Geraldine Dray

Background: Male infertility is evaluated using standard semen parameters. However, these criteria offer limited insight into sperm functionality and poorly predict natural fertility or assisted reproductive technology (ART) outcomes. Methods: In this study, the expression levels of three genes (AURKA, HDAC4, and CARHSP1) involved in mitosis regulation, epigenetic modulation and early embryonic development, were measured by RT-qPCR in sperm samples (training dataset). For each gene, thresholds of normal and reduced expression were established by biostatistical modeling and combined with the number of motile spermatozoa to develop the Spermatozoa Function Index (SFI). Results: The ROC analysis was used to interpret the SFI values: SFI > 320 (normal), 290–320 (intermediate), and <290 (low). Then, this index was validated using 627 fresh semen samples from 25- to 60-year-old men at our ART center. Based on the World Health Organization criteria, 54.5% of the 627 sperm samples were normospermic, 8.8% showed oligo-astheno-teratospermia, and 36.6% had one or two abnormal parameters. According to the SFI values, 41% of sperm samples displayed normal expression, 55.9% low expression, and 4.1% intermediate expression. Only 57% of the 342 normospermic samples had normal SFI values and 37% had low SFI values. Among the 81 samples with stringent normal criteria (≥50 million/mL, ≥50% total motility, ≥14% normal morphology), 67.9% displayed normal SFI and 22.2% low SFI values. These findings suggest that even sperm with normal parameters may harbor dysfunctions. Conclusions: Our data highlight a gene signature with strong discriminatory power and promising diagnostic value for detecting subclinical sperm defects and improving male infertility assessment.

Background: Sperm samples are separated into bad and good quality samples in function of their phenotype, but this does not indicate their genetic quality. Methods: Here, we used GeneChip miRNA arrays to analyze microRNA expression in ten semen samples selected based on high-magnification morphology (score 6 vs. score 0) to identify miRNAs linked to sperm phenotype. Results: We found 86 upregulated and 21 downregulated miRNAs in good-quality sperm (score 6) compared with bad-quality sperm samples (score 0) (fold change > 2 and p-value < 0.05). MiR-34 (FC × 30, p = 8.43 × 10⁻⁸), miR-30 (FC × 12, p = 3.75 × 10⁻⁶), miR-122 (FC × 8, p = 0.0031), miR-20 (FC × 5.6, p = 0.0223), miR-182 (FC × 4.83, p = 0.0008) and miR-191 (FC × 4, p = 1.61 × 10⁻⁶) were among these upregulated miRNAs. In silico prediction algorithms predicted that miRNAs upregulated in good-quality sperm targeted 910 genes involved in key biological functions of spermatozoa, such as cell death and survival, cellular movement, molecular transport, response to stimuli, metabolism, and the regulation of oxidative stress. Genes deregulated in bad-quality sperm were involved in cell growth and proliferation. Conclusions: This study reveals that miRNA profiling may provide potential biomarkers of sperm quality.