Biology subjects

Matthews, B.

Publications and source records attributed to Matthews, B..

12 recordsLinked to original sources

bioRxiv2026

Glaciers retreat, stickleback advance: variation in demographic histories of freshwater stickleback populations

When colonizing new habitats, the evolution of populations can be affected by their demographic history and structure, including their effective population sizes and the time and nature of their divergence from an ancestral population. The deglaciation of Southern Greenland following the last glacial maximum offers the opportunity to comparatively study colonization events of fish populations among lakes of varying size and altitude, the latter of which partially reflects their geological age and isolation. This study investigates the demographic history of threespine stickleback (Gasterosteus aculeatus) from 17 freshwater lake populations in comparison to a marine population sampled across four fjord sites in Southern Greenland using whole-genome data. Our findings suggest that divergence of freshwater from ocean populations was concurrent with ice sheet retreat and lake formation, which occurred ~4-13 thousand years ago. In all 17 freshwater populations, demographic modeling revealed a population decline upon lake colonization and prolonged periods of gene flow between freshwater and marine populations. We also found that bigger lakes had larger effective population sizes. These findings emphasize the interplay between local landscape and demographic history in shaping the population genetic structure of Southern Greenland stickleback populations. Our results underscore the relevance of such small scale variation in demographic history across the landscape for understanding postglacial colonization into freshwater ecosystems.

evolutionary biology↗

bioRxiv2026

An Eco-Evolutionary Modelling Framework for Mosquito Host Specialisation

Host preference varies widely across mosquitoes, with many species feeding opportunistically on diverse vertebrate hosts, while others show strong fidelity to specific hosts. Anthropophilia, the behavioural preference for feeding on humans, is a defining characteristic of some mosquito species responsible for the transmission of major human diseases, including malaria, dengue, and yellow fever. The evolution of anthropophilia therefore has profound epidemiological implications because increased human biting elevates vectorial capacity and disease transmission potential. However, the ecological and evolutionary mechanisms driving this extreme specialisation have not yet been fully elucidated and remain difficult to unify across laboratory and field studies. Here we present an eco-evolutionary modelling framework that links genetically determined mosquito traits with spatially structured host environments. Our framework integrates innate olfactory sensitivity, blood meal-derived fitness benefits, and spatio-temporal host accessibility. Two complementary indices are introduced: a local specialisation index, capturing short-term ecological feeding strategies, and a co-evolutionary index, capturing long-term genetic coupling between host detection and resource utilisation. Our results demonstrate that host specialisation is not a default evolutionary outcome but an environmentally gated process, which is favoured in resource-poor or temporally varying habitats and strongly filtered by seasonality. The framework yields testable predictions regarding when specialisation emerges, persists, or collapses, with direct implications for predicting vector-borne disease risk in changing environments

evolutionary biology↗

bioRxiv2026

FcϵRI+IgE+ monocytes are linked to atopy and allergic inflammation with distinct phenotypes and enhanced antiviral responses

Monocytes are critical regulators of allergic inflammation, whose functions are modified by IgE-driven processes. Monocytes are heterogeneous; comprised of multiple subsets which implies differential functions. In allergic inflammation, this heterogeneity is likely influenced by IgE-mediated effects. We sought to identify phenotypically distinct monocyte subsets related to allergic disease and then further delineate functional differences in cytokine release and antiviral responses. Using high dimensional spectral flow cytometry, we identified monocyte surface phenotypes directly related to surface levels of the high affinity IgE receptor (Fc{varepsilon}RI) and surface-bound IgE. Fc{varepsilon}RI+IgE+ monocytes, or FIMs, correlated with allergic disease and the level of atopy (i.e. serum IgE levels) of individual subjects. The FIM population also had differential surface expression of other molecules of monocyte maturation, which closely resembled a type 2 conventional dendritic cell (cDC2) phenotype. Functionally, FIMs had enhanced antiviral responses and IgE-driven IL-10 cytokine release. Finally, we showed that FIMs could be identified at higher levels in lung tissue from individuals with asthma. This study supports that atopic disease drives differential monocyte phenotypes, with the FIM population, specifically, as a more mature cell population closely related to dendritic cells with enhanced antiviral responses. The presence of monocytes in lung tissue during lethal asthma exacerbation further supports a role in regulating tissue inflammatory responses in allergic airway disease.

immunology↗

bioRxiv2026

Biodiversity dynamics with complex genotype-to-phenotype architecture in multilayer networks

