Biology subjects

Bintu, L.

Publications and source records attributed to Bintu, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens

Noncoding regulatory elements in the human genome can control gene expression through distinct mechanisms, including acting as enhancers, silencers, or topological elements. However, we lack tools to identify all such classes systematically and quantify their effects on gene expression in an endogenous genomic context. Here we develop Swap-seq, a method that combines pooled twin prime editing with flow-sorting to delete hundreds of endogenous regulatory sequences and measure their quantitative effects on the expression of a nearby gene. We apply Swap-seq to dissect the regulatory elements that control PPIF expression in THP-1 monocytes. We quantify the effects of distal and intronic enhancers, characterize CTCF insulators, and discover a silencer that regulates distal genes through histone deacetylase (HDAC)-mediated repression of neighboring enhancers. Hundreds of other genomic elements share its chromatin signatures and transcription factor binding sites, suggesting that this silencer represents a broader class of repressive elements. We leverage Swap-seq data to benchmark sequence-to-function models and show that they often capture the local activity of regulatory elements but fail to link them to distal target genes. Swap-seq thus provides a generalizable tool to discover regulatory elements across mechanistic classes, characterize their effects on gene expression, and generate large-scale datasets for evaluating sequence-to-function models.

genomics↗

bioRxiv2026

In vivo intercellular CRISPR screens using viral proximity barcoding reveal regulators of tumor-immune interactions

Cell-cell interactions shape tumor growth, immune evasion, and therapeutic response, but systematically dissecting their genetic regulators in vivo remains challenging. Existing cell-cell interaction tracing technologies rely largely on protein labels or enzymatic reactions, limiting their information content and scalability. To overcome these limitations, we developed match-seq, an imaging-free, sequencing-based cell proximity tracing system that uses virus-like particles to transmit barcodes between neighboring cells. Barcoded mRNAs are transmitted from sender cells to nearby receiver cells, thereby establishing a spatial linkage that can be computationally reconstructed by barcode sequencing after tissue dissociation. We apply this system to an in vivo syngeneic murine tumor model, where we observe robust labeling of all immune cell lineages, and leverage barcode labeling to reconstruct cell type niches that recapitulate known tumor spatial biology. Furthermore, we couple this system with CRISPR perturbations and single-cell RNA sequencing to perform genetic screens in vivo on tumor-immune interactions at single-cell resolution. We use this method to infer cell-cell spatial relationships and uncover genetic dependencies in cancer cells that change the composition and cell state of their local microenvironments. By calculating a local immune activation signature, we are able to prioritize targets whose deletion enhances anti-tumor immunity through distinct effector cell types: loss of Tgfb1 engages CD8 T cells and macrophages, Traf7 loss elicits a CD4 T cell response, and Nectin3 loss promotes NK cell-mediated immunity, relationships that we validate by in vivo immune cell depletion. Notably, Tgfb1 and Nectin3 deletion had little effect on cancer cell fitness in the pooled screen yet suppressed tumor growth when deleted throughout the tumor, demonstrating the potential of match-seq to reveal functionally important tumor-immune interactions that would otherwise remain hidden. Together, these results establish a general framework for pooled genetic dissection of cell-cell interactions in vivo, extending CRISPR screening from cell-intrinsic phenotypes to the mechanisms by which cells shape and respond to their local microenvironments.

cancer biology↗

bioRxiv2026

Stable epigenetic states set single-cell activation thresholds in mammalian expression systems

Quantitatively relating transcription factor (TF) input to gene expression output is central to understanding mammalian gene regulation and essential for designing predictable synthetic expression systems. However, even minimal synthetic systems often exhibit unexplained behaviors. In a widely used inducible mammalian expression system, we show that transcriptional responses appear graded and sigmoidal at the population level but are largely all-or-none at the single-cell level. By combining single-cell sorting and single-molecule footprinting with mathematical modeling of transcriptional regulation, we found that this behavior is not caused by bursty transcription or bistability, but by long-lived, chromatin-encoded variability in TF occupancy and activation strength. This variability produced a range of activation thresholds in switch-like single-cell responses that were stable over time, resulting in bimodal gene expression across the population. These results advance our basic understanding of how TFs interact with chromatin to modulate quantitative features of single-cell and population level transcriptional responses.

