    Results of geneome mapping and distribution of RNAs among different categories.

    The most abundant of these smRNAs in cryptorchid tissue were 21 nt in length, and these smRNAs were more abundant than the 22-nt and 23-nt RNAs which were in second and third place, respectively. However, the most abundant smRNAs in normal tissue were 22 nt in length, and these were more abundant than the 21-nt and 23-nt RNAs which were in second and third place, respectively (see Additional file 2).

    Understanding the distribution pattern of miRNA genes may help to elucidate their roles, therefore the chromosomal locations of miRNA genes were evaluated. In cryptorchid tissue, most miRNA genes were located on chromosomes X, 9, 3 and 21. Similarly, in normal tissues, most miRNA genes were located on chromosome X, 15, 9 and 5 (see Additional file 3).

    Features of the most abundant miRNAs in cryptorchid and normal tissues

    The NGS results were used to compile a list of the 20 most abundant and known miRNAs in cryptorchid tissue and the 10 most abundant and novel miRNAs in normal tissue. In cryptorchid tissue, miR-514a-3p, miR-143-3p, miR-26a-5p, miR-99a-5p, miR-202-5p, miR-509-3-5p, miR-10b-5p, miR-508-3p, let-7 g-5p and let-7f-5p were the most abundant known miRNAs. In normal tissue, miR-514a-3p, miR-143-3p, miR-26a-5p, miR-509-3-5p, miR-99a-5p, miR-202-5p, miR-10b-5p, let-7f-5p, miR-508-3p and let-7 g-5p were the most abundant known miRNAs (Table 1). Detailed information is shown in Table 1. Of the 10 most abundant novel miRNAs, only one was different between cryptorchid and normal tissues. Detailed information is shown in Table 2.
    Table 1

    The top 20 most abundant known miRNAs expressed in cryptorchid and normal tissues