2The genotype-to-phenotype architecture (GPA), defined by complex interactions such as pleiotropy, epistasis, and regulatory control, is a fundamental yet often overlooked driver of biodiversity dynamics. While empirical evidence suggests that traits mediating species interactions (biotic) and environmental responses (abiotic) are frequently correlated, most eco-evolutionary theories treat these traits as independent, leaving a gap in our understanding of how genomic architecture influences community-level outcomes. In this study, we contrast two distinct GPAs, modular (independent trait evolution) and correlated (integrated trait evolution), within a spatially explicit multilayer network framework. We evaluate their impact on biodiversity across varying regimes of selection, migration, and biotic and environmental filtering. Our results reveal a hierarchy of drivers: selection strength dictates the absolute magnitude of the architectural effect, while migration and context-dependent biotic and abiotic effects determine which architecture yields a diversity advantage. Correlated GPAs enhance species coexistence and diversity in low-migration landscapes characterized by strong selection and moderate, balanced biotic and abiotic pressures. In these contexts, trait integration serves as a buffer against selective noise. Conversely, modular GPAs support higher diversity under high migration and strong biotic interactions, where the decoupling of trait modules provides the adaptive flexibility necessary to navigate spatially conflicting selective pressures. Our findings demonstrate that genomic architecture acts as a critical filter for environmental perturbations. Integrating complex GPAs into multispecies models is essential for quantifying the co-evolutionary feedbacks among traits, population adaptation, and species persistence. Our framework provides a path for predicting how biodiversity emerges and persists across biological scales, from genomics to communities and food webs, under the accelerating pressures of global change. 1 ConclusionsO_LIWe integrate trait architecture to spatial biodiversity to show biodiversity patterns are not merely products of ecological interactions, but are fundamentally constrained by Genotype-to-Phenotype Architecture (GPA). By linking GPA to biodiversity we show the interplay between the complexity of an organism and community structure in determining diversity patterns. C_LIO_LIThe hierarchy of Eco-Evolutionary Drivers: We establish a new conceptual hierarchy where selection strength acts as the fundamental governor of architectural impact, while the specific architecture predicting higher diversity (Correlational vs. Modular) is dictated by the interplay of migration scales and context-dependent biotic and abiotic dynamics. C_LIO_LISelection-Migration contingency for coexistence: We provide a new hypothesis for species coexistence: Correlational selection serves as a stabilizing force under dispersal limitation, whereas Modular trait architecture provides the adaptive flexibility to maintain diversity in high-migration, spatially heterogeneous landscapes. C_LIO_LIAdaptive decoupling as a diversity engine: We propose that trait modularity functions as a "buffer" against extinction by decoupling phenotypic responses. This allows populations to navigate conflicting selective pressures, effectively facilitating evolutionary rescue in complex biotic environments. C_LIO_LIMethodological framework for empirical inference: To bridge the gap between theory and data, we provide a novel likelihood-based framework. This enables researchers to infer latent trait architectures from population genomic samplings, turning GPA from a theoretical construct into a measurable sampling variable in natural populations. C_LIO_LIWe define a new roadmap for the next generation of eco-evolutionary modeling. By identifying the gaps between existing simulation engines, we provide a conceptual "blueprint" for a digital ecosystem that fully integrates complex genetic architecture with global bio-diversity dynamics. C_LI

evolutionary biology↗

bioRxiv2025

Nutritional effects on the expression of cryptic pigmentation in freshwater isopods

Cryptic pigmentation is a key phenotypic adaptation that helps many invertebrates evade visual predators. However, little is known about whether and how the expression of pigmentation phenotypes that match the habitat background is influenced by the availability of nutritional resources. Here we investigated whether variation in both the background and the nutritive composition of benthic substrates affect the expressed pigmentation of freshwater isopods (Asellus hilgendorfii). We collected isopods and their predominant substrate from 17 locations across Hokkaido, northern Japan, and quantified substrate background and nutritional composition (total protein, 18 amino acids, and C:N and C:P ratios). We found that variation in isopod pigmentation was better explained by the substrates nutritive composition than by its background darkness. Specifically, isopods were more pigmented when substrates had intermediate C:P ratios, lower C:N ratios, and a higher proportion of tryptophan - an essential amino acid involved in the isopods pigmentation pathway. These results are consistent with previous experiments showing that isopods reared under diets with higher protein concentrations developed more pigmentation, advancing our understanding about the environmental sources of phenotypic variation in natural populations. By demonstrating that nutritional constraints may shape the expression of key phenotypic adaptations in natural populations, our study opens new directions for exploring how organisms navigate adaptive landscapes; particularly in those organisms that rely on pigmentation for signaling and camouflage. Finally, we demonstrate how macronutrients, amino acids, and elemental ratios can serve as biotracers in ecological studies of adaptation, offering new opportunities to examine how stoichiometric traits influence phenotypic plasticity and adaptive capacity, especially in detritivorous taxa.

