Abstract

Genetic defects in sperm are responsible for a great percentage of male infertility. Dysregulation of these genes directly influences sperm morphology, motility, and viability. Therefore, analyzing gene expression aberrancies is a must in male infertility. Microarray analysis is practically used for several aspects of male infertility, including the detection of differentially expressed genes and the identification of potential infertility biomarkers. We conducted a meta-analysis using microarray datasets, including the datasets containing sperm tissues from both healthy and infertile males. Seven datasets qualified for inclusion in this study and were then transformed into a single set of meta-data. For these genes, expression and diagnostic analyses were conducted. Additionally, enrichment analysis revealed the role and function of these genes in cellular processes. Six genes—S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1—were found to be significantly down-regulated in the sperm of infertile men. Notably, the expression of these genes was highly correlated in the sperm of these men. In addition, receiver operating curve analysis indicated that these genes may serve as useful biomarkers for infertility diagnosis. The role of these genes in transporting glucose, vitamins, and fructose as the sperm’s primary fuel source was suggested by pathway analysis.

Introduction

Infertility is a developing concern, affecting between 10 and 15% of couples worldwide (Wasilewski et al., 2020). Male factor infertility accounts for approximately 50 % of all sterility cases, with sperm abnormalities being the most common cause (Kumar and Singh, 2015). The traditional analysis of sperm provides fundamental information regarding sperm count, motility, and morphology but fails to identify the underlying molecular abnormalities (Boitrelle et al., 2021; Vasan, 2011; Auger, 2010).

Microarray analysis is a high-throughput technique that permits the simultaneous identification of numerous gene expression alterations (Mutch et al., 2001). Recently, it has been utilized to investigate the genetic basis of male infertility and sperm dysfunction (Garrido et al., 2013; Garrido et al., 2009). Sperm microarray analysis can provide a comprehensive picture of the sperm’s molecular landscape, including gene expression patterns and DNA copy number variations (CNVs) that may be responsible for infertility (Waclawska and Kurpisz, 2012). The identification of specific genes and pathways involved in sperm function and fertilization can lead to the development of novel therapeutic targets for male infertility (You et al., 2019). So far, over 2000 genes and pathways are recognized to be involved in spermatogenesis; hence, spotting differentially expressed genes (DEGs) may disclose infertility etiology (Babakhanzadeh et al., 2020; Lee and Ramasamy, 2018). Among the genes associated with male infertility and the disorders caused by their defect are CFTR (congenital unilateral/bilateral absence of vas deferens), AR (non-obstructive azoospermia) (Bieniek et al., 2021), LRRC6 (primary ciliary dyskinesia) (Li et al., 2023), APOA1 (testicular amyloidosis) (Houston et al., 2022), and SRY (sexual development disorders) (Jiang et al., 2013). Signaling pathways such as JAKs, MAPKs, etc. are also involved in this disease (Fig. 1). Genetic factors account for approximately 15% of the causes of male infertility, which may manifest as deficiencies in sperm count or sperm quality (Salas-Huetos and Aston, 2021). However, the genetic cause of male infertility remains unknown in about 40% of cases (Krausz and Riera-Escamilla, 2018). Several DEGs and CNVs have been confirmed to play a role in spermatogenesis and fertilization as a result of microarray analysis of sperm from infertile men. For instance, Hashemi et al. used microarray analysis to reveal multiple gene dysregulations such as RAD23B, OBFC2A, CHEK2, TRIP13, and POLD4, which primarily mediate DNA damage detection and repair and regulate cell proliferation (Hashemi Karoii et al., 2022).

Conclusion

Downregulation of six novel genes (S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1) in the sperm of infertile males was identified by this study via meta-analysis of microarray data. Furthermore, the potential use of these dysregulations as diagnostic biomarkers was demonstrated through ROC curve analysis.

۵٫۱٫ Future prospective
Advantages of using microarrays for infertile sperm include the discovery of underlying genetic pathways and the suggestion of novel diagnostic biomarkers. Our findings may prove useful in the treatment and accurate diagnosis of male infertility.

۵٫۲٫ Limitations
Although the study of microarrays is a high-throughput method, we recommend verifying the data with a gold-standard method, such as real-time polymerase chain reaction (RT-PCR). Given the scarcity of samples, we would be overjoyed if subsequent research confirmed the molecular level findings reported here. As for the diagnostics, commenting on ideal biomarkers require several studies with a wide sample size, therefore, testing our findings on a cohort will disclose the accurate and real ability of these DEGs in identifying infertility.