Biology subjects

Garcia-Ojalvo, J.

Publications and source records attributed to Garcia-Ojalvo, J..

At least 19 recordsLinked to original sources

bioRxiv2025

Network-Based Analysis of Human Astrocytes Links Aging to Neurodegenerative and Cardiovascular Diseases

Astrocytes are central to brain homeostasis, supporting neuronal metabolism, synaptic activity, and the blood-brain barrier. With aging, these glial cells undergo molecular and functional changes that weaken support functions and promote neuroinflammation, contributing to neurodegeneration. Yet the systems-level mechanisms of astrocytic aging remain poorly defined in human models. Because aging also heightens risk for cardiovascular disease, cognitive impairment, type 2 diabetes, and systemic inflammation, clarifying shared astrocytic pathways is critical for understanding brain-body crosstalk. Using an in vitro human astrocyte model exposed to sublethal oxidative stress (10 {micro}M H2O2), we profiled transcriptomic changes and identified differentially expressed genes across antioxidant defences, proteostasis, transcriptional regulation, vesicular trafficking, and inflammatory signalling. We then performed seven network-prioritization analyses on a curated human protein-protein interactome: one seeded with the astrocyte H2O2-responsive genes and six with phenotype-associated gene sets (Alzheimers disease, cardiovascular disease, cognitive impairment, type 2 diabetes, oxidative stress, and inflammation). Intersecting the top 5% scoring genes from each run yielded a 127-gene core shared across all seven, enriched for proteostasis, DNA repair, mitochondrial regulation, and telomere and nuclear envelope maintenance. Structure-guided analyses highlighted vulnerable interfaces, including lamin A/C-lamin B1, -actinin-filamins, 14-3-3 dimers, and aminoacyl-tRNA synthetase assemblies, where pathogenic variants are predicted to destabilize or aberrantly stabilize protein interactions. Structure-based interface predictions also highlight potential interactions between APP-VCP/p97 and p53-14-3-3{zeta} that link proteostasis and stress signalling. Together, these findings define a conserved astrocytic vulnerability network that may couple neurodegeneration with cardiovascular disease and nominate structurally testable targets for biomarkers and interventions.

bioinformatics↗

bioRxiv2025

Emergence of multifrequency activity in a laminar neural mass model

Neural mass models (NMMs) aim to capture the principles underlying mesoscopic neural activity representing the average behavior of large neural populations in the brain. Recently, a biophysically grounded laminar NMM (LaNMM) has been proposed, capable of generating coupled slow and fast oscillations resulting from interactions between different cortical layers. This concurrent oscillatory activity provides a mechanistic framework for studying information processing mechanisms and various disease-related oscillatory dysfunctions. We show that this model can exhibit periodic, quasiperiodic, and chaotic oscillations. Additionally we demonstrate, through bifurcation analysis and numerical simulations, the emergence of rhythmic activity and various frequency couplings in the model, including delta-gamma, theta-gamma, and alpha-gamma couplings. We also examine how alterations linked with Alzheimers disease impair the models ability to display multifrequency activity. Furthermore, we show that the model remains robust when coupled to another neural mass. Together, our results offer a dynamical systems perspective of the laminar NMM model, thereby providing a foundation for future modeling studies and investigations into cognitive processes that depend on cross-frequency coupling. Author SummaryUnderstanding how the brain generates and coordinates rhythms across different layers is essential for uncovering the mechanisms underlying perception, memory, and cognition. In this work, we analyze a previously developed model of mesoscopic brain activity that simulates the layered structure of the cortex and its ability to produce coupled slow and fast neural oscillations. Using tools from dynamical systems theory, we reveal how the model gives rise to a rich repertoire of dynamical patterns--including periodic, quasiperiodic, and chaotic activity--through the coexistence of multiple oscillatory modes. We also investigate how pathological changes, such as those linked to Alzheimers disease, alter the models dynamics and impair its capacity to sustain complex cross-frequency interactions. Finally, we show that the model remains stable when connected to another brain region, highlighting its robustness. Our findings provide a deeper understanding of how multifrequency neural rhythms may emerge, how they might break down in disease, and how this modeling framework can inform both future theoretical studies and the development of new brain models.

