Biology subjects

Valencia, A.

Publications and source records attributed to Valencia, A..

At least 19 recordsLinked to original sources

bioRxiv2026

PhysiCelldFBA: Linking single-cell genome-scale metabolism to spatially explicit multicellular dynamics

Genome-scale metabolic models can predict how individual cells allocate resources and respond to their environment, yet few frameworks link single-cell metabolism to the spatial organisation of multicellular systems. Here we introduce PhysiCelldFBA, an extension of the PhysiCell agent-based framework that couples genome-scale dynamic flux balance analysis to off-lattice multicellular simulations. Each simulated cell carries its own metabolic model, allowing local environmental conditions to shape metabolism while metabolic activity feeds back on the surrounding environment, cellular behaviour, and spatial organisation. We first validate this coupling by showing that glucose consumption, CO2 production, and biomass accumulation remain mass-balanced in a closed E. coli system, with simulated biomass agreeing with analytical predictions to within 1%. We then demonstrate how metabolic phenotypes emerge from this coupling across microbial and mammalian systems. Spatial nutrient gradients generate metabolic stratification and acetate cross-feeding in growing E. coli colonies; diffusion-limited metabolism produces proliferative, hypoxic, and necrotic zones across a broad panel of metabolites in a tumour-like tissue; distinct, organism-specific metabolic networks give rise to syntrophic cross-feeding and spatial niche formation in a two-species consortium; and metabolic state couples energy availability to transitions between cellular motility and growth. Across these examples, metabolic stratification, cross-feeding, and phenotypic adaptation emerge from local metabolic optimisation and environmental feedback rather than being explicitly prescribed. PhysiCelldFBA therefore provides a general framework for simulating genome-scale metabolism at single-cell resolution and linking intracellular metabolic state to cellular behaviour and emergent organisation across scales.

systems biology↗

bioRxiv2026

Interpretable Forecasting of Kidney Cancer Progression via Generative AI and Symbolic Reasoning

Predicting cancer stage progression from omics data, and deriving molecular insight into the mechanisms driving it, remains a major challenge, owing in part to the lack of adequate longitudinal data and the interpretability limitations of current forecasting models. Large cancer datasets such as TCGA capture patient profiles cross-sectionally rather than longitudinally, complicating timely treatment decisions as tumors become more invasive. Deep neural networks typically used for forecasting, such as LSTMs, compound this problem by remaining largely opaque and offering clinicians no straightforward way to audit their predictions. Clear cell renal cell carcinoma (ccRCC) illustrates the clinical stakes of both challenges. Five-year survival falls from over 94% at stage I to 28% at stage IV, yet early-stage tumors are often managed under active surveillance, a strategy constrained by sparse molecular evidence of progression risk. Detecting progression in time, meanwhile, demands forecasts clinicians can interpret and trust, not black-box predictions. We address both challenges by combining generative and symbolic AI: a Variational Autoencoder trained on bulk RNA-Seq profiles of 530 TCGA ccRCC patients generates synthetic pseudo-time trajectories that overcome the absence of longitudinal data, while a symbolic rule-induction framework (ASAL) learns finite-state automata from these trajectories, encoding stage transition as human-readable Boolean conditions over gene expression, which a complex event forecasting system (Wayeb) converts into probabilistic forecasts of stage advancement. An independent XGBoost classifier trained on real patients (F1 score = 0.71-0.81) shows a gradual early-to-late probability shift along the synthetic trajectories, absent in non-progressing control trajectories. Pathway enrichment of those trajectories reveals stage-dependent changes in established kidney cancer-related processes, including the TCA cycle and DNA repair. Finally, our symbolic forecaster nearly matches an LSTM baseline (macro F1 = 0.928 vs. 0.964), while additionally offering an inspectable rule set and a probability distribution over transition timing rather than a single opaque score. This work shows that generative and symbolic AI, paired together, can turn cross-sectional cohorts into a transparent, forecast-oriented framework for modeling disease progression, demonstrated here in ccRCC.

