Introduction

Obstructive (OA) and non-obstructive azoospermia (NOA) denote normal and abnormal spermatogenesis, respectively. Aberrant spermatogenesis is also classified into five main pathologic patterns1: seminiferous tubule hyalinization (SH), Sertoli cell-only syndrome (SCOS), early maturation arrest (eMA), late maturation arrest (lMA), and hypospermatogenesis (Hypo). Ultimately, pathological analyses can identify spermatogenesis failure and ductal obstruction; however, sperm retrieval (SR) cannot be predicted solely based on the current approach.

Medical expenses and loss of golden time are two factors preventing the treatment of azoospermic men wishing to have biological children. Reliable and precise molecular markers, especially those detecting spermatogenesis pathology, could be a boon for would-be parents. To reduce infertility stress on couples and improve male fertility, especially for NOA men, we previously introduced the KDM3A to PRM1 expression ratio as a reliable molecular indicator of SR2. However, we have thus far not been able to detect any association between the aforementioned genes and the pathological features of the biopsies. It is critical to identify the gene(s) that will allow us to predict the success of SR while confirming testicular pathology. By joining pathology and genetics in this manner, we can determine the possibility of SR. This information could persuade surgeons to explore tissues from NOA men to extract any residual sperm during the first micro-TESE surgery.

Thioredoxins are intracellular and extracellular scavengers of the oxidative stress system. Reactive oxygen species (ROS) are one of their main targets, and the regulation of redox signaling plays pivotal roles in sperm fertility3. Thioredoxin domain-containing 2 (TXNDC2, ENSG00000168454) is transiently expressed in the haploid phase of spermatogenesis and, as a sperm-specific oxidoreductase, is only detected in round and elongating spermatids4,5. Double inactivation of TXNDC2/TXNDC3 was performed in animal models, and the output was impaired chromatin protamination6. A DNA safeguard, protamination not only condensates sperm chromatin but also replaces most histones during spermiogenesis; male infertility is conclusively associated with impaired protamination7. Known to begin with the expression of transition protein 1 (TNP1), protamination is followed by protamine (PRM1 and PRM2) replacement in the nucleus8. Thereafter, mature spermatozoa are released into the lumen of seminiferous tubules9, and capacitation then starts as the final step of sperm maturation. Even after capacitation, decondensation of sperm chromatin would be triggered by heparin sulfate of mammalian oocytes10, a phenomenon highlighting how previous chromatin condensation is necessary for male fertility.

In this study, TXNDC3 was not evaluated as it is ubiquitously expressed in all tissues and is no longer considered testis specific11. Considering TXNDC2 is localized in the nucleus and TXNDC8 is distributed extracellularly, the latter was also removed from analyses. Therefore, the aim of this study was to evaluate the expression levels of TXNDC2 concomitantly with protamination genes in different azoospermia pathologies. We showed that PRM1 and PRM2, but not TNP1, are excellent indicators of SR. We also showed that TXNDC2 expression levels were consistent with tissue pathologies. Moreover, logistic regression model analysis of combined TXNDC2, PRM1, and PRM2 genes was a robust predictor of SR, providing a sensitivity of 96.8% and specificity of 95.5%.

Results

Data quality control and pre-processing

The assessment of data normalization revealed that parts of the data were log2 scaled, and the remainder were transformed. The second round of quality control was carried out to assess the quality of sample quantiles (Supplementary Fig. 1). For each dataset, hierarchical cluster analysis of samples, based on Euclidian Distance of the Pearson correlation coefficient, grouped similar objects into clusters. Clustering was followed by dimension reduction using the Eigenvector with the highest Eigenvalue (Supplementary Fig. 2). The decision to remove 27 outliers out of 89 samples was based on advanced knowledge of biology, combined with clustering and PCA (supplementary Fig. 3). Consequently, a total of 62 samples were pooled for further analyses.

Limma and SVA algorithms were applied to the pool to correct their batch. Hierarchical clustering and PCA were performed, and the outcome provided the confidence about the correction (Fig. 1).

