BACKGROUND: Assessment of male fertility is traditionally based on microscopic evaluation of semen. However, the classical semen parameters do not adequately reflect sperm function, and their clinical value in predicting fertility is limited. We hypothesize that the sperm expression profile could reflect the fertilizing quality of spermatozoa and could be more informative for predicting the in vivo reproductive fitness of men with normal semen parameters. METHODS: Sperm gene expression patterns of 68 normozoospermic donors (43 Phase I and 25 Phase II), used for therapeutic IUI, were analysed via TaqMan Arrays. RESULTS: Significant differences in the expression of individual genes were observed between groups of donors with the lowest and highest pregnancy rates (PRs) after IUI. Additionally, we have developed a molecular means to classify the fertility status of semen donors for IUI based on the expression signature of four genes. In the Phase I study, this model had 90% sensitivity and 97% specificity for discriminating donors resulting in low PRs (cut-off value: <13.6%), far better than that obtained from the combination of sperm parameters. The translation of the model was validated in Phase II donors resulting in a sensitivity of 71.5% and a specificity of 78%. CONCLUSIONS: Our findings contribute to the search for the most valuable genetic markers which are potentially useful as tools for predicting pregnancy. Our expression model could complement classical semen analysis in order to identify sperm donors with a less favourable IUI reproductive outcome despite having normal semen parameters. It may also be useful for the study of sperm function in couples with unexplained infertility.
RCT Entities:
BACKGROUND: Assessment of male fertility is traditionally based on microscopic evaluation of semen. However, the classical semen parameters do not adequately reflect sperm function, and their clinical value in predicting fertility is limited. We hypothesize that the sperm expression profile could reflect the fertilizing quality of spermatozoa and could be more informative for predicting the in vivo reproductive fitness of men with normal semen parameters. METHODS: Sperm gene expression patterns of 68 normozoospermic donors (43 Phase I and 25 Phase II), used for therapeutic IUI, were analysed via TaqMan Arrays. RESULTS: Significant differences in the expression of individual genes were observed between groups of donors with the lowest and highest pregnancy rates (PRs) after IUI. Additionally, we have developed a molecular means to classify the fertility status of semen donors for IUI based on the expression signature of four genes. In the Phase I study, this model had 90% sensitivity and 97% specificity for discriminating donors resulting in low PRs (cut-off value: <13.6%), far better than that obtained from the combination of sperm parameters. The translation of the model was validated in Phase II donors resulting in a sensitivity of 71.5% and a specificity of 78%. CONCLUSIONS: Our findings contribute to the search for the most valuable genetic markers which are potentially useful as tools for predicting pregnancy. Our expression model could complement classical semen analysis in order to identify sperm donors with a less favourable IUI reproductive outcome despite having normal semen parameters. It may also be useful for the study of sperm function in couples with unexplained infertility.