EvoDCode · Deep representational learning of the evolutionary DNA code in the vertebrate pallium

Deep representational learning of the evolutionary DNA code in the vertebrate pallium

Vertebrate genomes generally consist of billions of nucleotides and encode tens of thousands of genes. Yet, while these genes could theoretically be expressed in almost any combination, evolution has resulted in a large, but finite number of stable configurations with distinct downstream functions. These configurations form the basis of cell types, groups of cells that share a core identity. However, our understanding of how an organisms’ cell types are encoded in the genome is still lacking. While genes themselves can be homologous between species to a certain extent, the regulatory DNA is much less conserved. The expression of genes is regulated by enhancer elements that bind transcription factors, which can be located far away from the target gene. To investigate the regulatory logic that encodes cell types in the genome, a systematic approach must be taken towards sequence phylogeny, where we identify which sequences are active in each of the different types of cells across a wide variety of species, before asking what the defining features of these sequences are. The rapidly advancing field of artificial intelligence holds great promise for comparative genomics and Convolutional Neural Networks (CNNs) and DNA language models have already been successfully used to model gene regulatory logic in an interpretable way by revealing the transcription factor binding sites within cis-regulatory elements and their co-regulatory relationships. These models require large amounts of training data, and the introduction of the single-cell Assay for Transposase Accessible Chromatin sequencing (scATAC-seq) has enabled us to collect the large amounts of data stratified by cell type, which are required to train these artificial intelligence models. The overarching goal of this proposal is to better understand how the genome sequence underlies cell identity in the pallium across vertebrate species. I hypothesize that gene regulatory logic can be directly learned from the genomic sequence and used to model and predict cell types. I specifically focus on the pallium, a part of the brain that is strongly tied to species-specific behaviour and underwent strong divergent evolution. In humans the dorsal pallium is expanded into the cerebral cortex, providing most of our expanded cortical abilities, while the avian dorsal pallium consists of only a single cortical layer (Wulst). Our understanding of how these large differences came to be is still limited. Using modern single-cell epigenomic methods we can study how evolutionary changes impact gene regulation by sampling across a wide set of vertebrate species and using this data to model cell type evolution.

Data: CORDIS, © European Union