    miRNA name

    Cryptorchid

    miRNA name

    Control

    Reads count

    Normalized reads count

    Reads count

    Normalized reads count

    hsa-miR-514a-3p

    2,313,282

    109,499

    hsa-miR-514a-3p

    1,008,914

    98,435

    hsa-miR-143-3p

    864,140

    45,306

    hsa-miR-143-3p

    677,433

    65,245

    hsa-miR-26a-5p

    829,953

    46,035

    hsa-miR-26a-5p

    407,763

    39,613

    hsa-miR-99a-5p

    705,575

    38,041

    hsa-miR-509-3-5p

    392,074

    37,711

    hsa-miR-202-5p

    616,580

    30,770

    hsa-miR-99a-5p

    346,310

    33,871

    hsa-miR-509-3-5p

    593,363

    28,054

    hsa-miR-202-5p

    258,855

    25,790

    hsa-miR-10b-5p

    428,806

    24,984

    hsa-miR-10b-5p

    248,352

    24,218

    hsa-miR-508-3p

    303,445

    14,625

    hsa-let-7f-5p

    154,806

    15,116

    hsa-let-7 g-5p

    296,741

    15,472

    hsa-miR-508-3p

    153,631

    15,059

    hsa-let-7f-5p

    266,118

    14,707

    hsa-let-7 g-5p

    151,745

    14,851

    hsa-let-7a-5p

    265,013

    14,644

    hsa-let-7a-5p

    137,886

    13,412

    hsa-miR-21-5p

    248,959

    12,378

    hsa-miR-21-5p

    132,197

    12,931

    hsa-miR-509-5p

    194,212

    9293

    hsa-miR-148a-3p

    119,576

    11,671

    hsa-miR-148a-3p

    188,828

    10,645

    hsa-miR-100-5p

    103,191

    10,071

    hsa-miR-125b-5p

    172,432

    9013

    hsa-miR-125b-5p

    93,627

    9134

    hsa-miR-100-5p

    169,375

    9160

    hsa-miR-27b-3p

    92,637

    8927

    hsa-miR-199a-3p

    154,971

    8592

    hsa-miR-509-5p

    81,898

    8124

    hsa-miR-27b-3p

    144,132

    7610

    hsa-miR-126-3p

    79,980

    7687

    hsa-let-7i-5p

    140,689

    7772

    hsa-miR-125a-5p

    72,130

    7013

    hsa-let-7b-5p

    112,327

    6040

    hsa-miR-34c-5p

    69,568

    6885

    hsa-miR-125a-5p

    107,449

    5594

    hsa-let-7i-5p

    66,915

    6560

    Table 2

    The list of top 10 most abundant novel miRNAs expressed in cryptorchid and normal tissues

    Cryptorchid tissues

    miRNA ID

    Mature Sequence

    Reads count

    Location of novel miRNA precusor

    chrX_47246

    AUUGACACUUCUGUGAGUAGA

    2,280,438

    chrX:146366172..146366230:-

    chr12_27425

    UUCAAGUAAUCCAGGAUAGGCU

    826,714

    chr12:58218403..58218462:-

    chr3_5958

    UUCAAGUAAUCCAGGAUAGGCU

    826,558

    chr3:38010903..38010964:+

    chr21_44054

    AACCCGUAGAUCCGAUCUUGU

    693,017

    chr21:17911420..17911480:+

    chrX_47235

    UACUGCAGACGUGGCAAUCAUG

    592,879

    chrX:146341178..146341235:-

    chr10_23103

    UUCCUAUGCAUAUACUUCUUU

    586,995

    chr10:135061041..135061097:-

    chr5_9937

    UGAGAUGAAGCACUGUAGCUC

    534,462

    chr5:148808506..148808561:+

    chr2_3766

    UACCCUGUAGAACCGAAUUUGU

    428,617

    chr2:177015056..177015117:+

    chrX_47228

    UGAUUGUAGCCUUUUGGAGUAGA

    298,225

    chrX:146318462..146318520:-

    chr3_7283

    UGAGGUAGUAGUUUGUACAGUU

    295,643

    chr3:52302295..52302373:-

    Normal tissues

    miRNA ID

    Mature Sequence

    Reads count

    Location of novel miRNA precusor

    chrX_47246

    AUUGACACUUCUGUGAGUAGA

    996,346

    chrX:146366172..146366230:-

    chr5_9937

    UGAGAUGAAGCACUGUAGCUC

    419,103

    chr5:148808506..148808561:+

    chr12_27425

    UUCAAGUAAUCCAGGAUAGGCU

    405,817

    chr12:58218403..58218462:-

    chr3_5958

    UUCAAGUAAUCCAGGAUAGGCU

    405,621

    chr3:38010903..38010964:+

    chrX_47235

    UACUGCAGACGUGGCAAUCAUG

    391,755

    chrX:146341178..146341235:-

    chr21_44054

    AACCCGUAGAUCCGAUCUUGU

    340,009

    chr21:17911420..17911480:+

    chr2_3766

    UACCCUGUAGAACCGAAUUUGU

    248,236

    chr2:177015056..177015117:+

    chr10_23103

    UUCCUAUGCAUAUACUUCUUU

    246,558

    chr10:135061041..135061097:-

    chr9_18744

    UGAGGUAGUAGAUUGUAUAGUU

    154,925

    chr9:96938634..96938712:+

    chr3_7283

    UGAGGUAGUAGUUUGUACAGUU

    151,154

    chr3:52302295..52302373:-

    Differential expression of miRNAs between cryptorchid and normal tissues

    As described previously by Zhang et al. [16], miRNAs were considered to be significantly differentially expressed between cryptorchid and normal tissues if they were altered by at least two-fold with P < 0.05 on the t-test. The results showed that 449 miRNAs were significantly differentially expressed in cryptorchid tissue (Fig. 3). Of these, 297 were downregulated and 152 were upregulated compared to normal tissue. The 30 most downregulated and upregulated known miRNAs are listed in Tables 3 and 4, respectively.
    Fig. 3
    Fig. 3

    The overview of the volcano plot generated by miRNAs profile in cryptorchid tissues and control tissues.

