Our long-term research goal is to understand and predict gene regulation based on DNA sequence information and genome-wide experimental data.
Kansas City, MO
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What a wonderful collaboration, excited to see this released to the public!
We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes.
Here’s how it could help researchers better understand our biology 🧵
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Excited to be a part of this collaboration! The AlphaGenome Atlas will be a huge accelerant in our field for understanding regulatory encoding.
Excited to release AlphaGenome Atlas 🧬 We used AlphaGenome to predict the regulatory impact of all 9B possible SNVs in the human genome. We collaborated with amazing scientists to analyze and apply it, and developed a portal to browse the genome using this new lens 🔬
🌐 Portal: alphagenome.google/atlas
📖 Blog: goo.gle/4heuCvn
📄 Preprint: deepmind.google/blog/alphage…
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(9/10) Our BPReveal package provides tools to engineer sequences with desired properties. For example, we designed mutations to alter a nucleosome’s presence in vivo, and our design was corroborated experimentally.
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(8/10) These models detect barrier elements in nucleosome occupancy and chromatin organization. Around barriers, motif effects are asymmetric, and the most asymmetric regions align with known domain boundaries.
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(3/10) PISA can visualize the influence range of sequences, including TF motifs.
In a mESC BPNet model predicting Nanog binding, PISA can distinguish the nucleosome-range effects of pioneering motif Oct4-Sox2 versus the binding-range effects of the Nanog motif itself.
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The new updates for Charles McAnany’s preprint “Positional Interpretation of Cis-Regulatory Code and Nucleosome Organization with Deep Learning Models” (biorxiv.org/content/10.1101/…) are up!
We introduce PISA, a tool to visualize the cis-regulatory code. See a recap below:
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(1/7) How does TFIID function across promoter types in vivo? We mapped all 14 TFIID subunits at base-pair resolution using ChIP-nexus in Drosophila. This lets us directly connect cryo-EM structures, biochemistry, and genetics to promoter behavior in vivo.
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The @ZeitlingerLab is excited to release @Sergiogma91’s preprint “High-resolution binding data of TFIID and cofactors show promoter-specific differences in vivo” (biorxiv.org/content/10.64898…).
TLDR; TFIID behaves differently based on promoter type. More below:
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Thanks to our coauthors @kaelanbrennan , @08Kats05, @haining_jiang, and Sabrina Krueger. Thanks again @rmartinezcorral for your mechanistic modeling, we learned so much from you! Finally, thank you to @JuliaZeitlinger for your guidance and inspiration along this journey!
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(12) Putting it together, it seems that low-affinity motifs likely evolve easily in enhancers because (1) they arise often, (2) the syntax is flexible, and (3) the effect is relatively large. Due to motif cooperativity, even small changes can affect enhancer function.
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(11) We tested this idea to an accessibility time-course on decreasing Oct4 concentrations (Xiong et al, from Hans Schöler's lab). When a pioneer motif was in a cooperative vs. single configuration, the enhancer was more sensitive to changing Oct4 conc., regardless of affinity.
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(10) We found that the regulatory potential increases when two pioneer TFs cooperate. Motif affinity shifts the curve towards higher or lower TF concentrations, but does not change the regulatory potential. Thus, cooperativity and motif affinity have distinct effects.
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(9) By simulating pioneering across changing TF concentrations, we found that if a pioneer TF is bound 100%, it does not guarantee 100% accessibility. We referred to how open chromatin could be at full TF occupancy as the “regulatory potential”.
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(7) This means that low-affinity motifs cooperate as readily as high-affinity motifs, but their relative gain is higher, which is why they produce strong effects.
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(6) Looking further, we find that arrangements of pioneer motifs tend to cooperate within nucleosome distances (~200 bp). This cooperative soft syntax applies to every examined pioneering motif pair and all mixtures of motif pair affinities.
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(5) Depending on the distance to a strong pioneer motif, the same motif sequence may have different effects on accessibility. We validated with CRISPR/Cas9 editing on the Akr1cl enhancer, where two identical and bound Sox2 motifs have very different effects on pioneering.
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(3) To map low-affinity motifs in their genomic context, we trained ChromBPNet (from @AnshulKundaje’s lab) deep learning models in mESCs, learning expected pluripotency TF motifs. We then validated the Oct4-Sox2 low-affinity mappings through high-resolution TF binding footprints.
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The @ZeitlingerLab is pleased to announce @MelanieWeilert’s preprint “Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs” (biorxiv.org/content/10.1101/…). Summary below!
(TLDR; low-affinity motifs are common and strong pioneers in vivo!)
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We are now on Bluesky! Follow us here: bsky.app/profile/zeitlingerl…
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We are thrilled to announce the release of Khyati Dalal's (@08Kats05) work, leveraging deep learning and experimental techniques to decipher regulatory mechanisms of Hippo signaling in mTSCs. Congratuations!
cell.com/cell-genomics/fullt…
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