Liu, Xinxin

The periconception period is critical for embryo development, pregnancy outcomes, and offspring health. During this stage, oviductal and uterine fluids facilitate embryo-maternal interactions and support early embryonic development. Using PANDORA-seq, we identify a diverse repertoire of small non-coding RNAs in female mouse oviduct fluid and uterine fluid during preimplantation, with tRNA-derived small RNAs and rRNA-derived small RNAs being predominant. Maternal high-fat diet during preimplantation period significantly alters tsRNA and rsRNA expression in oviduct fluid and uterine fluid compared to normal diet, disrupting blastocyst metabolic gene expression. While implantation remained unaffected, these alterations impair mid-gestation embryonic and placental growth, resulting in reduced birth weight and length, as well as metabolic disorders in offspring. Furthermore, transfecting embryos with uterine fluid-derived sncRNAs altered by maternal high-fat diet mimics the in vivo effects. These findings suggest that tsRNAs and rsRNAs in reproductive fluids may reflect maternal metabolic status and transmit dietary information to the early embryo, which might influence pregnancy outcomes and offspring health.

Polycystic Ovary Syndrome (PCOS) is the most prevalent ovulatory and endocrine disorder affecting reproductive-aged women, yet the absence of a specific, rapid molecular diagnostic marker results in diagnostic delays and inaccuracies. Given the critical role of RNA modifications in disease pathology, this study utilized a high-throughput RNA modification profiling platform to investigate 15 types of peripheral blood RNA modification patterns in individuals with ovulatory disorders, including PCOS and Primary Ovarian Insufficiency (POI), and control subjects. Our results revealed that distinct modification profiles correspond to specific disease states, with significant shifts in RNA modification inter-correlations observed across conditions. Additionally, specific RNA modifications were associated with clinical features, such as serum levels of testosterone and the follicle number per ovary (FNPO). To optimize diagnostic precision, we evaluated various machine learning models, identifying that combining m6A and m7G modifications in a light gradient boosting machine model achieves the highest accuracy in distinguishing PCOS, outperforming traditional diagnostic markers. This highlights the potential of RNA modification profiling as a novel, high-accuracy diagnostic tool for PCOS in clinical settings.