neuroscience↗

bioRxiv2025

A mosaic gastruloid model highlights the developmental stage-specific restriction of cell competition in mammalian pre-gastrulation

In early mammalian embryogenesis, the selective elimination of suboptimal cells is critical for developmental integrity. Cell competition (CC) is a cell non-autonomous quality control in which "winner" cells eliminate viable but suboptimal "loser" cells based on their relative difference in fitness. Due to its central role in fitness perception, loss of p53 results in the emergence of "supercompetitor" cells, which stand at the apex of cell competition and induce apoptosis in neighboring wild-type (WT) cells. Here, we investigate CC dynamics using mosaic 3D mouse gastruloids, an embryonic stem cell (ESC)-based in vitro model of gastrulation, composed of defined numbers of WT and p53-KO cells. In mosaic gastruloids, even low numbers of p53-KO cells robustly outcompete WT cells, and introduction of as few as two p53-KO cells is sufficient to measurably impair neighboring WT cell growth. CC in gastruloids is independent of cell proliferation rates, nutrient availability, or reactive oxygen species (ROS), and not influenced by Nodal and ERK signaling. However, we observe that Wnt and BMP signaling protect from CC, which is exclusively driven by intrinsic apoptosis, as indicated by Bcl2-mediated complete rescue of WT cells. During gastruloid development, CC is temporally restricted to a window of 48-96 hours after aggregation, mirroring embryonic days E5.5-E7.5 in the mouse. Heterochronic mosaic gastruloid experiments demonstrate that relative differences in pluripotency levels are neither necessary nor sufficient to cause supercompetition, but that CC is contingent on both competitors residing within the developmental window permissive to CC. Neither pluripotent mosaic 3D aggregates, nor 3D EpiGastruloids, which model more advanced developmental processes, display any competition, supporting the hypothesis that developmental CC is specific to the onset of gastrulation. Our findings offer insights into the mechanisms of cell fitness evaluation in mammalian embryogenesis and establish gastruloids as a powerful 3D model for investigating developmental stage-specific cell competition.

developmental biology↗

bioRxiv2024

Cell-cell communication controls the timing of gastruloid symmetry-breaking

How cell fate decisions coordinate with tissue-scale morphogenesis remains a major challenge in developmental biology. Gastruloids, three-dimensional aggregates of pluripotent stem cells that self-organise and break symmetry via polarised Brachyury/T expression, provide an ideal system to address this question. By generating gastruloids with defined initial proportions of T-expressing cells we show that fate decisions occur collectively, with cell-fate proportions influencing the transition rates. Mechanical measurements reveal differences in surface tension between T-positive and T-negative tissues, consistent with radial cell sorting. Finally, incorporating fate dynamics and mechanics into a computational model recapitulates the sequential symmetry-breaking events observed in vitro. Our findings identify a mechanochemical mechanism underlying axis formation, and demonstrate how multicellular systems can robustly self-organise without external signalling cues.

biophysics↗

bioRxiv2024

Learning in single cells: biochemically-plausible models of habituation

The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells. Habituation has been codified from studies in both invertebrate and vertebrate animals, as having ten characteristic hallmarks, seven of which involve a single stimulus. Here, we show by mathematical modelling that simple molecular networks, based on plausible biochemistry with common motifs of negative feedback and incoherent feedforward, can robustly exhibit all single-stimulus hallmarks. The models reveal how the hallmarks arise from underlying properties of timescale separation and reversal behaviour of memory variables and they reconcile opposing views of frequency and intensity sensitivity expressed within the neuroscience and cognitive science traditions. Our results suggest that individual cells may exhibit habituation behaviour as rich as that in multi-cellular animals with central nervous systems and that the relative simplicity of the biomolecular level may enhance our understanding of the mechanisms of learning.