systems biology↗

bioRxiv2026

A microproteome screen identifies immunomodulatory bacterial microproteins encoded in expanded gene arrays in Leptotrichia

The human microbiome exerts broad influence in health and disease with associative studies implicating the microbiome in influencing immunity, cancer outcomes, and neurodegeneration. However, the molecular mediators of microbe-host communication remain poorly defined. Bacterial microproteins from the microbiome represent a largely uncharacterized class of potential regulators of host immunity. Here, we utilize functional genomics to interrogate 3,552 microproteins in order to identify novel microbial-immune interactions. We constructed a microproteome library from microbial metagenomic datasets, expressed it in macrophages and assayed for immunomodulatory activity. We identify several bacterial microproteins that drive macrophage M1 polarization. Among the strongest hits are a cluster of structurally related microproteins from Leptotrichia species, which are oral Gram-negative commensals associated with differential cancer outcomes. Genomic analysis reveals that Leptotrichia species encode these putative immunomodulatory microproteins in tandem arrays of up to 44 copies. These genes encode microproteins with varying sequences but conserved predicted structures. In an orthogonal approach, we demonstrate that bacterial expression of Leptotrichia microproteins influences macrophage cell state and function. As a whole, our findings identify novel microbial microproteins with immunomodulatory activity and provide a framework for future discovery of host-microbe interactions that influence human health.

microbiology↗

bioRxiv2026

Structural probing of RNA hairpins quantifies protein occupancy on RNA and links it to function in human cells

RNA structures that form inside living cells influence processes ranging from translation to RNA decay, many of which are controlled by RNA-binding proteins (RBPs). Because RBP specificity depends on both local RNA structure and sequence motifs, traditional pulldown-based methods often obscure the structural context of bound RNAs. Here, a quantitative framework based on dimethyl sulfate mutational profiling and sequencing (DMS-MaPseq) is introduced that jointly measures RNA structure and protein binding at single-nucleotide resolution in human cells, enabling estimation of effective RNA-protein affinities and fractional occupancy directly in cells. Application of this approach to a 1,600-member library of MS2 hairpin mutants reveals that RNA folding is the strongest determinant of MS2 coat protein (MCP) recognition, and that stable MS2 structures must form for MCP to bind its target sequence. MCP also shows strong preference for its consensus loop sequence while displaying minimal dependence on stem length beyond ten base pairs or on stem GC content. Incorporation of an inducible degron fused to MCP allows precise tuning of intracellular protein concentrations analogous to those of many endogenous RBPs and show that DMS reactivity changes can be used to infer binding specificities across a subsaturating regime. A quantitative occupancy framework further shows that inferred fraction-bound values accurately predict how efficiently MCP fused to a downregulatory-domain drives RNA degradation. Together, these results establish a generalizable approach for measuring RBP-RNA affinities with structural resolution in living cells, dissecting how sequence and structure contribute to RBP recognition, and quantitatively linking occupancy to functional output.

biochemistry↗

bioRxiv2025

Bifunctional transcriptional effector domains control gene expression pulses in an occupancy-dependent manner

