Biology subjects

Seelig, G.

Publications and source records attributed to Seelig, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Transforming the cytokine literature into a resource for experimental analysis and discovery

Cytokine biology is dispersed across hundreds of thousands of publications, making it difficult to use systematically when interpreting new experiments. Large language models (LLMs) can assist with focused literature interpretation, but ad hoc retrieval remains incomplete and unreliable. We present the Cytokine Effect Database (CytED), a framework for interfacing user-supplied experimental datasets with literature knowledge at scale. CytED uses a multi-step LLM pipeline to generate over a million cytokine-cell type-effect triples from 110,000 full-text publications, with annotations for experimental context and directional changes in genes, pathways, and cellular processes. This structure enables quantitative comparison between observed perturbation responses and prior literature across cytokines, cell types, and experimental contexts. Applied to in vitro IL-10 stimulation of PBMCs, CytED identifies unexpected pro-inflammatory features in monocytes and systematic in vivo-in vitro differences in cytotoxicity responses in CD8+ T cells. CytED infers cytokine signaling, distinguishes primary from secondary cytokine effects, and guides the design of combinatorial perturbation screens. Together, CytED establishes a general paradigm for converting unstructured domain literature into analytical tools that bridge literature and experiment.

immunology↗

bioRxiv2025

Pooled single-cell CRISPRa/i screens for functional genomics in bacteria at scale

Pooled CRISPR screens using single-cell RNA sequencing (scRNA-seq) have emerged as powerful tools to uncover gene function, map regulatory networks, and identify genetic interactions. However, the inherent sparsity of bacterial scRNA-seq data has posed a major challenge toward applying these approaches to bacteria. Here, we present mapSPLiT, a pooled bacterial CRISPR activation and interference (CRISPRa/i) screening platform that enables large-scale experiments that simultaneously link hundreds of perturbations to their corresponding single-cell transcriptomes. By targeting 52 known or putative transcription factors with 118 perturbations in a pooled experiment, we expanded the E. coli regulatory network map, determined the function of putative regulators, and identified emergent global phenotypes. By targeting combinations of transcription factors simultaneously, we uncovered genetic interactions and regulatory logic between them. Mapping regulatory networks for carbon utilization in P. putida revealed control points that could expand metabolic flexibility and improve biomanufacturing. Together, these results establish mapSPLiT as a generalizable platform for single-cell functional genomics in bacteria.

genomics↗

bioRxiv2025

A single-cell cytokine dictionary of human peripheral blood

Cytokines orchestrate immune responses, yet we still lack a comprehensive understanding of their specific effects across human immune cells due to their pleiotropy, context dependence and extensive functional redundancy. Here, we present a Human Cytokine Dictionary, created from high-resolution single-cell transcriptomes of 9,697,974 human peripheral blood mononuclear cells (PBMC) from 12 donors stimulated in vitro with 90 different cytokines. We describe donor-specific response variation and uncover robust consensus cytokine signatures across individuals. We then delineate similarities between cytokine response profiles, and derive cytokine-induced immune programs that organize responsive genes into data-driven, biologically interpretable functional modules. By integrating cell type-specific responses with expression of cytokines, we infer higher-order cell-to-cell and cytokine-to-cytokine communication networks exemplified by an IL-32-{beta}-initiated signaling cascade, which rewires myeloid programs by inducing neutrophil-recruiting factors while suppressing Th1-responses and promoting IL-10-family cytokines. Finally, we show how the Human Cytokine Dictionary enables the interpretation of cytokine-driven immune responses in other studies and disease contexts, including systemic lupus erythematosus, multiple sclerosis, and non-small cell lung carcinoma. Together, the Human Cytokine Dictionary constitutes the first comprehensive cell type-resolved transcriptional screen of human cytokine responses and provides an essential open-access, easy-to-use community resource with accompanying software package to advance our understanding of cytokine biology in human disease and guide therapeutic discovery.

