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Poetsch, A. R.

Publications and source records attributed to Poetsch, A. R..

11 recordsLinked to original sources
bioRxiv2026

Nup153 regulates neuronal responsiveness through HDAC1-mediated epigenetic modulation

Neural activity-dependent gene regulation is central to the development of neural networks and neuronal plasticity. Induction of activity-dependent gene programs is equally important as repression of these programs, and both need to be balanced carefully. However, little is known about how repressive mechanisms modulate neuronal responsiveness across the genome before stimulation. Here, we identify nucleoporin-dependent regulation of neuronal responsiveness, in which Nup153 represses neuronal genes including activity-regulated genes (ARGs), in the basal state. By characterizing the genome-wide landscape of chromatin accessibility, histone modifications and Nup153 chromatin binding, we show that Nup153 bidirectionally regulates chromatin states through both basal activity-dependent and -independent mechanisms and influences associated genes. Mechanistically, Nup153 associates with HDAC1 to modulate histone acetylation and chromatin accessibility at target regulatory regions. Our data suggests Nup153 organizes chromatin states that regulate neuronal gene programs involved in maintaining neuronal responsiveness. TeaserNup153 primes neuronal responsiveness through multi-layered epigenetic regulation.

neuroscience↗
bioRxiv2026

Baseline regulatory programs in larval and adult neural progenitors converge towards an injury-induced state after spinal cord injury

Regeneration after spinal cord injury requires progenitor cells to convert injury-associated signals into coordinated remodeling of gene regulatory programs. Mammalian spinal progenitors show limited neurogenic output after injury, whereas zebrafish regenerate spinal neurons and recover motor function. To investigate the regulatory changes that allow ependymo-radial glia (ERG) cells, the progenitor cells of the zebrafish spinal cord, to generate new neurons, we combined single-nucleus gene expression and chromatin accessibility profiling across embryonic, larval, and adult stages with topic-based gene regulatory network (GRN) inference. We found that larval and adult ERGs enter the injury response from distinct regulatory baselines: larval progenitors are characterized by a gliogenic program, whereas adult progenitors maintain a comparatively quiescent state. Following injury, both populations gradually change their baseline programs and shift towards a lesion-associated module marked by stress-responsive and chromatin-associated regulators, including jun, hmga1a, hmga2, ybx1, and foxj1a. The shift away from homeostatic states is supported by decreased expression of the Notch-associated regulators nuclear factor I A (nfia) and hey1 in larvae, while in adults, downregulation of the same nuclear factor and other TFs such as bhlhe41 is associated with quiescence exit. Pathway analysis showed stage-specific alterations after injury, characterized predominantly by extracellular signaling and cytoskeletal reorganization in larvae and by metabolic and translational remodeling in adults. Despite divergence from the homeostatic states, injury-induced larval and adult GRNs remain distinct from embryonic hERG regulatory programs. Thus, larval and adult progenitors follow different trajectories from their baselines towards a related lesion-reactive state, in which shared regeneration-associated features are acquired within respective contexts.

bioinformatics↗
bioRxiv2026

Using Deep Learning to predict replication timing reveals baseline control of genomic DNA sequence

Human DNA replicates according to a precise schedule: certain regions are replicated early, while others replicate later in the cell cycle. The DNA sequence and the epigenome are both indicative for replication timing (RT), their relative contributions and potential complementarity yet remain unclear. Here, we utilize Repli-Seq data and machine learning to investigate these relationships in seven human cell lines. We show that GROVER (Genome Rules Obtained Via Extracted Representations), a DNA language model, trained purely on DNA sequence can be fine-tuned for RT, which indicates that information on DNA sequence is largely sufficient to predict RT. Integrating GROVERs sequence predictions as an additional feature with diverse epigenetic sequencing data, yields a multimodal model that outperforms approaches that use exclusively the DNA sequence or the epigenome, which demonstrates predictive synergy. By evaluating input feature contributions, we find that epigenetic features vary in their RT prediction contribution. Some epigenetic features see their contribution diminish in the presence of DNA sequence information, others remain predictive. GROVERs sequence representations emerge as the most dominant feature. Furthermore, to assess cell-type specificity of RT, we partitioned the genome into constitutive domains that replicate uniformly across cell lines and cell-type-specific domains that shift during differentiation. While DNA sequence features contribute more to constitutive predictions, they also remain informative within cell-type-specific regions. Together, our findings dissect the relationship between DNA sequence and the epigenome relative to RT and suggest that primary DNA sequence instructions exert a powerful baseline control over constitutive and cell-type-specific RT, fine-tuned by regulatory cues beyond the DNA sequence.

genomics↗
bioRxiv2025

Tumor clone dynamics in gastro-esophageal cancer organoids reveal a non-genetic memory of neoadjuvant chemotherapy via downregulation of NFκB signaling

