Cell-type-directed design of synthetic enhancers

EIciRNAs in focus: current understanding and future perspectives

Circular RNAs (circRNAs) are a unique class of covalently closed single-stranded RNA molecules that play diverse roles in normal physiology and pathology. Among the major types of circRNA, exon-intron circRNA (EIciRNA) distinguishes itself by its sequence composition and nuclear localization. Recent RNA-seq technologies and computational methods have facilitated the detection and characterization of EIciRNAs, with features like circRNA intron retention (CIR) and tissue-specificity being characterized. EIciRNAs have been identified to exert their functions via mechanisms such as regulating gene transcription, and the physiological relevance of EIciRNAs has been reported. Within this review, we present a summary of the current understanding of EIciRNAs, delving into their identification and molecular functions. Additionally, we emphasize factors regulating EIciRNA biogenesis and the physiological roles of EIciRNAs based on recent research. We also discuss the future challenges in EIciRNA exploration, underscoring the potential for novel functions and functional mechanisms of EIciRNAs for further investigation.

Comparative Perspectives on Neuropeptide Function and Social Isolation

Chronic social isolation alters behavior across animal species. Genetic model organisms such as mice and flies provide crucial insight into the molecular and physiological effects of social isolation on brain cells and circuits. Here, we comparatively review recent findings regarding the function of conserved neuropeptides in social isolation in mice and flies. Analogous functions of 3 classes of neuropeptides-tachykinins, cholecystokinins, and neuropeptide Y/F-in the two model organisms suggest that these molecules may be involved in modulating behavioral changes induced by social isolation across a wider range of species, including humans. Comparative approaches armed with tools to dissect neuropeptidergic function can lead to an integrated understanding of the impacts of social isolation on brain circuits and behavior.

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.

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the 'Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics' to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for identifying functional enhancers in mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian cortex and to designing more efficient genetic tools to target cortical cell types.

From computational models of the splicing code to regulatory mechanisms and therapeutic implications

Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.

Progress in AAV-Mediated In Vivo Gene Therapy and Its Applications in Central Nervous System Diseases

As the blood-brain barrier (BBB) prevents molecules from accessing the central nervous system (CNS), the traditional systemic delivery of chemical drugs limits the development of neurological drugs. However, in recent years, innovative therapeutic strategies have tried to bypass the restriction of traditional drug delivery methods. In vivo gene therapy refers to emerging biopharma vectors that carry the specific genes and target and infect specific tissues; these infected cells and tissues then undergo fundamental changes at the genetic level and produce therapeutic proteins or substances, thus providing therapeutic benefits. Clinical and preclinical trials mainly utilize adeno-associated viruses (AAVs), lentiviruses (LVs), and other viruses as gene vectors for disease investigation. Although LVs have a higher gene-carrying capacity, the vector of choice for many neurological diseases is the AAV vector due to its safety and long-term transgene expression in neurons. Here, we review the basic biology of AAVs and summarize some key issues in recombinant AAV (rAAV) engineering in gene therapy research; then, we summarize recent clinical trials using rAAV treatment for neurological diseases and provide translational perspectives and future challenges on target selection.

Training deep learning models on personalized genomic sequences improves variant effect prediction

Sequence-to-function models have broad applications in interpreting the molecular impact of genetic variation, yet have been criticized for poor performance in this task. Here we show that training models on functional genomic data with matched personal genomes improves their performance at variant effect prediction. Variant effect representations are retained even when fine tuning models to unseen cellular contexts and experimental readouts. Our results have implications for interpreting trait-associated genetic variation.

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors like cell state, histone mark function or distal effects, which impact the relationship, limiting their findings. Moreover, downstream use of these models for new biological insight is lacking. Here, we present the most comprehensive study of this relationship to date - investigating seven histone marks in eleven cell types across a diverse range of cell states. We used convolutional and attention-based models to predict transcription from histone mark activity at promoters and distal regulatory elements. Our work shows that histone mark function, genomic distance and cellular states collectively influence a histone mark's relationship with transcription. We found that no individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight.

Modeling and designing enhancers by introducing and harnessing transcription factor binding units

Enhancers serve as pivotal regulators of gene expression throughout various biological processes by interacting with transcription factors (TFs). While transcription factor binding sites (TFBSs) are widely acknowledged as key determinants of TF binding and enhancer activity, the significant role of their surrounding context sequences remains to be quantitatively characterized. Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. We demonstrate that designing TFBS context sequences can significantly modulate enhancer activities and produce cell type-specific responses. DeepTFBU is also highly efficient in the de novo design of enhancers containing multiple TFBSs. Furthermore, DeepTFBU enables flexible decoupling and optimization of generalized enhancers. We prove that TFBU is a crucial concept, and DeepTFBU is highly effective for rational enhancer design.