immunology↗

bioRxiv2025

Massively parallel assay of human splice variants reveals cis-regulatory drivers of disease-associated and cell type-specific splicing regulation

Splice-disrupting variants (SDVs) underlie many human diseases, yet systematic functional maps of their effects across cell types remain limited. We developed Cell-type Oriented Massively Parallel reporter Assay of Splicing Signatures (COMPASS) to measure splicing outcomes for 87,546 single and double variants across more than 1,700 genes in five human cell lines of diverse tissue origin. COMPASS targets disease relevant gene sets, including ACMG actionable genes and SFARI autism-associated genes, enabling systematic dissection of splicing impacts in health and disease. Our measurements reveal numerous SDVs, including ClinVar variants currently classified as variants of uncertain significance. Using prime editing, we validate variant effects in the genome for ClinVar variants in BIN1, a gene implicated in Alzheimers disease, cancer, and cardiac pathology. Benchmarking our MPRA against state-of-the-art predictive models shows that while these approaches capture broad variant effects, important gaps remain. Our data reveal putative RNA-binding protein motifs whose disruption drives splicing changes, providing mechanistic insight into variant impact. Finally, cross-cell line comparisons reveal a distinct subset of variants that drive cell type-specific splicing programs. This study delivers the largest cell type-resolved, base-resolution atlas of splicing variant effects to date, providing a resource to support variant reclassification in clinical genomics and aid in the selection of therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/681955v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@81765dorg.highwire.dtl.DTLVardef@181e55dorg.highwire.dtl.DTLVardef@f96eaeorg.highwire.dtl.DTLVardef@1d16d4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Programming human cell type-specific gene expression via an atlas of AI-designed enhancers

Differentially active enhancers are key drivers of cell type specific gene expression. Active enhancers are found in open chromatin, which can be mapped at genome scale across tissue and cell types. Though incompletely understood, the relationship between chromatin accessibility and enhancer activity has been exploited to identify, model, and even design functional enhancers for selected cell types, but to what extent this design strategy can generalize across human cell and tissue types remains unclear. Here, we trained deep neural networks on a large corpus of chromatin accessibility data from hundreds of human biosamples. We used these models to generate an atlas of tens of thousands of synthetic enhancers, targeting hundreds of cell lines, tissues, and differentiation states, aiming to maximize accessibility in target samples and minimize it in all off-target ones. Experimental testing of thousands of designs in a representative subset of ten human cell types and in mouse retina demonstrated their function as specific enhancers, not only in the case of one-versus-all objectives but also when targeting two or three cell types. An explainable AI analysis, enabled by our large-scale enhancer measurements, allowed us to identify similarities and differences between the sequence grammar underlying accessibility and enhancer activity. Our results show that model-guided design of enhancers can help us decipher the cis-regulatory code governing cell type specificity and generate novel tools for selective targeting of human cell states.

synthetic biology↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

Design of overlapping genes using deep generative models of protein sequences

In nature, viruses frequently evolve overlapping genes (OLG) in alternate reading frames of the same nucleotide sequence despite the drastically reduced protein sequence space resulting from the sharing of codon nucleotides. Their existence leads one to wonder whether amino acid sequences are sufficiently degenerate with respect to protein folding to broadly allow arbitrary pairs of functional proteins to be overlapped. Here, we investigate this question by engineering synthetic OLGs using state-of-the-art generative models. To evaluate the approach, we first design overlapped sequences targeting two different protein families. We then encode distinct highly ordered de novo protein structures and observe surprisingly high in silico and experimental success rates. This demonstrates that the overlap constraints under the structure of the standard genetic code do not significantly restrict simultaneous accommodation of well defined 3D folds in alternative reading frames. Our work suggests that OLG sequences may be frequently accessible in nature and could be readily exploited to compress and constrain synthetic genetic circuits.

synthetic biology↗

bioRxiv2025

Combinatorial phenotypic landscape enables bacterial resistance to phage infection