Dynamic gene expression pulses enable adaptive response to stimuli and can be generated in natural and synthetic systems. Controlling these dynamics typically involves circuits consisting of multiple genes and transcription factors (TFs). Here, we discover a new class of bifunctional transcriptional effector domains that can first activate and subsequently repress the same gene, producing dynamic gene expression pulses from a single input. These pulse dynamics arise from distinct, temporally separated chromatin states defined by active and repressive chromatin modifications. The balance between active and repressed states is determined by the DNA occupancy of the bifunctional TF. Bifunctional domains activate at low occupancy but switch to repression at high occupancy, resulting in a non-monotonic TF input-gene expression output relationship tunable by TF concentration and number of DNA binding sites. We develop a kinetic model that links TF occupancy to gene expression transitions, allowing for the programming of eight distinct cell "states" - combinations of On/Off states of 3 reporter genes - using a single bifunctional effector. This work establishes the theoretical framework and molecular mechanisms of pulse-generating gene regulation by bifunctional domains and creates a foundation for engineering complex multi-gene circuits.

systems biology↗

bioRxiv2025

Machine-Guided Dual-Objective Protein Engineering for Deimmunization and Therapeutic Functions

Cell and gene therapies often rely on the expression of exogenous proteins derived from nonhuman organisms. An emerging consensus is to reduce the potential immunogenicity of such therapies by instead using human protein domains. However, as we engineer these human-derived proteins, we create nonhuman peptides at the linkers or junctions between domains and at mutated residues within them, which still pose a risk of immunogenicity that has largely been left unaddressed. Here, we present a modular workflow to simultaneously optimize the functions of proteins and minimize their immunogenic risk using existing machine learning models that predict protein function and nonhuman peptide immunogenicity from their sequences. We first applied this workflow to existing transcriptional activation and bio-orthogonal RNA binding domains. Then we generated a set of small molecule-controllable transcription factors with human-derived zinc finger DNA-binding domains for targeting orthogonal non-genomic DNA sequences. Finally, we established a workflow for creating deimmunized zinc finger arrays to target arbitrary genomic DNA sequences and used it to upregulate expression of two therapeutically relevant genes, UTRN and SCN1A. Our future-proof, modular workflow offers a proof of principle for making cell and gene therapies safer and more efficacious through dual-objective protein optimization using state-of-the-art algorithms.

bioengineering↗

bioRxiv2024

Tunable, self-contained gene dosage control via proteolytic cleavage of CRISPR-Cas systems

The ability to modulate gene expression through modular and universal genetic tools like CRISPR-Cas has greatly advanced gene therapy for therapeutics and basic science. Yet, the inherent stochasticity of delivery methods cause variation in target gene expression at the single-cell level, limiting their applicability in systems that require more precise expression. Thus, we implement a modular incoherent feedforward loop based on proteolytic cleavage of Cas to reduce gene expression variability against the variability of vector delivery. We target a genome-integrated marker and demonstrate dosage control of gene activation and repression, post-delivery tuning, and RNA-based compatibility of the system. To illustrate therapeutic relevance, we target the gene RAI1, the haploinsufficiency and triplosensitivity of which cause two autism-related syndromes. We demonstrate dosage-controlled gene activation for both human and mouse Rai1 via viral delivery to patient-derived cell lines and mouse cortical neurons. Overall, we established a robust dosage control circuit for uniform gene expression, beneficial for basic and translational research.

synthetic biology↗

bioRxiv2024

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet (Transcriptional Effector Network) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

genomics↗

bioRxiv2024

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 Kds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

biophysics↗

bioRxiv2024

High-throughput discovery of regulatory effector domains in human RNA-binding proteins

RNA regulation plays an integral role in tuning gene expression and is controlled by thousands of RNA-binding proteins (RBPs). We develop and use a high-throughput recruitment assay (HT-RNA-Recruit) to identify regulatory domains within human RBPs by recruiting over 30,000 protein tiles from 367 RBPs to a reporter mRNA. We discover over 100 unique RNA-regulatory effectors in 86 distinct RBPs, presenting evidence that RBPs contain functionally separable domains that dictate their post-transcriptional control of gene expression, and identify some with unique activity at 5 or 3UTRs. We identify some domains that downregulate gene expression both when recruited to DNA and RNA, and dissect their mechanisms of regulation. Finally, we build a synthetic RNA regulator that can stably maintain gene expression at desired levels that are predictable by a mathematical model. This work serves as a resource for human RNA-regulatory effectors and expands the synthetic repertoire of RNA-based genetic control tools. Highlights- HT-RNA-Recruit identifies hundreds of RNA-regulatory effectors in human proteins. - Recruitment to 5 and 3 UTRs identifies regulatory domains unique to each position. - Some protein domains have both transcriptional and post-transcriptional regulatory activity. - We develop a synthetic RNA regulator and a mathematical model to describe its behavior.

