Biology subjects

Munoz, M. A.

Publications and source records attributed to Munoz, M. A..

11 recordsLinked to original sources

bioRxiv2025

NLRP3 is a thermosensor that is negatively regulated by high temperature

Inflammation is an essential response to infection and injury, but unregulated inflammation is damaging and must be limited by negative feedback signalling. Inflammasome signalling drives local inflammation and systemic responses like fever. However, our understanding of how inflammasome signalling is negatively regulated is limited. NLRP3 is activated by a vast number of stimuli and senses perturbations of cytoplasmic homeostasis. As temperature is a fundamental environmental stressor, we hypothesised that NLRP3 inflammasome signalling would be sensitive to increased temperatures and so we investigated the effects of high temperatures on NLRP3 in macrophages. Short-term incubation at high fever range temperatures significantly inhibits NLRP3 activation, while secretion of the inflammasome-independent cytokines TNF and IL-6 are much less affected. High temperature blocks NLRP3 inflammasome formation in a transcription-independent manner, and NLRP3 is highly sensitive to temperature-mediated inhibition relative to the NLRC4, AIM2, and NLRP1 inflammasomes. Using cellular assays and molecular simulations we show that the effect of high temperature on NLRP3 is protein intrinsic. NLRP3 activation is associated with a decrease in the thermal stability of the protein and multiscale molecular dynamics simulations identified a peptide in the C-terminal of the FISNA domain (COFI) that is highly flexible and undergoes a significant conformational shift at high temperature. Cellular assays demonstrate that the COFI regulates NLRP3 stability and is required for activation. Furthermore, mice exposed to high temperature display attenuated inflammatory cytokine production upon in vivo LPS challenge. Our studies thus reveal that high temperatures associated with fever limit NLRP3 activity and identify a novel role for NLRP3 as a protein thermosensor.

immunology↗

bioRxiv2025

Imbalance in gut microbial interactions as a marker of health and disease

Imbalances in the human gut microbiome (dysbioses) are linked to multiple diseases but remain poorly understood. Current biomarkers to identify dysbiosis are inconsistent and fail to capture the ecological mechanisms differentiating healthy from diseased microbiomes. We propose a general dysbiosis biomarker, inspired by phenomenology observed in a gut-microbiome theoretical model introduced here. The emergent communities show complex interaction networks and two distinct collective states, corresponding to healthy and dysbiotic microbiomes. Our robust metric for dysbiosis, by quantifying the balance between cooperation and competition, differentiates these states in both simulated and real datasets across diverse diseases. Moreover, it reveals that dysbiosis results from a shift toward greater cooperation in the community. Our metric further correlates with disease progression, highlighting its potential as a diagnostic tool.

microbiology↗

bioRxiv2023

Statistical mechanics of phenotypic eco-evolution:from adaptive dynamics to complex diversification

The ecological and evolutionary dynamics of large sets of individuals can be theoretically addressed using ideas and tools from statistical mechanics. This strategy has been addressed in the literature, both in the context of population genetics -whose focus is of genes or "genotypes"-- and in adaptive dynamics, putting the emphasis on traits or "phenotypes". Following this tradition, here we construct a framework allowing us to derive "macroscopic" evolutionary equations from a rather general "microscopic" stochastic dynamics representing the fundamental processes of reproduction, mutation and selection in a large community of individuals, each one characterized by its phenotypic features. Importantly, in our setup, ecological and evolutionary timescales are intertwined, which makes it particularly suitable to describe microbial communities, a timely topic of utmost relevance. Our framework leads to a probabilistic description of the distribution of individuals in phenotypic space --even in the case of arbitrarily large populations-- as encoded in what we call "generalized Crow-Kimura equation" or "generalized replicator-mutator equation". We discuss the limits in which such an equation reduces to the (deterministic) theory of "adaptive dynamics" (i.e. the standard approach to evolutionary dynamics in phenotypic space. Moreover, we emphasize the aspects of the theory that are beyond the reach of standard adaptive dynamics. In particular, by working out, as a guiding example, a simple model of a growing and competing population, we show that the resulting probability distribution can exhibit "dynamical phase transitions" changing from unimodal to bimodal --by means of an evolutionary branching-- or to multimodal, in a cascade of evolutionary branching events. Furthermore, our formalism allows us to rationalize these cascades of transitions using the parsimonious approach of Landaus theory of phase transitions. Finally, we extend the theory to account for finite populations and illustrate the possible consequences of the resulting stochastic or "demographic" effects. Altogether the present framework extends and/or complements existing approaches to evolutionary/adaptive dynamics and paves the way to more systematic studies of e.g. microbial communities as well as to future developments including theoretical analyses of the evolutionary process from the general perspective of non-equilibrium statistical mechanics.

