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
- Tweets 172
- Following 91
- Followers 884
- Likes 204
(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.
1
4
3
1,248
(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.
1
1
3
493
(7/10) Overcoming enzymatic sequence biases using PISA reveals the nucleosome motif grammar. Upon correcting the AT-rich sequences of a BPNet model trained to predict MNase-seq nucleosome occupancy, resulting attribution scores clearly identify motifs.
1
189
(6/10) PISA can detect enzymatic biases in various sequencing data.
Sequence-to-function models learn both enzymatic bias of the sequencing experiment and the underlying biology. Here, a BPNet MNase-seq model trained in yeast exhibits expected preference for AT-rich sequences.
2
1
197
(5/10) PISA reveals complex motif effects on histone modification ChIP-seq data.
In a H3K27ac BPNet model predicting activity in early embryo fly, a pioneering Zelda motif produces a dual response in H3K27ac profile: a central depletion, flanked by an increase in activity.
1
1
207
(4/10) PISA can reveal “hidden” motifs that possess disparate effects on the output window.
In a ChromBPNet model predicting accessibility in early embryo fly, a CA-rich “Cackle” motif is revealed to possess a positive and negative contributions depending on the output locus.
1
2
233
(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.
1
3
254
(2/10) Our new interpretation tool called PISA (pairwise influence by sequence attribution) overcomes this limitation and quantifies how each individual base impacts the predicted readout at each genomic coordinate.
1
326
(1/10) When interpreting sequence-to-function models, current attribution methods typically summarize an input base’s effect on the entire output. But what happens if a single nucleotide causes one effect at one output position, but a different effect elsewhere?
1
431
(7/7) Our Model: All promoters use TFIID to load TBP, but TATA promoters additionally allow direct TBP binding to the TATA box. Such dual initiation likely enables faster TBP re-loading and larger transcriptional bursts at TATA promoters.
For more details, check out our work!
91
(6/7) DPR promoters, which contain downstream sequences favorable for TFIID binding, show the highest levels of downstream TBP. Downstream TBP shows the strongest correlation with TAF2, TAF1 and TAF7, consistent with this being the promoter loading state of TFIID.
1
119
(5/7) When binding is normalized by transcription output, we see that TAFs are significantly depleted at TATA promoters. Here, TBP correlates more strongly with TFIIA, TFIIB, TFIIF, NC2, and Mot1 than with the TAFs. Binding at DPR promoters is more homogeneous.
1
56
(3/7) Across promoter types, TAF footprints are strikingly similar, but TBP shows strong promoter-type–specific binding patterns. Using TBP binding patterns alone, we could classify promoters de novo into TATA, DPR, and TCT/housekeeping—and recover their core promoter motifs.
1
69
(2/7) We observe all TFIID subunits at active promoters with highly correlated binding, arguing against promoter-specific partial TFIID complexes. Also, our high-resolution DNA footprints match cryo-EM structures —validating them in vivo.
1
1
82
(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.
1
3
176
(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.
1
2
216
(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.
1
1
5
705
(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.
1
1
3
268
(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.
1
2
259
(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.
1
2
278
(4) Our low-affinity motifs were predicted to have outsized effects on pioneering due to the motif’s arrangement in the genomic region. Surprisingly, this context is a stronger determinant of pioneering than the motif’s affinity alone, as confirmed with CRISPR/Cas9 editing.
1
1
299
(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.
1
2
371
(2) Described as “futility theorem” in the early 2000s, it’s hard to map functional low-affinity motifs based on low PWM match scores, yet some low-affinity motifs have crucial phenotypic consequences in vivo.
So what makes low-affinity motifs important for enhancer regulation?