Mechanisms of enhancer-driven oncogene activation

An aggressive subtype of acute myeloid leukemia (AML) is caused by enhancer hijacking resulting in MECOM overexpression. Several chromosomal rearrangements can lead to this: the most common (inv(3)/t(3;3)) results in a hijacked GATA2 enhancer, and there are several atypical MECOM rearrangements involving enhancers from other hematopoietic genes. The set of enhancers which can be hijacked by MECOM can also be hijacked by BCL11B. Enhancer deregulation is also a driver of oncogenesis in a range of other malignancies. The mechanisms of enhancer deregulation observed in other cancer types, including TAD boundary disruptions and the creation of de novo (super-) enhancers, may explain overexpression of MECOM or other oncogenes in AML without enhancer hijacking upon translocation. Gaining mechanistic insight in both enhancer deregulation and super-enhancer activity is critical to pave the way for new treatments for AML and other cancers that are the result of enhancer deregulation.

Improving the generalization of protein expression models with mechanistic sequence information

The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences. Moreover, studies have consistently shown that training on mechanistic sequence features leads to much poorer predictions, even with features that are known to correlate with expression, such as DNA sequence motifs, codon usage, or properties of mRNA secondary structures. However, despite their excellent local accuracy, current sequence-to-expression models can fail to generalize predictions far away from the training data. Through a comparative study across datasets in Escherichia coli and Saccharomyces cerevisiae, here we show that mechanistic sequence features can provide gains on model generalization, and thus improve their utility for predictive sequence design. We explore several strategies to integrate one-hot encodings and mechanistic features into a single predictive model, including feature stacking, ensemble model stacking, and geometric stacking, a novel architecture based on graph convolutional neural networks. Our work casts new light on mechanistic sequence features, underscoring the importance of domain-knowledge and feature engineering for accurate prediction of protein expression levels.

Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays. We identified sets of putative regulatory elements with shared TF binding (TF-pRE modules) that orchestrate distinct processes of GABAergic neurogenesis and suppress other cell fates. The majority of pREs were bound by one or two TFs; however, a small proportion were extensively bound. These sequences had exceptional evolutionary conservation and motif density, complex chromosomal interactions, and activity as in vivo enhancers. Our results provide insights into the combinatorial TF-pRE interactions that activate and repress expression programs during telencephalon neurogenesis and demonstrate the value of TF binding toward modeling developmental transcriptional wiring.

Abundant repressor binding sites in human enhancers are associated with the fine-tuning of gene regulation

The regulation of gene expression relies on the coordinated action of transcription factors (TFs) at enhancers, including both activator and repressor TFs. We employed deep learning (DL) to dissect HepG2 enhancers into positive (PAR), negative (NAR), and neutral activity regions. Sharpr-MPRA and STARR-seq highlight the dichotomy impact of NARs and PARs on modulating and catalyzing the activity of enhancers, respectively. Approximately 22% of HepG2 enhancers, termed "repressive impact enhancers" (RIEs), are predominantly populated by NARs and transcriptional repression motifs. Genes flanking RIEs exhibit a stage-specific decline in expression during late development, suggesting RIEs' role in trimming enhancer activities. About 16.7% of human NARs emerge from neutral rhesus macaque DNA. This gain of repressor binding sites in RIEs is associated with a 30% decrease in the average expression of flanking genes in humans compared to rhesus macaque. Our work reveals modulated enhancer activity and adaptable gene regulation through the evolutionary dynamics of TF binding sites.

Active learning of enhancers and silencers in the developing neural retina

Deep learning is a promising strategy for modeling cis-regulatory elements. However, models trained on genomic sequences often fail to explain why the same transcription factor can activate or repress transcription in different contexts. To address this limitation, we developed an active learning approach to train models that distinguish between enhancers and silencers composed of binding sites for the photoreceptor transcription factor cone-rod homeobox (CRX). After training the model on nearly all bound CRX sites from the genome, we coupled synthetic biology with uncertainty sampling to generate additional rounds of informative training data. This allowed us to iteratively train models on data from multiple rounds of massively parallel reporter assays. The ability of the resulting models to discriminate between CRX sites with identical sequence but opposite functions establishes active learning as an effective strategy to train models of regulatory DNA. A record of this paper's transparent peer review process is included in the supplemental information.