Adenocarcinomas of the gastroesophageal junction exhibit genetic and non-genetic heterogeneity that impact clinical outcomes, though the underlying mechanisms behind drug resistance remain poorly understood. We integrated bulk whole-genome sequencing (WGS) and single cell RNA sequencing (scRNA-seq) data from patient-derived organoid lines generated from drug resistant gastric tumors of three patients before and after chemotherapy with FLOT (5-fluorouracil, leucovorin, oxaliplatin, and docetaxel), investigating both in vivo and ex vivo treatment effects. We found that inter-patient variability of gene expression exceeds intra-patient differences and predominantly shapes the expression profiles. Integration of WGS-inferred cancer phylogenies with scRNA-seq data allowed us to associate genetic clones with the individual cells transcriptional program and to track the genetic and transcriptomic history of dominant genetic clones in post-treatment samples relative to the corresponding primary tumor. Notably, in vivo treated samples appeared to be transcriptionally distinct from the untreated counterparts, marked by sustained NF-{kappa}B down-regulation, which suggests that they retain an immune-mediated imprint of the prior therapy. Changes in the clonal composition of a tumor alone cannot explain the post-chemotherapy NF-{kappa}B-associated transcriptional reprogramming. Instead, non-genetic mechanisms shape the altered transcriptomic landscape, particularly a distinct subpopulation of epithelial cells that specifically express pro-inflammatory cytokines, key components of the NF-{kappa}B regulatory network. These observations support a model of transcriptional reprogramming after FLOT treatment, which is most likely independent of genetic evolution and consequently potentially reversible. Downregulated NF-{kappa}B signaling may thus represent a candidate pathway change for predictive response assessment and/or NF-{kappa}B-stimulating co-therapeutic strategies to overcome FLOT resistance.

cancer biology↗
bioRxiv2025

Using the DNA language model, GROVER, to parse sequence and epigenetic effects on genome stability

AbstractGenome stability is shaped by DNA sequence and chromatin context, but their relative contributions to double-strand break (DSB) sensitivity remain unclear. We show that the DNA language model, GROVER, can infer DSB location based on sequence. DSB hotspots tend to contain GC-rich sequences that belong to promoters, genes and short interspersed nuclear elements (SINEs). Additionally, we identified several specific short sequences (tokens) that are associated with modulating DSB sensitivity. Another model using chromatin and genome regulatory features outperforms the sequence-only model, highlighting complementary and cell-type specific information. Integrating sequence and genome biological features yields the best performance, demonstrating their synergy. Analyzing this model revealed that, dependent on the sample, genome stability information encoded in H3K36me3 and DNase-seq can be learned from the sequence, but not H3K27ac or H3K9me3. Embedding chromatin data directly into the GROVER architecture enabled cell-type specific modeling with performance matching the full chromatin feature model. Our results suggest that while chromatin and regulatory context provides important information, such as cell-type specificity, much of the information shaping DSB patterns is already encoded in the DNA sequence itself. Our integrative modeling approach not only reveals DSB patterns but also provides a generalizable strategy for tracing predictions in genomic data. O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/666402v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@af3ea5org.highwire.dtl.DTLVardef@139d159org.highwire.dtl.DTLVardef@df455borg.highwire.dtl.DTLVardef@428e20_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗
bioRxiv2024

Click-chemistry-aided quantitation and sequencing of oxidized guanines and apurinic sites uncovers their transcription-linked strand bias in human cells

DNA modifications drive aging, neurodegeneration, carcinogenesis, and chemotherapy drug action. To understand the functional genomic roles of DNA modifications, it is critical to accurately map their diverse chemical forms with single-nucleotide precision in complex genomes, but it remains challenging. Click-code-seq is a click-chemistry-aided single-nucleotide-resolution strategy for guanine-oxidation mapping, used in yeast DNA but having poor applicability to human genomes. Here, we upgraded click-code-seq to enable its first application for sequencing DNA oxidation and depurination in human genomes. For this, we developed a companion fluorescence assay, click-fluoro-quant, to rapidly quantify different common DNA modifications, and devised novel adapters to minimize false modification detection and assess modification frequency in cell populations. We uncovered that endogenous DNA oxidation in a human cell line has a highly similar pattern to cancer mutational signatures associated with reactive oxygen species. We established that the DNA-alkylating chemotherapy drug irofulven preferentially induces depurination in ApA dimers and promoter regions. Intriguingly, we revealed that oxidized guanines and apurinic sites, both irofulven-induced and endogenous, are depleted in gene transcribed strands, and the strand bias widens with increasing gene expression. This work substantially advances click-code-seq for deciphering the impacts of key modifications in human DNA on cellular physiology and toxicological responses.