bioinformatics↗

bioRxiv2026

A Genetic Atlas of Direct and Inverse Neuropsychiatric-Cancer Comorbidity

Direct and inverse comorbidities between neuropsychiatric disorders and cancer are increasingly recognised as important features of the nervous system-cancer relationship, yet the inherited genetic architecture underlying these patterns remains poorly understood. Here, we analysed pairwise genetic correlations across 35 diseases represented by 115 GWAS datasets, including 9 psychiatric disorders, 10 neurological diseases and 16 cancers, using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL), complemented by meta-analysis, subtype-resolved analyses, local covariance mapping and multi-omic benchmarking. Genetic correlations were predominantly positive and strongest within disease categories, whereas cancer-neurological pairs showed the weakest overall genetic affinity. Meta-analysis and subtype resolution uncovered associations obscured in aggregate analyses, including opposing correlations between familial and late-onset Alzheimers disease and lung cancer, revealing subtype-dependent neuro-oncological biology. Local analyses identified recurrent genomic loci where direct comorbidities are consistent with shared inflammatory, interferon, survival and tissue-remodelling programs, whereas inverse comorbidities suggest competing demands on apoptotic regulation, immune tone and stress-response calibration between neuronal and tumour-cell states. Together, these findings provide a genome-scale genetic framework for neuropsychiatric-cancer comorbidity and identify shared inherited biological programs as candidates for mechanistic investigation and therapeutic translation.

genomics↗

bioRxiv2026

An enhancer-centric approach applied to human immune system epigenomes revealed the association of macrophage enhancers with cardiovascular disease

Complex diseases are influenced by both genetic and environmental factors. Immune cells are key mediating interactions with the environment, but the impact of genetic variation on the immune system and how it influences complex diseases is not fully understood. Moreover, most genome-wide analyses (GWAS) variants associated with complex diseases are non-coding and difficult to interpret. Here, we investigated the association of non-coding variants with immune cell enhancers. As part of BLUEPRINT and the International Human Epigenome (IHEC) consortia, we generated and analysed a comprehensive set of epigenomes for human primary immune cells, including 107 epigenomes derived from 749 ChIP-Seq experiments across 24 cell types. We identified multicell enhancer activity patterns across the genome and examined their links with non-coding variants from 518 GWAS traits. This analysis revealed 117 significant associations, including novel links between cardiovascular disease variants and macrophage-specific enhancers that regulate genes involved in lipid metabolism and immunity, such as the gene encoding for the nuclear receptor LXR-alpha (NR1H3) and many of its known target genes. Together, these data will help to better understand the influence of genetic variability in immune function and related diseases.

genomics↗

bioRxiv2026

EBEx: an Ensemble-Based Explainable Framework for Gene Calling in Heterogeneous Diseases

Complex and clinically heterogeneous diseases pose significant challenges for gene prioritisation and patient stratification, as relevant genes often show weak or context-specific signals and transcriptomic datasets are limited in size. These limitations hinder the discovery of robust molecular signatures using traditional case-control approaches and motivate computational pipelines capable of capturing molecular diversity. Here, we present an explainable ensemble-based AI pipeline to prioritise disease-relevant genes from transcriptomic data, using Chronic Obstructive Pulmonary Disease (COPD) as a use case. To retain biologically relevant interactors obscured by molecular heterogeneity, the framework integrates data-driven signals with curated COPD-related gene sets, further expanded through network-based prioritisation and supported by molecular interactions. Gene relevance is evaluated via aggregated explainability scores across multiple classifier configurations to ensure robust candidate selection. The final set comprised < 8% of evaluated genes, [~] 62% arising from network-based expansion, substantially reducing dimensionality while preserving biological heterogeneity. Beyond case-control classification, the approach identified candidate genes and molecular subgroups associated with specific clinical features, capturing patient-level heterogeneity. The prioritised genes recapitulated key disease-related processes, including immune responses and extracellular matrix degradation, and highlighted additional associations like the enrichment of the IL-4 and IL-13 signalling pathway, which is of clinical interest given ongoing biologic developments targeting these axes. Our pipeline outperformed existing methods in discriminating COPD from controls, and the final gene list was validated in independent cohorts. Implemented as a scalable and reusable R package, this framework facilitates the study of molecular heterogeneity in complex diseases like COPD, supporting advances in diagnosis and precision medicine. Availability and implementationEBEx code and tutorials can be found in: https://iposelag.github.io/EBEx/