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (TF) binding and chromatin accessibility at context-specific cREs remain elusive. We introduce ChromBPNet, a deep learning DNA sequence model of base-resolution accessibility profiles that detects, learns and deconvolves assay-specific enzyme biases from regulatory sequence determinants of accessibility, enabling robust discovery of compact TF motif lexicons, cooperative motif syntax and precision footprints across assays and sequencing depths. Extensive benchmarks show that ChromBPNet, despite its lightweight design, is competitive with much larger contemporary models at predicting variant effects on chromatin accessibility, pioneer TF binding and reporter activity across assays, cell contexts and ancestry, while providing interpretation of disrupted regulatory syntax. ChromBPNet also helps prioritize and interpret regulatory variants that influence complex traits and rare diseases, thereby providing a powerful lens to decode regulatory DNA and genetic variation.

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these enhancer codes and devised three metrics to compare cell types in the telencephalon across amniotes. To this end, we generated single-cell multiome and spatially resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous nonneuronal and γ-aminobutyric acid-mediated (GABAergic) cell types show a high degree of similarity across amniotes, whereas excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types on the basis of genomic regulatory sequences.

The neurobiology of parenting and infant-evoked aggression

Parenting behavior comprises a variety of adult-infant and adult-adult interactions across multiple timescales. The state transition from nonparent to parent requires an extensive reorganization of individual priorities and physiology and is facilitated by combinatorial hormone action on specific cell types that are integrated throughout interconnected and brainwide neuronal circuits. In this review, we take a comprehensive approach to integrate historical and current literature on each of these topics across multiple species, with a focus on rodents. New and emerging molecular, circuit-based, and computational technologies have recently been used to address outstanding gaps in our current framework of knowledge on infant-directed behavior. This work is raising fundamental questions about the interplay between instinctive and learned components of parenting and the mutual regulation of affiliative versus agonistic infant-directed behaviors in health and disease. Whenever possible, we point to how these technologies have helped gain novel insights and opened new avenues of research into the neurobiology of parenting. We hope this review will serve as an introduction for those new to the field, a comprehensive resource for those already studying parenting, and a guidepost for designing future studies.

Predicting genome-wide tissue-specific enhancers via combinatorial transcription factor genomic occupancy analysis

Enhancers are non-coding cis-regulatory elements crucial for transcriptional regulation. Mutations in enhancers can disrupt gene regulation, leading to disease phenotypes. Identifying enhancers and their tissue-specific activity is challenging due to their lack of stereotyped sequences. This study presents a sequence-based computational model that uses combinatorial transcription factor (TF) genomic occupancy to predict tissue-specific enhancers. Trained on diverse datasets, including ENCODE and Vista enhancer browser data, the model predicted 25 000 forebrain-specific cis-regulatory modules (CRMs) in the human genome. Validation using biochemical features, disease-associated SNPs, and in vivo zebrafish analysis confirmed its effectiveness. This model aids in predicting enhancers lacking well-characterized chromatin features, complementing experimental approaches in tissue-specific enhancer discovery.

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Crosstalk between paralogs and isoforms influences p63-dependent regulatory element activity

The p53 family of transcription factors (p53, p63 and p73) regulate diverse organismal processes including tumor suppression, maintenance of genome integrity and the development of skin and limbs. Crosstalk between transcription factors with highly similar DNA binding profiles, like those in the p53 family, can dramatically alter gene regulation. While p53 is primarily associated with transcriptional activation, p63 mediates both activation and repression. The specific mechanisms controlling p63-dependent gene regulatory activity are not well understood. Here, we use massively parallel reporter assays (MPRA) to investigate how local DNA sequence context influences p63-dependent transcriptional activity. Most regulatory elements with a p63 response element motif (p63RE) activate transcription, although binding of the p63 paralog, p53, drives a substantial proportion of that activity. p63RE sequence content and co-enrichment with other known activating and repressing transcription factors, including lineage-specific factors, correlates with differential p63RE-mediated activities. p63 isoforms dramatically alter transcriptional behavior, primarily shifting inactive regulatory elements towards high p63-dependent activity. Our analysis provides novel insight into how local sequence and cellular context influences p63-dependent behaviors and highlights the key, yet still understudied, role of transcription factor paralogs and isoforms in controlling gene regulatory element activity.

