Enhancer architecture and chromatin accessibility constrain phenotypic space during Drosophila development

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context.

Conservation of regulatory elements with highly diverged sequences across large evolutionary distances

Developmental gene expression is a remarkably conserved process, yet most cis-regulatory elements (CREs) lack sequence conservation, especially at larger evolutionary distances. Some evidence suggests that CREs at the same genomic position remain functionally conserved independent of sequence conservation. However, the extent of such positional conservation remains unclear. Here, we profiled the regulatory genome in mouse and chicken embryonic hearts at equivalent developmental stages and found that most CREs lack sequence conservation. To identify positionally conserved CREs, we introduced the synteny-based algorithm interspecies point projection, which identifies up to fivefold more orthologs than alignment-based approaches. We termed positionally conserved orthologs 'indirectly conserved' and showed that they exhibited chromatin signatures and sequence composition similar to sequence-conserved CREs but greater shuffling of transcription factor binding sites between orthologs. Finally, we validated indirectly conserved chicken enhancers using in vivo reporter assays in mouse. By overcoming alignment-based limitations, we revealed widespread functional conservation of sequence-divergent CREs.

Antagonizing cis-regulatory elements of a conserved flowering gene mediate developmental robustness

Developmental transitions require precise temporal and spatial control of gene expression. In plants, such regulation is critical for flower formation, which involves the progressive maturation of stem cell populations within shoot meristems to floral meristems, followed by rapid sequential differentiation into floral organs. Across plant taxa, these transitions are orchestrated by the F-box transcriptional cofactor gene UNUSUAL FLORAL ORGANS (UFO). The conserved and pleiotropic functions of UFO offer a useful framework for investigating how evolutionary processes have shaped the intricate cis-regulation of key developmental genes. By pinpointing a conserved promoter sequence in an accessible chromatin region of the tomato ortholog of UFO, we engineered in vivo a series of cis-regulatory alleles that caused both loss- and gain-of-function floral defects. These mutant phenotypes were linked to disruptions in predicted transcription factor binding sites for known transcriptional activators and repressors. Allelic combinations revealed dosage-dependent interactions between opposing alleles, influencing the penetrance and expressivity of gain-of-function phenotypes. These phenotypic differences support that robustness in tomato flower development requires precise temporal control of UFO expression dosage. Bridging our analysis to Arabidopsis, we found that although homologous sequences to the tomato regulatory region are dispersed within the UFO promoter, they maintain similar control over floral development. However, phenotypes from disrupting these sequences differ due to the differing expression patterns of UFO. Our study underscores the complex cis-regulatory control of dynamic developmental genes and demonstrates that critical short stretches of regulatory sequences that recruit both activating and repressing machinery are conserved to maintain developmental robustness.

Novelty versus innovation of gene regulatory elements in human evolution and disease

It is not currently understood how much of human evolution is due to modifying existing functional elements in the genome versus forging novel elements from nonfunctional DNA. Many early experiments that aimed to assign genetic changes on the human lineage to their resulting phenotypic change have focused on mutations that modify existing elements. However, a number of recent studies have highlighted the potential ease and importance of forging novel gene regulatory elements from nonfunctional sequences on the human lineage. In this review, we distinguish gene regulatory element novelty from innovation. We propose definitions for these terms and emphasize their importance in studying the genetic basis of human uniqueness. We discuss why the forging of novel regulatory elements may have been less emphasized during the previous decades, and why novel regulatory elements are likely to play a significant role in both human adaptation and disease.

Ancient biases in phenotype production drove the functional evolution of a protein family

Biological systems may be biased in the phenotypes they can access by mutation1-7, but the extent of these biases and their causal role in the evolution of extant phenotypic diversity remains unclear. There are three major challenges: it is difficult to isolate the effect of bias in the genotype-phenotype (GP) map from that of natural selection in producing natural diversity6,8-11, the universe of possible genotypes and phenotypes is so vast and complex that a direct characterization has been impossible, and most extant phenotypes evolved long ago in species whose GP maps cannot be recovered. Here we develop exhaustive multi-phenotype deep mutational scanning to experimentally characterize the complete GP maps of two reconstructed ancestral steroid receptor proteins, which existed during an ancient phylogenetic interval when a new phenotype-specific binding of a new DNA response element-evolved12. We measured all possible DNA specificity phenotypes encoded by all possible amino acid combinations at sites in the protein's DNA binding interface. We found that the ancestral GP maps are structured by strong global bias-unequal propensity to encode the various phenotypes-and extreme heterogeneity in the phenotypes accessible around each genotype, which strongly affect evolution on both long and short timescales. Distinct biases in the two ancestral maps steered evolution toward the lineage-specific functional phenotypes that evolved during history. Our findings establish that ancient biases in the GP relationship were causal factors in the evolutionary process that produced the present-day patterns of phenotypic conservation and diversity in this protein family.