ecology↗

bioRxiv2024

Mapping planktonic communities: a network approach to assess the role of scale and centrality on their diversity and composition

The distribution of habitats across a landscape and their centrality gradient are key elements defining the effective pathways of dispersal, and thus of metacommunity assembly. Understanding how centrality shapes diversity patterns is essential for predicting the impact of future landscape changes on diversity. While alpine lakes have been extensively studied, often considering the fluvial network as a potential landscape, small planktonic communities have frequently been overlooked as potential dispersers due to their assumed ubiquity. In this study, we investigate the diversity patterns of alpine lake planktonic communities along lake networks constructed at different scales, ranging from 6.5 to 650 km and the fluvial network. We sampled 55 lakes in the northern Alps (16S, 18S, phytoplankton and zooplankton) and calculated several diversity metrics (alpha, beta diversity and LCBD) and multivariate analysis. We then constructed several networks responding to different scales, determined their centrality gradients, and finally explored their relationship with the diversity of each planktonic group. We expected that a groups diversity would relate differently across scales based on body size, but the outcomes were varied. Bacterioplankton and zooplankton diversity were both affected across scales higher than 100 km, whereas phytoplankton appeared completely unrelated to centrality. Nonetheless, we could observe that when significant, the relationships between diversity and centrality were shared among organisms. These findings not only underscore that planktonic organisms are influenced by landscape configurations larger than the fluvial system but also emphasise the critical role of dispersal for these groups and the scales at which it impacts metacommunity assembly. Significance statementWhile dispersal is widely recognized as a key driver of assembly, some groups and systems remain insufficiently explored to fully grasp the impact of landscape and dispersal on their assembly. Planktonic communities have traditionally been considered ubiquitous and detached from regional-level structure, primarily due to their small size, leading to the notion that "everything is everywhere". Additionally, alpine lake communities have traditionally been perceived as solely connected through fluvial systems. In this study, we challenge these notions by demonstrating how planktonic communities are indeed influenced by the relative positioning of lakes in the landscape, with significant impacts occurring at larger scales, spanning hundreds of kilometres. However, not all planktonic groups responded uniformly to the analysed factors, emphasizing the marked differences among groups and the diverging drivers shaping planktonic metacommunities.

ecology↗

bioRxiv2023

DDHD2 interacts with STXBP1 to mediate long-term memory via the generation of myristic acid

The phospholipid and free fatty acid (FFA) composition of neuronal membranes plays a crucial role in learning and memory, but the mechanisms through which neuronal activity affects the brains lipid landscape remain largely unexplored. Saturated FFAs, particularly myristic acid (C14:0), strongly increase during neuronal stimulation and memory acquisition, suggesting the involvement of phospholipase A1 (PLA1) activity in synaptic plasticity. Here, we show that genetic ablation of the DDHD2 isoform of PLA1 in mice reduced memory performance in reward-based learning and spatial memory models prior to the development of neuromuscular deficits, and markedly reduced saturated FFAs across the brain. DDHD2 was shown to bind to the key synaptic protein STXBP1. Using STXBP1/2 knockout neurosecretory cells and a haploinsufficient STXBP1+/- mouse model of STXBP1 encephalopathy that is also associated with intellectual disability and motor dysfunction, we show that STXBP1 controls the targeting of DDHD2 to the plasma membrane and the generation of saturated FFAs in the brain. Our findings suggest key roles for DDHD2 and STXBP1 in the lipid metabolism underlying synaptic plasticity, learning and memory.

neuroscience↗

bioRxiv2023

Transcriptional characterization of iPSC-derived microglia as a model for therapeutic development in neurodegeneration

