Biology subjects

Schreiber, J.

Publications and source records attributed to Schreiber, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Quantitative profiling of intrinsic dCas9-DNA recognition reveals key determinants of guide RNA performance

CRISPR technologies based on nuclease-deactivated Cas9 (dCas9) rely on programmable DNA binding rather than DNA cleavage, yet the intrinsic DNA-recognition properties that govern optimal guide RNA (gRNA) performance remain poorly understood. Existing approaches either measure genomic occupancy in cells or infer dCas9 behavior from cleavage-based Cas9 datasets, despite DNA binding being substantially more permissive than DNA cleavage. Here we introduce TANGO (Targeted Array-based Nucleic acid-Guided Occupancy), a high-density DNA-array platform that quantitatively profiles intrinsic dCas9:gRNA binding across tens of thousands of DNA targets in a cell-free system. TANGO captures established features of dCas9 target recognition, while providing substantially greater sensitivity than prior assays. Comparison with ChIP-seq data demonstrates that intrinsic DNA-binding specificity is a major driver of genomic occupancy and reveals that chromatin accessibility modulates the intrinsic binding affinity required for dCas9 recruitment. Across CRISPRi/a guides, TANGO identifies multiple independent biochemical determinants of guide performance--including on-target affinity, mismatch tolerance, and ribonucleoprotein assembly--and flags problematic and highly promiscuous guides overlooked by current specificity metrics. Unexpectedly, some guides retain substantial guide-directed DNA binding even in the absence of a protospacer-adjacent motif (PAM), revealing an additional dimension of dCas9 specificity. Together, these results establish intrinsic DNA recognition as a quantitative and experimentally accessible determinant of dCas9 function, providing a framework for improving guide selection and enhancing the precision of CRISPR technologies.

genomics↗

bioRxiv2026

A sequence variation between two orthologues alters functional expression of the potassium channel Kesv of Ectocarpus siliculosus virus

The potassium channel Kesv encoded by the Ectocarpus siliculosus virus (Kesv 1) differs by seven amino acid residues from its host-derived homolog (Kesv 2), resulting from lysogenic integration. When expressed in Xenopus laevis oocytes, Kesv 1 displayed significantly higher ion conductance and functional expression than Kesv 2, as demonstrated by GFP fluorescence and voltage clamp measurements. This study provides the first structural and functional analysis of Kesv 2, uncovering key differences between the original and host-derived variant. The systematic residue substitutions - based on location- from Kesv 2 to the corresponding residues in Kesv 1 illustrated that two amino acid exchanges in close proximity to the pore region (Q61H and T66A), albeit not individually but in combination, significantly resulted in a loss-of-function phenotype in Kesv 1. AlphaFold predictions and subsequent molecular dynamics simulations did not reveal significant differences between Kesv 1 and Kesv 2 structural models, suggesting that the loss of function cannot be attributed to differences at the structural level. Instead, a reduced surface expression of Kesv 2, caused by the sequence modulations in the brown algal host, appears more plausible. Notably, the pharmacological profiling with Linopirdine and Sotalol highlights differences in drug sensitivity, establishing these minimalist channels (core channel structure without regulatory domains) as tractable models for dissecting novel fundamental principles of ion channel function and drug interaction, while highlighting key differences from more complex channel systems. Significance StatementPotassium channels are essential for cellular excitability, yet their large size and structural complexity limit our understanding of the core features underlying channel function. Here, we identified and established an orthologous model to compare the effects of evolutionarily acquired mutations in two voltage-sensing potassium channels-the viral potassium channel from Ectocarpus siliculosus virus (Kesv 1) and its host-homolog derivative (Kesv 2) as simplified model systems for understanding ion channel physiology and host-viral interactions. We provide the first functional characterization of Kesv 2 in Xenopus laevis oocytes using two-electrode voltage-clamp and site-directed mutagenesis, revealing that, despite sharing an identical SVGYG selectivity-filter motif and differing by only 7 residues, they exhibit distinct ion-conduction properties.

