Cis-regulatory Code Research Articles

Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Dissecting the cis-regulatory syntax of transcription initiation with deep learning.

Despite extensive characterization of mammalian Pol II transcription, the DNA sequence determinants of transcription initiation at a third of human promoters and most enhancers remain poorly understood. Hence, we trained and interpreted a neural network called ProCapNet that accurately models base-resolution initiation profiles from PRO-cap experiments using local DNA sequence. ProCapNet learns sequence motifs with distinct effects on initiation rates and TSS positioning and uncovers context-specific cryptic initiator elements intertwined within other TF motifs. ProCapNet annotates predictive motifs in nearly all actively transcribed regulatory elements across multiple cell-lines, revealing a shared cis-regulatory logic across promoters and enhancers mediated by a highly epistatic sequence syntax of cooperative and competitive motif interactions. ProCapNet models of RAMPAGE profiles measuring steady-state RNA abundance at TSSs distill initiation signals on par with models trained directly on PRO-cap profiles. ProCapNet learns a largely cell-type-agnostic cis-regulatory code of initiation complementing sequence drivers of cell-type-specific chromatin state critical for accurate prediction of cell-type-specific transcription initiation.

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Deep learning approaches have made significant advances in predicting cell type-specific chromatin patterns from the identity and arrangement of transcription factor (TF) binding motifs. However, most models have been applied in unperturbed contexts, precluding a predictive understanding of how chromatin state responds to TF perturbation. Here, we used transfer learning to train and interpret deep learning models that use DNA sequence to predict, with accuracy approaching experimental reproducibility, how the concentration of two dosage-sensitive TFs (TWIST1, SOX9) affects regulatory element (RE) chromatin accessibility in facial progenitor cells. High-affinity motifs that allow for heterotypic TF co-binding and are concentrated at the center of REs buffer against quantitative changes in TF dosage and strongly predict unperturbed accessibility. In contrast, motifs with low-affinity or homotypic binding distributed throughout REs lead to sensitive responses with minimal contributions to unperturbed accessibility. Both buffering and sensitizing features show signatures of purifying selection. We validated these predictive sequence features using reporter assays and showed that a biophysical model of TF-nucleosome competition can explain the sensitizing effect of low-affinity motifs. Our approach of combining transfer learning and quantitative measurements of the chromatin response to TF dosage therefore represents a powerful method to reveal additional layers of the cis-regulatory code.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

A computational approach for deciphering the interactions between proximal and distal gene regulators in GC B-cell response.

Delineating the intricate interplay between promoter-proximal and -distal regulators is crucial for understanding the function of transcriptional mediator complexes implicated in the regulation of gene expression. The present study aimed to develop a computational method for accurately modeling the spatial proximal and distal regulatory interactions. Our method combined regression-based models to identify key regulators through gene expression prediction and a graph-embedding approach to detect coregulated genes. This approach enabled a detailed investigation of the gene regulatory mechanisms for germinal center B cells, accompanied by dramatic rearrangements of the genome structure. We found that while the promoter-proximal regulatory elements were the principal regulators of gene expression, the distal regulators fine-tuned transcription. Moreover, our approach unveiled the presence of modular regulators, such as cofactors and proximal/distal transcription factors, which were co-expressed with their target genes. Some of these modules exhibited abnormal expression patterns in lymphoma. These findings suggest that the dysregulation of interactions between transcriptional and architectural factors is associated with chromatin reorganization failure, which may increase the risk of malignancy. Therefore,our computational approachhelps decipherthe transcriptional cis-regulatory code spatially interacting.

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it has been suggested that the genome contains insufficient sequence variation to train models with suitable complexity. Data augmentation is a widely used approach to increase the data variation available for model training, however current data augmentation methods for genomic sequence data are limited. Inspired by the success of comparative genomics, we show that augmenting genomic sequences with evolutionarily related sequences from other species, which we term phylogenetic augmentation, improves the performance of deep learning models trained on regulatory genomic sequences to predict high-throughput functional assay measurements. Additionally, we show that phylogenetic augmentation can rescue model performance when the training set is down-sampled and permits deep learning on a real-world small dataset, demonstrating that this approach improves data efficiency. Overall, this data augmentation method represents a solution for improving model performance that is applicable to many supervised deep-learning problems in genomics. The open-source GitHub repository agduncan94/phylogenetic_augmentation_paper includes the code for rerunning the analyses here and recreating the figures.

