Biology subjects

Granados, A.

Publications and source records attributed to Granados, A..

13 recordsLinked to original sources

bioRxiv2025

EMILIN1 emerges as a TGFbeta/SETDB1-regulated secreted biomarker in Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is an incurable muscle-wasting disorder characterized by chronic membrane damage, inflammation, and progressive fibrosis. Fibrosis in DMD is driven by sustained TGF{beta} signaling, which promotes extracellular matrix (ECM) accumulation. We previously showed that SETDB1 sustains the TGF{beta}-induced fibrotic response in DMD myotubes. Here, we further show that SETDB1 modulates the TGF{beta}-induced secretome, particularly by regulating ECM-related proteins. Comparison of the basal secretome from DMD patient-derived myotubes and healthy controls revealed a distinct disease-specific profile. Integrating both secretome analyses, we identified EMILIN1, an ECM glycoprotein not previously studied in skeletal muscle, as a robust shared candidate; EMILIN1 is enriched in the DMD secretome, further upregulated by TGF{beta}, and downregulated upon SETDB1 depletion. We confirmed EMILIN1 overexpression in DMD patient muscle biopsies, validating its pathological relevance. Functionally, EMILIN1 depletion modulated myogenic differentiation and reduced expression of the fibrotic marker SERPINE1. These findings establish EMILIN1 as a novel secreted regulator of myogenesis and fibrosis, and implicate SETDB1 in shaping the TGF{beta}-dependent secretome in DMD. Our integrative proteomic approach provides new insights into the molecular drivers of impaired regeneration in DMD and highlights potential therapeutic targets.

genetics↗

bioRxiv2025

In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer

Antibodies are central mediators of the adaptive immune response, and they are powerful research tools and therapeutics. Antibody discovery requires substantial experimental effort, such as immunization campaigns or in vitro library screening. Predicting antibody-antigen binding a priori remains challenging. However, recent machine learning methods raise the possibility of in silico antibody discovery, bypassing or reducing initial experimental bottlenecks. Here, we report a virtual screen using AlphaFold-Multimer (AF-M) that prospectively identified nanobody binders to MRGPRX2, a G protein-coupled receptor (GPCR) and therapeutic target for the treatment of pseudoallergic inflammation and itch. Using previously reported nanobody-GPCR structures, we identified a set of AF-M outputs that effectively discriminate between interacting and non-interacting nanobody-GPCR pairs. We used these outputs to perform a prospective in silico screen, identified nanobodies that bind MRGPRX2 with high affinity, and confirmed activity in signaling and functional cellular assays. Our results provide a proof of concept for fully computational antibody discovery pipelines that can circumvent laboratory experiments.

biochemistry↗

bioRxiv2024

Generative Models Validation via Manifold Recapitulation Analysis

Comparing empirical distributions is central to generative model evaluation, hypothesis testing and data augmentation in high-dimensional biological data. Established methods such as energy distance summarize each point's relationship to the opposing distribution through a single expected distance, providing sensitivity to location shifts. We introduce Signature Distance (SD), a statistical distance that compares empirical distributions through the mean absolute difference of their sorted pointwise distance profiles. SD is a structural generalization of energy distance and matches its quadratic pairwise-distance cost, with an additional sorting step. In controlled experiments and on TCGA pan-cancer transcriptomic data, we show that (1) SD detects density changes with greater sensitivity than energy distance in the tested scale-contraction scenarios; (2) per-point mean-distance and signature-profile landscapes reveal the geometric mechanisms behind their different penalties; (3) linearly interpolated biological samples that receive no increased penalty from energy distance are penalized by SD; (4) SD provides a direct differentiable potential energy for model-free Langevin data expansion, with a bootstrap resampling protocol to assess the stopping epoch; and (5) SD is directly usable as a differentiable generative training loss. Code to reproduce all experiments is available at github.com/lazzaronico/signature-distance.

bioinformatics↗

bioRxiv2024

Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis

During embryonic development, gene regulatory networks (GRNs) drive molecular differentiation of cell types. However, the temporal dynamics of these networks remain poorly understood. Here, we present Zebrahub-Multiome, a single-cell multiomic atlas that captures chromatin accessibility and gene expression from 94,562 cells across six stages of zebrafish embryogenesis (10-24 hours post-fertilization), capturing key developmental stages from the end of gastrulation to the onset of organogenesis. By measuring regulatory element activity alongside transcriptional output from the same cells, we identify 640,000 cis-regulatory elements organized into 402 hierarchically structured modules corresponding to specific developmental pathways. Early embryonic stages employ broadly shared regulatory programs that progressively fragment into lineage-specific modules. Timeresolved gene regulatory network inference reveals that transcription factors undergo functional transitions - from multilineage regulators to specialized, lineage-committed factors. These quantitative measurements reveal the regulatory network rewiring that drives cell fate specification. Our interactive web portal (zebrahub.org/epigenomics) enables exploration of gene dynamics, regulatory networks, and perturbation predictions, providing a quantitative framework for understanding vertebrate developmental regulation.