Figure 1
figure 1

PCA of pooled samples before and after the batch effect removal (using different algorithms). (a) Before the batch effect removal, samples with identical or similar pathology were separated based on their batches. After the removal, PCA separated samples according to their pathology and the samples were grouped regardless of their batches using limma algorithm (b) and SVA algorithm (c). Batch 1–4 represents GSE145467, GSE45885, GSE108886, GSE14310. mei (meiotic arrest); norm_oa (normal spermatogenesis or obstructive azoospermia); oligo (oligospermia); post (post meiotic arrest); pre (pre meiotic arrest); SCOS (Sertoli cell-only syndrome); unknown (azoospermia with unknown pathology).

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Meta-analysis

The gene expression of pooled data with pathological phenotypes of SCOS (7 samples), pre-meiotic arrest (5 samples), meiotic arrest (12 samples), and post-meiotic arrest (11 samples) was evaluated (Fig. 2). Based on the goal of this study, protamination genes (PRM1, PRM2, TNP1) with respect to testis-specific thioredoxin genes (TXNDC2, TXNDC8) were analyzed (Table 1 and Fig. 3). SCOS patients’ meta-analysis revealed meaningful downregulation of TXNDC2 (effect size = − 2.42, FDR = 7.86E−07), PRM1 (effect size = − 4.28, FDR = 5.89E−07), PRM2 (effect size = − 3.98, FDR = 1.77E−06), and TNP1 (effect size = − 4.75, FDR = 8.32E−09). Similar meaningful downregulation of the genes was also recorded in pre-meiotic arrest and meiotic arrest phenotypes, but not in post-meiotic arrest. TXNDC2 (effect size = − 4.25, FDR = 1.44E−15), PRM1 (effect size = − 5.37, FDR = 1.99E−10), PRM2 (effect size = − 5.16, FDR = 3.60E−10), and TNP1 (effect size = − 7.05, FDR = 6.48E−16) were all downregulated in the idiopathic azoospermia dataset. Except for post-meiotic arrest, TXNDC8 meaningful downregulation was detected for SCOS (effect size = − 1.59, FDR = 3.97E−05), pre-meiotic arrest (effect size = − 1.79, FDR = 8.63E−05) and meiotic arrest (effect size = − 1.55, FDR = 4.53E−05).

Figure 2
figure 2

A heatmap representing 71 samples, clustered based on correlation coefficient of 788 genes with standard deviation greater than 1. Group indicates the pathology of samples and the batch represents different datasets. Batch effect removal was approved as the heatmap clusters genes based on their pathologic groups and separates them based on their batches. Batch 1–4 represents GSE145467, GSE45885, GSE108886, GSE14310. mei (meiotic arrest); norm_oa (normal spermatogenesis or obstructive azoospermia); oligo (oligospermia); post (post meiotic arrest); pre (pre meiotic arrest); SCOS (Sertoli cellonly syndrome); unknown (azoospermia with unknown pathology).

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Table 1 GEO analysis results.
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Figure 3
figure 3

Log fold changes of TXNDC2, PRM1, PRM2 and TNP1 genes in different pathologies were illustrated. (a) After the batch effect removal using limma package, different log2FC of individual genes was visualized in different aberrant pathologies. (b) A same pattern of log2FC differences were also observed after batch effect removal, using SVA algorithm. In all comparisons, normal spermatogenesis was used as control. mei (meiotic arrest); norm_oa (normal spermatogenesis or obstructive azoospermia); oligo (oligospermia); post (post meiotic arrest); pre (pre meiotic arrest); SCOS (Sertoli cell-only syndrome); unknown (azoospermia with unknown pathology); TXNDC2 (Thioredoxin Domain Containing 2); TXNDC8 (Thioredoxin Domain Containing 8); PRM1 (Protamine 1); PRM2 (Protamine 2); TNP1 (Transition Protein 1).

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RT-qPCR data analysis

The mean expression level of GAPDH, RPL37, TXNDC2, PRM1, PRM2, and TNP1 was compared between positive and negative SR (Supplementary Table 1). Reference genes GAPDH and RPL37 showed the minimal mean differences between positive and negative SR individuals (0.59 and 0.97, respectively). High positive mean differences were detected for TXNDC2, PRM1, and PRM2 (considering positive SR as the control). However, TNP1 showed a negative (− 1.52) mean difference. Therefore, TXNDC2 was differentially expressed in homology and protamination genes PRM1 and PRM2. Unexpectedly, the expression of TNP1 was overlapping (Fig. 4). A t-test was performed on normalized data to determine the significance of the observed differences (Table 2). A significant differential expression for TXNDC2, PRM1, and PRM2 (p = 0.000) was observed between positive and negative SR, but not for TNP1 (p = 0.558).