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements.

Uncertainty-aware genomic deep learning with knowledge distillation

Deep neural networks (DNNs) have advanced predictive modeling for regulatory genomics, but challenges remain in ensuring the reliability of their predictions and understanding the key factors behind their decision making. Here we introduce DEGU (Distilling Ensembles for Genomic Uncertainty-aware models), a method that integrates ensemble learning and knowledge distillation to improve the robustness and explainability of DNN predictions. DEGU distills the predictions of an ensemble of DNNs into a single model, capturing both the average of the ensemble's predictions and the variability across them, with the latter representing epistemic (or model-based) uncertainty. DEGU also includes an optional auxiliary task to estimate aleatoric, or data-based, uncertainty by modeling variability across experimental replicates. By applying DEGU across various functional genomic prediction tasks, we demonstrate that DEGU-trained models inherit the performance benefits of ensembles in a single model, with improved generalization to out-of-distribution sequences and more consistent explanations of cis-regulatory mechanisms through attribution analysis. Moreover, DEGU-trained models provide calibrated uncertainty estimates, with conformal prediction offering coverage guarantees under minimal assumptions. Overall, DEGU paves the way for robust and trustworthy applications of deep learning in genomics research.

Quantitative design of cell type-specific mRNA stability from microRNA expression data

Limiting expression to target cell types is a longstanding goal in gene therapy, which could be met by sensing endogenous microRNA. However, an unclear association between microRNA expression and activity currently hampers such an approach. Here, we probe this relationship by measuring the stability of synthetic microRNA-responsive 3'UTRs across 10 cell lines in a library format. By systematically addressing biases in microRNA expression data and confounding factors such as microRNA crosstalk, we demonstrate that a straightforward model can quantitatively predict reporter stability purely from expression data. We use this model to design constructs with previously unattainable response patterns across our cell lines. The rules we derive for microRNA expression data selection and processing should apply to microRNA- responsive devices for any environment with available expression data.

Decoding biology with massively parallel reporter assays and machine learning

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

Designing realistic regulatory DNA with autoregressive language models

Cis-regulatory elements (CREs), such as promoters and enhancers, are DNA sequences that regulate the expression of genes. The activity of a CRE is influenced by the order, composition, and spacing of sequence motifs that are bound by proteins called transcription factors (TFs). Synthetic CREs with specific properties are needed for biomanufacturing as well as for many therapeutic applications including cell and gene therapy. Here, we present regLM, a framework to design synthetic CREs with desired properties, such as high, low, or cell type-specific activity, using autoregressive language models in conjunction with supervised sequence-to-function models. We used our framework to design synthetic yeast promoters and cell type-specific human enhancers. We demonstrate that the synthetic CREs generated by our approach are not only predicted to have the desired functionality but also contain biological features similar to experimentally validated CREs. regLM thus facilitates the design of realistic regulatory DNA elements while providing insights into the cis-regulatory code.

Model-directed generation of artificial CRISPR-Cas13a guide RNA sequences improves nucleic acid detection

CRISPR guide RNA sequences deriving exactly from natural sequences may not perform optimally in every application. Here we implement and evaluate algorithms for designing maximally fit, artificial CRISPR-Cas13a guides with multiple mismatches to natural sequences that are tailored for diagnostic applications. These guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared with guides derived directly from natural sequences and illuminate design principles that broaden Cas13a targeting.

Machine-guided design of cell-type-targeting cis-regulatory elements

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing and stimulus responses, which collectively define the thousands of unique cell types in the body1-3. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell-type specificity. We take advantage of innovations in deep neural network modelling of CRE activity across three cell types, efficient in silico optimization and massively parallel reporter assays to design and empirically test thousands of CREs4-8. Through large-scale in vitro validation, we show that synthetic sequences are more effective at driving cell-type-specific expression in three cell lines compared with natural sequences from the human genome and achieve specificity in analogous tissues when tested in vivo. Synthetic sequences exhibit distinct motif vocabulary associated with activity in the on-target cell type and a simultaneous reduction in the activity of off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from massively parallel reporter assay models and demonstrate the required literacy to write fit-for-purpose regulatory code.

