Gametogenesis requires coordinated signaling between germ cells and somatic cells. We previously showed that Gap junction (GJ)-mediated soma-germline communication is essential for fly spermatogenesis. Specifically, the GJ protein Innexin4/Zero population growth (Zpg) is necessary for somatic and germline stem cell maintenance and differentiation. It remains unknown how GJ-mediated signals regulate spermatogenesis or whether the function of these signals is restricted to the earliest stages of spermatogenesis. Here we carried out comprehensive structure/function analysis of Zpg using insights obtained from the protein structure of innexins to design mutations aimed at selectively perturbing different regulatory regions as well as the channel pore of Zpg. We identify the roles of various regulatory sites in Zpg in the assembly and maintenance of GJs at the plasma membrane. Moreover, mutations designed to selectively disrupt, based on size and charge, the passage of cargos through the Zpg channel pore, blocked different stages of spermatogenesis. Mutations were identified that progressed through early germline and soma development, but exhibited defects in entry to meiosis or sperm individualisation, resulting in reduced fertility or sterility. Our work shows that specific signals that pass through GJs regulate the transition between different stages of gametogenesis.

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The US Food and Drug Administration Adverse Event Reporting System (FAERS) contains over 24 million individual case safety reports (ICSRs). In this research project, we evaluated a natural language processing (NLP) tool’s ability to extract four demographic variables (gender, weight, ethnicity, race) from ICSR narratives. Specificity of the NLP algorithm was over 94% for all demographics, while sensitivity varied between the demographics: 98.6% (gender), 45.5% (weight), 100% (ethnicity), and 85.3% (race). Among ICSRs missing weight, ethnicity, and race in the structured field, few cases had this information in the narrative (>95% missing); consequently, the positive predictive value (PPV) for these three demographics had wide 95% confidence intervals. After NLP implementation, the total number of ICSRs missing gender was reduced by 33% (i.e., NLP identified 472 thousand reports having a gender value in the narrative that was not in the structured field), while the total number of ICSRs missing weight, ethnicity, or race was reduced by less than 4%. This study demonstrated that the implementation of an NLP tool can provide meaningful improvements in the availability of gender information for pharmacovigilance activities conducted with FAERS data. In contrast, NLP tools targeting the extraction of weight, ethnicity, or race from free-text fields have minimal impact largely because the information was infrequently provided by the reporter. Further gains in completeness of these fields must originate from increases in provision of demographic information from the reporter rather than informatic solutions.