Chromatin enables precise and scalable gene regulation with factors of limited specificity

Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs). Under what conditions and by how much can chromatin reduce regulatory errors on a global scale? We use a theoretical approach to compare two scenarios for gene regulation: one that relies on TF binding to free DNA alone and one that uses a combination of TFs and chromatin-regulating PFs to achieve desired gene expression patterns. We find, first, that chromatin effectively silences groups of genes that should be simultaneously OFF, thereby allowing more accurate graded control of expression for the remaining ON genes. Second, chromatin buffers the deleterious consequences of nontarget binding as the number of OFF genes grows, permitting a substantial expansion in regulatory complexity. Third, chromatin-based regulation productively co-opts nontarget TF binding for ON genes in order to establish a "leaky" baseline expression level, which targeted activator or repressor binding subsequently up- or down-modulates. Thus, on a global scale, using chromatin simultaneously alleviates pressure for high specificity of regulatory interactions and enables an increase in genome size with minimal impact on global expression error.

Spherical harmonics texture extraction for versatile analysis of biological objects

The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract the polarization direction and quantify the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik to provide a parameter-free and data-agnostic application to any segmented 3D or 2D dataset. Additionally, this technique can also be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.

The emergence and evolution of gene expression in genome regions replete with regulatory motifs

Gene regulation is essential for life and controlled by regulatory DNA. Mutations can modify the activity of regulatory DNA, and also create new regulatory DNA, a process called regulatory emergence. Non-regulatory and regulatory DNA contain motifs to which transcription factors may bind. In prokaryotes, gene expression requires a stretch of DNA called a promoter, which contains two motifs called -10 and -35 boxes. However, these motifs may occur in both promoters and non-promoter DNA in multiple copies. They have been implicated in some studies to improve promoter activity, and in others to repress it. Here, we ask whether the presence of such motifs in different genetic sequences influences promoter evolution and emergence. To understand whether and how promoter motifs influence promoter emergence and evolution, we start from 50 'promoter islands', DNA sequences enriched with -10 and -35 boxes. We mutagenize these starting 'parent' sequences, and measure gene expression driven by 240,000 of the resulting mutants. We find that the probability that mutations create an active promoter varies more than 200-fold, and is not correlated with the number of promoter motifs. For parent sequences without promoter activity, mutations created over 1500 new -10 and -35 boxes at unique positions in the library, but only ~0.3% of these resulted in de-novo promoter activity. Only ~13% of all -10 and -35 boxes contribute to de-novo promoter activity. For parent sequences with promoter activity, mutations created new -10 and -35 boxes in 11 specific positions that partially overlap with preexisting ones to modulate expression. We also find that -10 and -35 boxes do not repress promoter activity. Overall, our work demonstrates how promoter motifs influence promoter emergence and evolution. It has implications for predicting and understanding regulatory evolution, de novo genes, and phenotypic evolution.

Beyond modular enhancers: new questions in cis-regulatory evolution

Our understanding of how cis-regulatory elements work has advanced rapidly, outpacing our evolutionary models. In this review, we consider the implications of new mechanistic findings for evolutionary developmental biology. We focus on three different debates: whether evolutionary innovation occurs more often via the modification of old cis-regulatory elements or the emergence of new ones; the extent to which individual elements are specific and autonomous or multifunctional and interdependent; and how the robustness of cis-regulatory architectures influences the rate of trait evolution. These discussions lead us to propose new questions for the evo-devo of cis-regulation.

De Novo Genes

Although the majority of annotated new genes in a given genome appear to have arisen from duplication-related mechanisms, recent studies have shown that genes can also originate de novo from ancestrally nongenic sequences. Investigating de novo-originated genes offers rich opportunities to understand the origin and functions of new genes, their regulatory mechanisms, and the associated evolutionary processes. Such studies have uncovered unexpected and intriguing facets of gene origination, offering novel perspectives on the complexity of the genome and gene evolution. In this review, we provide an overview of the research progress in this field, highlight recent advancements, identify key technical and conceptual challenges, and underscore critical questions that remain to be addressed.

TF-High-Evolutionary: In Vivo Mutagenesis of Gene Regulatory Networks for the Study of the Genetics and Evolution of the Drosophila Regulatory Genome

Understanding the evolutionary potential of mutations in gene regulatory networks is essential to furthering the study of evolution and development. However, in multicellular systems, genetic manipulation of regulatory networks in a targeted and high-throughput way remains challenging. In this study, we designed TF-High-Evolutionary (HighEvo), a transcription factor (TF) fused with a base editor (activation-induced deaminase), to continuously induce germline mutations at TF-binding sites across regulatory networks in Drosophila. Populations of flies expressing TF-HighEvo in their germlines accumulated mutations at rates an order of magnitude higher than natural populations. Importantly, these mutations accumulated around the targeted TF-binding sites across the genome, leading to distinct morphological phenotypes consistent with the developmental roles of the tagged TFs. As such, this TF-HighEvo method allows the interrogation of the mutational space of gene regulatory networks at scale and can serve as a powerful reagent for experimental evolution and genetic screens focused on the regulatory genome.