    Table 3

    A collection of the top 30 most downregulated known miRNAs detected by deep sequencing in cryptorchid tissues

    MiRNA name

    baseMean

    log2FoldChange

    lfcSE

    stat

    p

    Adjust p

    hsa-miR-3663-5p

    41.936

    −4.426

    0.624

    −7.089

    1.35E-12

    2.39E-10

    hsa-miR-1233-3p

    25.216

    −4.227

    0.679

    −6.225

    4.79E-10

    1.84E-08

    hsa-miR-552-5p

    66.556

    −4.055

    0.563

    −7.195

    6.24E-13

    1.21E-10

    hsa-miR-449b-5p

    392.523

    −3.972

    0.496

    −8.001

    1.23E-15

    5.26E-13

    hsa-miR-7153-5p

    108.897

    − 3.812

    0.634

    −6.010

    1.84E-09

    5.18E-08

    hsa-miR-122-5p

    525.785

    −3.790

    0.562

    −6.741

    1.57E-11

    1.60E-09

    hsa-miR-552-3p

    65.189

    −3.760

    0.562

    −6.680

    2.38E-11

    2.31E-09

    hsa-miR-449a

    5575.001

    −3.740

    0.511

    −7.317

    2.52E-13

    5.97E-11

    hsa-miR-122-3p

    4.738

    −3.722

    1.011

    −3.679

    0.00023

    0.0016

    hsa-miR-34b-5p

    123.524

    −3.688

    0.558

    −6.610

    3.84E-11

    3.56E-09

    hsa-miR-449c-5p

    2234.173

    −3.637

    0.465

    −7.816

    5.42E-15

    1.93E-12

    hsa-miR-34c-5p

    39,328.272

    −3.553

    0.440

    −8.060

    7.58E-16

    5.26E-13

    hsa-miR-449c-3p

    7.961

    −3.441

    0.902

    −3.812

    0.00014

    0.0011

    hsa-miR-375

    491.449

    −3.408

    0.362

    −9.416

    4.68E-21

    9.99E-18

    hsa-miR-3663-3p

    37.612

    −3.385

    0.676

    −5.001

    5.68E-07

    9.63E-06

    hsa-miR-7159-5p

    20.897

    −3.259

    0.705

    −4.618

    3.87E-06

    5.29E-05

    hsa-miR-449b-3p

    142.460

    −3.212

    0.610

    −5.262

    1.42E-07

    2.75E-06

    hsa-miR-4700-5p

    4.985

    −3.208

    0.951

    −3.370

    0.00075

    0.0043

    hsa-miR-522-3p

    121.036

    −3.153

    0.465

    −6.768

    1.30E-11

    1.46E-09

    hsa-miR-1273a

    38.566

    −3.118

    0.508

    −6.135

    8.47E-10

    2.44E-08

    hsa-miR-1295a

    11.735

    −3.075

    0.760

    −4.041

    5.31E-05

    0.0005

    hsa-miR-34b-3p

    1137.731

    −2.970

    0.516

    −5.753

    8.72E-09

    2.16E-07

    hsa-miR-1283

    139.436

    −2.798

    0.488

    −5.731

    9.95E-09

    2.41E-07

    hsa-miR-3150b-3p

    3.547

    −2.768

    0.991

    −2.791

    0.0052

    0.020

    hsa-miR-4423-3p

    16.582

    −2.702

    0.755

    −3.578

    0.00035

    0.0023

    hsa-miR-6507-5p

    7.696

    −2.698

    0.811

    −3.325

    0.00088

    0.0049

    hsa-miR-7154-5p

    406.827

    −2.646

    0.981

    −2.697

    0.0070

    0.025

    hsa-miR-517c-3p

    95.074

    −2.639

    0.386

    −6.832

    8.37E-12

    9.92E-10

    hsa-miR-3925-3p

    10.324

    −2.613

    0.735

    −3.553

    0.00038

    0.0025

    hsa-miR-515-5p

    84.007

    −2.600

    0.379

    −6.856

    7.04E-12

    8.84E-10

    Table 4

    A collection of the top 30 most upregulated known miRNAs detected by deep sequencing in cryptorchid tissues

