Soluble adenylyl cyclase inhibition prevents human sperm functions essential for fertilization.

Soluble adenylyl cyclase (sAC: ADCY10) has been genetically confirmed to be essential for male fertility in mice and humans. In mice, ex vivo studies of dormant, caudal epididymal sperm demonstrated that sAC is required for initiating capacitation and activating motility. We now use an improved sAC inhibitor, TDI-10229, for a comprehensive analysis of sAC function in mouse and human sperm. In contrast to caudal epididymal mouse sperm, human sperm are collected post-ejaculation, after sAC activity has already been stimulated. In addition to preventing the capacitation-induced stimulation of sAC and protein kinase A activities, tyrosine phosphorylation, alkalinization, beat frequency and acrosome reaction in dormant mouse sperm, sAC inhibitors interrupt each of these capacitation-induced changes in ejaculated human sperm. Furthermore, we show for the first time that sAC is required during acrosomal exocytosis in mouse and human sperm. These data define sAC inhibitors as candidates for non-hormonal, on-demand contraceptives suitable for delivery via intravaginal devices in women.

Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients.

OBJECTIVE: To measure human sperm intracellular pH (pHi) and develop a machine-learning algorithm to predict successful conventional in vitro fertilization (IVF) in normospermic patients. DESIGN: Spermatozoa from 76 IVF patients were capacitated in vitro. Flow cytometry was used to measure sperm pHi, and computer-assisted semen analysis was used to measure hyperactivated motility. A gradient-boosted machine-learning algorithm was trained on clinical data and sperm pHi and membrane potential from 58 patients to predict successful conventional IVF, defined as a fertilization ratio (number of fertilized oocytes [2 pronuclei]/number of mature oocytes) greater than 0.66. The algorithm was validated on an independent set of data from 18 patients. SETTING: Academic medical center. PATIENT(S): Normospermic men undergoing IVF. Patients were excluded if they used frozen sperm, had known male factor infertility, or used intracytoplasmic sperm injection only. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful conventional IVF. RESULT(S): Sperm pHi positively correlated with hyperactivated motility and with conventional IVF ratio (n = 76) but not with intracytoplasmic sperm injection fertilization ratio (n = 38). In receiver operating curve analysis of data from the test set (n = 58), the machine-learning algorithm predicted successful conventional IVF with a mean accuracy of 0.72 (n = 18), a mean area under the curve of 0.81, a mean sensitivity of 0.65, and a mean specificity of 0.80. CONCLUSION(S): Sperm pHi correlates with conventional fertilization outcomes in normospermic patients undergoing IVF. A machine-learning algorithm can use clinical parameters and markers of capacitation to accurately predict successful fertilization in normospermic men undergoing conventional IVF.