Biology subjects

Fortelny, N.

Publications and source records attributed to Fortelny, N..

10 recordsLinked to original sources

bioRxiv2025

Differences between in vivo and ex vivo hematopoietic model systems modulate the outcomes of genetic perturbations.

Ex vivo experimental models are extensively used as reductionist models, yet the impact of model choice on molecular states and perturbation responses has not been systematically evaluated. Here, we compared the outcomes of genetic perturbations in hematopoietic stem cells between in vivo and ex vivo model systems. This revealed strong baseline differences characterized by a reduced interferon response and elevated growth and metabolism signatures in ex vivo models, a pattern recapitulated in various orthogonal human and mouse systems. We further found substantial differences between perturbation effects between in vivo and ex vivo models, with some perturbations showing opposite effects. Finally, we evaluated whether AI-based perturbation prediction models can predict in vivo perturbation effects from ex vivo perturbation effects, which proved challenging. Together, our findings reveal systematic biases in ex vivo models, demonstrate how these biases influence perturbation outcomes, suggest approaches to enhance ex vivo systems, and provide a test case for computational prediction of perturbation effects.

systems biology↗

bioRxiv2025

Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens

Cardiac fibrosis is mediated by the persistent activity of myofibroblasts, which differentiate from resident cardiac fibroblasts in response to tissue damage and stress signals. The signaling pathways and transcription factors regulating fibrotic transformation have been thoroughly studied. In contrast, the roles of chromatin factors in myofibroblast differentiation and their contribution to pathogenic cardiac fibrosis remain poorly understood. Here, we combined bulk and single-cell CRISPR screens to characterize the roles of chromatin factors in the fibrotic transformation of primary cardiac fibroblasts. We uncover strong regulators of fibrotic states including Srcap and Kat5 chromatin remodelers. We confirm that these factors are required for functional processes underlying fibrosis including collagen synthesis and cell contractility. Using chromatin profiling in perturbed cardiac fibroblasts, we demonstrate that pro-fibrotic chromatin complexes facilitate the activity of well-characterized pro-fibrotic transcription factors. Finally, we show that KAT5 inhibition alleviates fibrotic responses in patient-derived human fibroblasts.

cell biology↗

bioRxiv2024

Hedgehog-Hippo pathway interactions promote T cell exclusion from the tumor microenvironment in basal cell carcinoma

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer driven primarily by genetic activation of the Hedgehog/GLI (HH/GLI) signaling pathway. BCC also displays a high mutational burden, with frequent co-occurrence of mutations in the TP53, NOTCH, N- MYC, and Hippo/YAP pathways, though the impact of these mutations on HH/GLI-driven tumorigenesis remains poorly understood. This study examines the interaction between the HH/GLI and Hippo/YAP signaling pathways in the pathogenesis of BCC and the establishment of an immunosuppressive tumor microenvironment (TME). The study used 3D human organotypic skin models and humanized mouse models to demonstrate that co-activation of HH/GLI and Hippo/YAP in epidermal cells suppresses T cell chemotaxis. Transcriptome analysis revealed a significant downregulation of chemotactic and inflammatory genes, particularly under combined GLI-YAP activation. Analysis of primary human BCC biopsies confirmed these findings and demonstrated a marked suppression of T cell chemo-attractants, including CCL22 and CCL27. Spatial profiling of immune cells in BCC tissues revealed preferential exclusion of CD8+ T cells from the TME, which correlated with high HH/GLI and Hippo/YAP activity. Further, functional assays in humanized mice showed that the GLI-YAP interaction controls immune cell distribution by inhibiting T cell recruitment and migration into the skin. These findings suggest that cooperation between oncogenic HH/GLI and Hippo/YAP signaling contributes to the development of an immunosuppressive TME in BCC by disrupting local chemokine signaling. Understanding these mechanisms provides a basis for the development of targeted combination therapies to enhance the efficacy of existing treatments, such as Smoothened (SMO) inhibitors and immune checkpoint blockers, by promoting T cell infiltration and activation within the TME.

cancer biology↗

bioRxiv2023

Reliable interpretability of biology-inspired deep neural networks

Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.

bioinformatics↗

bioRxiv2022

Systematic functional screening of chromatin factors identifies strong lineage and disease dependencies in normal and malignant haematopoiesis

Interactions between transcription factors (TF) and chromatin factors (CF) regulate gene expression programmes to determine cellular fate. However, unlike for TF, the exact role of CF in this process is poorly understood. Using haematopoiesis as a model system and utilising novel functional CRISPR screens ex vivo and in vivo, coupled with Perturb-Seq, CF binding and genome-wide chromatin accessibility in primary murine cells, we assess the role of 550 chromatin factors in lineage choice in normal haematopoiesis and the maintenance of acute myeloid leukaemia (AML). These studies demonstrate marked specificity for a large number of CFs in lineage determination, highlighting functional diversity within specific families of chromatin regulators, including MLL-H3K4-methyltransferases and different BAF-complexes, that regulate disparate lineage decisions across haematopoiesis. Conversely, we demonstrate that unrelated Repressive complexes function similarly to restrain excessive myeloid differentiation and protect lineage diversity. We identify interactions between CF and TF that, at least in part, explain the regulatory function of CF and link Brd9-loss to a premalignant state. Utilising similar experiments in a relevant murine AML model, we demonstrate opposing effects for CF in normal haematopoiesis and AML, where MLL-H3K4-methyltransferases, c-BAF-remodelers and Repressive complexes prevent differentiation and maintain leukaemic fitness. We show that this alteration relates to differential utilisation of TF by CF complexes between normal and malignant haematopoiesis, highlighting corrupted TF-CF interactions as potential novel avenues for therapeutic intervention in AML. Together, this study provides novel insights on the functional diversity of chromatin factors in governing cell-fate.