    MiRNA name

    baseMean

    log2FoldChange

    lfcSE

    stat

    p

    Adjust p

    hsa-miR-7151-3p

    6.026

    2.634

    0.892

    2.953

    0.0031

    0.014

    hsa-miR-376a-2-5p

    10.918

    2.202

    0.724

    3.042

    0.0023

    0.011

    hsa-miR-1224-5p

    17.708

    2.193

    0.615

    3.565

    0.00036

    0.0024

    hsa-miR-1299

    187.854

    1.958

    0.426

    4.600

    4.22E-06

    5.73E-05

    hsa-miR-142-5p

    697.547

    1.898

    0.583

    3.255

    0.0011

    0.0060

    hsa-miR-543

    1281.559

    1.869

    0.450

    4.152

    3.29E-05

    0.00036

    hsa-miR-487a-3p

    80.564

    1.865

    0.591

    3.155

    0.0016

    0.0079

    hsa-miR-584-3p

    19.666

    1.829

    0.562

    3.254

    0.0011

    0.0060

    hsa-miR-665

    18.416

    1.798

    0.710

    2.534

    0.011

    0.036

    hsa-miR-134-3p

    29.541

    1.778

    0.598

    2.975

    0.0029

    0.013

    hsa-miR-369-3p

    500.851

    1.692

    0.432

    3.916

    8.99E-05

    0.00082

    hsa-miR-377-3p

    96.245

    1.665

    0.551

    3.023

    0.0025

    0.011

    hsa-miR-33a-5p

    28.103

    1.664

    0.550

    3.025

    0.0025

    0.011

    hsa-miR-376a-3p

    112.0733

    1.602

    0.436

    3.704

    0.00021

    0.0015

    hsa-miR-758-3p

    520.1303

    1.589

    0.439

    3.620

    0.00029

    0.0020

    hsa-miR-654-3p

    4175.568

    1.587

    0.388

    4.095

    4.22E-05

    0.00044

    hsa-miR-134-5p

    2747.859

    1.558

    0.424

    3.675

    0.00024

    0.0017

    hsa-miR-889-3p

    740.3619

    1.552

    0.468

    3.312

    0.00093

    0.0052

    hsa-miR-127-3p

    40,871.646

    1.548

    0.392

    3.955

    7.65E-05

    0.00071

    hsa-miR-1185-1-3p

    161.457

    1.539

    0.506

    3.039

    0.0024

    0.011

    hsa-miR-1185-2-3p

    38.541

    1.534

    0.587

    2.614

    0.0089

    0.030

    hsa-miR-154-5p

    267.267

    1.516

    0.346

    4.385

    1.16E-05

    0.00014

    hsa-miR-381-3p

    7512.422

    1.511

    0.382

    3.957

    7.57E-05

    0.00070

    hsa-miR-127-5p

    768.176

    1.511

    0.401

    3.765

    0.00017

    0.0013

    hsa-miR-337-5p

    44.570

    1.510

    0.439

    3.437

    0.00059

    0.0036

    hsa-miR-379-3p

    262.022

    1.508

    0.401

    3.756

    0.00017

    0.0013

    hsa-miR-136-3p

    937.135

    1.506

    0.389

    3.868

    0.00011

    0.00096

    hsa-miR-376c-3p

    327.216

    1.492

    0.402

    3.713

    0.00020

    0.0015

    hsa-miR-495-3p

    884.797

    1.443

    0.390

    3.696

    0.00022

    0.0016

    hsa-miR-376b-5p

    24.828

    1.442

    0.590

    2.445

    0.014

    0.045

    Validating the altered expression level of miRNAs by qRT-PCR

    QRT-PCR was performed to validate the altered miRNA expression. Among these deregulated miRNAs, we firstly selected two well-established spermatogenesis-associated miRNAs, miR-449a and miR-34c-5p [5, 11]. Additionally, to better proving the accuracy of NGS, the other validated miRNAs were picked from the non-top 30 most deregulated known miRNAs (see Additional file 4 and Additional file 5), so that the relatively small fold changes could be validated. According to the previous studies, ten miRNAs were picked for qRT-PCR validation randomly [16, 19]. Eventually, a total of 12 differentially-expressed miRNAs (seven upregulated and five downregulated) were selected for qRT-PCR analysis. The results showed that the expression levels of most miRNAs (9/12; P < 0.05) were consistent with the results of NGS technology. Detailed information is shown in Fig. 4.
    Fig. 4
    Fig. 4

    Confirmation of differentially expressed miRNAs between cryptorchid tissues and control tissues obtained by NGS using qRT-PCR. (* P <0.05)

    GO enrichment analysis of differentially-expressed genes in cryptorchid and normal tissues

    After predicting the target genes of differentially-expressed miRNAs in cryptorchid and normal tissues, GO enrichment analysis was conducted. The 10 most enriched GO terms, including signal transduction and adaptive immune response, are shown in Table 5.
    Table 5

    Top 30 most enriched GO terms for predicted targets of differentially expressed miRNAs between cryptorchid and normal tissues

