It has been demonstrated that melatonin influences the developmental competence of both in vivo and in vitro matured oocytes. It modulates oocyte-specific gene expression patterns among mammalian species. Due to differences among study systems, the identification of the classifier orthologs-the homologous genes related among mammals that could universally categorize oocytes matured in environments with varied melatonin levels is still limitedly studied. To gain insight into such orthologs, cross-species transcription profiling meta-analysis of in vitro matured bovine oocytes and in vivo matured human oocytes in low and high melatonin environments was demonstrated in the current study. RNA-Seq data of bovine and human oocytes were retrieved from the Sequence Read Archive database and pre-processed. The used datasets of bovine oocytes obtained from culturing in the absence of melatonin and human oocytes from old patients were regarded as oocytes in the low melatonin environment (Low). Datasets from bovine oocytes cultured in 10-9 M melatonin and human oocytes from young patients were considered as oocytes in the high melatonin environment (High). Candidate orthologs differentially expressed between Low and High melatonin environments were selected by a linear model, and were further verified by Zero-inflated regression analysis. Support Vector Machine (SVM) was applied to determine the potentials of the verified orthologs as classifiers of melatonin environments. According to the acquired results, linear model analysis identified 284 candidate orthologs differentially expressed between Low and High melatonin environments. Among them, only 15 candidate orthologs were verified by Zero-inflated regression analysis (FDR ≤ 0.05). Utilization of the verified orthologs as classifiers in SVM resulted in the precise classification of oocyte learning datasets according to their melatonin environments (Misclassification rates < 0.18, area under curves > 0.9). In conclusion, the cross-species RNA-Seq meta-analysis to identify novel classifier orthologs of matured oocytes under different melatonin environments was successfully demonstrated in this study-delivering candidate orthologs for future studies at biological levels. Such verified orthologs might provide valuable evidence about melatonin sufficiency in target oocytes-by which, the decision on melatonin supplementation could be implied.

中文翻译：

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在不同褪黑激素环境中成熟的牛和人卵母细胞的新型分类器直向同源物

已经证明褪黑激素影响体内和体外成熟卵母细胞的发育能力。它调节哺乳动物物种中的卵母细胞特异性基因表达模式。由于研究系统之间的差异，分类器直向同源物的鉴定 - 哺乳动物之间相关的同源基因可以对在不同褪黑激素水平的环境中成熟的卵母细胞进行普遍分类的鉴定仍然有限。为了深入了解此类直向同源物，本研究证明了在低褪黑激素和高褪黑激素环境中体外成熟牛卵母细胞和体内成熟人类卵母细胞的跨物种转录分析荟萃分析。从序列读取存档数据库中检索牛和人卵母细胞的 RNA-Seq 数据并进行预处理。在没有褪黑激素的情况下培养的牛卵母细胞和来自老年患者的人卵母细胞的使用数据集被视为低褪黑激素环境（低）中的卵母细胞。来自在 10-9 M 褪黑激素中培养的牛卵母细胞和来自年轻患者的人卵母细胞的数据集被认为是高褪黑激素环境 (High) 中的卵母细胞。通过线性模型选择在低和高褪黑激素环境之间差异表达的候选直向同源物，并通过零膨胀回归分析进一步验证。应用支持向量机 (SVM) 来确定经验证的直系同源物作为褪黑激素环境分类器的潜力。根据获得的结果，线性模型分析确定了在低和高褪黑激素环境之间差异表达的 284 个候选直向同源物。他们之中，只有 15 个候选直系同源物通过零膨胀回归分析 (FDR ≤ 0.05) 进行验证。在 SVM 中使用经过验证的直向同源物作为分类器导致根据其褪黑激素环境对卵母细胞学习数据集进行精确分类（错误分类率 < 0.18，曲线下面积 > 0.9）。总之，本研究成功证明了跨物种 RNA-Seq 荟萃分析，以识别不同褪黑激素环境下成熟卵母细胞的新型分类器直向同源物，为未来的生物学研究提供候选直向同源物。这种经过验证的直向同源物可能会提供有关目标卵母细胞中褪黑激素充足的有价值的证据——由此可以暗示补充褪黑激素的决定。在 SVM 中使用经过验证的直向同源物作为分类器导致根据其褪黑激素环境对卵母细胞学习数据集进行精确分类（错误分类率 < 0.18，曲线下面积 > 0.9）。总之，本研究成功证明了跨物种 RNA-Seq 荟萃分析，以识别不同褪黑激素环境下成熟卵母细胞的新型分类器直向同源物，为未来的生物学研究提供候选直向同源物。这种经过验证的直向同源物可能会提供有关目标卵母细胞中褪黑激素充足的有价值的证据——由此可以暗示补充褪黑激素的决定。在 SVM 中使用经过验证的直向同源物作为分类器导致根据其褪黑激素环境对卵母细胞学习数据集进行精确分类（错误分类率 < 0.18，曲线下面积 > 0.9）。总之，本研究成功证明了跨物种 RNA-Seq 荟萃分析，以识别不同褪黑激素环境下成熟卵母细胞的新型分类器直向同源物，为未来的生物学研究提供候选直向同源物。这种经过验证的直向同源物可能会提供有关目标卵母细胞中褪黑激素充足的有价值的证据——由此可以暗示补充褪黑激素的决定。