美国麻省理工学院和哈佛大学的布罗德研究所Aviv Regev，美国麻省理工学院Eeshit Dhaval Vaishnav和加拿大英属哥伦比亚大学Carl G. de Boer共同合作取得重要工作进展。他们构建了从序列到表达的模型，并利用模型来破译调节进化的原理。该项研究成果2022年3月9日在线发表于《自然》杂志上。

在这里，研究人员构建了从序列到表达的模型，这些模型可以捕捉适应度景观，并使用它们来破译调节进化的原理。使用数百万个随机采样的启动子 DNA 序列及其在酿酒酵母中测量的表达水平，研究人员获取了具有出色预测性能的深度神经网络模型，并为表达工程设计了序列。他们利用模型研究了遗传漂移和强选择弱突变机制下的表达差异，发现调节进化是快速的并且受到收益递减上位性的影响；不同环境中表达目标的冲突制约了表达的适应；并且稳定基因表达的选择导致调节复杂性的缓和。

他们提出了一种方法，利用这种模型从调控序列的自然变异中检测出对表达的选择特征，并利用它来发现趋同调控进化的实例。研究团队评估了突变的稳健性，发现调控突变效应大小遵循幂次定律，表征了调控的可进化性，可视化了启动子适应度景观，发现了可进化原型，并揭示了自然调控序列群体的突变稳健性。他们的工作为设计调控序列和解决调控进化中的基本问题提供了一个总体框架。

据了解，非编码调控DNA序列的突变可以改变基因表达、有机体表型和适应度。构建完整的适应度景观，其中 DNA 序列映射到适应度，是生物学的长期目标，但仍然难以实现，因为难以可靠地推广到广阔的序列空间。

附：英文原文

Title: The evolution, evolvability and engineering of gene regulatory DNA

Author: Vaishnav, Eeshit Dhaval, de Boer, Carl G., Molinet, Jennifer, Yassour, Moran, Fan, Lin, Adiconis, Xian, Thompson, Dawn A., Levin, Joshua Z., Cubillos, Francisco A., Regev, Aviv

Issue&Volume: 2022-03-09

Abstract: Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1,2,3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4,5,6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

DOI: 10.1038/s41586-022-04506-6

Source: https://www.nature.com/articles/s41586-022-04506-6