developmental biology↗

bioRxiv2024

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

bioinformatics↗

bioRxiv2023

Prior Fc Receptor activation primes macrophage for increased sensitivity to IgG via long term and short term mechanisms

Macrophages measure the eat-me signal IgG to identify targets for phagocytosis. We wondered if prior encounters with IgG influence macrophage appetite. IgG is recognized by the Fc Receptor. To temporally control Fc Receptor activation, we engineered an Fc Receptor that is activated by light-induced oligomerization of Cry2, triggering phagocytosis. Using this tool, we demonstrate that Fc Receptor activation primes macrophages to be more sensitive to IgG in future encounters. Macrophages that have previously experienced Fc Receptor activation eat more IgG-bound cancer cells. Increased phagocytosis occurs by two discrete mechanisms - a short- and long-term priming. Long term priming requires new protein synthesis and Erk activity. Short term priming does not require new protein synthesis and correlates with an increase in Fc Receptor mobility. Our work demonstrates that IgG primes macrophages for increased phagocytosis, suggesting that therapeutic antibodies may become more effective after initial priming doses.

cell biology↗

bioRxiv2023

Single-nucleus RNA sequencing provides insights into the GL261-GSC syngeneic mouse model of glioblastoma

Glioblastoma (GBM) is an aggressive tumor with very bad prognosis. The urgent need to find new effective therapies is challenged by the unique characteristics of GBM, including high intra and intertumoral heterogeneity. Using single-nucleus transcriptomics (snRNA-seq), we characterized the panorama of a preclinical immunocompetent murine model based in the implantation of mouse glioblastoma stem cells (GL261-GSCs) into the brain parenchyma. Additionally, we performed Visium spatial transcriptomics in the in vivo model to confirm the location of annotated cells. To understand the technical bias of this approach, we performed two scRNA-seq methods in GBM cells. We thoroughly characterized the tumor microenvironment (TME) at early and late stages of tumor development and upon treatment with temozolomide (TMZ), the standard of care for patients with GBM, and with Tat-Cx43266-283, a promising experimental treatment. We identified prominent GBM targets that can be addressed using this preclinical model, such as Grik2, Nlgn3, Gap43 or Kcnn4, which are involved in electrical and synaptic integration of GBM cells into neural circuits, as well as the expression of Nt5e, Cd274 or Irf8, which indicates the development of immune evasive properties in these GBM cells. In agreement, snRNA-seq unveiled high expression of several immunosuppressive-associated molecules in immune cells, such as Csf1r, Arg1, Mrc1 and Tgfb1, suggesting the development of an immunosuppressive microenvironment. We also show the landscape of cytokines, cytokine receptors, checkpoint ligands and receptors in tumor and TME cells, which are crucial data for a rational design of immunotherapy studies. Thus, Mrc1, PD-L1, TIM-3 or B7-H3 are among the immunotherapy targets that can be addressed in this model. Finally, the comparison of the preclinical GL261-GSC GBM model with human GBM subtypes unveiled important similarities with the recently identified TMEmed human GBM, indicating that preclinical data obtained in GL261-GSC GBM model might be applied to TMEmed human GBM, improving patient stratification in clinical trials. In conclusion, this work provides crucial information for future preclinical studies in GBM improving their clinical application.

neuroscience↗

bioRxiv2023

Tutorial: guidelines for manual cell type annotation of single-cell multi-omics datasets using interactive software

Assigning cell identity to clusters of single cells is an essential step towards extracting biological insights from many genomics datasets. Although annotation workflows for datasets built with a single modality are well established, limitations exist in annotating cell types in datasets with multiple modalities due to the need for a framework to exploit them jointly. While, in principle, different modalities could convey complementary information about cell identity, it is unclear to what extent they can be combined to improve the accuracy and resolution of cell type annotations. Here, we present a conceptual framework to examine and jointly interrogate distinct modalities to identify cell types. We integrated our framework into a series of vignettes, using immune cells as a well-studied example, and demonstrate cell type annotation workflows ranging from using single-cell RNA-seq datasets alone, to using multiple modalities such as single-cell Multiome (RNA and chromatin accessibility), CITE-seq (RNA and surface proteins). In some cases, one or other single modality is superior to the other for identification of specific cell types, in others combining the two modalities improves resolution and the ability to identify finer subpopulations. Finally, we use interactive software from CZ CELLxGENE community tools to visualize and integrate histological and spatial transcriptomic data.

