Liu, C.-L. | bioRxiv Science

bioRxiv2025

Sperm-specific fertility factors SMZ-1/2 promote FB-MO biogenesis and MSP filament assembly in C. elegans

In nematode sperm, motility is powered by polymerization of major sperm protein (MSP) into filaments assembled from a specialized organelle (FB-MO), but how filament formation begins is unclear. We identify two sperm-specific PDZ proteins, SMZ-1 and SMZ-2 (SMZ-1/2), that initiate MSP loading into pre-formed membranous organelles in Caenorhabditis elegans. SMZ-1/2 were expressed specifically in spermatogenic germ cells, colocalized with developing FB-MOs from diplotene through spermatid formation, and were redundant for fertility. Loss of SMZ-1/2 abolished MSP filaments, left MOs MSP-negative and morphologically immature, and caused primary spermatocyte arrest. Electron microscopy showed fewer, smaller FB-MOs lacking crystalline FBs, consistent with a defect in assembly initiation rather than late polymerization. SMZ-1/2 cytoplasmic structures were absent in spe-6 casein kinase mutants, placing SMZ-1/2 within a SPE-6-dependent initiation module. These findings identify SMZ-1/2 as PDZ scaffold proteins that initiate MSP assembly and couple kinase signaling to FB-MO biogenesis during nematode spermatogenesis.

genetics↗

bioRxiv2019

Integration of gut microbiome, host biomarkers, and urine metabolome data reveals networks of interactions associated with distinct clinical phenotypes

BackgroundComprehensive analyses of multi-omics data may provide insights into interactions between different biological layers in relation to distinct clinical features. Here we integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations.\n\nResultsTrans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite features covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. Of specific note in relation to clinical parameters of liver function, we identified Eubacterium eligens, Faecalibacterium prausnitzii and Ruminococcus lactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerella nexills, Ruminococcus gnavus, Blautia hansenii, and Atopobium parvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome.\n\nConclusionsDistinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases.

microbiology↗