Search

Identifying the molecular origins by which new morphological structures evolve is one of the long-standing problems in evolutionary biology. To date, vanishingly few examples provide a compelling account of how new morphologies were initially formed, thereby limiting our understanding of how diverse forms of life derived their complex features. Here, we provide evidence that the large projections on the Drosophila eugracilis phallus that are implicated in sexual conflict have evolved through the partial co-option of the trichome genetic network. These unicellular apical projections on the phallus postgonal sheath are reminiscent of trichomes that cover the Drosophila body but are up to 20-fold larger in size. During their development, they express the transcription factor Shavenbaby, the master regulator of the trichome network. Consistent with the co-option of the Shavenbaby network during the evolution of the D. eugracilis projections, somatic mosaic CRISPR-Cas9 mutagenesis shows that shavenbaby is necessary for their proper length. Moreover, misexpression of Shavenbaby in the sheath of D. melanogaster, a naive species that lacks these projections, is sufficient to induce small trichomes. These induced projections rely on a genetic network that is shared to a large extent with the D. eugracilis projections, indicating its partial co-option but also some genetic rewiring. Thus, by leveraging a genetically tractable evolutionary novelty, our work shows that the trichome-forming network is flexible enough that it can be partially co-opted in a new context and subsequently refined to produce unique apical projections that are barely recognizable compared with their simpler ancestral beginnings., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Identifying the molecular origins by which new morphological structures evolve is one of the long standing problems in evolutionary biology. To date, vanishingly few examples provide a compelling account of how new morphologies were initially formed, thereby limiting our understanding of how diverse forms of life derived their complex features. Here, we provide evidence that the large projections on the Drosophila eugracilis phallus that are implicated in sexual conflict have evolved through co-option of the trichome genetic network. These unicellular apical projections on the phallus postgonal sheath are reminiscent of trichomes that cover the Drosophila body but are up to 20-fold larger in size. During their development, they express the transcription factor Shavenbaby, the master regulator of the trichome network. Consistent with the co-option of the Shavenbaby network during the evolution of the D. eugracilis projections, somatic mosaic CRISPR/Cas9 mutagenesis shows that shavenbaby is necessary for their proper length. Moreover, mis-expression of Shavenbaby in the sheath of D. melanogaster , a naïve species that lacks these extensions, is sufficient to induce small trichomes. These induced extensions rely on a genetic network that is shared to a large extent with the D. eugracilis projections, indicating its co-option but also some genetic rewiring. Thus, by leveraging a genetically tractable evolutionarily novelty, our work shows that the trichome-forming network is flexible enough that it can be co-opted in a new context, and subsequently refined to produce unique apical projections that are barely recognizable compared to their simpler ancestral beginnings.

Congenital heart disease often arises from perturbations of transcription factors (TFs) that guide cardiac development. ISLET1 (ISL1) is a TF that influences early cardiac cell fate, as well as differentiation of other cell types including motor neuron progenitors (MNPs) and pancreatic islet cells. While lineage specificity of ISL1 function is likely achieved through combinatorial interactions, its essential cardiac interacting partners are unknown. By assaying ISL1 genomic occupancy in human induced pluripotent stem cell-derived cardiac progenitors (CPs) or MNPs and leveraging the deep learning approach BPNet, we identified motifs of other TFs that predicted ISL1 occupancy in each lineage, with NKX2.5 and GATA motifs being most closely associated to ISL1 in CPs. Experimentally, nearly two-thirds of ISL1-bound loci were co-occupied by NKX2.5 and/or GATA4. Removal of NKX2.5 from CPs led to widespread ISL1 redistribution, and overexpression of NKX2.5 in MNPs led to ISL1 occupancy of CP-specific loci. These results reveal how ISL1 guides lineage choices through a combinatorial code that dictates genomic occupancy and transcription., Competing Interests: Declaration of interests D.S. is a co-founder and member of the board of directors of Tenaya Therapeutics and has equity in Tenaya Therapeutics. K.N.I. is an employee and shareholder of Tenaya Therapeutics. N.J.K. has received research support from Vir Biotechnology, F. Hoffmann-La Roche, and Rezo Therapeutics. N.J.K. has financially compensated consulting agreements with Maze Therapeutics, Interline Therapeutics, Rezo Therapeutics, and GEn1E Lifesciences, Inc. He is on the Board of Directors of Rezo Therapeutics and is a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics, and Interline Therapeutics., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding and chromatin accessibility data with interpretable deep learning and performed genetic validation experiments. We identify a hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, whereas patterning TFs augment chromatin accessibility in sequence contexts where they mediate enhancer activation. We conclude that chromatin accessibility occurs in two tiers: one through pioneering, which makes enhancers accessible but not necessarily active, and the second when the correct combination of TFs leads to enhancer activation., Competing Interests: Declaration of interests J.Z. owns a patent on ChIP-nexus (no. 10287628). A.K. is on the scientific advisory board of PatchBio, SerImmune, AINovo, TensorBio and OpenTargets, was a consultant with Illumina, and owns shares in Illumina, Deep Genomics, Immunai, and Freenome Inc. All other authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Short tandem repeats (STRs) are enriched in eukaryotic cis -regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis -regulatory mechanism to target TFs to genomic sites.

While the accessibility of enhancers is dynamically regulated during development, promoters tend to be constitutively accessible and poised for activation by paused Pol II. By studying Lola-I, a Drosophila zinc finger transcription factor, we show here that the promoter state can also be subject to developmental regulation independently of gene activation. Lola-I is ubiquitously expressed at the end of embryogenesis and causes its target promoters to become accessible and acquire paused Pol II throughout the embryo. This promoter transition is required but not sufficient for tissue-specific target gene activation. Lola-I mediates this function by depleting promoter nucleosomes, similar to the action of pioneer factors at enhancers. These results uncover a level of regulation for promoters that is normally found at enhancers and reveal a mechanism for the de novo establishment of paused Pol II at promoters., (© 2023. Springer Nature Limited.)

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy., Competing Interests: Competing interests A.K. is on the scientific advisory board of PatchBio, SerImmune, AINovo, TensorBio and OpenTargets, was a consulting Fellow with Illumina and owns shares in DeepGenomics, Immuni and Freenome. All other authors have no competing interests to declare.

What new questions can we ask about transcriptional regulation given recent developments in large-scale approaches?, Competing Interests: Declaration of interests J.Z. is an investigator at the Stowers Institute for Medical Research and also a professor (affiliate track) at the Department of Pathology and Laboratory Medicine at the University of Kansas Medical Center. H.J.B. is a co-founder and shareholder of Metric Biotechnologies, Inc. Columbia University has filed a patent on a technology tangentially related, on which H.J.B. is one of the inventors, to the topic of the Voices piece that H.J.B. contributed., (Copyright © 2023. Published by Elsevier Inc.)

After a COVID-related hiatus, the fifth biennial symposium on Evolution and Core Processes in Gene Regulation met at the Stowers Institute in Kansas City, Missouri July 21 to 24, 2022. This symposium, sponsored by the American Society for Biochemistry and Molecular Biology (ASBMB), featured experts in gene regulation and evolutionary biology. Topic areas covered enhancer evolution, the cis-regulatory code, and regulatory variation, with an overall focus on bringing the power of deep learning (DL) to decipher DNA sequence information. DL is a machine learning method that uses neural networks to learn complex rules that make predictions about diverse types of data. When DL models are trained to predict genomic data from DNA sequence information, their high prediction accuracy allows the identification of impactful genetic variants within and across species. In addition, the learned sequence rules can be extracted from the model and provide important clues about the mechanistic underpinnings of the cis-regulatory code., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of the article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)