evolutionary biology↗

bioRxiv2022

A macroecological law links abundance correlations with phylogenetic similarity in microbiomes

Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches --which combine phylogenetic relationships with community ecology-- have the potential to disentangle such forces, but are often hard to connect with quantitative predictions from theoretical models. On the other hand, macroecology, which focuses on statistical patterns of abundance and diversity, provides natural connections with theoretical models but often neglects inter-speficic correlations and interactions. Here, we propose a unified framework combining both such approaches to analyze microbial communities. In particular, by using both cross-sectional and longitudinal metagenomic data for species abundances, we reveal the existence of a novel empirical macroecological law establishing that correlations in species-abundance fluctuations across communities decay from positive to null values as a function of phylogenetic similarity in a consistent manner across ecologically distinct microbiomes. We formulate three mechanistic models --relying on alternative ecological forces-- that lead to radically different predictions. We conclude that the empirically observed macroecological pattern can be quantitatively explained as a result of shared fluctuating resources, i.e. environmental filtering and not e.g. as a consequence of species competition. Finally, we also show that the macroecological law is also valid for temporal data of a single community, and that the properties of delayed temporal correlations are reproduced by the model with environmental filtering.

ecology↗

bioRxiv2022

Transformation of Human Mesenchymal Stem Cells into High-Grade Sarcomas by YAP1 and K-RAS Reflects the Undifferentiated Pleomorphic Sarcoma-Myxofibrosarcoma Disease Spectrum

High-grade complex karyotype sarcomas are a heterogeneous group of more than seventy tumors that vary in histology, clinical course, and patient demographics. Despite these clear differences, these high-grade sarcomas are treated similarly with a uniformly high metastatic rate. Pre-clinical models that allow for rigorous comparisons of distinct human sarcoma subtypes would advance insights into the relationships between sarcomas and inform therapeutic decisions. We describe the robust transformation of human mesenchymal stem cells into multiple subtypes of high-grade sarcoma. Using a pooled genetic screening approach, we identified key drivers and potential modifiers of transformation. YAP1 and KRAS were validated as drivers of two distinct sarcoma subtypes, undifferentiated pleomorphic sarcoma (UPS) and myxofibrosarcoma (MFS), respectively. In addition, the pathology of tumors driven by CDK4 and PIK3CA reflected leiomyosarcoma (LMS) and osteosarcoma (OS) indicating that further iterations of this model could result in additional sarcoma subtypes. Histologically and phenotypically these tumors reflect human sarcomas including the pathognomonic complex karyotype. In addition, CDK4 and PIK3CA driven tumors demonstrated endogenous YAP1 amplification which is seen across a subset of human tumors. While all tumors overlapped transcriptionally with the TCGA sarcoma data, further analysis confirmed that YAP1 and KRAS tumors recapitulate the UPS and MFS subtypes. Co-analysis of TCGA and model tumors support that these sarcoma subtypes lie along a spectrum of disease and adds guidance for further transcriptome-based refinement of sarcoma subtyping. Within complex karyotype sarcomas, there are multiple genetic changes but identifying those that are clinically relevant has been challenging. Comparing differentially expressed genes in YAP1 and KRAS tumors to human UPS and MFS identified the enrichment of oxidative phosphorylation pathways in both YAP1 tumors and UPS. Treatment of a panel of sarcoma cell lines with the combination of an oxidative phosphorylation inhibitor and Hippo pathway inhibitor led to a significant impairment in growth identifying new therapeutic targets. A subset of human UPS tumors showed an even greater enrichment in these pathways indicating this model can be used to identify clinically relevant subtypes. This model can be used to begin to understand pathways and mechanisms driving human sarcoma development, the relationship between sarcoma subtypes and to identify and test new therapeutic vulnerabilities for this aggressive and heterogeneous disease. Statement of SignificanceWe have created the first model to study the development, growth, and metastasis of multiple human sarcoma subtypes. This system can be used as a platform to investigate sarcoma biology and identify new therapeutic targets across a heterogeneous disease.