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.

Computational analysis on the dissemination of non-B DNA structural motifs in promoter regions of 1180 cellular genomes

The promoter regions of gene regulation are under evolutionary constraints and earlier studies uncovered that they are characterized by enrichment of functional non-B DNA structural signatures like curved DNA, cruciform DNA, G-quadruplex, triple-helical DNA, slipped DNA structures, and Z-DNA. However, these studies are restricted to a few model organisms, single non-B DNA motif types, or whole genomic sequences, and their comparative accumulation in promoter regions of different domains of life has not been reported comprehensively. In this study, for the first time, we investigated the preponderance of non-B DNA-prone motifs in promoter regions in 1180 genomes belonging to 28 taxonomic groups using the non-B DNA Motif Search Tool (nBMST). The trends suggest that they are predominant in promoters compared to the upstream and downstream regions of all three domains of life and variably linked to taxonomic groups. Cruciform DNA motif is the most abundant form of non-B DNA, spanning from archaea to lower eukaryotes. Curved DNA motifs are prominent in host-associated bacteria, and suppressed in mammals. Triplex-DNA and slipped DNA structure repeats are discretely dispersed in all lineages. G-quadruplex motifs are significantly enriched in mammals. We also observed that the unique enrichment of non-B DNA in promoters is strongly linked to genome GC, size, evolutionary time divergence, and ecological adaptations. Overall, our work systematically reports the unique non-B DNA structural landscape of cellular organisms from the perspective of the cis-regulatory code of genomes.

Off the deep end: What can deep learning do for the gene expression field?

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.

Deciphering the impact of genetic variation on human polyadenylation using APARENT2

Background3′-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging.ResultsWe introduce a residual neural network model, APARENT2, that can infer 3′-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2’s performance on several variant datasets, including functional reporter data and human 3′ aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3′ untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of {>}43 million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3′-end and autism spectrum disorder. To experimentally validate APARENT2’s predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells.ConclusionsA sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3′-end mutations and human health.

Convergent evolution of AP2/ERF III and IX subfamilies through recurrent polyploidization and tandem duplication during eudicot adaptation to paleoenvironmental changes

Whole-genome duplication (WGD or polyploidization) has been suggested as a genetic contributor to angiosperm adaptation to environmental changes. However, many eudicot lineages did not undergo recent WGD (R-WGD) around and/or after the Cretaceous-Paleogene (K-Pg) boundary, times of severe environmental changes; how those plants survived has been largely ignored. Here, we collected 22 plants from major branches of the eudicot phylogeny and classified them into two groups according to the occurrence or absence of R-WGD: 12 R-WGD-containing plants (R-WGD-Y) and 10 R-WGD-lacking plants (R-WGD-N). Subsequently, we identified 496 gene-rich families in R-WGD-Y and revealed that members of the AP2/ERF transcription factor family were convergently over-retained after R-WGDs and showed exceptional cold stimulation. The evolutionary trajectories of the AP2/ERF family were then compared between R-WGD-Y and R-WGD-N to reveal convergent expansions of the AP2/ERF III and IX subfamilies through recurrent independent WGDs and tandem duplications (TDs) after the radiation of the plants. The expansions showed coincident enrichments in- times around and/or after the K-Pg boundary, when global cooling was a major environmental stressor. Consequently, convergent expansions and co-retentions of AP2/ERF III C-repeat binding factor (CBF) duplicates and their regulons in different eudicot lineages contributed to the rewiring of cold-specific regulatory networks. Moreover, promoter analysis of cold-responsive AP2/ERF genes revealed an underlying cis-regulatory code (G-box: CACGTG). We propose a seesaw model of WGDs and TDs in the convergent expansion of AP2/ERF III and IX genes that has contributed to eudicot adaptation during paleoenvironmental changes, and we suggest that TD may be a reciprocal/alternative mechanism for genetic innovation in plants that lack WGD.

