System analysis of teratozoospermia mRNA profile based on integrated bioinformatics tools

Identification of hub genes and their expression profiling for predicting buffalo (Bubalus bubalis) semen quality and fertility

Sperm transcriptomics provide insights into subtle differences in sperm fertilization competence. For predicting the success of complex traits like male fertility, identification of hub genes involved in various sperm functions are essential. The bulls from the transcriptome profiled samples (n = 21), were grouped into good and poor progressive motility (PM), acrosome integrity (AI), functional membrane integrity (FMI) and fertility rate (FR) groups. The up-regulated genes identified in each group were 87, 470, 1715 and 36, respectively. Gene networks were constructed using up- and down-regulated genes from each group. The top clusters from the upregulated gene networks of the PM, AI, FMI and FR groups were involved in tyrosine kinase (FDR = 1.61E-11), apoptosis (FDR = 1.65E-8), translation (FDR = 2.2E-16) and ribosomal pathway (FDR = 1.98E-21), respectively. From the clusters, the hub genes were identified and validated in a fresh set of semen samples (n = 12) using RT-qPCR. Importantly, the genes (fold change) RPL36AL (14.99) in AI, EIF5A (54.32) in FMI, and RPLP0 (8.55) and RPS28 (13.42) in FR were significantly (p < 0.05) up-regulated. The study suggests that the expression levels of MAPK3 (PM), RPL36AL + RPS27A or RPL36AL + EXT2 (AI), RPL36AL or RPS27A (FMI) and RPS18 + RPS28 (FR) are potential markers for diagnosing the semen quality and fertility status of bulls which can be used for the breeding program.

Identification of hub genes associated with spermatogenesis by bioinformatics analysis

Spermatogenesis is a complex process related to male infertility. Till now, the critical genes and specific mechanisms have not been elucidated clearly. Our objective was to determine the hub genes that play a crucial role in spermatogenesis by analyzing the differentially expressed genes (DEGs) present in non-obstructive azoospermia (NOA) compared to OA and normal samples using bioinformatics analysis. Four datasets, namely GSE45885, GSE45887, GSE9210 and GSE145467 were used. Functional enrichment analyses were performed on the DEGs. Hub genes were identified based on protein-protein interactions between DEGs. The expression of the hub genes was further examined in the testicular germ cell tumors from the TCGA by the GEPIA and validated by qRT-PCR in the testes of lipopolysaccharide-induced acute orchitis mice with impaired spermatogenesis. A total of 203 DEGs including 34 up-regulated and 169 down-regulated were identified. Functional enrichment analysis showed DEGs were mainly involved in microtubule motility, the process of cell growth and protein transport. PRM2, TEKT2, FSCN3, UBQLN3, SPATS1 and GTSF1L were identified and validated as hub genes for spermatogenesis. Three of them (PRM2, FSCN3 and TEKT2) were significantly down-regulated in the testicular germ cell tumors and their methylation levels were associated with the pathogenesis. In summary, the hub genes identified may be related to spermatogenesis and may act as potential therapeutic targets for NOA and testicular germ cell tumors.

Integrative Bioinformatics Analysis of The Cell Division Cycle and Ribosomal Pathways in The Rat Varicocele: Implications for Drug Discovery

OBJECTIVE

Varicocele is a common cause of male infertility, affecting a substantial proportion of infertile men. Recent studies have employed transcriptomic analysis to identify candidate genes that may be implicated in the pathogenesis of this condition. Accordingly, this study sought to leverage rat gene expression profiling, along with protein-protein interaction networks, to identify key regulatory genes, related pathways, and potentially effective drugs for the treatment of varicocele.

MATERIALS AND METHODS

In this in-silico study, differentially expressed genes (DEGs) from the testicular tissue of 3 rats were screened using the edgeR package in R software and the results were compared to 3 rats in the control group. Data was obtained from GSE139447. Setting a -11 and P<0.05 as cutoff points for statistical significance, up and down-regulated genes were identified. Based on Cytoscape plugins, protein-protein interaction (PPI) networks were drawn, and hub genes were highlighted. ShinyGO was used for pathway enrichment. Finally, effective drugs were identified from the drug database.

RESULTS

Among the 1277 DEGs in this study, 677 genes were up-regulated while 600 genes were down-regulated in rats with varicocele compared to the control group. Using protein-protein interaction networks, we identified the top five up-regulated genes and the top five down-regulated genes. Enrichment analysis showed that the up-regulated genes were associated with the cell division cycle pathway, while the down-regulated genes were linked to the ribosome pathway. Notably, our findings suggested that dexamethasone may be a promising therapeutic option for individuals with varicocele.

CONCLUSION

The current investigation indicates that in varicocele the cell division cycle pathway is up-regulated while the ribosome pathway is down-regulated compared to controls. Based on these findings, dexamethasone could be considered a future candidate drug for the treatment of individuals with varicocele.

Development and validation of a conventional in vitro total fertilization failure prediction model

BACKGROUND

Conventional total fertilization failure (TFF) is a challenging problem for clinicians. The predictive model developed in this study aims to predict the individual probability of conventional in vitro total fertilization failure.