cancer biology↗

bioRxiv2022

Increased core body temperature exacerbates defective protein prenylation in mouse avatars of mevalonate kinase deficiency

Mevalonate kinase deficiency (MKD) is caused by biallelic loss-of-function mutations in MVK, leading to recurrent fevers and systemic inflammation. We describe new mouse avatars of MKD bearing p.Val377Ile (the commonest variant) or deletions in Mvk. Compound heterozygous mice recapitulated the biochemical phenotype of MKD, with build-up of unprenylated GTPases and increased plasma mevalonic acid. Mice with different deficiencies in mevalonate kinase revealed new insights into the genotype-phenotype relationship and mirrored the variability in the prenylation defect in human MKD, with p.V377I homozygous mice having a milder phenotype than compound heterozygous animals. The inflammatory response to LPS was enhanced in compound heterozygous mice in vivo and elevated serum interleukin-1{beta} was abrogated by NLRP3 inflammasome inhibition. Increased temperature dramatically but reversibly exacerbated the deficit in the mevalonate pathway and defective prenylation in vitro and in vivo, highlighting increased body temperature as a likely trigger of inflammatory flares and an additional potential target for future therapeutic approaches.

physiology↗

bioRxiv2021

Quasi-universal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics

The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime near the edge of a phase transition. Moreover, the resulting critical behavior, with its concomitant scale invariance, is assumed to carry crucial functional advantages. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we synergistically combine cutting-edge methods for the study of brain activity (such as a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population), while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that is "quasi-universal" across brain regions and reveal that all these areas operate, to a greater or lesser extent, near the edge of instability. Furthermore, this framework allows us to distinguish between quasi-universal background activity and non-universal input-related activity. Taken together, this study provides strong evidence that brain networks actually operate in a critical regime which, among other functional advantages, provides them with a scale-invariant substrate of activity covariances that can sustain optimal input representations.

neuroscience↗

bioRxiv2021

Bisphosphonate drugs have actions outside the skeleton and inhibit the mevalonate pathway in alveolar macrophages

Bisphosphonates drugs target the skeleton and are used globally for the treatment of common bone disorders. Nitrogen-containing bisphosphonates act by inhibiting the mevalonate pathway in bone-resorbing osteoclasts but, surprisingly, also appear to reduce the risk of death from pneumonia. We overturn the long-held belief that these drugs act only in the skeleton and show that a fluorescently-labelled bisphosphonate is internalised by alveolar macrophages and peritoneal macrophages in vivo. Furthermore, a single dose of a nitrogen-containing bisphosphonate (zoledronic acid) in mice was sufficient to inhibit the mevalonate pathway in tissue-resident macrophages, causing the build-up of a mevalonate metabolite and preventing protein prenylation. Importantly, one dose of bisphosphonate enhanced the immune response to bacterial endotoxin in the lung and increased the level of cytokines and chemokines in bronchoalveolar fluid. These studies suggest that bisphosphonates, as well as preventing bone loss, may boost immune responses to infection in the lung and provide a mechanistic basis to fully examine the potential of bisphosphonates to help combat respiratory infections that cause pneumonia.