AbstractSuccess of phage therapies is limited by bacterial defenses against phages. While a large variety of anti- phage defense mechanisms has been characterized, how expression of these systems is distributed across individual cells and how their combined activities translate into protection from phages has not been studied. Using bacterial single-cell RNA sequencing, we profiled the transcriptomes of [~]50,000 cells from cultures of a human pathobiont, Bacteroides fragilis, infected with a lytic bacteriophage. We quantified the asynchronous progression of phage infection in single bacterial cells and reconstructed the infection timeline, characterizing both host and phage transcriptomic changes as infection unfolded. We discovered a subpopulation of bacteria that remained uninfected and determined the heterogeneously expressed host factors associated with protection. Each cells vulnerability to phage infection was defined by combinatorial phase-variable expression of multiple genetic loci, including capsular polysaccharide (CPS) biosynthesis pathways, restriction-modification systems (RM), and a previously uncharacterized operon likely encoding fimbrial genes. By acting together, these heterogeneously expressed phase-variable systems and anti-phage defense mechanisms create a phenotypic landscape where distinct protective combinations enable the survival and re-growth of bacteria expressing these phenotypes without acquiring additional mutations. The emerging model of complementary action of multiple protective mechanisms heterogeneously expressed across an isogenic bacterial population showcases the potent role of phase variation and stochasticity in bacterial anti-phage defenses. One Sentence SummaryCombinatorial phenotypic states with differential vulnerability to phage infection across a Bacteroides fragilis population enable a small number of super-resistant bacterial cells to evade the phage without the need for acquiring mutations.

systems biology↗

bioRxiv2024

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

Limiting expression to target cell types or states 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 3UTRs 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 the stability of reporters containing full microRNA target sites purely from expression data. We use this model to design constructs with previously unattainable response patterns across our cell lines. We then generalize this approach to primary tissues and cells and show that mRNA can be engineered to be differentially active between rested and exhausted T cell states. The rules we derive for microRNA expression data selection and processing should apply to microRNA-responsive devices for any environment with available expression data.

synthetic biology↗

bioRxiv2024

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.

developmental biology↗

bioRxiv2024

A nuclear RNA degradation code for eukaryotic transcriptome surveillance

The RNA exosome plays critical roles in eukaryotic RNA degradation, but it remains unclear how the exosome specifically recognizes its targets. The PAXT connection is an adaptor that recruits the exosome to polyadenylated RNAs in the nucleus, especially transcripts polyadenylated at intronic poly(A) sites. Here we show that PAXT-mediated RNA degradation is induced by the combination of a 5' splice site and a poly(A) junction, but not by either sequence alone. These sequences are bound by U1 snRNP and cleavage/polyadenylation factors, which in turn cooperatively recruit PAXT. As the 5' splice site-poly(A) junction combination is typically not found on correctly processed full-length RNAs, we propose that it functions as a "nuclear RNA degradation code" (NRDC). Importantly, disease-associated single nucleotide polymorphisms that create novel 5' splice sites in 3' untranslated regions can induce aberrant mRNA degradation via the NRDC mechanism. Together our study identified the first NRDC, revealed its recognition mechanism, and characterized its role in human diseases.

molecular biology↗

bioRxiv2024

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.

synthetic biology↗

bioRxiv2023

The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis

The 5' UTRs of mRNAs are critical for translation regulation, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massively parallel reporter assay of 18,154 sequences coupled to polysome profiling. We found that the 5' UTR is sufficient to confer temporal dynamics to translation initiation, and identified 86 motifs enriched in 5' UTRs with distinct ribosome recruitment capabilities. A quantitative deep learning model, DaniO5P, revealed a combined role for 5' UTR length, translation initiation site context, upstream AUGs and sequence motifs on in vivo ribosome recruitment. DaniO5P predicts the activities of 5' UTR isoforms and indicates that modulating 5' UTR length and motif grammar contributes to translation initiation dynamics. This study provides a first quantitative model of 5' UTR-based translation regulation in early vertebrate development and lays the foundation for identifying the underlying molecular effectors. HighlightsO_LIIn vivo MPRA systematically interrogates the regulatory potential of endogenous 5' UTRs C_LIO_LIThe 5' UTR alone is sufficient to regulate the dynamics of ribosome recruitment during early embryogenesis C_LIO_LIThe MPRA identifies 5' UTR cis-regulatory motifs for translation initiation control C_LIO_LI5' UTR length, upstream AUGs and motif grammar contribute to the differential regulatory capability of 5' UTR switching isoforms C_LI