BackgroundMicroglia are the resident immune cells in the brain that play a key role in driving neuroinflammation, a hallmark of neurodegenerative disorders. Inducible microglia-like cells have been developed as an in vitro platform for molecular and therapeutic hypothesis generation and testing. However, there has been no systematic assessment of similarity of these cells to primary human microglia along with their responsiveness to external cues expected of primary cells in the brain. MethodsIn this study, we performed transcriptional characterization of commercially available human inducible pluripotent stem cell (iPSC)-derived microglia-like (iMGL) cells by bulk and single cell RNA sequencing to assess their similarity with primary human microglia. To evaluate their stimulation responsiveness, iMGL cells were treated with Liver X Receptor (LXR) pathway agonists and their transcriptional responses characterized by bulk and single cell RNA sequencing. ResultsBulk transcriptome analyses demonstrate that iMGL cells have a similar overall expression profile to freshly isolated human primary microglia and express many key microglial transcription factors and functional and disease-associated genes. Notably, at the single-cell level, iMGL cells exhibit distinct transcriptional subpopulations, representing both homeostatic and activated states present in normal and diseased primary microglia. Treatment of iMGL cells with LXR pathway agonists induces robust transcriptional changes in lipid metabolism and cell cycle at the bulk level. At the single cell level, we observe heterogeneity in responses between cell subpopulations in homeostatic and activated states and deconvolute bulk expression changes into their corresponding single cell states. ConclusionsIn summary, our results demonstrate that iMGL cells exhibit a complex transcriptional profile and responsiveness, reminiscent of in vivo microglia, and thus represent a promising model system for therapeutic development in neurodegeneration.

genomics↗

bioRxiv2022

Oxysterols drive inflammation via GPR183 during influenza virus and SARS-CoV-2 infection

RationaleSevere viral respiratory infections are often characterized by extensive myeloid cell infiltration and activation and persistent lung tissue injury. However, the immunological mechanisms driving excessive inflammation in the lung remain elusive. ObjectivesTo identify the mechanisms that drive immune cell recruitment in the lung during viral respiratory infections and identify novel drug targets to reduce inflammation and disease severity. MethodsPreclinical murine models of influenza virus and SARS-CoV-2 infection. ResultsOxidized cholesterols and the oxysterol-sensing receptor GPR183 were identified as drivers of monocyte-macrophage infiltration to the lung during influenza virus (IAV) and SARS-CoV-2 infections. Both IAV and SARS-CoV-2 infections upregulated the enzymes cholesterol 25-hydroxylase (CH25H) and cytochrome P450 family 7 subfamily member B1 (CYP7B1) in the lung, resulting in local production of the oxidized cholesterols 25-hydroxycholesterol and 7,25-dihydroxycholesterol (7,25-OHC). Loss-of-function mutation of GPR183, or treatment with a GPR183 antagonist, reduced macrophage infiltration and inflammatory cytokine production in the lungs of IAV- or SARS-CoV-2-infected mice. The GPR183 antagonist also significantly attenuated the severity of SARS-CoV-2 infection by reducing weight loss and viral loads. ConclusionThis study demonstrates that oxysterols drive inflammation in the lung and provides the first preclinical evidence for therapeutic benefit of targeting GPR183 during severe viral respiratory infections. Author SummaryViral infections trigger oxysterol production in the lung, attracting macrophages via GPR183. Blocking GPR183 reduced inflammation and disease severity in SARS-CoV-2 infection, making GPR183 a putative target for therapeutic intervention.

immunology↗

bioRxiv2021

The interaction of climate history and evolution impacts alpine biodiversity assembly differently in freshwater and on land

Quaternary climate fluctuations can affect biodiversity assembly through speciation in two non-mutually-exclusive ways: a glacial species pump, where isolation in glacial refugia accelerates allopatric speciation, and adaptive radiation during ice-free periods. Here we detected biogeographic and genetic signatures associated with both mechanisms in the generation of the European Alps biodiversity. Age distributions of endemic and widespread species within aquatic and terrestrial taxa (amphipods, fishes, amphibians, butterflies and flowering plants) revealed that endemic fish evolved only in lakes, are highly sympatric and mainly of Holocene age, consistent with adaptive radiation. Endemic amphipods are ancient, suggesting preglacial radiation with limited range expansion and local Pleistocene survival, perhaps facilitated by a groundwater-dwelling lifestyle. Terrestrial endemics are mostly of Pleistocene age, and are thus more consistent with the glacial species pump. The lack of evidence for Holocene adaptive radiation in the terrestrial biome may be attributable to a faster range expansion of these taxa after glacial retreats, though fewer stable environments may also have contributed to differences between terrestrial areas and lakes. The high proportion of young, endemic species make the Alps vulnerable to climate change, but the mechanisms and consequences of species loss will likely differ between biomes because of their distinct histories.

