McAnany, C. | Search bioRxiv

bioRxiv2024

Interpretable deep learning reveals the sequence rules of Hippo signaling

The response to signaling pathways is highly context-specific, and identifying the transcription factors and mechanisms that are responsible is very challenging. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show here that this information is encoded in cis-regulatory sequences and can be learned from high-resolution binding data of signaling transcription factors. Using interpretable deep learning, we show that the binding levels of TEAD4 and YAP1 are enhanced in a distance-dependent manner by cell type-specific transcription factors, including TFAP2C. We also discovered that strictly spaced Tead double motifs are widespread highly active canonical response elements that mediate cooperativity by promoting labile TEAD4 protein-protein interactions on DNA. These syntax rules and mechanisms apply genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/580842v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1ee22e9org.highwire.dtl.DTLVardef@1362c82org.highwire.dtl.DTLVardef@1a42512org.highwire.dtl.DTLVardef@17dd619_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2019

Deep learning at base-resolution reveals motif syntax of the cis-regulatory code

The arrangement of transcription factor (TF) binding motifs (syntax) is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution ChIP-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using CRISPR-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data. HighlightsO_LIThe neural network BPNet accurately predicts TF binding data at base-resolution. C_LIO_LIModel interpretation discovers TF motifs and TF interactions dependent on soft syntax. C_LIO_LIMotifs for Nanog and partners are preferentially spaced at [~]10.5 bp periodicity. C_LIO_LIDirectional cooperativity is validated: Sox2 enhances Nanog binding, but not vice versa. C_LI

genomics↗