bioinformatics↗

bioRxiv2026

Adaptive and Spandrel-like Constraints at Functional Sites in Protein Folds

How new molecular functions emerge during protein evolution remains a fundamental question in molecular biology. The energy landscape theory states that proteins are minimally frustrated, i.e. they have minimized their internal conflicts, to allow robust folding. Yet, not all energetic conflicts are eliminated, with functional regions such as catalytic residues and ligand-binding sites being often enriched in frustrated interactions, trading localized stability for biological activity. However, it is still uncertain whether this functional frustration is an evolutionary adaptation, positively selected despite its energetic cost or an inevitable physical byproduct of the fold architecture. Here, we combine reverse folding, structure prediction, and sequence analysis with local frustration profiling to address this long-standing question. Unexpectedly, we found that reverse folding algorithms are unable to energetically minimize evolutionary conserved frustration at specific residues, even when detrimental to overall structural stability. We propose that these frustration hotspots act as architectural spandrels, inherent physical constraints of the fold that evolution subsequently co-opts for function. Our findings connect biophysical constraints and evolutionary selection, providing a new framework to understand how functional specificity emerges in protein landscapes.

bioinformatics↗

bioRxiv2025

Three-Dimensional Epigenome Roadmap of Human B-cell Differentiation Uncovers Mechanisms of Humoral Immunity and Oncogenesis

Human B-cell differentiation underlies humoral immunity, and its disruption leads to cancer, autoimmunity, and immunodeficiencies. However, the enhancer-based regulatory mechanisms governing this process remain poorly understood. To address this gap, we generated the first three-dimensional epigenomic roadmap of human B-cell differentiation in vivo. By profiling nine differentiation stages, we linked nearly half a million enhancers to their target genes through chromatin interactions, defined their activity states, and predicted bound transcription factors. We show that the acquisition of B-cell identity, distinct from common cellular processes, relies on large enhancer networks acting additively and synergistically to finely tune transcription. We further identify a 3D epigenetic priming mechanism underlying immune memory, whereby memory B cells retain primed DNA loops that enable faster responses to antigen re-exposure. Extending this roadmap to disease, we demonstrate that B-cell malignancies preserve enhancer signatures of their cell of origin while silencing distal tumor suppressor control. Finally, we uncover a previously unappreciated oncogenic mechanism in which intragenic deletions disrupt distal gene regulation via enhancer loss. Together, this resource provides a framework for understanding the genetic and epigenetic basis of humoral immunity and immune-related diseases.

genomics↗

bioRxiv2025

Airway organoids reveal patterns of Influenza A tropism and adaptation in wildlife species

Identifying animal species that are susceptible to the plethora of existing and emerging viruses is critical for predicting and containing disease outbreaks. Current efforts to assess viral tropism largely rely on experimental infection models, but such experiments are logistically and ethically infeasible for many wildlife species. To tackle this challenge, we developed a panel of airway organoids from ten taxonomically diverse wildlife and livestock species and evaluated their susceptibility to influenza viruses of mammalian (pH1N1) and avian (H5N1) origin. Our analyses revealed large species-specific differences in infection rate and cytopathogenicity that aligned with known in vivo data and field observations. Furthermore, we demonstrated that this organoid panel can serve as a powerful tool to elucidate receptor-binding mechanisms, viral dynamics, and early host adaptation in poorly characterized animal species. In summary, this work provides a robust and ethically viable approach for evaluating viral tropism and adaptation in wildlife species, and fills a critical gap in current pandemic preparedness, zoonotic disease surveillance, and wildlife conservation efforts.

microbiology↗

bioRxiv2025

A Unified Framework for TCR-pMHC Structural Model Assessment

T cell receptor (TCR) recognition of peptide-MHC complexes (pMHCs) is a central determinant of cellular immunity. Structural insights are key to understanding TCR-pMHC interactions, however progress is limited by the scarcity of experimental structures (PDB = 232 TCR-pMHC class I complexes, date 01/2025). Although protein modelling tools have rapidly advanced, their accuracy on TCR-pMHC complexes is hampered by CDR loop hypervariability, conformational flexibility, and the lack of reliable quality assessment without experimental references. To evaluate the performance of recent protein modelling algorithms on TCR-pMHC complexes, we benchmarked three general-purpose (AlphaFold3, Boltz-2, Chai-1) and two TCR-specific (tFold-TCR, TCRmodel2) modelling tools using a benchmark set of 20 experimentally determined PDB structures, demonstrating AlphaFold3 superior performance. To expand structural coverage beyond the existing experimental landscape, we present a framework that enables quality assessment of TCR-pMHC structural models without requiring experimental reference structures. To this end, we integrate multiple modelling and interface confidence metrics (pLDDT, ipTM, iPAE, pDockQ) into explainable random forest classifiers trained on 1160 models of 232 experimentally determined PDB TCR-pMHC class I complexes. This approach reliably distinguishes low-, acceptable-, medium-, and high-quality models, outperforming the combination of these metrics using literature-established thresholds. Leveraging our quality tier framework, we used AlphaFold3 to generate and evaluate the largest synthetic dataset of TCR-pMHC class I structural models to date from VDJdb-annotated sequences, comprising 33,820 complexes (169,100 models, 5 models per complex) and representing a >70-fold expansion of available structures. We demonstrate the applicability of our quality tier stratification in three settings: 1) filtering validating vs. non-validating TCR-pMHC interaction in sequence databases (VDJdb), 2) enrichment of biologically validated interactions versus synthetic negatives amongst higher quality complexes, and 3) enhancing the predictive performance of TCR-pMHC pairing models. Altogether our TCR-pMHC structural quality tier framework provides a scalable and interpretable approach for improving modelling and functional analyses of TCR-pMHC class I complexes, with translational applications in T-cell and TCR-based immunotherapies.