Rapid response of fly populations to gene dosage across development and generations

Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges remains limited. Here, we have examined the phenotypic consequences of disturbing the bicoid regulatory network in early Drosophila embryos. We generated flies with two extra copies of bicoid, which causes a posterior shift of the network's regulatory outputs and a decrease in fitness. We subjected these flies to EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level.

Synthetic reversed sequences reveal default genomic states

Pervasive transcriptional activity is observed across diverse species. The genomes of extant organisms have undergone billions of years of evolution, making it unclear whether these genomic activities represent effects of selection or 'noise'1-4. Characterizing default genome states could help understand whether pervasive transcriptional activity has biological meaning. Here we addressed this question by introducing a synthetic 101-kb locus into the genomes of Saccharomyces cerevisiae and Mus musculus and characterizing genomic activity. The locus was designed by reversing but not complementing human HPRT1, including its flanking regions, thus retaining basic features of the natural sequence but ablating evolved coding or regulatory information. We observed widespread activity of both reversed and native HPRT1 loci in yeast, despite the lack of evolved yeast promoters. By contrast, the reversed locus displayed no activity at all in mouse embryonic stem cells, and instead exhibited repressive chromatin signatures. The repressive signature was alleviated in a locus variant lacking CpG dinucleotides; nevertheless, this variant was also transcriptionally inactive. These results show that synthetic genomic sequences that lack coding information are active in yeast, but inactive in mouse embryonic stem cells, consistent with a major difference in 'default genomic states' between these two divergent eukaryotic cell types, with implications for understanding pervasive transcription, horizontal transfer of genetic information and the birth of new genes.

Regulatory activity is the default DNA state in eukaryotes

Genomes encode for genes and non-coding DNA, both capable of transcriptional activity. However, unlike canonical genes, many transcripts from non-coding DNA have limited evidence of conservation or function. Here, to determine how much biological noise is expected from non-genic sequences, we quantify the regulatory activity of evolutionarily naive DNA using RNA-seq in yeast and computational predictions in humans. In yeast, more than 99% of naive DNA bases were transcribed. Unlike the evolved transcriptome, naive transcripts frequently overlapped with opposite sense transcripts, suggesting selection favored coherent gene structures in the yeast genome. In humans, regulation-associated chromatin activity is predicted to be common in naive dinucleotide-content-matched randomized DNA. Here, naive and evolved DNA have similar co-occurrence and cell-type specificity of chromatin marks, challenging these as indicators of selection. However, in both yeast and humans, extreme high activities were rare in naive DNA, suggesting they result from selection. Overall, basal regulatory activity seems to be the default, which selection can hone to evolve a function or, if detrimental, repress.

Shaping gene expression and its evolution by chromatin architecture and enhancer activity

Transcriptional regulation plays a pivotal role in orchestrating the intricate genetic programs governing embryonic development. The expression of developmental genes relies on the combined activity of several cis-regulatory elements (CREs), such as enhancers and silencers, which can be located at long linear distances from the genes that they regulate and that interact with them through establishment of chromatin loops. Mutations affecting their activity or interaction with their target genes can lead to developmental disorders and are thought to have importantly contributed to the evolution of the animal body plan. The income of next-generation-sequencing approaches has allowed identifying over a million of sequences with putative regulatory potential in the human genome. Characterizing their function and establishing gene-CREs maps is essential to decode the logic governing developmental gene expression and is one of the major challenges of the post-genomic era. Chromatin 3D organization plays an essential role in determining how CREs specifically contact their target genes while avoiding deleterious off-target interactions. Our understanding of these aspects has greatly advanced with the income of chromatin conformation capture techniques and fluorescence microscopy approaches to visualize the organization of DNA elements in the nucleus. Here we will summarize relevant aspects of how the interplay between CRE activity and chromatin 3D organization regulates developmental gene expression and how it relates to pathological conditions and the evolution of animal body plan.

Hold out the genome: a roadmap to solving the cis-regulatory code

Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven to be exceedingly complex. Recently, advances in the scale and resolution of functional genomics assays and machine learning have enabled substantial progress towards deciphering this code. However, the cis-regulatory code will probably never be solved if models are trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and our genome is too short and has insufficient sequence diversity to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable testing a far larger sequence space than exists in our genomes, and designed DNA sequences enable targeted queries to maximally improve the models. As the same biochemical principles are used to interpret DNA regardless of its source, models trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by a combination of machine learning and massively parallel assays using synthetic DNA.