Figure 4
figure 4

Relative expression of TXNDC2 and protamination genes were compared between men with positive (blue bars) and negative (red bars) sperm retrieval. Mean Cqs of both reference genes, GAPDH and RPL37, were calculated and used for relative expression. Meaningful intra-gene differences were illustrated for TXNDC2, PRM1 and PRM2. TNP1 showed overlapped relative expression between samples with positive and negative sperm retrieval. p-value less than 0.05 were considered as significant.

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Table 2 Sperm retrieval and mRNA expression.
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REST2009 relative expression analysis results are presented in Table 3. Data analysis showed significant downregulation of TXNDC2 with an expression ratio of 0.047 (p = 0.000). PRM1 and PRM2 genes were also significantly (p = 0.000) downregulated with an expression ratio of 0.000. TNP1, on the other hand, was insignificantly (p = 0.301) upregulated with a minor expression ratio of 4.078.

Table 3 Relative expression report deduced from REST2009.
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Discussion

Discovering a suitable molecular marker to predict SR is a topic of current substantial research interest in andrology. In the first attempt between different azoospermia phenotypes, only SCOS was successfully correlated with RBMY1 and DAZ genes, suggesting a significant positive association between these genes and successful SR12. The BOll/GAPDH mRNA ratio was assessed in different pathological phenotypes of azoospermia, and using a cut-off value of 0.5, sensitivity and specificity of 100% was achieved for SR13.

Technical improvements made the methodology of previous studies challenging, and therefore, the demand has risen for accurate and precise methods capable of diminishing biases. To address this urgency, RT-qPCR was introduced and applied in numerous recent studies. ESX1 was the first reliable spermatogenesis molecular marker introduced with a significant (p = 0.04) concordance of 73.7%14. Additional testing of seminal fluid also confirmed the capacity of ESX1 as a molecular marker of SR with 84% sensitivity, notwithstanding discrepancies between molecular and clinical outputs15. In a previous study, we improved the sensitivity of SR to 95.5% using KDM3A histone demethylase. However, we were unable to produce concordance between our molecular markers and pathological phenotypes2.

TXNDC2 was correlated with SH phenotype in the present study, while PRM1 and PRM2 showed additional association with GCA/SCOS (Table 4). Notably, genome-wide integration of transcriptomics and antibody-based proteomics had previously determined that TXNDC8 was a testis-specific protein as well, albeit as an extracellular equivalent of nuclear TXNDC211,16. It seems logical to consider TXNDC2 over TXNDC8, as protamine activation takes place in the nucleus. Furthermore, the association of PRM2 but not PRM1 with eMA was also notable. Specifically, these three genes could be altered at the very early stages of spermatogenesis, and when being expressed, could indicate the existence of germ cells. As we know, protamine activation occurs before they bind DNA, a potential role for thioredoxin. After the release of protamine precursors, a round of sequential phosphorylation and dephosphorylation strengthen protamines’ binding power to wrap around the corresponding DNA. A key event after dephosphorylation, completing the activation process, is the oxidation of protamine monomers to produce a head-to-tail dimer. Thioredoxins are oxidizing molecules acting on Cys residues, which are abundantly present in protamines. Therefore, synchronous downregulation of TXNDC2 and PRM1/PRM2 in SH and SCOS (the phenotypes of the most severe pathologies of sperm failure) could imply their importance for sperm production.

Table 4 mRNA expression and histological phenotypes.
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To future examine the observed synchronicity, a linear regression model was developed (Table 5). TXNDC2 showed a strong correlation with PRM1 (r = 0.761) and PRM2 (r = 0.767). The coefficient of determination correlated up to 60% of PRM1 and PRM2 expression solely with TXNDC2 expression. Moreover, PRM1 was perfectly correlated (r = 0.993, p = 0.000) with PRM2, indicating that the value of PRM2 could be anticipated from PRM1 by 98.6%. Previous observations proposed similar correlations between two co-expressed protamines. Animal knockout models and our previous study confirmed KDM3A, itself under the control of HIF1-a, as the transcription factor of PRM1 and PRM22,8,17. It was also shown that the overexpression of thioredoxin could increase HIF1-a activity18.