METHODS

The prediction model was developed based on 1635 patients who underwent first-attempt in vitro fertilization (IVF) cycles from January 2018 to January 2020. Total fertilization failure and normal fertilization occurred in 218 and 1417 cycles, respectively. Multivariate logistic regression analyses were used to develop the prediction model. Performance of our model was evaluated using calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve [AUC]).

RESULTS

Thirteen risk factors for TFF were included in the prediction model, as follows: female age; female body mass index; infertility duration; number of oocytes retrieved; stimulation protocol; infertility etiology; infertility diagnosis; male age; sperm concentration; total sperm motility; normal sperm morphology percentage; swim-up sperm motility; and swim-up sperm concentration. The AUC of our model was 0.815 (95% CI: 0.783-0.846), indicating satisfactory discrimination performance.

CONCLUSION

Considering female and male factors (especially sperm parameters), we established a model that predicts the probability of TFF in conventional IVF procedures that will be helpful in the laboratory supporting IVF to facilitate physicians in determining optimal treatment.

Evaluation of SEPT2 and SEPT4 transcript contents in spermatozoa from men with asthenozoospermia and teratozoospermia

BACKGROUND AND AIMS

Motility and morphological defects of spermatozoa can cause male infertility. Sperm RNAs are related to sperm quality. They are considered to have clinical values as a biomarker for assessing sperm quality and fertility potential. The annulus, located in the mammalian sperm tail, is required for motility and terminal differentiation of the spermatozoa. SEPT2, 4, 6, 7, and 12 proteins are the main components of the annulus in the sperm tail. The study aimed to evaluate SEPT2 and SEPT4 mRNA contents in the spermatozoa of patients with asthenozoospermia and teratozoospermia.

METHODS

We evaluated transcript levels of SEPT2 and SEPT4 in the sperm samples of 20 asthenozoospermic, 20 teratozoospermic, and 20 normozoospermic samples using quantitative PCR.

RESULTS

The SEPT2 transcript level was significantly decreased in the asthenozoospermia samples compared with the normal group (P = .013). However, SEPT4 was not significantly different between these two groups. The transcript levels of SEPT2 and SEPT4 were not statistically different between teratozoospermic and normozoospermic groups.

CONCLUSION

In conclusion, downregulation of SEPT2 in patients with asthenozoospermia appears to be associated with poor sperm motility.

Yeast Two-Hybrid Screen Identifies PKA-Riα Interacting Proteins during Mouse Spermiogenesis

cAMP-dependent protein kinase (PKA) signaling plays various roles during mammalian spermatogenesis, ranging from the regulation of gene expression to the modulation of sperm motility. However, the molecular mechanisms that govern the multifaceted functions of PKA during spermatogenesis remain largely unclear. We previously found that PKA regulatory subunit I α (RIα) and catalytic subunit α (Cα) co-sediment with polyribosomal fractions of mouse testis lysate on sucrose gradient and the stimulation of PKA activity facilitates protein synthesis in post-meiotic elongating spermatids, indicating that type I PKA is intricately associated with protein translation machinery and regulates protein synthesis during mouse spermiogenesis. Since PKA activity is often regulated by interacting proteins that form complexes with its regulatory subunits, the identification of PKA-RIα interacting proteins in post-meiotic spermatogenic cells will facilitate our understanding of its regulatory roles in protein synthesis and spermiogenesis. In the present study, we applied a yeast two-hybrid screen to identify PKA-Riα-binding proteins using a cDNA library generated from mouse round and elongating spermatids. Numerous proteins were found to potentially interact with PKA-RIα, including proteostasis modulators, metabolic enzymes, cytoskeletal regulators, and mitochondrial proteins, many of which are specifically expressed in testes. Consistently, the examination of MENA (mouse ENA/VASP homolog) in developing mouse testes suggested that post-meiotic spermatogenic cells express a short isoform of MENA that interacts with PKA-RIα in yeast two-hybrid assay. The identification of PKA-RIα interacting proteins provides us solid basis to further explore how PKA signaling regulates protein synthesis and cellular morphogenesis during mouse spermatogenesis.

Downregulation of KIF2C and TEKT2 is associated with male infertility and testicular carcinoma

Background: Genetic factors are important in spermatogenesis and fertility maintenance, and are potentially significant biomarkers for the early detection of infertility. However, further understanding of these biological processes is required.

Methods: In the present study, we sought to identify associated genes by reanalyzing separate studies from Gene Expression Omnibus datasets (GSE45885, GSE45887 and GSE9210) and validation datasets (GSE4797, 145467, 108886, 6872). The differential genes were used the limma package in R language. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by the clusterprofier package. The protein-protein interaction network was constructed by the STRING database. The interaction between mRNA and TF was predicted by miRWalk web. At last, The Cancer Genome Atlas data were used to identify hub gene expression levels in GEPIA web.