bioinformatics↗

bioRxiv2025

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces

The construction of multicellular mechanistic models in systems biology typically requires months of literature research, programming expertise, and deep knowledge of specialized computational tools. Here we present intelligent tool orchestration through Model Context Protocol (MCP) servers that enable Large Language Models (LLMs) agents to function as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF, entirely through natural language interactions, using an AI agent connected to MCP servers that interface with three complementary modeling software: NeKo for constructing gene regulatory networks from prior-knowledge databases, MaBoSS for simulating and analyzing Boolean models, and PhysiCell for setting up multicellular agent-based models. Our architecture encompasses more than 60 specialized tools, covering the entire workflow from data collection to multicellular simulation setup. From this use case we derived three key design principles for biological AI-tool integration: first, optimal tool granularity is best defined at biological decision points, where domain knowledge guides modeling choices; second, comprehensive session management is essential for tracking complex workflows and ensuring reproducibility across long interactions; and third, effective orchestration must be established from the start to let LLMs combine tools flexibly while maintaining biological coherence. In applying this framework across three different LLMs and scenarios, we observed consistent end-to-end orchestration as well as model-dependent differences in network size, dynamical behaviors, and integration details. This variability highlights both the portability of our approach and the importance of careful reporting and validation when deploying different LLMs in scientific contexts. While the resulting models require refinement, this work establishes the foundation for AI-assisted rapid prototyping in systems biology, enabling researchers to explore computational hypotheses at unprecedented speed while maintaining biological fidelity.

bioinformatics↗

bioRxiv2025

Open Benchmarking for Cell-Based Multiscale Models: Lessons from a Community Initiative

The emergence of virtual human twins (VHT) in biomedical research has sparked interest in multiscale in silico modelling frameworks, particularly in their application bridging cellular to tissue levels. Among the diverse array of multiscale modelling tools, off-lattice center-based agent-based models (CBM) offer a promising approach due to their depiction of cells in 3D space, closely resembling biological reality. Despite the proliferation of CBM tools addressing various biomedical challenges, a comprehensive and systematic comparison among them has been elusive. This paper presents a community-driven benchmark initiative aimed at evaluating and comparing CBM for biomedical applications, akin to successful efforts in other scientific domains such as the Critical Assessment of Protein Structure Prediction (CASP). Enlisting developers from leading tools like BioDynaMo, Chaste, PhysiCell,TiSim, and CompuTiX, we devised a benchmark scope, defined metrics, and established reference datasets to ensure a meaningful and equitable evaluation. Unit tests targeting different solvers within these tools were designed, ranging from diffusion and mechanics to cell cycle simulations and growth scenarios. Results from these tests demonstrate varying tool implementations in handling diffusion, mechanics, and cell cycle equations, emphasising the need for standardised benchmarks and interoperability. Discussions among the community underscore the necessity for defining gold standards, fostering interoperability, and drawing lessons from analogous benchmarking experiences. The outcomes, disseminated through a public platform in collaboration with OpenEBench, aim to catalyse advancements in computational biology, offering a comprehensive resource for tool evaluation and guiding future developments in cell-level simulations. This initiative endeavours to strengthen and expand the computational biology simulation community through continued dissemination and performance-oriented benchmarking efforts to enable the use of VHT in biomedicine.