bioinformatics↗

bioRxiv2023

SETDB1 modulates the TGFbeta response in Duchenne muscular dystrophy myotubes

Overactivation of the TGF{beta} signaling in Duchenne muscular dystrophy (DMD) is a major hallmark of disease progression, leading to fibrosis and muscle dysfunction. Here, we investigated the role of SETDB1, a histone lysine methyltransferase involved in muscle differentiation. Our data show that, following TGF{beta} induction, SETDB1 accumulates in the nuclei of healthy myotubes, while being already present in the nuclei of DMD myotubes where TGF{beta} signaling is constitutively activated. Interestingly, transcriptomics revealed that depletion of SETDB1 in DMD myotubes leads to downregulation of TGF{beta}-target genes coding for secreted factors involved in extracellular matrix remodeling and inflammation. Consequently, SETDB1 silencing in DMD myotubes abrogates the deleterious effect of their secretome on myoblast differentiation by impairing myoblast pro-fibrotic response. Our findings indicate that SETDB1 potentiates the TGF{beta}-driven fibrotic response in DMD muscles, providing a new axis for therapeutic intervention. Key resultsO_LITGF{beta} induces nuclear accumulation of SETDB1 in healthy myotubes C_LIO_LISETDB1 is enriched in DMD myotube nuclei with intrinsic TGF{beta} pathway overactivation C_LIO_LISETDB1 LOF in DMD myotubes attenuates TGF{beta}-induced pro-fibrotic response C_LIO_LISecretome of TGF{beta}-treated DMD myotubes with SETDB1 LOF is less deleterious on myoblast differentiation C_LI

cell biology↗

bioRxiv2023

Zebrahub - Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors

Elucidating the developmental processes of organisms requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate mapping experiments. To demonstrate the versatility of our multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

developmental biology↗

bioRxiv2022

Schwann cells promote sensory neuron excitability during development

Excitability--the ability to fire action potentials--is a signature feature of neurons. How neurons become excitable during development, and whether excitability is an intrinsic property of neurons or requires signaling from glial cells, remain unclear. Here we demonstrate that Schwann cells, the most abundant glia in the peripheral nervous system, promote somatosensory neuron excitability during development. We find that Schwann cells secrete prostaglandin E2, which is necessary and sufficient to induce developing somatosensory neurons to express normal levels of voltage-gated sodium channels and fire action potential trains. Inactivating this signaling pathway specifically in Schwann cells selectively impairs the maturation of nociceptor and proprioceptor somatosensory neuron subtypes, leading to corresponding sensory defects in thermoception, inflammatory pain, and proprioception. Our studies thus reveal a cell non-autonomous mechanism by which glia regulate neuronal excitability to enable the development of normal sensory functions.

neuroscience↗

bioRxiv2017

Dietary xenoestrogens significantly alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy

HighlightsO_LISynergism of combined palbociclib/letrozole chemotherapy was examined by global metabolomics\nC_LIO_LICombination therapy led to more pronounced effects on the MCF-7 metabolome than single agents\nC_LIO_LIDietary phyto- and mycoestrogens significantly affected the metabolic and anti-oncogenic response of the drugs\nC_LIO_LIImplications of these bio-active chemicals on therapeutic success in breast cancer patients appear plausible\nC_LI\n\nIn BriefWarth et al. used innovative global metabolomics and pathway prediction technology to describe the metabolic effects of the combined palbociclib/letrozole breast cancer therapy. Moreover, the role of dietary xenoestrogens on this treatment was examined by metabolite data, proliferation experiments, and functional assays.\n\nSummaryRecently, the palbociclib/letrozole combination therapy was granted accelerated FDA approval for the treatment of estrogen receptor (ER) positive breast cancer. Since the underlying metabolic effects of these drugs are yet unknown, we investigated their synergism at the metabolome level in MCF-7 cells. As xenoestrogens interact with the ER, we additionally aimed at deciphering the impact of the phytoestrogen genistein, and the estrogenic mycotoxin zearalenone on this treatment. A global metabolomics approach was applied to unravel metabolite and pathway modifications. The results clearly showed that the combined effects of palbociclib and letrozole on cellular metabolism were far more pronounced than that of each agent alone and potently influenced by xenoestrogens. This behavior was confirmed in proliferation experiments and functional assays. Specifically, amino acids and central carbon metabolites were attenuated while higher abundances were observed for fatty acids and most nucleic acid related metabolites. Interestingly, exposure to model xenoestrogens appeared to partially counteract these effects.

cancer biology↗

bioRxiv2017

Exposing the Exposome with Global Metabolomics andCognitive Computing

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e. the exposome, and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700,000 chemical structures to now include more than 950,000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis and further, cognitive computing provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the outstanding methodological challenges current exposome research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.

epidemiology↗