pharmacology and toxicology↗

bioRxiv2026

CACNA2D4 variants are associated with exertional heat stroke susceptibility

Exertional Heat Stroke (EHS) is a life-threatening disease defined by severe hyperthermia and sudden neurological dysfunction in healthy individuals exposed to intense physical exercise. As is the case with some forms of malignant hyperthermia, mutations in genes involved in skeletal muscle contraction could predispose some patients to EHS. However, the genetic basis of EHS remains poorly documented. Here we identify EHS-associated variants in the CACNA2D4 gene encoding the Cav Ca2+ channel subunit, 2{delta}4, and describe their impact in heterologous expression systems and a knock-in mutant mouse strain heterozygous for the S299R variant (S299R+/-). In transfected cells, each CACNA2D4 variant, including S299R, affected the trafficking and function of 2{delta}4 and its regulation of the Cav1.1 channel. S299R+/-mice showed EHS-like crisis with signs of rhabdomyolysis and an elevation in core body temperature when submitted to an intense exercise protocol. We show that a CACNA2D4 transcript is expressed in mouse skeletal muscle and that the presence of the S299R variant in 2{delta}4 induces a modification in the calcium flux triggered by muscle cell depolarization. Altogether, our data reveal an unexpected connection between CACNA2D4 gene variants and pathophysiological alterations in skeletal muscle Ca2+ signaling that may increase EHS susceptibility.

genetics↗

bioRxiv2025

Predictive design of tissue-specific mammalian enhancers that function in vivo in the mouse embryo

Enhancers control tissue-specific gene expression across metazoans. Although deep learning has enabled enhancer prediction and design in mammalian cell lines and invertebrate systems, it remains unclear whether such approaches can operate within the regulatory complexity of mammalian tissues in vivo. Here, we present a general strategy for designing tissue-specific enhancers that function reliably in mice. We use deep learning to train compact convolutional neural networks (CNNs) on genome-wide chromatin accessibility and fine-tune them via transfer learning on validated human and mouse enhancers. Guided by these models, we design fifteen synthetic enhancers for the heart, limb, and central nervous system (CNS) in mouse embryos, all of which are active in their intended target tissue. Our work establishes a generalizable framework for programmable control of mammalian gene expression in vivo, opening new avenues in functional genomics, synthetic biology, and gene therapy.

synthetic biology↗

bioRxiv2025

CD36 is a metabolic checkpoint for Th2 cell tissue residency during allergic airway inflammation

The prevalence of allergic diseases, including asthma, continues to rise in industrialized societies, yet the mechanisms sustaining pathogenic T helper 2 (Th2) responses remain incompletely understood. Here, we show that patients with allergic asthma exhibit elevated lipophilic volatile organic compounds in exhaled air and altered fatty acid-metabolism gene expression in sputum-derived Th2 cells. Using a mouse model of house dust mite-induced allergic airway inflammation, we find that the lipid transporter CD36 is dispensable for T follicular helper and germinal center B cell responses but is critical for maintaining lung-resident memory Th2 cells. CD36 regulates GATA3 and PPAR{gamma} expression in lung-resident memory Th2 cells and their interaction with type-2 conventional dendritic cells during airway inflammation. In human T cells, pharmacological inhibition of CD36 does not impair initial activation but blocks terminal Th2 differentiation. These findings identify CD36 as a metabolic checkpoint that sustains Th2 effector function and tissue residency, and establish lipid metabolism as a yet unrecognized therapeutic target in allergic asthma. SummaryAllergic asthma is marked by rising prevalence yet the drivers of persistent T helper 2 (Th2) immunity remain unclear. We show that asthma patients exhibit altered fatty acid-metabolism signatures in sputum Th2 cells and elevated lipophilic volatile organic compounds in exhaled air. In a mouse model of house dust mite-induced airway inflammation, the lipid transporter CD36 was dispensable for germinal center responses but essential for lung-resident memory Th2 cells, controlling GATA3 and PPAR{gamma} expression and promoting cDC2 interactions. Pharmacological inhibition of CD36 in human T cells preserved activation but blocked terminal Th2 differentiation. These findings identify CD36 as a metabolic checkpoint that sustains Th2 effector function and tissue residency, and nominate it as a therapeutic target in allergic asthma. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/676489v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@117da6eorg.highwire.dtl.DTLVardef@105934corg.highwire.dtl.DTLVardef@109903corg.highwire.dtl.DTLVardef@dc9345_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

tangermeme: A toolkit for understanding cis-regulatory logic using deep learning models

