Sequence variation in enhancers that control cell-type-specific gene transcription contributes significantly to phenotypic variation within human populations. However, it remains difficult to predict precisely the effect of any given sequence variant on enhancer function due to the complexity of DNA sequence motifs that determine transcription factor (TF) binding to enhancers in their native genomic context. Using F1-hybrid cells derived from crosses between distantly related inbred strains of mice, we identified thousands of enhancers with allele-specific TF binding and/or activity. We find that genetic variants located within the central region of enhancers are most likely to alter TF binding and enhancer activity. We observe that the AP-1 family of TFs (Fos/Jun) are frequently required for binding of TEAD TFs and for enhancer function. However, many sequence variants outside of core motifs for AP-1 and TEAD also impact enhancer function, including sequences flanking core TF motifs and AP-1 half sites. Taken together, these data represent one of the most comprehensive assessments of allele-specific TF binding and enhancer function to date and reveal how sequence changes at enhancers alter their function across evolutionary timescales.
Keywords: chromosomes; enhancers; gene expression; gene regulation; genetics; genomics; mouse; transcription factors.
There are hundreds of different types of cells in the body. Each one performs a unique role, but they all share the same genes. Sequences of the genetic code called enhancers decide which genes each cell uses. Enhancers work like genetic switches: to turn a gene on, proteins called transcription factors assemble on an enhancer. Each transcription factor recognises a short sequence on the enhancer, and several distinct transcription factors work together to promote the activatation of a gene. The relationship between transcription factors, enhancers, and gene activation is complex. The specific genetic sequences of enhancers differ between species, changing the way these genetic switches work. But scientists are not yet able to reliably predict the effects of small changes in the DNA sequence of an enhancer. One way to tackle this problem is to look at different versions of the same enhancers side by side to see how small mutations change their behaviour. Mammalian cells generally carry two copies of each chromosome (the molecules that contain the genetic code), one inherited from each parent. Each of the two copies carries the same genes and enhancers, but there are many small differences in the DNA sequences of enhancers between the chromosomes inherited from each parent, which can potentially alter their function Yang, Ling et al. generated cells from mice that come from different inbred strains, which are similar to purebred dogs. By breeding two distinct inbred mouse strains together that are very different from one another, they generated a panel of hybrid mouse cell lines that have a relatively large number of differences in their DNA sequence between the maternal and paternal chromosomes. Looking at the different versions of each enhancer side-by-side revealed thousands of single letter changes in the DNA sequence of enhancers that changed how they work. Mutations affecting the binding site of one transcription factor within an enhancer can indirectly affect the binding of other types of transcription factors. Yang, Ling et al. found that if a transcription factor could no longer find its place on an enhancer, it stopped others from binding even if their own places had not changed. Sometimes, mutations on either side of the binding sequences also affected transcription factor binding. This suggests a more complex relationship than previously thought may exist between the DNA sequence of an enhancer and the transcription factors that bind to it. Spotting the differences caused by mutations could help further the efforts of scientists to read and write the genetic code. This could have many benefits. It would allow scientists to control natural or artificial genes, and to predict the effects of genetic changes that are identified in humans with genetic diseases. This might improve genetic experiments, medical screening, gene therapy, and our understanding of evolution.
© 2022, Yang, Ling et al.