genomics↗
bioRxiv2023

The human genome's vocabulary as proposed by the DNA language model GROVER

Large Language Models (LLMs) on natural language have achieved a level of performance that allows the generation of coherent and syntactically correct text. DNA sequence of genomes follows rules similar to natural language, but a distinguishing factor is the absence of a concept analogous to words. We established byte-pair tokenization on the human genome and trained a foundation language model called GROVER ("Genome Rules Obtained Via Extracted Representations") to select the optimal vocabulary with a custom fine-tuning task of next-k-mer prediction. We thus defined a dictionary of words/tokens in the human genome that best carries the information content for DNA language models. Analyzing GROVERs learned representations, we observed that token embeddings primarily encode information related to their frequency, sequence content, and length. Some tokens are almost exclusively localized in repeats, while the vast majority widely distributes over the genome. The model also learns context and lexical ambiguity. Average embeddings of genomic regions relate to functional genomics annotation and thus indicate that GROVER has learned these structures purely from the contextual relationships of tokens. That we can extract functional annotations from the genome, purely based on sequence representation to the trained model, highlights the extent of information content encoded by the sequence. This is supported by fine-tuning tasks on genome biology with questions of promoter identity and protein-DNA binding. GROVER learns sequence context, a sense for grammatical structures and language rules in the genome. This knowledge can be extracted and used to compose a grammar book for the code of life.

genomics↗
bioRxiv2023

Distinguishing word identity and sequence context in DNA language models

Transformer-based large language models (LLMs) are very suited for biological sequence data, because the structure of protein and nucleic acid sequences show many analogies to natural language. Complex relationships in biological sequence can be learned, although there may not be a clear concept of words, because they can be generated through tokenization. Training is subsequently performed for masked token prediction. With this strategy, the models learn both the token sequence identity and a larger sequence context. We developed a framework to interrogate what a model learns, which is both relevant for the interpretability of the model and to evaluate its potential for specific tasks. We used a DNA language model, which is trained on the human reference genome with a Bidirectional Encoder Representations from Transformers (BERT) model. In this model, tokens are defined with overlapping k-mers. To gain insight into the models learning, we interrogated how the model performs predictions, extracted token embeddings, and defined a fine-tuning benchmarking task to predict the next tokens of different sizes without overlaps. This task is very suited to evaluate different pretrained DNA language models, also called foundation models, since it does not interrogate specific genome biology, does not depend on the tokenization strategy, the size of the vocabulary, the dictionary, or the number of parameters used to train the model. Lastly, the task performs without leakage of information from token identity into the prediction task, which makes it particularly useful to evaluate the learning of sequence context. Through this assessment we discovered that the model with overlapping k-mers struggles to learn larger sequence context. Instead, the learned embeddings largely represent token sequence. Still, good performance is achieved for genome biology inspired fine-tuning tasks. Models with overlapping tokens may be used for tasks where a larger sequence context is of less relevance, but the token sequence directly represents the desired learning feature. This emphasizes the need to interrogate knowledge representation in biological large language models. Transparency is particularly important for biomedical use cases and an understanding of what the models are learning can be used to match the model to the desired task.

bioinformatics↗
bioRxiv2018

Target-specific precision of CRISPR-mediated genome editing

The CRISPR-Cas9 system has successfully been adapted to edit the genome of various organisms. However, our ability to predict editing accuracy, efficacy and outcome at specific sites is limited by an incomplete understanding of how the bacterial system interacts with eukaryotic genomes and DNA repair machineries. Here, we performed the largest comparison of indel profiles to date, examining over one thousand sites in the genome of human cells, and uncovered general principles guiding CRISPR-mediated DNA editing. We find that precision of DNA editing varies considerably among sites, with some targets showing one highly-preferred indel and others displaying a wide range of infrequent indels. Editing precision correlates with editing efficiency, homology-associated end-joining for both insertions and deletions, and a preference for single-nucleotide insertions. Precise targets and the identity of their preferred indel can be predicted based on simple rules that mainly depend on the fourth nucleotide upstream of the PAM sequence. Regardless of precision, site-specific indel profiles are highly robust and depend on both DNA sequence and chromatin features. Our findings have important implications for clinical applications of CRISPR technology and reveal general patterns of broken end-joining that can inform us on DNA repair mechanisms in human cells.

molecular biology↗
bioRxiv2017

Genomic landscape of oxidative DNA damage and repair reveals regioselective protection from mutagenesis

DNA is subject to constant chemical modification and damage, which eventually results in variable mutation rates throughout the genome. Although detailed molecular mechanisms of DNA damage and repair are well-understood, damage impact and execution of repair across a genome remains poorly defined. To bridge the gap between our understanding of DNA repair and mutation distributions we developed a novel method, AP-seq, capable of mapping apurinic sitesand 8-oxo-7,8-dihydroguanine bases at [~]300bp resolution on a genome-wide scale. We directly demonstrate that the accumulation rate of oxidative damage varies widely across the genome, with hot spots acquiring many times more damage than cold spots. Unlike SNVs in cancers, damage burden correlates with marks for open chromatin notably H3K9ac and H3K4me2. Oxidative damage is also highly enriched in transposable elements and other repetitive sequences. In contrast, we observe decreased damage at promoters, exons and termination sites, but not introns, in a seemingly transcription-independent manner. Leveraging cancer genomic data, we also find locally reduced SNV rates in promoters, genes and other functional elements. Taken together, our study reveals that oxidative DNA damage accumulation and repair differ strongly across the genome, but culminate in a previously unappreciated mechanism that safe-guards the regulatory sequences and the coding regions of genes from mutations.

genomics↗