1
1
626
These genome-wide sequence rules and insights allowed us to design small sequence changes that alter the activity of enhancers in vivo, showing that deep learning can learn rules of signaling pathways in a cell-type-specific way. 🧬🔬(6/8)
1
1
478
We find that strictly spaced Tead double motifs are widespread, active canonical response elements that mediate cooperativity by promoting labile TEAD4 protein-protein interactions on DNA. Ever heard of protein-protein interactions at the scale of 100 nanoseconds? 🤔 (5/8)
1
2
360
Based on the extracted syntax rules, we validate that the binding of YAP1 can be enhanced, along with TEAD4, by cell-specific transcription factors in a distance-dependent manner, thereby conferring cell-specific response. (4/8)
1
2
387
Next, we found that YAP1 binding is an important determinant for enhancer activation in mouse trophoblast stem cells and driven by the combinatorial rules. (3/8)
1
3
454
Using deep learning on binding data, we extracted sequence rules that drive enhancer activation downstream of the Hippo signaling pathway. We found that binding of the Hippo transcription factors TEAD (DNA-binding) and YAP1 depend on specific motifs of partner TFs (2/8)
1
4
534
In our latest work spearheaded by Khyati Dalal @08Kats05 , we use interpretable deep learning of genomic sequence information to tackle a long-standing question: How do signaling pathways regulate transcription in a cell-type-specific fashion? (biorxiv.org/cgi/content/shor…) (1/8)
1
35
125
22,398
Are promoters just landing pads for the transcriptional machinery? No! They can be key regulatory centers during development. Vivek Ramalingam shows this in some beautiful new work published in @NatureComms.
Thread below: (1/6)
doi.org/10.1038/s41467-023-4…
1
27
121
14,459
In summary, chromatin accessibility is two-tiers and includes pioneering and activation. Pioneering is a consistent effect that is commensurate with motif affinity. Accessibility is potentiated during enhancer activation, when combinations of TFs mediate transactivation. (5/6)
1
8
1,045
@JuliaZeitlinger presenting at Day 1 of @ASBMB's Evolution & Core Processes in Gene Expression at @ScienceStowers in Kansas City, Missouri! We are so proud to be a part of this conference and thanks to all the attendees and organizers!
1
4
26
Lola-I opens promoters by binding preferentially to the edge of nucleosomes and depleting them. This is similar to the function of pioneer factors at enhancers, the first step towards enhancer activation. Thus, promoters can be subject to similar regulation as enhancers. (3/4)
1
4
When Lola-I becomes ubiquitously expressed in late-stage embryos, it prompts paused polymerases to be loaded onto target promoters in all tissues. This does not cause gene activation on its own - it allows promoters to better respond to activation signals from enhancers. (2/4)
1
4
Promoters are thought to be always accessible and not subject to developmental regulation. Here we show that Lola-I is a pioneer factor that opens promoters at the end of Drosophila embryogenesis. This regulatory step at promoters is critical for development to proceed. (1/4)
Lola-I is a developmentally regulated promoter pioneer factor biorxiv.org/cgi/content/shor… #bioRxiv
3
24
122
When Lola-I becomes ubiquitously expressed in late-stage embryos, it prompts paused polymerases to be loaded onto target promoters in all tissues. This does not cause gene activation on its own - it allows promoters to better respond to activation signals from enhancers. (2/4)
Join us for the 2022 ASBMB Special Symposia Series "Evolution and Core Processes in Gene Expression," in Kansas City, Missouri at the Stowers Institute for Medical Research from July 21-24, 2022! Abstract deadline is May 6 (May 25 for posters).
asbmb.org/meetings-events/ge…
10
19
As summer settles in, our lab is excited to attend the Stowers Fitness Extravaganza to celebrate the reopening of the fitness center! #fitness @ScienceStowers
1
15
Sushi happy hour and the long-awaited MinION sequencer arriving! @BourdareauSimon had a good birthday last week! 🎂🍣🧪
5
@anshulkundaje just gave a wonderful talk on his work in applying deep learning towards regulatory genomics! Thank you for visiting @JuliaZeitinger and @ScienceStowers, it is so exciting to have you here! 👨💻🧬👩🔬
1
1
19
In honor of Post-doc Appreciation Week, we took a lab pic with matching fleeces. We are definitely one of the most stylish labs in regulatory genomics! 💃😘💃 @kaelanbrennan @SabKrueger @nilay28shah @BourdareauSimon @08Kats05 @Sergiogma91 @MelanieWeilert @JuliaZeitinger
3
15
@ScienceStowers was awesome enough to take pictures of some of our lab members at our in-house science retreat. We look super cool when we present science! 👩🔬😎👨🔬 @08Kats05 @BourdareauSimon @MelanieWeilert #YISR2019
1
8
It is the last week of our summer rotation students Erik and Ana so we thought it would be fun to snap a pic! Wishing them the best for their future scientific endeavors! 👨🔬👩💻👩🔬👨💻
2
15
Wanqing Shao, a graduated student, just published her final paper from our lab! This paper is an excellent investigation into the underlying effects of initiation sequences on Pol II pausing in flies! Go Wanqing and @Sergiogma91! elifesciences.org/articles/4…
4
27