Hot 'n cold: Applying the cis-regulatory code to predict heat and cold stress response in maize.

Hot 'n cold: Applying the cis-regulatory code to predict heat and cold stress response in maize.

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation.

Transcription factors bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multiomic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep-learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter assay analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach shows the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation.

DNA features beyond the transcription factor binding site specify target recognition by plant MYC2-related bHLH proteins

Transcription factors (TFs) regulate gene expression by binding to cis-regulatory sequences in the promoters of target genes. Recent research is helping to decipher in part the cis-regulatory code in eukaryotes, including plants, but it is not yet fully understood how paralogous TFs select their targets. Here we addressed this question by studying several proteins of the basic helix-loop-helix (bHLH) family of plant TFs, all of which recognize the same DNA motif. We focused on the MYC-related group of bHLHs, that redundantly regulate the jasmonate (JA) signaling pathway, and we observed a high correspondence between DNA-binding profiles in vitro and MYC function in vivo. We demonstrated that A/T-rich modules flanking the MYC-binding motif, conserved from bryophytes to higher plants, are essential for TF recognition. We observed particular DNA-shape features associated with A/T modules, indicating that the DNA shape may contribute to MYC DNA binding. We extended this analysis to 20 additional bHLHs and observed correspondence between in vitro binding and protein function, but it could not be attributed to A/T modules as in MYCs. We conclude that different bHLHs may have their own codes for DNA binding and specific selection of targets that, at least in the case of MYCs, depend on the TF-DNA interplay.

Cis-regulatory code for determining the action of Foxd as both an activator and a repressor in ascidian embryos

In early embryos of Ciona, an invertebrate chordate, the animal-vegetal axis is established by the combinatorial actions of maternal factors. One target of these maternal factors, Foxd, is specifically expressed in the vegetal hemisphere and stabilizes the animal-vegetal axis by activating vegetal hemisphere-specific genes and repressing animal hemisphere-specific genes. This dual functionality is essential for the embryogenesis of early ascidian embryos; however, the mechanism by which Foxd can act as both a repressor and an activator is unknown. Here, we identify a Foxd binding site upstream of Lhx3/4, which is activated by Foxd, and compare it with a repressive Foxd binding site upstream of Dmrt.a. We found that activating sites bind Foxd with low affinity while repressive sites bind Foxd with high affinity. Reporter assays confirm that this qualitative difference between activating and repressive Foxd binding sites is sufficient to change Foxd functionality. We therefore conclude that the outcome of Foxd transcriptional regulation is encoded in cis-regulatory elements.

Base-resolution models of transcription-factor binding reveal soft motif syntax.

The arrangement of transcription factor (TF) binding motifs (syntax) is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution ChIP-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using CRISPR-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

Systemic evolutionary changes in mammalian gene expression

Changes in gene expression play an important role in evolution and can be relevant to evolutionary medicine. In this work, a strong relationship was found between the statistical significance of evolutionary changes in the expression of orthologous genes in the five or six homologous mammalian tissues and the across-tissues unidirectionality of changes (i.e., they occur in the same direction in different tissues -- all upward or all downward). In the area of highly significant changes, the fraction of unidirectionally changed genes (UCG) was above 0.9 (random expectation is 0.03). This observation indicates that the most pronounced evolutionary changes in mammalian gene expression are systemic (i.e., they operate at the whole-organism level). The UCG are strongly enriched in the housekeeping genes. More specifically, in the human-chimpanzee comparison, the UCG are enriched in the pathways belonging to gene expression (translation is prominent), cell cycle control, ubiquitin-dependent protein degradation (mostly related to cell cycle control), apoptosis, and Parkinson's disease. In the human-macaque comparison, the two other neurodegenerative diseases (Alzheimer's and Huntington's) are added to the enriched pathways. The consolidation of gene expression changes at the level of pathways indicates that they are not neutral but functional. The systemic expression changes probably maintain the across-tissues balance of basic physiological processes in the course of evolution (e.g., during the movement along the fast-slow life axis). These results can be useful for understanding the variation in longevity and susceptibility to cancer and widespread neurodegenerative diseases. This approach can also guide the choice of prospective genes for studies aiming to decipher cis-regulatory code (the gene list is provided).