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We used transcriptomic and epigenomic cortical cell type taxonomies from mouse and human to define marker genes and putative enhancers and created a large toolkit of transgenic lines and enhancer AAVs for selective targeting of cortical cell populations. We report evaluation of fifteen new transgenic driver lines, two new reporter lines, and >800 different enhancer AAVs covering most subclasses of cortical cells. The tools reported here as well as the scaled process of tool creation and modification enable diverse experimental strategies towards understanding mammalian cortex and brain function.

Oncogenic Enhancers in Leukemia

Although the study of leukemogenesis has traditionally focused on protein-coding genes, the role of enhancer dysregulation is becoming increasingly recognized. The advent of high-throughput sequencing, together with a better understanding of enhancer biology, has revealed how various genetic and epigenetic lesions produce oncogenic enhancers that drive transformation. These aberrations include translocations that lead to enhancer hijacking, point mutations that modulate enhancer activity, and copy number alterations that modify enhancer dosage. In this review, we describe these mechanisms in the context of leukemia and discuss potential therapeutic avenues to target these regulatory elements. Significance: Large-scale sequencing projects have uncovered recurrent gene mutations in leukemia, but the picture remains incomplete: some patients harbor no such aberrations, whereas others carry only a few that are insufficient to bring about transformation on their own. One of the missing pieces is enhancer dysfunction, which only recently has emerged as a critical driver of leukemogenesis. Knowledge of the various mechanisms of enhancer dysregulation is thus key for a complete understanding of leukemia and its causes, as well as the development of targeted therapies in the era of precision medicine.

W2V-repeated index: Prediction of enhancers and their strength based on repeated fragments

Enhancers are crucial in gene expression regulation, dictating the specificity and timing of transcriptional activity, which highlights the importance of their identification for unravelling the intricacies of genetic regulation. Therefore, it is critical to identify enhancers and their strengths. Repeated sequences in the genome are repeats of the same or symmetrical fragments. There has been a great deal of evidence that repetitive sequences contain enormous amounts of genetic information. Thus, We introduce the W2V-Repeated Index, designed to identify enhancer sequence fragments and evaluates their strength through the analysis of repeated K-mer sequences in enhancer regions. Utilizing the word2vector algorithm for numerical conversion and Manta Ray Foraging Optimization for feature selection, this method effectively captures the frequency and distribution of K-mer sequences. By concentrating on repeated K-mer sequences, it minimizes computational complexity and facilitates the analysis of larger K values. Experiments indicate that our method performs better than all other advanced methods on almost all indicators.

Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesis

The interplay between transcription factors and chromatin accessibility regulates cell type diversification during vertebrate embryogenesis. To systematically decipher the gene regulatory logic guiding this process, we generated a single-cell multi-omics atlas of RNA expression and chromatin accessibility during early zebrafish embryogenesis. We developed a deep learning model to predict chromatin accessibility based on DNA sequence and found that a small number of transcription factors underlie cell-type-specific chromatin landscapes. While Nanog is well-established in promoting pluripotency, we discovered a new function in priming the enhancer accessibility of mesendodermal genes. In addition to the classical stepwise mode of differentiation, we describe instant differentiation, where pluripotent cells skip intermediate fate transitions and terminally differentiate. Reconstruction of gene regulatory interactions reveals that this process is driven by a shallow network in which maternally deposited regulators activate a small set of transcription factors that co-regulate hundreds of differentiation genes. Notably, misexpression of these transcription factors in pluripotent cells is sufficient to ectopically activate their targets. This study provides a rich resource for analyzing embryonic gene regulation and reveals the regulatory logic of instant differentiation.

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 42 CPA 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.

Identification, Design, and Application of Noncoding Cis-Regulatory Elements

Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.

Symbolic recording of signalling and cis-regulatory element activity to DNA

Measurements of gene expression or signal transduction activity are conventionally performed using methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm in which such biological activities are stably recorded to the genome. Enhancer-driven genomic recording of transcriptional activity in multiplex (ENGRAM) is based on the signal-dependent production of prime editing guide RNAs that mediate the insertion of signal-specific barcodes (symbols) into a genomically encoded recording unit. We show how this strategy can be used for multiplex recording of the cell-type-specific activities of dozens to hundreds of cis-regulatory elements with high fidelity, sensitivity and reproducibility. Leveraging signal transduction pathway-responsive cis-regulatory elements, we also demonstrate time- and concentration-dependent genomic recording of WNT, NF-κB and Tet-On activities. By coupling ENGRAM to sequential genome editing via DNA Typewriter1, we stably record information about the temporal dynamics of two orthogonal signalling pathways to genomic DNA. Finally we apply ENGRAM to integratively record the transient activity of nearly 100 transcription factor consensus motifs across daily windows spanning the differentiation of mouse embryonic stem cells into gastruloids, an in vitro model of early mammalian development. Although these are proof-of-concept experiments and much work remains to fully realize the possibilities, the symbolic recording of biological signals or states within cells, to the genome and over time, has broad potential to complement contemporary paradigms for how we make measurements in biological systems.

