Biology subjects

Kraft, F.

Publications and source records attributed to Kraft, F..

8 recordsLinked to original sources

bioRxiv2026

A relational architecture of the human genome

Complex systems exhibit organization not only through their components but also via their interdependencies. Local nucleotide dependencies underpin regulatory motifs and RNA structures, yet principles governing a higher-order organization across the human genome remain unknown. Here we systematically perturbed every position of the com- plete telomere-to-telomere human genome using Nucleotide Transformer v3 with an ex- ascale compute cluster, generating nucleotide-resolution dependency maps that reveal a directed, weighted network of position-to-position genomic relationships. This network recapitulates known biological features and uncovers recurrent, directional architec- tures termed 'relons', spanning coding, non-coding, and unannotated regions. These re- lational modules reflect evolutionary and functional constraints and associate with hu- man genetic variation and pathogenic variants. Our findings suggest a higher-order rela- tional layer of genome organization, providing a framework to interpret DNA as a complex system of dependencies beyond linear and 3D structure, with implications for under- standing genetic variation and disease.

genomics↗

bioRxiv2026

A Framework for NGN1-Induced Sensory Neuron Differentiation for Disease Modelling and Drug Screening

Background: Neuropathic pain is a burdensome, difficult-to-treat, and highly heterogeneous condition with limited therapeutic options, underscoring the need for robust and reproducible human disease models. Human induced pluripotent stem cell (iPSC)-derived sensory neurons provide a promising platform for patient specific disease modelling and drug screening; however, their translational use is hampered by variability in differentiation efficiency, cellular composition, and functional maturation across protocols and cell lines. Methods: Here, we present a standardized and potentially scalable framework for NGN1 driven differentiation of human iPSCs into sensory neurons. Building on a previously published two step protocol (1), we systematically deconstructed and optimized each stage of differentiation across a large panel of genetically diverse iPSC lines. Results: We identified robust parameters for neural crest like cell (NCLC) generation, established a flow cytometry-based quality control strategy for NCLCs, and defined optimal combinations of seeding density and lentiviral multiplicity of infection to maximize sensory neuron progenitor yield. To improve culture homogeneity, we compared antimitotic selection strategies and demonstrated that tightly timed Ara-C treatment combined with low progenitor seeding density yields consistently pure sensory neuron cultures. We further evaluated maturation under physiologically relevant glucose conditions and performed a systematic review of media compositions to derive two defined maturation media. Morphological, immunocytochemical, transcriptomic, and electrophysiological analyses revealed that time in culture is a major determinant of maturation, while specific supplements such as prostaglandin E2; (PGE2) selectively enhance transcriptional signatures associated with nociceptor identity without substantially altering global network activity. Bulk RNA sequencing demonstrated broad expression of sensory neuron and pain related markers and gene programs across conditions, with long term maturation and PGE2; treatment showing the highest similarity to human dorsal root ganglion reference data. Functional assessment using multi electrode arrays enabled the detection of donor specific electrophysiological phenotypes, including reproducible hyperexcitability in small fiber neuropathy patient derived lines. Conclusions: This study establishes a modular, reproducible NGN1 based differentiation workflow with integrated quality checkpoints that accommodates iPSC line to line variability. The framework provides a practical foundation for translational sensory neuron research, patient specific disease modelling, and scalable drug screening applications.

neuroscience↗

bioRxiv2026

Laudanosine restricts Ebola virus entry by targeting TPC2-dependent endolysosomal trafficking

Late endosome-dependent viruses, including filo- and arenaviruses, rely on host endolysosomal trafficking for productive infection. Here, we used a dual-colour Vesicular stomatitis virus (VSV) based pseudoparticle screen of CytoSorb-derived fractions to identify inhibitors of the Zaire Ebolavirus glycoprotein (GP)-mediated entry. Iterative chromatographic purification and mass spectrometry identified Laudanosine, a degradation product of the clinically used neuromuscular blocker Atracurium, as the antiviral compound. Laudanosine specifically inhibited entry mediated by Ebola, Marburg, Lymphocytic choriomeningitis and Lassa virus glycoproteins without affecting VSV-G-dependent entry. Importantly, Laudanosine inhibited authentic Ebola virus infection without detectable cytotoxicity in cell culture and embryonic zebrafish. Molecular dynamics simulations suggest stable association of Laudanosine with the allosteric inhibitory pocket of the lysosomal two-pore channel (TPC2). Consistently, Laudanosine impairs autophagic flux and disrupts endolysosomal trafficking. Together, our findings identify Laudanosine as a previously unrecognised inhibitor of TPC2-dependent entry of highly lethal viral pathogens.

microbiology↗

bioRxiv2026

Aberrant DNA methylation is co-regulated across the genome in leukemia and other types of cancer

Epigenetic dysregulation is a defining feature of cancer, but it remains poorly understood how this is coordinated across the genome. In this study we focusd on DNA methylation (DNAm) in acute myeloid leukemia (AML). Despite highly heterogeneous and largely patient-specific patterns, we identified co-regulated clusters of CpGs that could be assembled into reproducible epigenetic networks. Multilinear regression models accurately predicted the patient-specific DNAm deviations, even for CpGs located on different chromosomes. The alterations were mirrored on homologous chromosomes and there was no clear association with epigenetic driver mutations. Furthermore, we found very similar co-regulation patterns in acute lymphoblastic leukemia (ALL); with AML-derived models successfully predicting the ALL-associated DNAm changes. Notably, the top 1000 AML-associated CpGs showed also pronounced aberrations in DNAm levels across 46 other cancer types, whereas this was hardly observed across multiple non-malignant cell types. Co-regulation analysis realed very similar patterns in non-malignant blood and pan-cancer analysis, albeit the DNAm levels remained overall consistent in the controls. Collectively, our findings demonstrate that the complex, patient-specific DNAm landscapes observed in leukemia are not random. Instead, they are orchestrated within expanded epigenetic networks, which also exist in non-maligant cells, highlighting a higher-order regulatory layer in cancer epigenomics.