synthetic biology↗

bioRxiv2024

Single-molecule chromatin configurations link transcription factor binding to expression in human cells

The binding of multiple transcription factors (TFs) to genomic enhancers activates gene expression in mammalian cells. However, the molecular details that link enhancer sequence to TF binding, promoter state, and gene expression levels remain opaque. We applied single-molecule footprinting (SMF) to measure the simultaneous occupancy of TFs, nucleosomes, and components of the transcription machinery on engineered enhancer/promoter constructs with variable numbers of TF binding sites for both a synthetic and an endogenous TF. We find that activation domains enhance a TFs capacity to compete with nucleosomes for binding to DNA in a BAF-dependent manner, TF binding on nucleosome-free DNA is consistent with independent binding between TFs, and average TF occupancy linearly contributes to promoter activation rates. We also decompose TF strength into separable binding and activation terms, which can be tuned and perturbed independently. Finally, we develop thermodynamic and kinetic models that quantitatively predict both the binding microstates observed at the enhancer and subsequent time-dependent gene expression. This work provides a template for quantitative dissection of distinct contributors to gene activation, including the activity of chromatin remodelers, TF activation domains, chromatin acetylation, TF concentration, TF binding affinity, and TF binding site configuration.

molecular biology↗

bioRxiv2023

The H3.3 K36M oncohistone disrupts the establishment of epigenetic memory through loss of DNA methylation

Histone H3.3 is frequently mutated in cancers, with the lysine 36 to methionine mutation (K36M) being a hallmark of chondroblastomas. While it is known that H3.3K36M changes the cellular epigenetic landscape, it remains unclear how it affects the dynamics of gene expression. Here, we use a synthetic reporter to measure the effect of H3.3K36M on silencing and epigenetic memory after recruitment of KRAB: a member of the largest class of human repressors, commonly used in synthetic biology, and associated with H3K9me3. We find that H3.3K36M, which decreases H3K36 methylation, leads to a decrease in epigenetic memory and promoter methylation weeks after KRAB release. We propose a new model for establishment and maintenance of epigenetic memory, where H3K36 methylation is necessary to convert H3K9me3 domains into DNA methylation for stable epigenetic memory. Our quantitative model can inform oncogenic mechanisms and guide development of epigenetic editing tools.

cell biology↗

bioRxiv2023

Single-cell chromatin state transitions during epigenetic memory formation

Repressive chromatin modifications are thought to compact chromatin to silence transcription. However, it is unclear how chromatin structure changes during silencing and epigenetic memory formation. We measured gene expression and chromatin structure in single cells after recruitment and release of repressors at a reporter gene. Chromatin structure is heterogeneous, with open and compact conformations present in both active and silent states. Recruitment of repressors associated with epigenetic memory produces chromatin compaction across 10-20 kilobases, while reversible silencing does not cause compaction at this scale. Chromatin compaction is inherited, but changes molecularly over time from histone methylation (H3K9me3) to DNA methylation. The level of compaction at the end of silencing quantitatively predicts epigenetic memory weeks later. Similarly, chromatin compaction at the Nanog locus predicts the degree of stem-cell fate commitment. These findings suggest that the chromatin state across tens of kilobases, beyond the gene itself, is important for epigenetic memory formation.

genetics↗

bioRxiv2023

Development of compact transcriptional effectors using high-throughput measurements in diverse contexts