pharmacology and toxicology↗

bioRxiv2020

Low variability of dynamical functional connectivity in cerebellar networks

Brain networks can be defined and explored using different types of connectivity. Here, we studied P=48 healthy participants with neuroimaging state-of-the-art techniques and analyzed the relationship between the actual structural connectivity (SC) networks (between 2514 regions of interest covering the entire brain and brainstem) and the dynamical functional connectivity (DFC) among the same regions. To do so, we focused on a combination of two metrics: the first one measures the degree of SC-DFC similarity -i.e. how much functional correlations can be explained by structural pathways- and the second one, the intrinsic variability of DFC networks across time. Overall, we found that cerebellar networks have smaller DFC variability than other networks in the cerebrum. Moreover, our results clearly evidence the internal structure of the cerebellum, which is divided in two differentiated networks, the posterior and anterior parts, the latter also being connected to the brain stem. The mechanism for keeping the DFC variability low in the posterior part of the cerebellum is consistent with another finding, namely, it exhibits the highest SC-DFC similarity among all other sub-networks, i.e. its structure constrains very strongly its dynamics. On the other hand, the anterior part of the cerebellum, which also exhibits a low level of DFC variability, has the lowest SC-DFC similarity, suggesting very different dynamical mechanisms. It is likely that its connections with the brain stem -which regulates sleep cycles, cardiac and respiratory functioning- might have a critical role in DFC variations in the anterior part. A lot is known about cerebellar networks, such as having extremely rich and complex anatomy and functionality, connecting to the brainstem, and cerebral hemispheres, and participating in a large variety of cognitive functions, such as movement control and coordination, executive function, visual-spatial cognition, language processing, and emotional regulation. However, as far as we know, our findings of low variability in the dynamical functional connectivity of cerebellar networks and its possible relation with the above functions, have not been reported so far. Further research is still needed to shed light on these findings.

neuroscience↗

bioRxiv2018

Bet-hedging strategies in expanding populations

In ecology, species can mitigate their extinction risks in uncertain environments by diversifying individual phenotypes. This observation is quantified by the theory of bet-hedging, which provides a reason for the degree of phenotypic diversity observed even in clonal populations. The theory of bet-hedging in well-mixed populations is rather well developed. However, many species underwent range expansions during their evolutionary history, and the importance of phenotypic diversity in such scenarios still needs to be understood. In this paper, we develop a theory of bet-hedging for populations colonizing new, unknown environments that fluctuate either in space or time. In this case, we find that bet-hedging is a more favorable strategy than in well-mixed populations. For slow rates of variation, temporal and spatial fluctuations lead to different outcomes. In spatially fluctuating environments, bet-hedging is favored compared to temporally fluctuating environments. In the limit of frequent environmental variation, no opportunity for bet-hedging exists, regardless of the nature of the environmental fluctuations. For the same model, bet-hedging is never an advantageous strategy in the well-mixed case, supporting the view that range expansions strongly promote diversification. These conclusions are robust against stochasticity induced by finite population sizes. Our findings shed light on the importance of phenotypic heterogeneity in range expansions, paving the way to novel approaches to understand how biodiversity emerges and is maintained.\n\nAuthor summaryEcological populations are often exposed to unpredictable and variable environmental conditions. A number of strategies have evolved to cope with such uncertainty. One of them is stochastic phenotypic switching, by which some individuals in the community are enabled to tackle adverse conditions, even at the price of reducing overall growth in the short term. In this paper, we study the effectiveness of these \"bet-hedging\" strategies for a population in the process of colonizing new territory. We show that bet-hedging is more advantageous when the environment varies spatially rather than temporally, and infrequently rather than frequently.

ecology↗

bioRxiv2017

Diversity Of Biocrust-Forming Cyanobacteria In A Semiarid Gypsiferous Site From Central Spain

Cyanobacteria are an important constituent of biocrusts, communities dominated by lichens, mosses and associated microorganisms, which are prevalent in drylands worldwide and that largely determine their functioning. Despite their importance, there are large gaps in our knowledge of cyanobacteria associated to biocrusts, particularly in areas such as the Mediterranean Basin. We studied the diversity of these cyanobacteria in a gypsiferous grassland from Central Spain using both morphological identification after cultivation and genetic analyses with the 16S rRNA gene. Eight different morphotypes were observed, most of them corresponding to filamentous and one of them to unicellular cyanobacteria. We found cyanobacterial genera typical of biocrust communities, such as Microcoleus, Schizothrix or Tolypothrix, and N-fixing cyanobacteria as Scytonema or Nostoc. Genetic information allowed us to identify cultures belonging to recently described genera such as Roholtiella, Nodosilinea and Mojavia. In this study we describe two new phylotypes of Microcoleus and Scytonema, which are key genera contributing to ecosystem functioning in biocrust-dominated ecosystems worldwide.

microbiology↗