Results: The results showed that 27 shared genes associated with spermatogenesis. We effectively screen out two genes (KIF2C and TEKT2) and both validated by GSE4797, 145467, 108886 and 6872. Among 27 shared genes, KIF2C and TEKT2 both down-regulated in spermatogenesis. The network of TF-miRNA-target gene was established, we found KIF2C-miRNAs (has-miR-3154, 6075, 6760-5p, 1251-5p, 186-sp)-TFs (EP300, SP1) might work in spermatogenesis.

Conclusions: Our study might help to improve our understanding of the mechanisms in spermatogenesis and provide diagnostic biomarkers and therapeutics targets.

Gene transcription profiling of astheno- and normo-zoospermic sperm subpopulations

Spermatozoa contain a repertoire of RNAs considered to be potential functional fertility biomarkers. In this study, the gene expression of human sperm subpopulations with high (F1) and low (F2) motility from healthy normozoospermic (N) and asthenozoospermic (A) individuals was evaluated using RNA microarray followed by functional genomic analysis of differentially expressed genes. Results from A–F1 versus N–F1, A–F2 versus N–F2, N–F1 versus N–F2, and A–F1 versus A–F2 comparisons showed a considerably larger set of downregulated genes in tests versus controls. Gene ontology (GO) analysis of A–F1 versus N–F1 identified 507 overrepresented biological processes (BPs), several of which are associated with sperm physiology. In addition, gene set enrichment analysis of the same contrast showed 110 BPs, 36 cellular components, and 31 molecular functions, several of which are involved in sperm motility. A leading-edge analysis of selected GO terms resulted in several downregulated genes encoding to dyneins and kinesins, both related to sperm physiology. Furthermore, the predicted activation state of asthenozoospermia was increased, while fertility, cell movement of sperm, and gametogenesis were decreased. Interestingly, several downregulated genes characteristic of the canonical pathway protein ubiquitination were involved in asthenozoospermia activation. Conversely, GO analysis of A–F2 versus N–F2 did not identify overrepresented BPs, although the gene set enrichment analysis detected six enriched BPs, one cellular component, and two molecular functions. Overall, the results show differences in gene transcription between sperm subpopulations from asthenozoospermic and normozoospermic semen samples and allowed the identification of gene sets relevant to sperm physiology and reproduction.

Correlation among isolated teratozoospermia, sperm DNA fragmentation and markers of systemic inflammation in primary infertile men

Aim

To assess the prevalence of isolated teratozoospermia (iTZS) in a cohort of infertile and fertile men; explore the relationship between iTZS, inflammatory parameters and sperm DNA fragmentation index (SDF) in the same cohort.

Materials and methods

1824 infertile men and 103 fertile controls. Semen analysis, the neutrophil-to-lymphocyte ratio (NLR) and serum hormones were investigated. DFI was tested in infertile men only. According to 2010 WHO semen analysis, patients were categorized in 3 sub-groups of isolated sperm defects: isolated oligozoospermia (iOZS), isolated asthenozoospermia (iAZS) and iTZS. Descriptive statistics and linear regression models tested the association between clinical variables and inflammatory markers.

Results

Among infertile men, iAZS, iTZS, and iOZS were found in 13.9%, 11.9% and 4.1% participants, respectively. iTZS was found in 37 (35.9%) fertile men. Infertile men with iTZS had higher NLR values than those with iOZS, iAZS and men with normal semen parameters (all p<0.001). FSH and LH were higher and inhibin B lower in iOZS infertile men compared to all other groups (p≤0.001). Hormonal characteristics were similar between iTZS infertile and fertile men. Similarly, iTZS infertile men had higher SDF than all other groups (all p<0.001). Infertile men with iTZS had higher NLR values than fertile men with iTZS (p<0.01). Linear regression analysis showed that, in infertile men, iTZS was associated with SDF and NLR (all p≤0.01).

Conclusions

iTZS was found in 11.9% of infertile men but it was even more prevalent in fertile controls. Infertile men with iTZS had higher NLR than fertile controls and increased SDF values than infertile participant with iAZS, iOZS, or normal semen parameters. No differences in hormonal characteristics were found between infertile and fertile men with iTZS.

One potential biomarker for teratozoospermia identified by in-depth integrative analysis of multiple microarray data

Teratozoospermia is a common category of male infertility and with the increase in clinical patients and the increasing sophistication of assisted reproductive technology, there is an urgent need for an accurate semen diagnostic biomarker to accomplish rapid diagnosis of patients with teratozoospermia and accurately assess the success rate of assisted reproductive technologies. In this study, we performed gene differential expression analysis on two publicly available DNA microarray datasets (GSE6872 and GSE6967), followed by GSEA analysis to parse their enriched KEGG pathways, and WGCNA analysis to obtain the most highly correlated modules. Subsequent in-depth comparative analysis of the modules screened into the two datasets resulted in a gene set containing the identical expression trend, and then the differentially expressed genes in the set were screened using the corresponding criteria. Finally, three differentially expressed genes common to both datasets were selected. In addition, we validated the expression changes of this gene using another dataset (GSE6968) and in vitro experiments, and only screened one potential semen biomarker gene whose expression trend was identical to those in other datasets, which will also provide an important theoretical basis for the diagnosis and treatment of teratozoospermia.