Estrus and Data Science: Implications to the Pre-implantation Endometrium Biology and Genomic Selection

我的研究计划的主要目的是最大限度地利用来自精密传感器技术的大数据和构建工具，以揭示哺乳奶牛子宫内膜中与发情强度相关的特定生物机制，并提出提高奶牛发情表达、胚胎存活和遗传选择的策略。在最近使用不同自动化活动监测系统的一系列研究中，与低强度发情事件相比，表现出更高强度发情的奶牛的受孕率提高了35%。新的命题是，只有近年来随着传感器技术在奶牛养殖场的采用，才有可能在大量奶牛上详细和准确地显示发情的相对增长、持续时间和总体模式。峰值强度、持续时间和其他数字测量的使用可以帮助预测生育能力，并改进乳制品行业中使用的生殖项目的决策。此外，使用自动监测系统作为一种客观的数字工具来选择高繁殖力的动物，同时提高表型收集的精度，是真正有潜力的。数据科学工具和人工智能现在有能力探索来自活动监测器的大数据集，以便找到与生育和其他关键生理事件相关的更精确的测量结果。到目前为止，使用发情表达特征作为基因组选择表型的文献有限。选择与发情数字特征相关的遗传标记，可以加快选择繁殖力优异的奶牛，减少乳品行业对生殖激素的依赖，提高数据科学能力，创造精准乳品的数字表型。为了回答一些与动情表达相关的机制问题，我们选择了研究着床前阶段的子宫内膜，这一阶段以胚胎损失率高而闻名。在本课题组以往的研究中，我们发现子宫内膜中受动情检测影响的转录本属于免疫系统和黏附分子家族(如MX1、MX2、MYL12A、MMP19、CXCL10、IGLL1、SLPI、PTX3、IDO、MUC1、MUC4、SEL)，以及与前列腺素合成相关的转录本(Era、OTR和COX-2)。研究表明，发情奶牛在丝状阶段的胚胎大小增加了2倍。我们小组对这三个生物学功能特别感兴趣，由大约150个转录本组成，因为它们一直被发情修饰，应该使用更多样化和更复杂的细胞和分子生物学技术来进一步深入研究。然而，到目前为止，还没有使用数字表型的详细信息来进行研究，这些表型与动情期的行为强度有关，对子宫内膜的机械功能有影响。

The major aim of my research program is to maximize the use of big data from precision sensor technologies and construct tools to unveil specific biological mechanisms in the endometrium of lactating dairy cows associated with estrus strength, and to propose strategies that enhance estrous expression, embryonic survival, and the genetic selection of dairy cows. In a series of recent studies using different automated activity monitoring systems, cows displaying estrus events of greater intensity had a 35% increase in fertility compared with estrus events of low intensity. The novel proposition is that detailed and accurate relative increase, duration and overall pattern of estrus display have only been possible on large number of cows in recent years with the adoption of sensor technologies in dairy farms. The use of peak intensity, duration, and other digital measurements could assist in the prediction of fertility and improve decision-making of reproductive programs used in the dairy industry. Moreover, there is true potential to use automated monitor systems as an objective digital tool to select animals of superior fertility, while improving phenotype collection precision. Data science tools and artificial intelligence have now the capability to explore large datasets coming from activity monitors in order to find more precise measurements associated with fertility and other key physiological events. To date, there is limited literature using estrous expression characteristics as phenotypes for genomic selection. The selection of genetic markers associated with digital characteristics of estrus could accelerate the selection of cows with superior fertility, decrease reliance on reproductive hormones in the dairy industry, and improve the data science capabilities to create digital phenotypes in precision dairy. In order to answer some of the mechanistic questions associated with estrous expression, we chose to study the endometrium during the pre-implantation stage, a period known for the high rates of embryonic loss. In previous studies by our group we found that transcripts affected by estrous detection in the endometrium belong to the immune system and adhesion molecule family (e.g. MX1, MX2, MYL12A, MMP19, CXCL10, IGLL1, SLPI, PTX3, IDO, MUC1, MUC4, SELL), as well as those related with prostaglandin synthesis (ERa, OTR and COX-2). The studies have shown a 2-fold increase in conceptus size at the filamentous stage coming from cows that displayed estrus. Our group has been particularly interested in those three biological functions, comprised of around 150 transcripts, as they have been consistently modified by estrus, and should be further investigated in depth using a more varied and sophisticated cellular and molecular biology techniques. No study to date, however, have been performed using detailed information of digital phenotypes related with the behavioural strength of estrus on mechanistic functions of the endometrium.