    GO number

    Term*

    GO process

    Ratio in study

    Ratio in pop

    p

    GO:0007165

    BP

    signal transduction

    19.68%

    23.63%

    1.04E-05

    GO:0002250

    BP

    adaptive immune response

    0.70%

    1.83%

    1.56E-05

    GO:0050789

    BP

    regulation of biological process

    47.95%

    52.34%

    4.14E-05

    GO:0050794

    BP

    regulation of cellular process

    45.10%

    49.34%

    7.66E-05

    GO:0008150

    BP

    biological_process

    78.42%

    81.73%

    8.25E-05

    GO:0065007

    BP

    biological regulation

    51.05%

    55.25%

    8.51E-05

    GO:0006956

    BP

    complement activation

    0.25%

    0.98%

    0.000114

    GO:0006958

    BP

    complement activation, classical pathway

    0.20%

    0.88%

    0.000134

    GO:0048518

    BP

    positive regulation of biological process

    21.53%

    25.03%

    0.00014

    GO:0050776

    BP

    regulation of immune response

    3.95%

    5.72%

    0.000229

    GO:0044425

    CC

    membrane part

    28.37%

    34.86%

    1.03E-10

    GO:0005886

    CC

    plasma membrane

    17.68%

    23.38%

    1.11E-10

    GO:0031224

    CC

    intrinsic component of membrane

    23.23%

    29.30%

    2.06E-10

    GO:0016021

    CC

    integral component of membrane

    22.68%

    28.69%

    2.24E-10

    GO:0005575

    CC

    cellular_component

    84.22%

    88.01%

    1.38E-07

    GO:0005794

    CC

    Golgi apparatus

    3.35%

    5.84%

    1.42E-07

    GO:0005840

    CC

    ribosome

    2.20%

    1.09%

    7.38E-06

    GO:0000139

    CC

    Golgi membrane

    1.85%

    3.41%

    1.92E-05

    GO:0044459

    CC

    plasma membrane part

    9.84%

    12.81%

    1.93E-05

    GO:0004872

    MF

    receptor activity

    5.39%

    8.48%

    5.53E-08

    GO:0060089

    MF

    molecular transducer activity

    5.39%

    8.48%

    5.53E-08

    GO:0005179

    MF

    hormone activity

    1.55%

    0.54%

    6.49E-08

    GO:0004871

    MF

    signal transducer activity

    5.79%

    8.78%

    2.25E-07

    GO:0038023

    MF

    signaling receptor activity

    4.45%

    7.13%

    3.21E-07

    GO:0099600

    MF

    transmembrane receptor activity

    4.25%

    6.85%

    4.09E-07

    GO:0003823

    MF

    antigen binding

    0.50%

    1.75%

    4.18E-07

    GO:0004888

    MF

    transmembrane signaling receptor activity

    4.15%

    6.63%

    9.33E-07

    GO:0032553

    MF

    ribonucleotide binding

    6.34%

    8.94%

    1.10E-05

    GO:0003674

    MF

    molecular_function

    77.82%

    81.17%

    7.79E-05

    *BP Biological process; CC Cellular component; MF Molecular function

    KEGG pathway analysis of differentially-expressed genes in cryptorchid and normal tissues

    After GO analysis, KEGG pathway enrichment analysis was performed. A total of five KEGG pathways were enriched, including oxidative phosphorylation, Parkinson’s disease, Ribosomal pathways, Huntington’s disease and Alzheimer’s disease. The results are presented in Table 6.
    Table 6