Rewiring gene circuitry for plant improvement

Aspirations for high crop growth and yield, nutritional quality and bioproduction of materials are challenged by climate change and limited adoption of new technologies. Here, we review recent advances in approaches to profile and model gene regulatory activity over developmental and response time in specific cells, which have revealed the basis of variation in plant phenotypes: both redeployment of key regulators to new contexts and their repurposing to control different slates of genes. New synthetic biology tools allow tunable, spatiotemporal regulation of transgenes, while recent gene-editing technologies enable manipulation of the regulation of native genes. Ultimately, understanding how gene circuitry is wired to control form and function across varied plant species, combined with advanced technology to rewire that circuitry, will unlock solutions to our greatest challenges in agriculture, energy and the environment.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

The evolution of developmental biology through conceptual and technological revolutions

Developmental biology-the study of the processes by which cells, tissues, and organisms develop and change over time-has entered a new golden age. After the molecular genetics revolution in the 80s and 90s and the diversification of the field in the early 21st century, we have entered a phase when powerful technologies provide new approaches and open unexplored avenues. Progress in the field has been accelerated by advances in genomics, imaging, engineering, and computational biology and by emerging model systems ranging from tardigrades to organoids. We summarize how revolutionary technologies have led to remarkable progress in understanding animal development. We describe how classic questions in gene regulation, pattern formation, morphogenesis, organogenesis, and stem cell biology are being revisited. We discuss the connections of development with evolution, self-organization, metabolism, time, and ecology. We speculate how developmental biology might evolve in an era of synthetic biology, artificial intelligence, and human engineering.

Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity

An important and largely unsolved problem in synthetic biology is how to target gene expression to specific cell types. Here, we apply iterative deep learning to design synthetic enhancers with strong differential activity between two human cell lines. We initially train models on published datasets of enhancer activity and chromatin accessibility and use them to guide the design of synthetic enhancers that maximize predicted specificity. We experimentally validate these sequences, use the measurements to re-optimize the predictor, and design a second generation of enhancers with improved specificity. Our design methods embed relevant transcription factor binding site (TFBS) motifs with higher frequencies than comparable endogenous enhancers while using a more selective motif vocabulary, and we show that enhancer activity is correlated with transcription factor expression at the single cell level. Finally, we characterize causal features of top enhancers via perturbation experiments and show enhancers as short as 50bp can maintain specificity.

Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters

The inability to scalably and precisely measure the activity of developmental cis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.

Context dependent activity of p63-bound gene regulatory elements

The p53 family of transcription factors regulate numerous organismal processes including the development of skin and limbs, ciliogenesis, and preservation of genetic integrity and tumor suppression. p53 family members control these processes and gene expression networks through engagement with DNA sequences within gene regulatory elements. Whereas p53 binding to its cognate recognition sequence is strongly associated with transcriptional activation, p63 can mediate both activation and repression. How the DNA sequence of p63-bound gene regulatory elements is linked to these varied activities is not yet understood. Here, we use massively parallel reporter assays (MPRA) in a range of cellular and genetic contexts to investigate the influence of DNA sequence on p63-mediated transcription. Most regulatory elements with a p63 response element motif (p63RE) activate transcription, with those sites bound by p63 more frequently or adhering closer to canonical p53 family response element sequences driving higher transcriptional output. The most active regulatory elements are those also capable of binding p53. Elements uniquely bound by p63 have varied activity, with p63RE-mediated repression associated with lower overall GC content in flanking sequences. Comparison of activity across cell lines suggests differential activity of elements may be regulated by a combination of p63 abundance or context-specific cofactors. Finally, changes in p63 isoform expression dramatically alters regulatory element activity, primarily shifting inactive elements towards a strong p63-dependent activity. Our analysis of p63-bound gene regulatory elements provides new insight into how sequence, cellular context, and other transcription factors influence p63-dependent transcription. These studies provide a framework for understanding how p63 genomic binding locally regulates transcription. Additionally, these results can be extended to investigate the influence of sequence content, genomic context, chromatin structure on the interplay between p63 isoforms and p53 family paralogs.

