Thanks to coauthors Kaelan Brennan, @kats0805.bsky.social, @hainingjiang.bsky.social, and Sabrina Krueger. Thanks again @rmartinezcorral.bsky.social for your mechanistic modeling, we learned so much from you! Finally, thank you to @juliazeitlinger.bsky.social for your guidance along this journey!

(13) Putting it together, it seems that low-affinity motifs likely evolve easily in enhancers because (1) they arise often (futility theorem), (2) the syntax is flexible, (3) the effect is relatively large. Due to motif cooperativity, even small changes can affect enhancer function.

(12) 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.

(11) 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.

(10) 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”.

(9) To understand the implications of pioneering cooperativity, we collaborated with @rmartinezcorral.bsky.social at @crg.eu. We showed that a kinetic modeling framework of nucleosome-mediated TF cooperativity could readily simulate the data from the ChromBPNet model.

(8) 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.

(7) 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.

(6) Depending on the distance to a strong pioneer motif, the same motif sequence may have different effects on accessibility. This was validated with CRISPR/Cas9 editing on the Akr1cl enhancer, where two identical and bound Sox2 motifs have very different effects on pioneering.

(5) We then found that low-affinity motifs are 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.

(4) To map low-affinity motifs in their genomic context, we trained ChromBPNet (from the lab of @anshulkundaje.bsky.social) deep learning models in mESCs, learning the expected pluripotency TF motifs. We then validated the Oct4-Sox2 motif mappings through high-resolution TF binding footprints.

(3) Described as “futility theorem” in the 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.

What then makes low-affinity motifs important for enhancer regulation?

(2) We find that low-affinity motifs play a widespread role during pioneering through motif cooperativity in nucleosome distance, shaping an enhancer’s response to TF concentrations. Their frequency, flexible syntax, and outsized effect make them a likely source of evolutionary innovation.