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Yinjuan Wang

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Open access Jul 2026

Production of gene-edited cloned cattle embryos using the CRISPR/EOCas12i system

Introduction The rapid development of genome editing technologies has enabled precise manipulation of livestock genomes for the improvement of production traits such as meat yield and milk quality. Myostatin (MSTN) and β-lactoglobulin (BLG) are key gene targets for enhancing muscle growth and reducing lactose intolerance, respectively. Methods In this study, we employed an optimized CRISPR/EOCas12i system to simultaneously target MSTN and BLG in bovine fetal fibroblasts (BFFs) using a single plasmid. Results T7E1 and Sanger sequencing confirmed efficient editing at multiple target sites, with EOCas12i producing deletions ranging from tens to over 100 bp. Furthermore, no off-target (OT) effects were detected at predicted loci, supporting the high specificity of this system in large animals. Gene-edited single-cell clones (SCCs) were expanded in conditioned medium, and selected double-knockout (DKO) clones served as nuclear donors for somatic cell nuclear transfer (SCNT) to produce MSTN/BLG double gene-edited cattle embryos. Discussion Collectively, this study demonstrates the feasibility of generating MSTN/BLG double gene-edited cattle embryos using a single CRISPR/EOCas12i plasmid and SCNT, providing a robust platform for multiplex genome editing aimed at improving meat production and milk traits, with potential applications in both agricultural and biomedical research.

Furui Wang, Lei Chen, Yuting Ning et al. · 0 citations
Open access Jul 2026

A transcriptomic map of ligand-receptor interactions within bovine antral follicles predicts intrafollicular factors for improving oocyte quality

The ovarian follicle is an enclosed microenvironment that ensures oocyte growth and maturation, making it essential for female fertility. Normal folliculogenesis development and oocyte maturation strictly rely on orchestrated intercellular interactions among the mural granulosa cells (MGCs), cumulus cells (CCs), and the oocyte within antral follicles. Despite the importance, how these cell populations communicate remains largely unknown. Due to this knowledge gap, current oocyte in vitro maturation (IVM) systems fail to fully replicate the physiological follicular microenvironment due to the lack of MGC regulation, leading to poor oocyte quality and lower success rates of assisted reproductive technologies. RNA-seq was performed on MGCs, CCs, and oocytes isolated from single bovine antral follicles at different developmental stages (3–5 mm, 6–8 mm and 9–12 mm). Using a ligand-receptor interaction scoring strategy, we constructed a dynamic intrafollicular interaction map. We revealed that MGCs are the most active cell type, acting as either secretors or receivers, based on the number of ligands and receptors it expresses, as well as the number of interaction pairs it establishes with other cell types. Intriguingly, intrafollicular crosstalk exhibits spatiotemporal commonality, characterized by shared ligand-receptor usage and potential functional redundancy across cell types and stages. Notably, the intrafollicular communication map enables the prediction of MGC-secreted factors that can improve the oocyte quality after IVM. Based on the predicted candidate factors, we have confirmed that supplementation with pleiotrophin (PTN) significantly improved in vitro matured oocyte quality and subsequent embryonic development. In addition, we also revealed significant heterogeneity at single-follicle resolution, unhealthy follicles showed aberrant activation or loss of partial core interaction signaling, supporting the hypothesis that these interactions play a critical role in determining follicular fate. By reconstructing a map of intrafollicular interactions, our study provides novel insights and important clues for understanding cellular and molecular mechanisms that fine-tune antral folliculogenesis. We also offer a roadmap for predicting candidate factors that can be used for optimizing mammalian in vitro embryo production systems.

Zhaochen Wang, Zhenni Zhang, Yawen Tang et al. · 0 citations