systems biology↗

bioRxiv2024

Network-community analysis of cellular senescence

Most cellular phenotypes are genetically complex. Identifying the set of genes that are most closely associated with a specific cellular state is still an open question in many cases. Here we study the transcriptional profile of cellular senescence using a combination of network-based approaches, which include eigenvector centrality feature selection and community detection. We apply our method to cell-type-resolved RNA sequencing data obtained from injured muscle tissue in mice. The analysis identifies some genetic markers consistent with previous findings, and other previously unidentified ones, which are validated with previously published single-cell RNA sequencing data in a different type of tissue. The key identified genes, both those previously known and the newly identified ones, are transcriptional targets of factors known to be associated with established hallmarks of senescence, and can thus be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.

systems biology↗

bioRxiv2024

A multiscale sensorimotor model of experience-dependent behavior in a minimal organism

To survive in ever-changing environments, living organisms need to continuously combine the ongoing external inputs they receive, representing present conditions, with their dynamical internal state, which includes influences of past experiences. It is still unclear in general, however, (i) how this happens at the molecular and cellular levels, and (ii) how the corresponding molecular and cellular processes are integrated with the behavioral responses of the organism. Here we address these issues by modeling mathematically a particular behavioral paradigm in a minimal model organism, namely chemotaxis in the nematode C. elegans. Specifically, we use a long-standing collection of elegant experiments on salt chemotaxis in this animal, in which the migration direction varies depending on its previous experience. Our model integrates the molecular, cellular and organismal levels to reproduce the experimentally observed experience-dependent behavior. The model proposes specific molecular mechanisms for the encoding of current conditions and past experiences in key neurons associated with this response, predicting the behavior of various mutants associated with those molecular circuits.

systems biology↗

bioRxiv2024

Modeling the impact of neuromorphological alterations in Down syndrome on fast neural oscillations

Cognitive disorders, including Down syndrome (DS), present significant morphological alterations in neuron architectural complexity. However, the relationship between neuromorphological alterations and impaired brain function is not fully understood. To address this gap, we propose a novel computational model that accounts for the observed cell deformations in DS. The model consists of a cross-sectional layer of the mouse motor cortex, composed of 3000 neurons. The network connectivity is obtained by accounting explicitly for two single-neuron morphological parameters: the mean dendritic tree radius and the spine density in excitatory pyramidal cells. We obtained these values by fitting reconstructed neuron data corresponding to three mouse models: wild-type (WT), transgenic (TgDyrk1A), and trisomic (Ts65Dn). Our findings reveal a dynamic interplay between pyramidal and fast-spiking interneurons leading to the emergence of gamma activity ([~]40 Hz). In the DS models this gamma activity is diminished, corroborating experimental observations and validating our computational methodology. We further explore the impact of disrupted excitation-inhibition balance by mimicking the reduction recurrent inhibition present in DS. In this case, gamma power exhibits variable responses as a function of the external input to the network. Finally, we perform a numerical exploration of the morphological parameter space, unveiling the direct influence of each structural parameter on gamma frequency and power. Our research demonstrates a clear link between changes in morphology and the disruption of gamma oscillations in DS. This work underscores the potential of computational modeling to elucidate the relationship between neuron architecture and brain function, and ultimately improve our understanding of cognitive disorders. Author summaryThe structural integrity of individual brain neurons and the intricate networks they form are fundamental to all brain functions, with structural anomalies directly linked to neurological disorders. Deciphering these links is a leading question in developmental disorders such as Down syndrome. Our work sheds light on the pivotal role that the structure of complex neural systems plays in shaping emergent network activity. In particular, our anatomically informed network modeling enables determining the extent to which specific deficits in neuronal architecture and connectivity perturb oscillatory patterns of activity.

neuroscience↗

bioRxiv2023

Revealing Shared Proteins and Pathways in Cardiovascular and Cognitive Diseases Using Protein Interaction Network Analysis