Human nuclear proteins contain >1000 transcriptional effector domains that can activate or repress transcription of target genes. We lack a systematic understanding of which effector domains regulate transcription robustly across genomic, cell-type, and DNA-binding domain (DBD) contexts. Here, we developed dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous targets, and tested effector function for a library containing 5092 nuclear protein Pfam domains across varied contexts. We find many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. We then confirm these findings and further map context dependencies of effectors drawn from unannotated protein regions using a larger library containing 114,288 sequences tiling chromatin regulators and transcription factors. To enable efficient perturbations, we select effectors that are potent in diverse contexts, and engineer (1) improved ZNF705 KRAB CRISPRi tools to silence promoters and enhancers, and (2) a compact human activator combination NFZ for better CRISPRa and inducible circuit delivery. Together, this effector-by-context functional map reveals context-dependence across human effectors and guides effector selection for robustly manipulating transcription.

systems biology↗

bioRxiv2022

High-throughput functional characterization of combinations of transcriptional activators and repressors

Despite growing knowledge of the functions of individual human transcriptional effector domains, much less is understood about how multiple effector domains within the same protein combine to regulate gene expression. Here, we measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. In our assay, weak and moderate activation domains synergize to drive strong gene expression, while combining strong activators often results in weaker activation. In contrast, repressors combine linearly and produce full gene silencing, and repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small molecule drug. Altogether, we outline the basic principles of how effector domains combine to regulate gene expression and demonstrate their value in building precise and flexible synthetic biology tools.

synthetic biology↗

bioRxiv2022

High-Throughput Discovery and Characterization of Viral Transcriptional Effectors in Human Cells

Viruses encode transcriptional regulatory proteins critical for controlling viral and host gene expression. Given their multifunctional nature and high sequence divergence, it is unclear which viral proteins can affect transcription and which specific sequences contribute to this function. Using a high-throughput assay, we measured the transcriptional regulatory potential of over 60,000 protein tiles across [~]1,500 proteins from 11 coronaviruses and all nine human herpesviruses. We discovered hundreds of new transcriptional effector domains, including a conserved repression domain in all coronavirus Spike homologs, dual activation-repression domains in VIRFs, and an activation domain in six herpesvirus homologs of the single-stranded DNA-binding protein that we show is important for viral replication and late gene expression in KSHV. For the effector domains we identified, we investigated their mechanisms via high-throughput sequence and chemical perturbations, pinpointing sequence motifs essential for function. This work massively expands viral protein annotations, serving as a springboard for studying their biological and health implications and providing new candidates for compact gene regulation tools.

systems biology↗

bioRxiv2022

Large-scale mapping and systematic mutagenesis of human transcriptional effector domains

Human gene expression is regulated by over two thousand transcription factors and chromatin regulators1,2. Effector domains within these proteins can activate or repress transcription. However, for many of these regulators we do not know what type of transcriptional effector domains they contain, their location in the protein, their activation and repression strengths, and the amino acids that are necessary for their functions. Here, we systematically measure the transcriptional effector activity of >100,000 protein fragments (each 80 amino acids long) tiling across most chromatin regulators and transcription factors in human cells (2,047 proteins). By testing the effect they have when recruited at reporter genes, we annotate 307 new activation domains and 592 new repression domains, a [~]5-fold increase over the number of previously annotated effectors3,4. Complementary rational mutagenesis and deletion scans across all the effector domains reveal aromatic and/or leucine residues interspersed with acidic, proline, serine, and/or glutamine residues are necessary for activation domain activity. Additionally, the majority of repression domain sequences contain either sites for SUMOylation, short interaction motifs for recruiting co-repressors, or are structured binding domains for recruiting other repressive proteins. Surprisingly, we discover bifunctional domains that can both activate and repress and can dynamically split a cell population into high- and low-expression subpopulations. Our systematic annotation and characterization of transcriptional effector domains provides a rich resource for understanding the function of human transcription factors and chromatin regulators, engineering compact tools for controlling gene expression, and refining predictive computational models of effector domain function.

systems biology↗