Overlapping binding sites underlie TF genomic occupancy

Sequence-specific DNA binding by transcription factors (TFs) is a crucial step in gene regulation. However, current high-throughput in vitro approaches cannot reliably detect lower affinity TF-DNA interactions, which play key roles in gene regulation. Here, we developed PADIT-seq ( p rotein a ffinity to D NA by in vitro transcription and RNA seq uencing) to assay TF binding preferences to all 10-bp DNA sequences at far greater sensitivity than prior approaches. The expanded catalogs of low affinity DNA binding sites for the human TFs HOXD13 and EGR1 revealed that nucleotides flanking high affinity DNA binding sites create overlapping lower affinity sites that together modulate TF genomic occupancy in vivo . Formation of such extended recognition sequences stems from an inherent property of TF binding sites to interweave each other and expands the genomic sequence space for identifying noncoding variants that directly alter TF binding.

One-Sentence Summary

Overlapping DNA binding sites underlie TF genomic occupancy through their inherent propensity to interweave each other.

Logical design of synthetic cis-regulatory DNA for genetic tracing of cell identities and state changes

Descriptive data are rapidly expanding in biomedical research. Instead, functional validation methods with sufficient complexity remain underdeveloped. Transcriptional reporters allow experimental characterization and manipulation of developmental and disease cell states, but their design lacks flexibility. Here, we report logical design of synthetic cis-regulatory DNA (LSD), a computational framework leveraging phenotypic biomarkers and trans-regulatory networks as input to design reporters marking the activity of selected cellular states and pathways. LSD uses bulk or single-cell biomarkers and a reference genome or custom cis-regulatory DNA datasets with user-defined boundary regions. By benchmarking validated reporters, we integrate LSD with a computational ranking of phenotypic specificity of putative cis-regulatory DNA. Experimentally, LSD-designed reporters targeting a wide range of cell states are functional without minimal promoters. Applied to broadly expressed genes from human and mouse tissues, LSD generates functional housekeeper-like sLCRs compatible with size constraints of AAV vectors for gene therapy applications. A mesenchymal glioblastoma reporter designed by LSD outperforms previously validated ones and canonical cell surface markers. In genome-scale CRISPRa screens, LSD facilitates the discovery of known and novel bona fide cell-state drivers. Thus, LSD captures core principles of cis-regulation and is broadly applicable to studying complex cell states and mechanisms of transcriptional regulation.

DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements

The challenge of systematically modifying and optimizing regulatory elements for precise gene expression control is central to modern genomics and synthetic biology. Advancements in generative AI have paved the way for designing synthetic sequences with the aim of safely and accurately modulating gene expression. We leverage diffusion models to design context-specific DNA regulatory sequences, which hold significant potential toward enabling novel therapeutic applications requiring precise modulation of gene expression. Our framework uses a cell type-specific diffusion model to generate synthetic 200 bp regulatory elements based on chromatin accessibility across different cell types. We evaluate the generated sequences based on key metrics to ensure they retain properties of endogenous sequences: transcription factor binding site composition, potential for cell type-specific chromatin accessibility, and capacity for sequences generated by DNA diffusion to activate gene expression in different cell contexts using state-of-the-art prediction models. Our results demonstrate the ability to robustly generate DNA sequences with cell type-specific regulatory potential. DNA-Diffusion paves the way for revolutionizing a regulatory modulation approach to mammalian synthetic biology and precision gene therapy.

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo

Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.

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.

OnTarget: in silico design of MiniPromoters for targeted delivery of expression

MiniPromoters, or compact promoters, are short DNA sequences that can drive expression in specific cells and tissues. While broadly useful, they are of high relevance to gene therapy due to their role in enabling precise control of where a therapeutic gene will be expressed. Here, we present OnTarget (http://ontarget.cmmt.ubc.ca), a webserver that streamlines the MiniPromoter design process. Users only need to specify a gene of interest or custom genomic coordinates on which to focus the identification of promoters and enhancers, and can also provide relevant cell-type-specific genomic evidence (e.g. accessible chromatin regions, histone modifications, etc.). OnTarget combines the provided data with internal data to identify candidate promoters and enhancers and design MiniPromoters. To illustrate the utility of OnTarget, we designed and characterized two MiniPromoters targeting different cell populations relevant to Parkinson Disease.