Ly, B. | bioRxiv Science

bioRxiv2026

Auto-sumoylation of UBC9 coordinates meiotic prophase and protects ovarian reserves

The small ubiquitin-like modifier SUMO regulates key events of meiosis, including pairing and crossing over between homologous chromosomes. Auto-sumoylation of the SUMO E2-conjugating enzyme UBC9 at lysine 14 alters its substrate selectivity in vitro, but the role of this modification in vivo is unknown. Here, we show that UBC9-K14 auto-sumoylation helps coordinate meiotic prophase and is important for maintenance of the ovarian reserve. Ubc9K14R/K14R knock-in mice show a variety of defects in meiotic prophase I, including altered assembly of DNA strand-exchange complexes, delayed and defective homolog synapsis, and unstable crossover recombination complexes. In spermatocytes, these defects are associated with reduced efficiency of crossing over between the X and Y chromosomes. Oocytes from Ubc9K14R/K14R females show related but distinct defects in recombination and synapsis. Moreover, maintenance of the primordial follicle reserve is defective in Ubc9K14R/K14R females, and their fecundity is reduced. Finally, we identify the meiosis-specific homolog-axis protein SYCP3 as a direct target of UBC9 in vitro and show that SYCP3 modification is strongly stimulated by K14 auto-sumoylation. We infer that auto-sumoylation enables UBC9 to modify a subset of targets in vivo, helping to coordinate key events of meiotic prophase and enhance the survival of primordial follicles to maximize fecundity.

genetics↗

bioRxiv2026

Prediction of parsimonious and temporally sensitive sets of cell fate engineering transcription factors with IMCell

Transcription factor (TF) cocktails used in cell identity reprogramming protocols have largely been developed from experimental approaches. A handful of computational approaches have been reported, though have not been widely adopted by the scientific community. To standardize their use and assess their performance, we built CompForce, a platform that integrates these tools. Using CompForce, we found that existing computational methods offer modest improvements over differential expression on both synthetic and literature-curated data, and that their lackluster and inconsistent performance could be attributed to a reliance on local centrality metrics. To improve upon these methods, we developed IMCell, a prediction method that is inspired by the influence maximization problem. Unlike existing tools, IMCell returns optimized TF sets rather than ranked TF lists. We demonstrate that IMCell vastly out-performs existing tools, and further extend it to dynamic, stepwise contexts. The tools presented here are available in the R packages CompForce and IMCell.

bioinformatics↗