developmental biology↗

bioRxiv2023

Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning

mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on all our datasets and use them to guide the design of high-performing 5UTRs using gradient descent and generative neural networks. We experimentally test designed 5UTRs with mRNA encoding megaTALTM gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.

synthetic biology↗

bioRxiv2023

The anti-cancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3' processing machinery

JTE-607 is a small molecule compound with anti-inflammation and anti-cancer activities. Upon entering the cell, it is hydrolyzed to Compound 2, which directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-mRNA 3' processing. Although CPSF73 is universally required for mRNA 3' end formation, we have unexpectedly found that Compound 2- mediated inhibition of pre-mRNA 3' processing is sequence-specific and that the sequences flanking the cleavage site (CS) are a major determinant for drug sensitivity. By using massively parallel in vitro assays, we have measured the Compound 2 sensitivities of over 260,000 sequence variants and identified key sequence features that determine drug sensitivity. A machine learning model trained on these data can predict the impact of JTE-607 on poly(A) site (PAS) selection and transcription termination genome-wide. We propose a biochemical model in which CPSF73 and other mRNA 3' processing factors bind to RNA of the CS region in a sequence-specific manner and the affinity of such interaction determines the Compound 2 sensitivity of a PAS. As the Compound 2-resistant CS sequences, characterized by U/A-rich motifs, are prevalent in PASs from yeast to human, the CS region sequence may have more fundamental functions beyond determining drug resistance. Together, our study not only characterized the mechanism of action of a compound with clinical implications, but also revealed a previously unknown and evolutionarily conserved sequence-specificity of the mRNA 3' processing machinery.

molecular biology↗

bioRxiv2023

DNA storage in thermoresponsive microcapsules for repeated random multiplexed data access

Owing to its longevity and extremely high information density, DNA has emerged as an attractive medium for archival data storage. Scalable parallel random access of information is a desirable property of any storage system. For DNA-based storage systems, however, this yet has to be robustly established. Here we develop thermoconfined PCR, a novel method that enables multiplexed, repeated random access of compartmentalized DNA files. Our strategy is based on stable localization of biotin-functionalized oligonucleotides inside microcapsules with temperature-dependent membrane permeability. At low temperatures, microcapsules are permeable to enzymes, primers, and amplified products, while at high temperatures membrane collapse prevents molecular crosstalk during amplification. We demonstrate that our platform outperforms non-compartmentalized DNA storage with respect to repeated random access and reducing amplification bias during multiplex PCR. Using fluorescent sorting, we additionally demonstrate sample pooling and data retrieval by barcoding of microcapsules. Our thermoresponsive microcapsule technology offers a scalable, sequence-agnostic approach for repeated random access of archival DNA files.

synthetic biology↗

bioRxiv2023

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

synthetic biology↗

bioRxiv2022

Deciphering the Impact of Genetic Variation on Human Polyadenylation

Genetic variants that disrupt polyadenylation can cause or contribute to genetic disorders. Yet, due to the complex cis-regulation of polyadenylation, variant interpretation remains challenging. Here, we introduce a residual neural network model, APARENT2, that can infer 3-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2s performance on several variant datasets, including functional reporter data and human 3 aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. Finally, we perform in-silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of >44 million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, using APARENT2s predictions we detect an association between gain-of-function mutations in the 3-end and Autism Spectrum Disorder.

genomics↗