Deep learning models have achieved state-of-the-art performance at predicting diverse genomic modalities, yet their promise for biological discovery lies in how they are used after demonstrating their predictive performance. Here, we describe the functionality of tangermeme, a highly optimized toolkit for "everything-but-the-model" when it comes to genomic deep learning, and demonstrate how tangermeme can be used to distill the learned cis- regulatory patterns from models into human-interpretable insights.

bioinformatics↗

bioRxiv2025

Tomtom-lite: Accelerating Tomtom enables large-scale and real-time motif similarity scoring

SummaryPairwise sequence similarity is a core operation in genomic analysis, yet most attention has been given to sequences made up of discrete characters. With the growing prevalence of machine learning, calculating similarities for sequences of continuous representations, e.g. frequency-based position-weight matrices (PWMs), attribution-based contribution-weight matrices, and even learned embeddings, is taking on newfound importance. Tomtom has previously been proposed as an algorithm for identifying pairs of PWMs whose similarity is statistically significant, but the implementation remains inefficient for both real-time and large-scale analysis. Accordingly, we have re-implemented Tomtom as a numba-accelerated Python function that is natively multi-threaded, avoids cache misses, more efficiently caches intermediate values, and uses approximations at compute bottlenecks. Here, we provide a detailed description of the original Tomtom method (see Supplementary Note 1) and present results demonstrating that our re-implementation can achieve over a thousand-fold speedup compared with the original tool on reasonable tasks (see Supplementary Note 2). Availability and ImplementationOur implementation of Tomtom is freely available as a Python package at https://github.com/jmschrei/memesuite-lite, which can be downloaded via pip install memelite.

bioinformatics↗

bioRxiv2025

Programmatic design and editing of cis-regulatory elements

The development of modern genome editing and DNA synthesis has enabled researchers to edit DNA sequences with high precision but has left unsolved the problem of designing these edits. We introduce Ledidi, a computational method that rephrases the discrete design task of choosing which edits to make as an easily solvable continuous optimization problem. Ledidi can use any pre-trained deep learning model to guide the optimization, yielding an edited sequence that exhibits the desired outcome while explicitly minimizing the number of edits used. When applied in dozens of settings, we find that Ledidis designs can precisely control transcription factor binding, chromatin accessibility, transcription, and enhancer activity in silico. By using several deep learning models simultaneously, we design cell type-specific enhancers and experimentally validate them in cellulo. Finally, we introduce the concept of an "affinity catalog", where the design task is repeated multiple times across continuous variants of the design target. We demonstrate how these catalogs can be used to interpret deep learning models and the impact of starting template sequences, and also to design regulatory elements that control transcriptional dosage while maintaining cell type-specificity.

bioinformatics↗

bioRxiv2024

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. 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 and a highly epistatic sequence syntax of cooperative and competitive motif interactions. ProCapNet models of steady-state RAMPAGE profiles 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.

genomics↗

bioRxiv2024

Predicting chromatin conformation contact maps

Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets--each represented as a 2D contact map-- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.

bioinformatics↗

bioRxiv2023

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell mRNA sequencing (scRNA-seq) allows deep molecular and cellular profiling of immunological processes. Longitudinal scRNA-seq datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. Here, we derived longitudinal changes in the abundance of six colonic cell types during inflammatory bowel disease (IBD) from scRNA-seq data of a mouse model of colitis using ODE-based models. We then predicted the immune dynamics of a different mouse colitis protocol and confirmed these scRNA-seq-based predictions with our previously published single-cell-based flow cytometry data. We further hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the model simulations by predicting genes indicative of treatment response in human IBD patients. The predictive power of IBD deterministic modelling from scRNA-seq data highlights its potential to advance our understanding of immune dynamics in health and disease.