evolutionary biology↗

bioRxiv2018

Ecosystem tipping points in an evolving world

There is growing concern over tipping points arising in ecosystems due to the crossing of environmental thresholds. Tipping points lead to strong and possibly irreversible shifts between alternative ecosystem states incurring high societal costs. Traits are central to the feedbacks that maintain alternative ecosystem states, as they govern the responses of populations to environmental change that could stabilize or destabilize ecosystem states. However, we know little about how evolutionary changes in trait distributions over time affect the occurrence of tipping points, and even less about how big scale ecological shifts reciprocally interact with trait dynamics. We argue that interactions between ecological and evolutionary processes should be taken into account for understanding the balance of feedbacks governing tipping points in nature.

ecology↗

bioRxiv2018

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems intrinsic predictability - the highest achievable predictability given the degree to which system dynamics are the result of deterministic v. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. By means of simulations we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a dataset of 461 empirical ecological time series. We show how deviations from the expected PE-FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics.\n\nThese results demonstrate a theoretically-grounded basis for a model-free evaluation of a systems intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.\n\nGlossaryActive information: The amount of information that is available to forecasting models (redundant information minus lost information; Fig. 1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC=\"FIGDIR/small/350017_fig1a.gif\" ALT=\"Figure 1A\">\nView larger version (9K):\norg.highwire.dtl.DTLVardef@934d9eorg.highwire.dtl.DTLVardef@ccdc10org.highwire.dtl.DTLVardef@1839ed9org.highwire.dtl.DTLVardef@31bd70_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1A.C_FLOATNO The total information content of an observation of a system at a given state in time, St, is depicted by filled circles with past states (St-1 and St-2) represented by shades of grey, i) lack of overlap between past and present states illustrating a case where no information is transmitted from past states (i.e. a purely stochastic system), with low redundancy and high Shannon entropy rate, ii) intermediate overlap indicating a case when some information is transferred from past to present (i.e. a deterministic system strongly driven by stochastic forcing), with intermediate redundancy and Shannon entropy rate, iii) large overlap indicating a case when the current state is mostly determined by the previous state (i.e. a highly deterministic system), with high redundancy and low Shannon entropy rate. Note that both the redundancy and Shannon entropy rate of a system are intrinsic properties of the system and will only change if the system itself changes.\n\nC_FIG Forecasting error (FE): A measure of the discrepancy between a models forecasts and the observed dynamics of a system. Common measures of forecast error are root mean squared error and mean absolute error.\n\nEntropy: Measures the average amount of information in the outcome of a stochastic process.\n\nInformation: Any entity that provides answers and resolves uncertainty about a process. When information is calculated using logarithms to the base two (i.e. information in bits), it is the minimum number of yes/no questions required, on average, to determine the identity of the symbol (Jost 2006). The information in an observation consists of information inherited from the past (redundant information), and of new information.\n\nIntrinsic predictability: the maximum achievable predictability of a system (Beckage et al. 2011).\n\nLost information: The part of the redundant information lost due to measurement or sampling error, or transformations of the data (Fig. 1).\n\nNew information, Shannon entropy rate: The Shannon entropy rate quantifies the average amount of information per observation in a time series that is unrelated to the past, i.e., the new information (Fig. 1).\n\nNonlinearity: When the deterministic processes governing system dynamics depend on the state of the system.\n\nPermutation entropy (PE): permutation entropy is a measure of the complexity of a time series (Bandt & Pompe, 2002) that is negatively correlated with a systems predictability (Garland et al. 2015). Permutation entropy quantifies the combined new and lost information. PE is scaled to range between a minimum of 0 and a maximum of 1.\n\nRealized predictability: the achieved predictability of a system from a given forecasting model.\n\nRedundant information: The information inherited from the past, and thus the maximum amount of information available for use in forecasting (Fig. 1).\n\nSymbols, words, permutations: symbols are simply the smallest unit in a formal language such as the letters in the English alphabet i.e., {\"A\", \"B\",..., \"Z\"}. In information theory the alphabet is more abstract, such as elements in the set {\"up\", \"down\"} or {\"1\", \"2\", \"3\"}. Words, of length m refer to concatenations of the symbols (e.g., up-down-down) in a set. Permutations are the possible orderings of symbols in a set. In this manuscript, the words are the permutations that arise from the numerical ordering of m data points in a time series.\n\nWeighted permutation entropy (WPE): a modification of permutation entropy (Fadlallah et al., 2013) that distinguishes between small-scale, noise-driven variation and large-scale, system-driven variation by considering the magnitudes of changes in addition to the rank-order patterns of PE.

ecology↗