Decoding mRNA translatability and stability from the 5' UTR.

Precise control of protein synthesis by engineering sequence elements in 5' untranslated regions (5' UTRs) remains a fundamental challenge. To accelerate our understanding of the cis-regulatory code embedded in 5' UTRs, we devised massively parallel reporter assays from a synthetic messenger RNA library composed of over one million 5' UTR variants. A completely randomized 10-nucleotide sequence preceding an upstream open reading frame (uORF) and downstream GFP drives a broad range of translational outputs and mRNA stability in mammalian cells. While efficient translation protects mRNA from degradation, uORF translation triggers mRNA decay in a UPF1-dependent manner. We also identified translational inhibitory elements with G-quadruplexes as marks for mRNA decay in P-bodies. Unexpectedly, an unstructured A-rich element in 5' UTRs destabilizes mRNAs in the absence of translation, although it enables cap-independent translation. Our results not only identify diverse sequence features of 5' UTRs that control mRNA translatability, but they also reveal ribosome-dependent and ribosome-independent mRNA-surveillance pathways.

MetaRE R Package for Meta-Analysis of Transcriptome Data to Identify the cis-Regulatory Code behind the Transcriptional Reprogramming.

At the molecular level, response to an external factor or an internal condition causes reprogramming of temporal and spatial transcription. When an organism undergoes physiological and/or morphological changes, several signaling pathways are activated simultaneously. Examples of such complex reactions are the response to temperature changes, dehydration, various biologically active substances, and others. A significant part of the regulatory ensemble in such complex reactions remains unidentified. We developed metaRE, an R package for the systematic search for cis-regulatory elements enriched in the promoters of the genes significantly changed their transcription in a complex reaction. metaRE mines multiple expression profiling datasets generated to test the same organism’s response and identifies simple and composite cis-regulatory elements systematically associated with differential expression of genes. Here, we showed metaRE performance for the identification of low-temperature-responsive cis-regulatory code in Arabidopsis thaliana and Danio rerio. MetaRE identified potential binding sites for known as well as unknown cold response regulators. A notable part of cis-elements was found in both searches discovering great conservation in low-temperature responses between plants and animals.

Putative cis-Regulatory Elements Predict Iron Deficiency Responses in Arabidopsis Roots.

Plant iron deficiency (-Fe) activates a complex regulatory network that coordinates root Fe uptake and distribution to sink tissues. In Arabidopsis (Arabidopsis thaliana), FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT), a basic helix-loop-helix (bHLH) transcription factor (TF), regulates root Fe acquisition genes. Many other -Fe-induced genes are FIT independent, and instead regulated by other bHLH TFs and by yet unknown TFs. The cis-regulatory code, that is, the cis-regulatory elements (CREs) and their combinations that regulate plant -Fe-responses, remains largely elusive. Using Arabidopsis root transcriptome data and coexpression clustering, we identified over 100 putative CREs (pCREs) that predicted -Fe-induced gene expression in computational models. To assess pCRE properties and possible functions, we used large-scale in vitro TF binding data, positional bias, and evolutionary conservation. As one example, our approach uncovered pCREs resembling IDE1 (iron deficiency-responsive element 1), a known grass -Fe response CRE. Arabidopsis IDE1-likes were associated with FIT-dependent gene expression, more specifically with biosynthesis of Fe-chelating compounds. Thus, IDE1 seems to be conserved in grass and nongrass species. Our pCREs matched among others in vitro binding sites of B3, NAC, bZIP, and TCP TFs, which might be regulators of -Fe responses. Altogether, our findings provide a comprehensive source of cis-regulatory information for -Fe-responsive genes that advance our mechanistic understanding and inform future efforts in engineering plants with more efficient Fe uptake or transport systems.