Table 5 Linear regression analysis of target genes.
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Receiver operator characteristic (ROC) analysis was conducted to evaluate the predictive power of biomarkers. In the first step, the relative expression of TXNDC2 was analyzed to understand its predictive potential regardless of SR. ROC curve analysis showed ROC value (AUC) = 0.880 for TXNDC2 (Fig. 5, blue line). The recorded AUC value was statistically significant (p < 0.05). A sensitivity of 85% and specificity of 92.9% were determined for TXNDC2. To increase the diagnostic power of our potential biomarker, a logistic regression model of TXNDC2 alongside PRM1 and PRM1/PRM2 was built based on the relative expression values. A regression model based on TXNDC2 and PRM1, but not PRM2, showed an increased AUC value of 0.995 (p = 6.9279E−9). A 10% improvement in sensitivity was achieved at a cut-off value = 0. 2912 when PRM1 and PRM2 were introduced into the regression model (Fig. 5, green line). Therefore the improved sensitivity of 95% and specificity of 96.4% with the AUC value of 0.995 (SE = 0.0070, 95% CI 0.982–1.000) was revealed for the combined regression model of TXNDC2-PRM1-PRM2.

Figure 5
figure 5

ROC curve analysis. TXNDC2 alone (Blue line) showed AUC = 0.880 significantly (p = 000,008). To assess the effects of protamines, logistic Regression model was built and, ROC curve analysis was performed. TXNDC2, PRM1, and PRM2 were all included in the regression model (green line). AUC value was significant and even more improved to 0.995 (SE = 0.0070, 95% CI 0.9816–1.000). The sensitivity and specificity were 95% (10% improvement) and 96.4% respectively.

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Conclusions

TXNDC2 was differentially expressed between positive and negative SR. Moreover, TXNDC2 was correlated with phenotypes of severe azoospermia pathology (SH and SCOS). A strong correlation of TXNDC2 with protamination genes was observed. ROC analysis applied to the multiple regression model demonstrated TXNDC2-PRM1-PRM2 as robust molecular markers of SR with a sensitivity of 96.8% and specificity of 95.5%.

Materials and methods

Patients and samples

Azoospermic men were interviewed twice, before and after the operation. A sample was eliminated from analysis after the operation if the patient was unwilling to continue participating in the study. The mean age of the participating men was 30 ± 5 years old at the time of surgery. Inclusion criteria were men with primary idiopathic azoospermia who did not have any previous naturally born children. All the men were classified as having azoospermia by analyses of at least two semen samples, and they all suffered from a lack of sperm in the ejaculate. Men whom (i) had any chromosomal abnormality or (ii) AZF gene mutations, (iii) were severe smokers or addicted to drugs, (iv) had a history of testosterone therapy or (v) TESE or micro-TESE were excluded from this study. Approximately 50 mg of fresh testicular tissue was collected and submerged immediately into the RNAlater stabilizing reagent (Ambion Life Science, Austin, TX, USA, AM7024) according to the manufacturer’s instruction. The first piece of testicular tissue was used for RNA extraction, and the subsequent pieces for pathology and SR. Submerged samples were stored at 4 °C for 24 h and then processed for RNA extraction. A total number of 58 testicular tissue samples were collected entered this study. Nine of those samples were omitted as they presented with unknown pathology. According to the pathological results, out of the 50 samples included, 40 were diagnosed as non-obstructive and 10 as obstructive-control individuals. The exclusion criteria for samples were those with weak RNA integrity, variable Cqs even after multiple rounds of separate analyses, and without clear pathology.

Ethics statement

Written informed consent was collected and a full explanation of the study was provided to azoospermic men before sampling. The experimentation and consent forms were approved by the institutional review board of the Isfahan University Ethical Committee. All procedures performed in the study involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

SR technique

The Schlegel technique was employed and an expert surgeon performed all the micro-TESE open surgeries under a microscope to lessen the obstruction of testicular vessels19. Meticulous sperm processing with initial mechanical dissection of seminiferous tubules was followed by extensive exercise to ensure the maximum rate of retrieval20.

Histological analysis

Hematoxylin and eosin (H&E) staining of paraffin-embedded tissues was performed according to the standard protocol21. A specialist pathologist examined two microscopic slides containing at least 100 different sections of seminiferous tubules for each specimen. The results were reported as follows: (i) N = normal spermatogenesis with all types of spermatogenic cell lineages in sections, (ii) SH = seminiferous tubule hyalinization, (iii) SCOS = Sertoli cell-only syndrome or germ cell aplasia, (iv) eMA = early maturation arrest, (v) lMA = late maturation arrest, (vi) Hypo = hypospermatogenesis. Individuals with normal spermatogenesis were considered to have obstructive azoospermia (OA), and these were the control individuals as per previous reports15. Other pathologies with abnormal spermatogenesis were classified as non-obstructive azoospermia (NOA).