One of the primary goals of systems medicine is detecting putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular Cognitive Impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular-cognitive genes/proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

systems biology↗

bioRxiv2023

Parametric modeling of mechanical effects on circadian oscillators

Circadian rhythms are archetypical examples of nonlinear oscillations. While these oscillations are usually attributed to circuits of biochemical interactions among clock genes and proteins, recent experimental studies reveal that they are also affected by the cells mechanical environment. Here we extend a standard biochemical model of circadian rhythmicity to include mechanical effects in a parametric manner. Using experimental observations to constrain the model, we suggest specific ways in which the mechanical signal might affect the clock. Additionally, a bifurcation analysis of the system predicts that these mechanical signals need to be within an optimal range for circadian oscillations to occur. Cells are nonlinear dynamical elements, which in multicellular tissues are commonly coupled to one another. Much work has been done, both theoretically and experimentally, to understand this coupling and to identify its dynamical consequences from a biochemical viewpoint. In contrast, much less is known about how the mechanical interactions between cells affect these dynamics. Recent work has shown, for instance, that circadian oscillations degrade substantially in populations of cells in vitro when cell density decreases sufficiently. Here we use this fact to constrain a standard model of circadian oscillations, and propose a way through which external mechanical signals and internal biochemical interactions could combine in clock cells.

systems biology↗

bioRxiv2023

Multiscale networks in multiple sclerosis

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information, and Boolean simulations identified paths within and among all layers. The path more commonly found from the boolean simulations connects MP2K, with Th17 cells, the retinal nerve fiber layer (RNFL) thickness and the age related MS severity score (ARMSS). Combinations of several proteins (HSPB1, MP2K1, SR6, KS6B1, SRC, MK03, LCK and STAT6)) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/530153v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7dd26org.highwire.dtl.DTLVardef@482239org.highwire.dtl.DTLVardef@1bef03borg.highwire.dtl.DTLVardef@8dc6e6_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryComplex diseases such as Multiple Sclerosis (MS) involve the contribution of a wide range of biological processes. We conducted a systems biology study of MS based on network analysis and deep phenotyping in a prospective cohort of patients with clinical, imaging, genetics, and omics assessments. The gene, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.

systems biology↗

bioRxiv2022

Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 brain regions, whose structural connectivity is obtained from tractography data. The activity of each brain area is governed by a Jansen neural mass model and we normalize the total input received by each node so it amounts the same across all brain areas. This assumption allows for the existence of an homogeneous invariant manifold, i.e., a set of different stationary and oscillatory states in which all nodes behave identically. Stability analysis of these homogeneous solutions unveils a transverse instability of the synchronized state, which gives rise to different types of spatiotemporal dynamics, such as chaotic alpha activity. Additionally, we illustrate the ubiquity of this route towards complex spatiotemporal activity in a network of next-generation neural mass models. Altogehter, our results unveil the bifurcation landscape that underlies the emergence of function from structure in the brain. Author summaryMonitoring brain activity with techniques such as EEG and fMRI has revealed that normal brain function is characterized by complex spatiotemporal dynamics. This behavior is well captured by large-scale brain models that incorporate structural connectivity data obtained with MRI-based tractography methods. Nonetheless, it is not yet clear how these complex dynamics emerges from the interplay of the different brain regions. In this paper we show that complex spatiotemporal dynamics, including travelling waves and high-dimensional chaos can arise in simple large-scale brain models through the destabilization of a synchronized oscillatory state. Such transverse instabilities are akin to those observed in chemical reactions and turbulence, and allow for a semi-analytical treatment that uncovers the overall dynamical landscape of the system. Overall, our work establishes and characterizes a general route towards spatiotemporal oscillations in large-scale brain models.

neuroscience↗

bioRxiv2022

Dissecting infant leukemia developmental origins with a hemogenic gastruloid model