systems biology↗

bioRxiv2025

10 Years of Variational Autoencoder: Insights from Cancer Temporal Progression Studies, a Systematic Literature Review

Deep learning methods, including deep representation learning (DRL) approaches such as variational au-toencoders (VAEs), have been widely applied to cancer omics data to address the high dimensionality of these datasets. Despite remarkable advances, cancer remains a complex and dynamic disease that is challenging to study, and the temporal resolution of cancer progression captured by omics-based studies remains limited. In this systematic literature review, we explore the use of DRL, particularly the VAE, in cancer omics studies for modeling time-related processes, such as tumor progression and evolutionary dynamics. Our work reveals that these methods most commonly support subtyping, diagnosis, and prognosis in this context, but rarely emphasize temporal information. We observed that the scarcity of longitudinal omics data currently limits deeper temporal analyses that could enhance these applications. We propose that applying the VAE as a generative model to study cancer in time, for example, focusing on cancer staging, could lead to meaningful advancements in our understanding of the disease. Biographical NoteO_LIGuillermo Prol-Castelo is a PhD student at the Barcelona Supercomputing Center and Universitat Pompeu Fabra, where he works on the application of deep learning methods to cancer studies. C_LIO_LIDavide Cirillo is the head of the Machine Learning for Biomedical Research Unit at the Barcelona Supercomputing Center. He is an expert in predictive modeling for Precision Medicine using Network Biology and Machine Learning. C_LIO_LIAlfonso Valencia is the principal investigator of the Computational Biology Group at the Barcelona Supercomputing Center. He is a leading expert in protein coevolution, disease networks and modelling cellular systems. C_LIO_LIThe Barcelona Supercomputing Center is a public research center that provides high-performance computing infrastructure to support scientific research in a wide range of fields, including life sciences. C_LI Key PointsO_LIThere is a growing interest on the application of deep learning methods, such as Deep Representation Learning (DRL), to cancer studies. C_LIO_LICancer is a complex and dynamic disease, whose temporal dynamics are not yet fully captured in omics-based studies. C_LIO_LImong DRL methods, the Variational Autoencoder (VAE) using omics-based data has been widely used in cancer studies, particularly for subtyping, diagnosis, and prognosis. C_LIO_LIThe temporal aspects of cancer progression are often insufficiently captured in omics-based studies, primarily due to the scarcity of longitudinal data. C_LIO_LIApplying the VAE as a generative model to study cancer in time, such as focusing on cancer staging, could lead to significant advancements in our understanding of cancer. C_LI

bioinformatics↗

bioRxiv2025

Using Constraint-Based Metabolic Modeling to Elucidate Drug-Induced Metabolic Changes in a Cancer Cell Line

Cancer cells frequently reprogram their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the TIDE (Tasks Inferred from Differential Expression) algorithm to infer pathway activity changes and introduced TIDE-essential, a new variant that focuses on task-essential genes, enhancing results robustness. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki-MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.

systems biology↗

bioRxiv2025

Sex-specific transcriptome similarity networks elucidate comorbidity relationships

Humans present sex-driven biological differences. Consequently, the prevalence of analyzing specific diseases and comorbidities differs between the sexes, directly impacting patients management and treatment. Despite its relevance and the growing evidence of said differences across numerous diseases (with 4,370 PubMed results published within the past year), knowledge at the comorbidity level remains limited. In fact, to date, no study has attempted to identify the biological processes altered differently in women and men, promoting differences in comorbidities. To shed light on this problem, we analyze expression data for more than 100 diseases from public repositories, analyzing each sex independently. We calculate similarities between differential expression profiles by disease pairs and find that 13-16% of transcriptomically similar disease pairs are sex-specific. By comparing these results with epidemiological evidence, we recapitulate 53-60% of known comorbidities distinctly described for men and women, finding sex-specific transcriptomic similarities between sex-specific comorbid diseases. The analysis of shared underlying pathways shows that diseases can co-occur in men and women by altering alternative biological processes. Finally, we identify different drugs differentially associated with comorbid diseases depending on patients sex, highlighting the need to consider this relevant variable in the administration of drugs due to their possible influence on comorbidities.

bioinformatics↗

bioRxiv2024

Exploring the Boundaries of Medulloblastoma Subgroups with Synthetic Data Generation