    KEGG pathway analysis for predicted target genes of differentially expressed miRNAs between cryptorchid and normal tissues

    Pathway ID

    Description

    GeneRatio

    BgRatio

    p

    Adjust p

    GeneName

    hsa00190

    Oxidative phosphorylation

    28/664

    133/7297

    1.80E-05

    0.0053

    ATP5G2;COX6C;SDHD;COX7A2L;

    COX8C;ATP6V1D

    hsa05012

    Parkinson’s disease

    28/664

    142/7297

    6.29E-05

    0.0093

    ATP5G2;COX6C;SDHD;UBB;UBE2L6;COX7A2L;GNAL;COX8C

    hsa03010

    Ribosome

    29/664

    154/7297

    0.0001112

    0.0110

    MRPL16;RPL38;RPS4X;MRPL35;

    RPS6;MRPS18C;RPL26;RPS27L

    hsa05016

    Huntington’s disease

    33/664

    193/7297

    0.0002633

    0.0187

    ATP5G2;COX6C;UCP1;SDHD;

    POLR2J3;COX7A2L;COX8C;POLR2K

    hsa05010

    Alzheimer’s disease

    30/664

    171/7297

    0.0003147

    0.0187

    ATP5G2;COX6C;CASP12;SDHD;

    PPP3CC;COX7A2L;LPL;COX8C

    Discussion

    As one of the most common congenital defects in newborn boys, cryptorchidism influences male fertility and increases the risk of testicular cancer. Reductions in seminiferous tubules and germ cells are common histological changes in cryptorchid testis [20]. Despite surgery being recommended for many patients with cryptorchidism, the success of orchidopexy depends on the timing of the procedure and the position of the testis: some may not benefit from cryptorchidopexy [21, 22]. Although research has identified some biological processes involved in spermatogenic arrest in cryptorchid testis (i.e. significant apoptotic changes in germ cells), the causative roles of genes in spermatogenic arrest or apoptosis remain unclear [2326]. This is the first study to investigate the possible mechanisms of spermatogenic arrest in cryptorchid testes by assessing the miRNA profiles in post-cryptorchidopexy testes.

    Many rodent and primate models were developed to identify altered miRNAs in cryptorchid testis. For example, Duan et al. established a mouse model of cryptorchidism and showed that miR-210 was highly expressed in cryptorchid testes compared with control testes. Moreover, they showed that this miRNA regulated spermatogenesis by inhibiting the expression of NR1D2 [12]. Moritoki et al. compared the miRNA expression profiles of unilateral undescended testes with contralateral descended testes in a rat model of cryptorchidism using microarray analysis. These authors found that only miR-135a expression was lower in unilateral undescended testes and that its target, FoxO1, played essential roles in stem cell maintenance [13]. Furthermore, Duan et al., also found that miR-210 was upregulated in human cryptorchidism, thus suggesting a vital role for miRNAs in humans [12]. In this study, 297 downregulated and 152 upregulated miRNAs were identified in post-cryptorchidopexy testicular tissue compared with normal testis tissue. However, miR-210 was not significantly altered, which may be due to the different types of human cryptorchid tissue. For example, Duan et al. used cryptorchid testis tissue obtained during the cryptorchidopexy, whilst this study used post-cryptorchidopexy testicular tissue. Some miRNA expression levels may change after the operation.

