Svante Pääbo and colleagues used AlphaGenome as a time machine to reconstruct the evolution of human gene regulation. An impressive new bioRxiv preprint! 🧬⏩ Fifty years ago, scientists proposed that the differences between humans and chimpanzees might be driven less by changes in genes themselves and more by changes in how those genes are regulated. The authors revisited this hypothesis using deep learning and identified more than 71,000 genomic regions predicted to have gained regulatory activity along the human lineage. But the most interesting finding is how these changes happened! In 71% of cases, evolution repurposed existing regulatory elements rather than creating new ones. A single mutation could introduce a tissue-specific transcription factor binding site, activating an existing element in another tissue. To investigate this, the researchers used AlphaGenome to predict the regulatory effects of 17 million nucleotide substitutions, linking them to gene expression differences between humans and other great apes. Interestingly, the vast majority of these regions fell outside known Human Accelerated Regions, genomic regions that evolved unusually rapidly along the human lineage. This suggests that the rate of sequence evolution does not necessarily reflect its functional impact. Comparisons with Neanderthal and Denisovan genomes revealed that 99.6% of the regulatory gains fixed in modern humans and successfully dated arose before our evolutionary split from these archaic lineages. There are also fascinating examples involving individual genes. One is SLC47A1, which encodes a transporter involved in eliminating drugs and toxins. The researchers identified five regulatory gains associated with increased expression in the human liver. Reverting these changes to their ancestral states in silico reduced predicted gene expression by 60%. Of course, many of these predictions still require experimental validation. But the broader idea is remarkable: ancient DNA has allowed us to discover which mutations occurred in the past. Now, deep learning is beginning to reveal how those mutations may have changed the way genes worked. biorxiv.org/content/10.64898… #aDNA #evolution #regulation #AlphaGenome