Principles of regulatory evolution inferred from early differentiation in primates

调控序列的变化无疑在人类进化过程中发挥了重要作用。然而，我们对它们仍然知之甚少。这部分是因为我们不太了解基因调控是如何在人类基因组中编码的。在这里，我们建议采取一种进化的方法，通过产生和分析单细胞表达和表观基因组数据从四个灵长类动物物种在胚状体形成过程中，以了解更多关于这个“监管代码”。一个中心的见解，从最近的研究的演变的调控元件是，他们有一个高周转率，即使调节过程是非常保守的物种。因此，有人推测，补偿性进化是调节进化中常见的。通过拟议的研究设计，我们可以评估灵长类动物进化过程中转录调控元件（TREs）的补偿性进化有多常见：我们将从人类，大猩猩，猩猩和食蟹猴中分化诱导多能干细胞为胚状体（EB），并从总共约150，000个细胞中生成scRNA-seq和scATAC-seq数据。有了这个新的数据集，我们可以重建所有三个胚层的发育轨迹，并在四个物种之间进行比较。使用scRNA-seq数据，我们将能够估计早期灵长类分化期间基因调控和调控网络的保守性，并且使用scATAC-seq数据，我们可以推断和比较每个基因，物种和轨迹的活性TREs。然后，我们将整合scRNA-seq和scATAC-seq数据，将基因调控的保守措施与早期灵长类分化期间TRE可及性和TRE序列进化的保守措施联系起来。 现在，我们可以确定补偿进化的情况下，基因有一个保守的表达轨迹，但非保守的TREs。在最简单的情况下，我们会发现功能类似，但非正交TREs（faTREs）。 我们将使用大规模平行报告基因试验（MPRA）验证faTREs的功能等效性。 一旦我们建立了功能等价性，我们就可以确定序列特征，如可以预测补偿性进化的转录因子结合位点的多样性或数量。总之，我们的方法将揭示调控进化的原则，这将提高我们对人类基因调控的理解。

Changes in regulatory sequences have undoubtedly played an important role during human evolution. However, we still know very little about them. This is in part because we do not understand very well how gene regulation is encoded in the human genome. Here, we suggest taking an evolutionary approach by generating and analysing single-cell expression and epigenomic data from four primate species during embryoid body formation to learn more about this "regulatory code".One central insight from recent studies of the evolution of regulatory elements is that they have a high turnover rate even though the regulated processes are very conserved across species. Hence, it has been speculated that compensatory evolution is common in regulatory evolution. With the proposed study design we can evaluate how common compensatory evolution is for transcriptional regulatory elements (TREs) during primate evolution: We will differentiate induced pluripotent stem cells from humans, gorillas, orangutans and cynomolgus macaques to embryoid bodies (EBs) and generate scRNA-seq and scATAC-seq data from a total of ~150,000 cells. With this novel data set we can reconstruct the developmental trajectories from all three germ layers and compare them among the four species. Using the scRNA-seq data, we will be able to estimate the conservation of gene regulation and regulatory networks during early primate differentiation, and using the scATAC-seq data we can infer and compare the active TREs for each gene, species and trajectory. We will then integrate the scRNA-seq and the scATAC-seq data to connect measures for the conservation of gene regulation, with the conservation of TRE accessibility and TRE sequence evolution during early primate differentiation. Now, we can identify cases of compensatory evolution as genes that have a conserved expression trajectory, but non-conserved TREs. In the easiest case, we will find functionally analogous, but non-orthologous TREs (faTREs). We will validate the functional equivalence of the faTREs using a massively parallel reporter assay (MPRA). Once we have established functional equivalence, we can identify sequence features such as the diversity or number of transcription factor binding sites that can predict compensatory evolution. In summary, our approach will reveal principles of regulatory evolution that will improve our understanding of human gene regulation.