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Functional analysis of the Drosophila eve locus in response to non-canonical combinations of gap gene expression levels

Transcription factor combinations play a key role in shaping cellular identity. However, the precise relationship between specific combinations and downstream effects remains elusive. Here, we investigate this relationship within the context of the Drosophila eve locus, which is controlled by gap genes. We measure spatiotemporal levels of four gap genes in heterozygous and homozygous gap mutant embryos and correlate them with the striped eve activity pattern. Although changes in gap gene expression extend beyond the manipulated gene, the spatial patterns of Eve expression closely mirror canonical activation levels in wild type. Interestingly, some combinations deviate from the wild-type repertoire but still drive eve activation. Although in homozygous mutants some Eve stripes exhibit partial penetrance, stripes consistently emerge at reproducible positions, even with varying gap gene levels. Our findings suggest a robust molecular canalization of cell fates in gap mutants and provide insights into the regulatory constraints governing multi-enhancer gene loci.

Butterfly eyespots exhibit unique patterns of open chromatin

Background: How the precise spatial regulation of genes is correlated with spatial variation in chromatin accessibilities is not yet clear. Previous studies that analysed chromatin from homogenates of whole-body parts of insects found little variation in chromatin accessibility across those parts, but single-cell studies of Drosophila brains showed extensive spatial variation in chromatin accessibility across that organ. In this work we studied the chromatin accessibility of butterfly wing tissue fated to differentiate distinct colors and patterns in pupal wings of Bicyclus anynana. Methods: We dissected small eyespot and adjacent control tissues from 3h pupae and performed ATAC-Seq to identify the chromatin accessibility differences between different sections of the wings. Results: We observed that three dissected wing regions showed unique chromatin accessibilities. Open chromatin regions specific to eyespot color patterns were highly enriched for binding motifs recognized by Suppressor of Hairless (Su(H)), Krüppel (Kr), Buttonhead (Btd) and Nubbin (Nub) transcription factors. Genes in the vicinity of the eyespot-specific open chromatin regions included those involved in wound healing and SMAD signal transduction pathways, previously proposed to be involved in eyespot development. Conclusions: We conclude that eyespot and non-eyespot tissue samples taken from the same wing have distinct patterns of chromatin accessibility, possibly driven by the eyespot-restricted expression of potential pioneer factors, such as Kr.

Integrative approaches to study enhancer-promoter communication

The spatiotemporal control of gene expression in complex multicellular organisms relies on noncoding regulatory sequences such as enhancers, which activate transcription of target genes often over large genomic distances. Despite the advances in the identification and characterization of enhancers, the principles and mechanisms by which enhancers select and control their target genes remain largely unknown. Here, we review recent interdisciplinary and quantitative approaches based on emerging techniques that aim to address open questions in the field, notably how regulatory information is encoded in the DNA sequence, how this information is transferred from enhancers to promoters, and how these processes are regulated in time.

Mutational scans reveal differential evolvability of Drosophila promoters and enhancers

Rapid enhancer and slow promoter evolution have been demonstrated through comparative genomics. However, it is not clear how this information is encoded genetically and if this can be used to place evolution in a predictive context. Part of the challenge is that our understanding of the potential for regulatory evolution is biased primarily toward natural variation or limited experimental perturbations. Here, to explore the evolutionary capacity of promoter variation, we surveyed an unbiased mutation library for three promoters in Drosophila melanogaster. We found that mutations in promoters had limited to no effect on spatial patterns of gene expression. Compared to developmental enhancers, promoters are more robust to mutations and have more access to mutations that can increase gene expression, suggesting that their low activity might be a result of selection. Consistent with these observations, increasing the promoter activity at the endogenous locus of shavenbaby led to increased transcription yet limited phenotypic changes. Taken together, developmental promoters may encode robust transcriptional outputs allowing evolvability through the integration of diverse developmental enhancers.

This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.

Decoding enhancer complexity with machine learning and high-throughput discovery

Enhancers are genomic DNA elements controlling spatiotemporal gene expression. Their flexible organization and functional redundancies make deciphering their sequence-function relationships challenging. This article provides an overview of the current understanding of enhancer organization and evolution, with an emphasis on factors that influence these relationships. Technological advancements, particularly in machine learning and synthetic biology, are discussed in light of how they provide new ways to understand this complexity. Exciting opportunities lie ahead as we continue to unravel the intricacies of enhancer function.

Enhancers display constrained sequence flexibility and context-specific modulation of motif function

The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, whereas the enhancer syntax, that is, the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remains poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important TF motifs by all possible 65,536 eight-nucleotide-long sequences and (2) paste eight important TF motif types into 763 positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but these are only a fraction of all possible sequences and motif types. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, the presence and diversity of other motif types, and the distance between motifs), such that not all motif types can work in all positions. The context-specific modulation of motif function is also a hallmark of human enhancers, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution, and in disease.