Dataset Information

Cross-species analysis of melanoma enhancer logic using deep learning

ABSTRACT: Here, we combine comparative regulatory genomics with machine learning to investigate enhancer logic in melanoma. Through epigenomics profiling of 26 melanoma cell lines across six species, we examine the conservation of the two main melanoma states and underlying master regulators. By training a deep neural network on topic models derived from the human lines, we were able to classify not only human melanoma enhancers, but also regulatory regions in the other species. The deep learning model revealed important genomic features (i.e. TF binding motifs) for the different melanoma states, how they co-occur within melanoma enhancers, and where they are placed with respect to the central enhancer nucleosome. This in-depth analysis of the melanoma enhancer code allowed us to propose a mechanistic model of TF binding in MEL melanoma enhancers. Finally, by exploiting the deep layers of our model, we are able to identify causal mutations for melanoma enhancer loss and gain through evolution, not only affecting enhancer accessibility but also activity.

ORGANISM(S): Mus musculus Danio rerio Equus caballus Sus scrofa domesticus Canis lupus familiaris Homo sapiens

PROVIDER: GSE142238 | GEO | 2020/04/22

REPOSITORIES: GEO

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Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Dataset Information

Cross-species analysis of melanoma enhancer logic using deep learning

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