genomics↗

bioRxiv2022

Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species

Methylation of cytosines is the prototypic epigenetic modification of the DNA. It has been implicated in various regulatory mechanisms throughout the animal kingdom and particularly in vertebrates. We mapped DNA methylation in 580 animal species (535 vertebrates, 45 invertebrates), resulting in 2443 genome-scale, base-resolution DNA methylation profiles of primary tissue samples from various organs. Reference-genome independent analysis of this comprehensive dataset quantified the association of DNA methylation with the underlying genomic DNA sequence throughout vertebrate evolution. We observed a broadly conserved link with two major transitions - once in the first vertebrates and again with the emergence of reptiles. Cross-species comparisons focusing on individual organs supported a deeply conserved association of DNA methylation with tissue type, and cross-mapping analysis of DNA methylation at gene promoters revealed evolutionary changes for orthologous genes with conserved DNA methylation patterns. In summary, this study establishes a large resource of vertebrate and invertebrate DNA methylomes, it showcases the power of reference-free epigenome analysis in species for which no reference genomes are available, and it contributes an epigenetic perspective to the study of vertebrate evolution.

genomics↗

bioRxiv2019

Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data

Deep learning has emerged as a powerful methodology for predicting a variety of complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent (such as a protein or gene) and every edge has a mechanistic interpretation (e.g., a regulatory interaction along a signaling pathway).\n\nWith knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights to multi-layered networks for interpretable deep learning. We introduce three methodological advances in the learning algorithm that enhance interpretability of the learnt KPNNs: Stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for the uneven connectivity inherent to biological networks. We demonstrate the power of our approach on two single-cell RNA-seq datasets, predicting T cell receptor stimulation in a standardized in vitro model and inferring cell type in Human Cell Atlas reference data comprising 483,084 immune cells.\n\nIn summary, we introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.

bioinformatics↗

bioRxiv2019

Single-cell RNA-seq analysis reveals the crucial role of Collagen Triplex Helix Repeat Containing 1 (CTHRC1) cardiac fibroblasts for ventricular remodeling after myocardial infarction

Cardiac fibroblasts have a central role during the ventricular remodeling process associated with different types of cardiac injury. Recent studies have shown that fibroblasts do not respond homogeneously to heart damage, suggesting that the adult myocardium may contain specialized fibroblast subgroups with specific functions. Due to the limited set of bona fide fibroblast markers, a proper characterization of fibroblast population dynamics in response to cardiac damage is still missing. Using single-cell RNA-seq, we identified and characterized a fibroblast subpopulation that emerges in response to myocardial infarction (MI) in a murine model. These activated fibroblasts exhibit a clear pro-fibrotic signature, express high levels of the hormone CTHRC1 and of the immunomodulatory co-receptor CD200 and localize to the injured myocardium. Combining epigenomic profiling with functional assays, we show Sox9 and the non-canonical TGF-{beta} signaling as important regulators mediating their response to cardiac damage. We show that the absence of CTHRC1, in this activated fibroblast subpopulation, results in pronounced lethality due to ventricular rupture in a mouse model of myocardial infarction. Finally, we find evidence for the existence of similar mechanisms in a pig pre-clinical model of MI and establish a correlation between CTHRC1 levels and cardiac function after MI.

cell biology↗

bioRxiv2019

Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib drug response in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a genetically, epigenetically, and clinically heterogeneous disease. Despite this heterogeneity, the Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for the vast majority of CLL patients. To define the underlining regulatory program, we analyzed high-resolution time courses of ibrutinib treatment in closely monitored patients, combining cellular phenotyping (flow cytometry), single-cell transcriptome profiling (scRNA-seq), and chromatin mapping (ATAC-seq). We identified a consistent regulatory program shared across all patients, which was further validated by an independent CLL cohort. In CLL cells, this program starts with a sharp decrease of NF-{kappa}B binding, followed by reduced regulatory activity of lineage-defining transcription factors (including PAX5 and IRF4) and erosion of CLL cell identity, finally leading to the acquisition of a quiescence-like gene signature which was shared across several immune cell types. Nevertheless, we observed patient-to-patient variation in the speed of its execution, which we exploited to predict patient-specific dynamics in the response to ibrutinib based on pre-treatment samples. In aggregate, our study describes the cellular, molecular, and regulatory effects of therapeutic B cell receptor inhibition in CLL at high temporal resolution, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring.

cancer biology↗

bioRxiv2017

The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space

Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the role of the epigenome in glioblastoma disease progression. Here, we present genome-scale maps of the DNA methylation dynamics in matched primary and recurring glioblastoma tumors, based on a national population registry and a comprehensively annotated clinical cohort. We demonstrate the feasibility of DNA methylation mapping in a large set of routinely collected formalin-fixed paraffin-embedded (FFPE) samples, and we validate bisulfite sequencing as a multi-purpose assay that allowed us to infer a range of different genetic, epigenetic, and transcriptional tumor characteristics. Based on these data, we identified characteristic differences between primary and recurring tumors, links between DNA methylation and the tumor microenvironment, and an association of epigenetic tumor heterogeneity with patient survival. In summary, this study provides a resource for dissecting DNA methylation heterogeneity in genetically diverse and heterogeneous tumors, and it demonstrates the feasibility of integrating epigenomics, radiology, and digital pathology in a representative national cohort, leveraging samples and data collected as part of routine clinical practice.

cancer biology↗