Medulloblastoma is a childhood brain tumor traditionally classified into four molecular subgroups. Recent evidence suggests that Groups 3 and 4 represent a biological continuum rather than distinct entities, a paradigm shift with significant implications for understanding disease biology and treatment strategies. Nevertheless, assessing this hypothesis is challenging mainly due to data scarcity. In this study, we analyze the largest available transcriptomics dataset to provide compelling evidence for the existence of an intermediate subgroup between Groups 3 and 4, characterized by distinct molecular features. To overcome limitations posed by data scarcity, we employ synthetic data generation using a Variational Autoencoder and apply explainability techniques to identify key relationships between gene expression and disease subgroups. Furthermore, by incorporating Machine Learning Fairness approaches, we demonstrate that overlooking this intermediate subgroup can result in treatment disparities. Our findings are further supported by both existing and newly proposed studies using diverse datasets and methodologies, including graphbased analyses and multi-scale simulations, underscoring the robustness and reproducibility of our results. This study demonstrates the potential of synthetic data generation to refine rare disease subtyping and advance our understanding of the underlying biological mechanisms. Keywords: Medulloblastoma, pediatric cancer, representation learning, autoencoder, synthetic data

bioinformatics↗

bioRxiv2024

Sequence-based coevolutionary prediction of species-specific interactomes

Evolutionarily conserved protein-protein interactions (PPIs) reveal fundamental biological processes. However, predicting protein function solely from these interactions provides an incomplete picture of biological systems. Computational methods for predicting PPIs often struggle due to limited functional annotations in databases, making it difficult to fully understand unique biological systems.. This study introduces ContextMirror2.0 (CM2.0), a coevolution-based method designed to address these limitations. Coevolutionary approaches, unlike supervised machine learning methods, do not rely on labelled datasets, proving valuable in addressing the scarcity of data for species-specific PPIs. While around 40% of CM2.0s top-1000 predicted PPIs for E. coli, S. enterica, and S. aureus are present in experimental PPI databases, a comparative analysis of predicted functional communities revealed highly conserved interaction patterns and species-specific interactions. CM2.0 leverages coevolutionary information to explore protein interaction dynamics and understand the functional consequences of species-specific variations. This information can inform further studies on the emergence of novel functions, adaptation to specific environments, and the development of targeted therapies.

bioinformatics↗

bioRxiv2024

Translocations can drive expression changes of multiple genes in regulons covering entire chromosome arms

Translocations have largely been implicated in tumor development. However, beyond the consequences of aberrant gene expression near the breakpoint, their effects remain unexplored. In this work, we characterize the interplay between translocations, chromatin organization and gene expression using mantle cell lymphoma (MCL) as a model. We show that in vitro induced MCL-associated translocations can drive transcriptional changes at entire chromosome arms affecting multiple genes in a regulon-like fashion. Additionally, overexpressed genes in MCL patients are enriched in the exact same genomic regions, further underlining its potential relevance for lymphomagenesis. Moreover, we demonstrate a clear link between the translocation-induced transcriptional alterations and genome organization, with genes most susceptible to change expression residing in pre-existing long-range interacting loops spanning 50 megabases. The translocation places the strong immunoglobulin enhancer into this loop, allowing the spread of its regulatory potential over the entire affected chromosome arm. Finally, we show that translocation-induced effects mainly represent expression enhancement of genes already active prior to translocation formation, highlighting the importance of the epigenetic state of the cell in which this initial hit occurs. In summary, we show that translocations can induce ample, simultaneous gene expression changes affecting entire chromosome arms, representing an important new mechanism for tumorigenesis.

cancer biology↗

bioRxiv2024

Midline Assembloids Reveal Regulators of Human Axon Guidance

Organizers are specialized cell populations that orchestrate cell patterning and axon guidance in the developing nervous system. Although non-human models have led to fundamental discoveries about the organization of the nervous system midline by the floor plate, an experimental model of human floor plate would enable broader insights into regulation of human neurodevelopment and midline connectivity. Here, we have developed stem cell-derived organoids resembling human floor plate (hFpO) and assembled them with spinal cord organoids (hSpO) to generate midline assembloids (hMA). We demonstrate that hFpO promote Sonic hedgehog-dependent ventral patterning of human spinal progenitors and Netrin-dependent guidance of human commissural axons, paralleling non-human models. To investigate evolutionary-divergent midline regulators, we profiled the hFpO secretome and identified 27 evolutionarily divergent genes between human and mouse. Utilizing the hMA platform, we targeted these candidates in an arrayed CRISPR knockout screen and reveal that GALNT2, a gene involved in O-linked glycosylation, impairs floor plate-mediated guidance of commissural axons in humans. This novel platform extends prior axon guidance discoveries into human-specific neurobiology with implications for mechanisms of nervous system evolution and neurodevelopmental disorders.

neuroscience↗