cancer biology↗

bioRxiv2025

Genolator: A Multimodal Large Language Model Fusing Natural Language, Genomic, and Structural Tokens for Protein Function Interpretation

BackgroundDecoding the genetic code to unveil its genome functionality is a monumental task which would greatly advance the understanding of disease mechanisms and development of targeted treatment approaches. Although large language models (LLMs) have transformed natural language processing across diverse domains, translating the complex language of DNA into human-readable form remains challenging due to genomic data complexity and unexplored regions of the human genome. Current (genomic) language models either have a solid understanding of natural language or of the genomic code. Models fusing both aspects are largely lacking. ResultsHere we present Genolator, a multimodal large language model that integrates embeddings from DNA sequences, amino acid sequences, and protein structures with natural language queries. Fine-tuned on over 370,000 question-answer pairs generated using abstracted Gene-Ontology (GO) terms, Genolator effectively answers queries regarding protein subcellular localization, molecular function, and biological processes. Evaluation demonstrates high accuracy in confirming or denying protein function associations, outperforming baseline models such as openly available allrounder LLMs like GPT 4.1 as well as smaller domain specific models integrating knowledge from foundation models like Evo2 and ESM-2. Explorations of the Genolators hidden states unveil a biologically and linguistically plausible organization of its learned representations. ConclusionGenolator enhances accessibility to genomic information by enabling natural language interaction with protein data, facilitating biological discovery, and clinical research. It represents a step towards bridging genomic code and human language through the integration of a multimodal LLM.

bioinformatics↗

bioRxiv2024

Mutational Constraint Analysis Workflow for Overlapping Short Open Reading Frames and Genomic Neighbours

Understanding the dark genome is a priority task following the complete sequencing of the human genome. Short open reading frames (sORFs) are a group of largely unexplored elements of the dark genome with the potential for being translated into microproteins. The definitive number of coding and regulatory sORFs is not known, however they could account for up to 1-2% of the human genome. This corresponds to an order of magnitude in the range of canonical coding genes. For a few sORFs a clinical relevance has already been demonstrated, but for the majority of potential sORFs the biological function remains unclear. A major limitation in predicting their disease relevance using large-scale genomic data is the fact that no population-level constraint metrics for genetic variants in sORFs are yet available. To overcome this, we used the recently released gno-mAD 4.0 dataset and analysed the constraint of a consensus set of sORFs and their genomic neighbours. We demonstrate that sORFs are mostly embedded into a moderately constraint genomic context, but within the gencode dataset we identified a subset of highly constrained sORFs comparable to highly constrained canonical genes.

bioinformatics↗

bioRxiv2024

CNVizard: a lightweight streamlit application for an interactive analysis of copy number variants

Methods to call, analyze and visualize copy number variations (CNVs) from massive parallel sequencing data have been widely adopted in clinical practice and genetic research. To enable a streamlined analysis of CNV data, comprehensive annotation and good visualization are indispensable. The ability to detect single exon CNVs is another important feature for genetic testing. Nonetheless, most available open-source tools come with limitations in at least one of these areas. One drawback is that available tools deliver data in an unstructured and static format which requires subsequent visualization and formatting efforts. Here we present CNVizard, a lightweight streamlit app which requires minimal computational knowledge, and which is compatible with widely used CNV processing tools (CNVkit and AnnotSV). CNVizard can process short- and long-read sequencing data and provides an intuitive webapp-like experience enabling an interactive visualization of CNV data.

bioinformatics↗

bioRxiv2018

Aberrant DEGS1 sphingolipid metabolism impairs central and peripheral nervous system function in humans

Sphingolipids including ceramides are important components of cellular membranes and functionally associated with fundamental processes such as cell differentiation, neuronal signaling and myelin sheath formation. Defects in the synthesis or degradation of sphingolipids are associated with various neurological pathologies, however, the entire spectrum of disorders affecting sphingolipid metabolism remains elusive. By whole-exome sequencing in a patient with a multisystem neurological disorder of both the central and peripheral nervous system, we identified a homozygous variant p.(Ala280Val) in DEGS1, encoding an enzyme of the ceramide synthesis pathway. The blood sphingolipid profile and patient-derived fibroblasts both showed a significant shift from the unsaturated to the dihydro-forms of sphingolipids. Moreover, an atypical and potentially toxic sphingolipid metabolite is formed as consequence of the altered synthesis pathway. The changes in the sphingolipid profile were recapitulated in a CRISPR/Cas-based DEGS1 knockout HAP1-cell model and by chemical inhibition of DEGS1, suggesting a loss of DEGS1 function in the disease. DEGS1 insufficiency is thus a novel cause for a multisystem neurological disorder. A sphingolipid-rich diet may correct the metabolic profile and improve the clinical outcome of affected individuals and suggests that this heritable condition might be treatable.\n\nAbbreviations

genetics↗