GEO meta-analysis

The GEO database was explored with the keyword “azoospermia” for microarray datasets. Rigid inclusion–exclusion criteria were applied as follows, and a total of nine datasets corresponding to Homo sapiens were found. Among these datasets, those including any treatments and therapies were excluded. Samples with the cryptorchidism phenotype and with detected mutations were also excluded. In this regard, GSE145467, GSE45885, GSE9194, GSE108886, GSE9210, GSE14310 were selected. All the candidate datasets were log2 scaled and quantile normalized if necessary. Hierarchical clustering of each dataset was illustrated using Euclidian distance. A principal component analysis (PCA) plot was drawn, and outliers were detected and removed. GSE9194 and GSE9210 were excluded due to low quality and low feature intersection with other datasets, respectively. SVA22 and Limma23 packages were used to remove batch effects, and subsequently, PCA and hierarchical clustering were used again to check the quality of the batch effect removal. The effect size of features was calculated using the Limma package with Benjamini–Hochberg correction. We applied p values to determine the corresponding false discovery rates (FDR). Finally, testis-specific thioredoxin gene 2 (TXNDC2) variation alongside protamination genes (TNP1, PRM1, PRM2) was recorded. Testis-specific thioredoxin gene 8 (TXNDC8) was not included in the GSE14310 dataset, and meta-analysis was performed on the resting GSE45885 and GSE108886 datasets. Software platform R 4.0.1 (R Foundation 3.6.2 for Statistical Computing, 2020, Austria) was used for meta-analysis.

RNA isolation and cDNA synthesis

RNA extraction was carried out as described previously2. Nanodrop One (Thermo Scientific, USA) was used for quantification, and 1 μg of total RNA was treated with DNase I (Thermo Scientific, Lithuania; EN0522) according to the manufacturer’s instruction. TaKaRa PrimerScript II 1st strand cDNA synthesis kit (TaKaRa, Otsu, Japan; 6210B) was used to prime the first strand of cDNA randomly. Qualities of the extracted RNAs were confirmed by 2% conventional agarose gel electrophoresis stained with ethidium bromide (data not shown).

Reverse transcription-quantitative real-time PCR (RT-qPCR)

Primers were adopted for RT-qPCR, and their concentration was optimized according to our previous study2. SYBR Premix Ex Taq II (TaKaRa; RR820L) was the quantifying dye in a Corbett 6000 Rotor-Gene thermocycler (Corbett Life Science, Mortlake, Australia). Equal amounts of cDNA were amplified in triplicate, and the values for the average cycle of quantification (Cq) were further analyzed.

Melting curve analysis

After the final amplification, a melting curve analysis via green channel was performed according to the thermocycler manufacturer’s manual. The temperature was gradually increased (1.0 °C/s) from 65 to 95 °C, and the amount of emitted fluorescence was recorded continuously. The deviation of fluorescence change over temperature was plotted on the y-axis against the temperature on the x-axis using the Rotor-Gene embedded software v. 1.7.

Gene expression analysis

GAPDH and RPL37 were used simultaneously as reference genes for RT-qPCR data normalization based on our previous finding2. REST2009 (Qiagen, Germany) was used for statistical analyses.

Statistical analyses

Raw mean Cqs were exported to SPSS v.21.0 (IBM Corp., Armonk, NY, USA), and normalization of the data was conducted if necessary. Normalized mean Cqs of the genes were compared between individuals with positive and negative SR using a t-test. A one-way between-subjects ANOVA-coupled with a Scheffe post hoc comparison was conducted to visualize the differences of mRNA expression levels between different testicular histopathologies. Multiple linear regression approaches were applied to model the relationship between the expression levels of PRM1, PRM2, and TXNDC2. A receiver operating characteristic curve (ROC) predictive model was obtained to demonstrate the predictive ability of the three expressed genes for SR. The area under the curve (AUC) was determined to assess the diagnostic accuracy. In all statistics, p values smaller than 0.05 were considered significant.