Current in vitro models of developmental blood formation lack spatio-temporal accuracy and weakly replicate successive waves of hematopoiesis. Herein, we describe a mouse embryonic stem cell (SC)-derived 3D hemogenic gastruloid (haemGx) that captures multi-wave blood formation, progenitor specification from hemogenic endothelium (HE), and generates hematopoietic progenitors capable of short-term engraftment of immunodeficient mice upon maturation in an in vivo niche. We took advantage of the haemGx model to interrogate the origins of infant acute myeloid leukemia (infAML). We focused on MNX1-driven leukemia, representing the commonest genetic abnormality unique to the infant group. Enforced MNX1 expression in haemGx promotes the expansion and in vitro transformation of yolk sac-like erythroid-myeloid progenitors at the HE-to-hematopoietic transition to faithfully recapitulate patient transcriptional signatures. By combining phenotypic, functional and transcriptional profiling, including at the single-cell level, we establish the haemGx as a useful new model for the study of normal and leukemic embryonic hematopoiesis.

developmental biology↗

bioRxiv2022

Comparison between an exact and a heuristic neural mass model with second order synapses

Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled non-linear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by non-invasive brain stimulation.

neuroscience↗

bioRxiv2022

Periodic spatial patterning with a single morphogen

Multicellular development employs periodic spatial patterning to generate repetitive structures such as digits, vertebrae, and teeth. Turing patterning has long provided a key paradigm for understanding such systems. The simplest Turing systems are believed to require at least two signals, or morphogens, that diffuse and react to spontaneously generate periodic patterns. Here, using mathematical modeling, we show that a minimal circuit comprising an intracellular positive feedback loop and a single diffusible morphogen is sufficient to generate stable, long-range spatially periodic cellular patterns. The model considers cells as discrete entities as a key feature, and incorporates transient boundary conditions. Linear stability analysis reveals that this single-morphogen Turing circuit can support a broad range of spatial wavelengths, including fine-grain patterns similar to those generated by classic lateral inhibition systems. Further, signals emanating from a boundary can initiate and stabilize propagating modes with a well-defined spatial wavelength. Once formed, patterns are self-sustaining and robust to noise. Finally, while noise can disrupt patterning in pre-patterned regions, its disruptive effect can be overcome by a bistable intracellular circuit loop, or by considering patterning in the context of growing tissue. Together, these results show that a single morphogen can be sufficient for robust spatial pattern formation, and should provide a foundation for engineering pattern formation in the emerging field of synthetic developmental biology.

synthetic biology↗

bioRxiv2022

Single-cell Bayesian deconvolution

Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, in a way that allows us to estimate confidence intervals. We use the approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.

developmental biology↗

bioRxiv2022

Mechanical control of the mammalian circadian clock via YAP/TAZ and TEAD

Circadian rhythms are a key survival mechanism that dictates biological activity according to the day-night cycle. In animals, cell-autonomous circadian clocks can be found in nearly every cell type and are subjected to multi-layered regulation. Although these peripheral clocks are remotely controlled by the master clock in the brain, they are also sensitive to their immediate mechano-chemical microenvironment. Whereas the mechanisms by which biochemical signalling controls the circadian clock at the single cell level are increasingly well understood, mechanisms underlying regulation by mechanical cues are still unknown. Here we show that the circadian clock in fibroblasts is regulated mechanically through YAP/TAZ and TEAD. We use high-throughput analysis of single-cell circadian rhythms and apply controlled mechanical, biochemical, and genetic perturbations to study the expression of the clock gene Rev-erb. We observe that Rev-erb circadian oscillations are disrupted concomitantly with the translocation of YAP/TAZ to the nucleus. By targeted mutations and tuning expression levels of YAP we identify TEAD as the transcriptional effector of this mechanosensitive regulatory pathway. Our findings establish a mechanism that links cell mechanobiology and the circadian clock, which could contribute to explain the circadian impairment observed in cancer and ageing, where the regulation of the mechanical environment and YAP/TAZ is lost.

cell biology↗

bioRxiv2021

Synthetic multistability in mammalian cells

In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate, a naturally-inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design, we engineered MultiFate circuits that generate up to seven states, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs, and can be easily expanded with additional transcription factors. Together, these results provide a foundation for engineering multicellular behaviors in mammalian cells.

synthetic biology↗