systems biology↗

bioRxiv2023

Cross-species imputation and comparison of single-cell transcriptomic profiles

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

bioinformatics↗

bioRxiv2023

Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency

The concentration and stoichiometry of transcription factors (TFs) determine cellular identity and can be manipulated to drive cell state transitions. Understanding how changes in TF concentration regulate chromatin state and expression across cell state transitions remains a challenge. We investigated this relationship by profiling chromatin accessibility and gene expression at single-cell resolution across a densely sampled time course of reprogramming human fibroblasts to induced pluripotent stem cells via ectopic expression of OCT4, SOX2, KLF4, and MYC (OSKM). Using deep learning sequence models of base-resolution chromatin accessibility profiles across cell states, we deciphered predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of TF footprints, linked peaks to putative target genes, and elucidated rewiring of cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution. We establish a quantitative, predictive framework that links TF stoichiometry, motif syntax, and somatic silencing to provide new perspectives on the control of cell identity by TFs during fate transitions.

genetics↗

bioRxiv2022

Mouse and cellular models of KPTN-related disorder implicate mTOR signalling in cognitive and progressive overgrowth phenotypes

KPTN-related disorder (KRD) is an autosomal recessive disorder associated with germline variants in KPTN (kaptin), a component of the mTOR regulatory complex KICSTOR. To gain further insights into the pathogenesis of KRD, we analysed mouse knockout and human stem cell KPTN loss-of-function models. Kptn-/- mice display many of the key KRD phenotypes, including brain overgrowth, behavioural abnormalities, and cognitive deficits. Assessment of affected individuals has identified concordant selectivity of cognitive deficits, postnatal onset of brain overgrowth, and a previously unrecognised KPTN dosage-sensitivity, resulting in increased head circumference in their heterozygous parents. Molecular and structural analysis of Kptn-/- mice revealed pathological changes, including differences in brain size, shape, and cell numbers primarily due to abnormal postnatal brain development. Both the mouse and differentiated iPSC models of the disorder display transcriptional and biochemical evidence for altered mTOR pathway signalling, supporting the role of KPTN in regulating mTORC1. Increased mTOR signalling downstream of KPTN is rapamycin sensitive, highlighting possible therapeutic avenues with currently available mTOR inhibitors. These findings place KRD in the broader group of mTORC1 related disorders affecting brain structure, cognitive function, and network integrity.

genetics↗

bioRxiv2022

Tracking influenza a virus infection in the lung from hematological data with machine learning

The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-{gamma} and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.

microbiology↗

bioRxiv2021

Optimizing dosage-specific treatments in a multi-scale model of a tumor growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell-cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool to study biology at a very different time and spatial scales, as they can integrate different processes that take place at the molecular, cellular and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells spatial geometry, as well as, population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

systems biology↗

bioRxiv2021

Accelerating in-silico saturation mutagenesis using compressed sensing

In-silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined. In this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. We have made this tool available at https://github.com/kundajelab/yuzu.

bioinformatics↗

bioRxiv2021

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants are typically unknown. Furthermore, a regulatory variant might influence a particular genes expression in a cell type or tissue-specific manner. Cell-type specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offers a powerful framework for understanding the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of model cell lines. To address this challenge, we have developed L-HiC-Reg, a Random Forests based regression method to predict high- resolution contact counts in new cell lines, and a network-based framework to identify candidate cell line-specific gene networks targeted by a set of variants from a Genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenome Consortium cell lines, which we used to interpret regulatory SNPs in the NHGRI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including Schizophrenia, Coronary Artery Disease (CAD) and Crohns disease. In CAD, we found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and associated network-based analysis pipeline offers a powerful resource to leverage long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

bioinformatics↗