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Wonderful - thank you for compiling this
We just published a short conceptual review together with @angela-taddei.bsky.social on the spatial controls of homology search in both bacteria and eukaryotes. We discuss an emerging framework for homology search in cells with two main phases. Check it out: authors.elsevier.com/c/1mjyh,LqAZ...
Wahoooo it’s online!!! 🎉🥳
Excited to share a glimpse into my postdoc work with @chorye.bsky.social at Duke where @stefan-golas.bsky.social and I developed TurboPRANCE, a robotics platform to scale and enable new phage-assisted continuous evolutions. bskytorial party!👇 1/n
Our most recent work on the “function and evolution” of #nuclear-speckles is now online at Cell @cp-cell.bsky.social
doi.org/10.1016/j.ce...
Read the thread👇 for the highlights of our findings.
🤩💪 Out now! 3D regulatory hubs in sex determination
With @mamartirenom.bsky.social & Capel labs, led by @imotagom.bsky.social & @jrotwitguez.bsky.social
1️⃣ METALoci – explore #3DGenome 🧬
2️⃣ Non-coding region controlling Fgf9 🧩
3️⃣ Meis genes = new key players 🌟
👉 rdcu.be/e5sm2
1/n Bluetorial 👇
Excited to share @suminkim.bsky.social and @mileshuseyin.bsky.social 's new Current Opinion review on how Polycomb complexes mediate 3D genome interactions including mechanistic models and potential roles in gene regulation:
www.sciencedirect.com/science/arti...
Very excited to share my postdoc research in the @jesserdixon.bsky.social lab at @salkinstitute.bsky.social, out online at @natgenet.nature.com today! www.nature.com/articles/s41... We investigated the function of the cohesin accessory protein NIPBL, making two particularly interesting findings:
24/ This work was led by multi-talented @erikacanderson.bsky.social
with expert contributions from:
Hadi Rahmaninejad, @gfudenberg.bsky.social
@aljahani.bsky.social , Ivana Cavka, Alistair Boettiger
@emilyarnold.bsky.social , Annie Adachi,
@rinishah.bsky.social
@karissalhansen.bsky.social
23/ Altogether genomic context shapes the local flux of loop extrusion within which enhancers operate.
So the next big question now becomes how cells control general mechanisms of looping to tune long-range enhancer action… 🧐
22/ Interestingly, Enformer, a transformer-based machine learning model, predicts that the same CTCF site retains less cohesin when it is flanked by other CTCF sites.
> Current ML models learned rules for CTCF-CTCF interference from genomic context, despite being unaware of loop extrusion.
21/ Combining simulations and ChIP-seq, we found that a CTCF site interferes with how much cohesin its neighbor can receive.
This interference patern explains variation in cohesin levels at CTCF sites genome-wide, without the need to invoke targeted loading at privileged elements like enhancers.
20/ The cohesin blocked at CTCF sites gives clues about where extrusion happens -- some CTCF sites retain way more cohesin that others, meaning the cohesin flux is not uniform across the genome.
19/ So the key question now becomes: what controls the flux of cohesin extrusion in the first place, if not targeted loading at enhancers?
Through careful ChIP-seq background subtraction we found cohesin is largely (~75%) between CTCF sites.
18/ All in all, we do not find evidence that enhancers load cohesin to support their own long-range activity on distal promoter targets.
Instead, enhancer action appears subordinate to the general flux of cohesin extrusion.
17/ 🖥️> Enhancers generally do not display such ‘jets’/’fountains’, at least in normal mouse ES cells. Yet cohesin can help them act long-range. How??
We find alternative regimes where enhancers may still modulate cohesin flux but without acting as loading sites, by tuning loading across their TAD
16/ 🖥️> Weaker targeted loading creates an antidiagonal on Hi-C maps (=‘jets’/’fountains’), but this does not increase interaction between the loading site and the rest of the domain.
i.e. targeted loading at enhancers does not by itself increase their chance to interact with distal promoters.
15/ 🖥️> In simulations, very strong targeted loading over several kilobases creates boundaries and stripes on contact maps. Yet enhancers generally do not display such patterns in Hi-C/micro-C.
14/🖥️> loading at enhancers would need to be rather unrealistic to overcome the flux of cohesin coming from the background and enrich cohesin even just 2x at nearby CTCF sites
(i.e. if your enhancer KO disrupts cohesin binding, it is unlikely to be just because of targeted loading at the enhancer)
13/ Many calculations later 📐📏:
🖥️> cohesin is not expected to be retained at loading sites
(i.e. if you see cohesin ChIP-seq at enhancers, it not likely to be a signature of targeted loading)
12/ Hadi Rahmaninejad in the group of @gfudenberg.bsky.social did just that, using biophysical simulations of loop extrusion. 🖥️
11/ 🤔…
Let’s reconsider our expectations of targeted cohesin loading then.
Enhancers represent such a small fraction of te genome, they would need to boost cohesin loading REALLY REALLY high to outweigh background loading rate.
Can we put numbers on this to help interpret experimental results?
10/ 🔬Chromosome tracing microscopy by @aljahani.bsky.social & Ivana Cavka in Alistair Boettiger’s group showed that targeted cohesin loading disrupts local 3D folding, creating a boundary.
Yet the negative impact on enhancer function must involve additional effects (cohesin::RNA-pol/TF clashes?).
9/ Nope. ❌
Quite the opposite in fact.
Using the TArgeted Cohesin Loader (TACL) approach (www.nature.com/articles/s41...) from Wouter de Laat’s group to increase loading was very detrimental to distal transcription.
So more cohesin loading by the enhancer =/= better enhancer function
8/ Let’s attack the problem more directly, from the functional angle.
Is increasing cohesin loading by an enhancer beneficial to its long-range activity? 📝
7/ Using an artificial tethering system we saw that transcription activators are sufficient to recruit NIPBL/MAU2. Yet we don’t see cohesin being recruited…
Does that mean NIPBL/MAU2 at enhancer does not load cohesin then? 😵💫
6/ Using new quantitative controls with clean tags, inducible degrons and calibrated ChIP-seq, we saw that NIPBL/MAU2 (cohesin cofactor complex necessary for initiation and progression of loop extrusion) binds at enhancers in mouse ES cells. Yet we don’t see much cohesin (RAD21)…
5/ Targeted vs. uniform loading models have VERY different implications for how we think about transcriptional regulation and enhancer functions.
So, what is happening in cells?
4/ Contrasting with this view is the idea that cohesin loads uniformly, and that enhancers are instead subordinate to the general dynamics of cohesin looping. 🤔
3/ This would mean that enhancers have some autonomy in setting up the DNA loops that support their role in long-range transcriptional regulation 💪