    Despite the insights gained into cryptorchidism over the years, the mechanism of spermatogenesis arrest in patients with this disease remains largely elusive. Germ cell apoptosis is commonly seen at the histological level in cryptorchid testes. Yin et al. revealed that cryptorchidism induced germ cell apoptosis in an experimental mouse model via p53-dependent and p53-independent pathways [23]. Liu et al. also found that the Hsf1/Phlda1 pathway participated in primary spermatocyte apoptosis in surgery-induced cryptorchid testes of rats [27]. The expression of many apoptosis-related miRNAs was also shown to be altered in post-cryptorchidopexy testicular tissues. It was reported that miR-299-5p could modulate apoptosis through autophagy in neurons and ameliorate the cognitive capacity of APPswe/PS1dE9 mice [28]. In addition, miR-299-5p was significantly upregulated in post-cryptorchidopexy testicular tissue. Similar results were also found for miR-217, miR-206 etc. Li et al. also found that miR-217 could regulate apoptosis by targeting TNFSF11 in human podocyte cells [29]. This study also identified a significant downregulation of miR-217 in post-cryptorchidopexy testicular tissue. Similarly, miR-206 was significantly upregulated in post-cryptorchidopexy testicular tissue and was shown to promoted cell apoptosis in Legg–Calvé–Perthes disease [30].

    Conclusions

    In summary, miRNA expression in post-cryptorchidopexy testicular tissue was profiled using NGS and compared with that of OA men with normal spermatogenesis. Several signalling pathways that are likely to be involved in spermatogenesis arrest in these patients were addressed. The results provide an important platform for future investigations into the roles of miRNAs in the progression of cryptorchidism as well as therapeutic targets to help these patients recover fertility. However, the comprehensive modulating behaviours of genes remain unclear, therefore determining the target genes and regulatory networks of these differentially-expressed miRNAs is essential in future investigations.

    Notes

    Abbreviations

    HE: 

    Hematoxylin-eosin

    miRNAs: 

    MicroRNAs

    NGS: 

    Next-generation sequencing

    NOA: 

    Non-obstructive azoospermia

    OA: 

    Obstructive azoospermia

    qRT-PCR: 

    Quantitative real-time reverse transcription-polymerase chain reaction

    Declarations

    Acknowledgements

    We thank all subjects who provided the tissues for this study.

    Funding

    This study was funded by the the National Natural Science Foundation of China (No. 81370749).

    Availability of data and materials

    The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

    Authors’ contributions

    DT, XH and XZ designed the study.ZH and HW performed the experiments.LZ and DP analyzed the data.DT and ZH wrote the paper. All authors read and approved the final manuscript.

    Ethics approval and consent to participate

    Written informed consent was obtained from all patients and this study was approved by the Ethics Review Board of The First Affiliated Hospital of Anhui Medical University.

    Consent for publication

    Not applicable.

    Competing interests

    The authors declare that they have no competing interests.

    Publisher’s Note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

    Authors’ Affiliations

    (1)
    Reproductive Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China
    (2)
    Anhui Province Key Laboratory of Reproductive Health and Genetics, Anhui Medical University, Hefei, Anhui, People’s Republic of China
    (3)
    Anhui Provincial Engineering Technology Research Center for Biopreservation and Artificial Organs, Hefei, Anhui, People’s Republic of China
    (4)
    Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China

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    Copyright

    © The Author(s). 2018

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