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Biology subjects

Lambert, S.

Publications and source records attributed to Lambert, S..

At least 19 recordsLinked to original sources

Structural order emerges without mechanical order inside living cells

Intracellular organization is vital for cell function, and disruption of organelle positioning is connected to severe diseases, such as cancer. Given the small length scale of cells and the importance of Brownian motion, maintaining intracellular organization requires mechanical interactions to remain stable. Here, we test the common view that intracellular organization is reflected in intracellular mechanical properties. Using the recently introduced active mechanical fingerprint, we quantify intracellular mechanical properties and active forces to create mechanical maps of patterned RPE1 cells. The organization of actin, microtubules, the Golgi apparatus, the endoplasmic reticulum, and the nucleus reveals pronounced structural polarization across the cell population. To overcome limitations of simple averages, we use a Wasserstein barycenter to calculate an optimal intracellular organization that explains all measurements with minimal cellular distortion. To our surprise, and in contrast to previous reports, we find neither a statistically significant spatial mechanical pattern nor any relevant correlation between the tested structures and intracellular mechanical properties. While there is clear local mechanical anisotropy, the large heterogeneity of mechanical values outweighs any intracellular mechanical gradient. This is further supported by large cell-to-cell variability. Thus, despite having the same genome, cells function properly while their intracellular mechanical properties vary over two orders of magnitude.

biophysics↗

A modelling challenge to improve preparedness for highly pathogenicavian influenza epidemics: objectives, design and synthetic datageneration

Highly pathogenic avian influenza (HPAI) is among the most significant disease threats to poultry worldwide. Modelling challenges, where independent teams address a common epidemiological scenario, can support the development of response capabilities and strengthen communication between modellers and risk managers. Despite the established benefits of these exercises, they remain uncommon in animal health, and none have previously addressed avian influenza. Here Jolly Island, an artificial landscape of 9160 poultry farms with five production types, was developed and seeded with stochastic HPAI epidemics that ran their course over a three-to-four month period. From January through April 2026, 45 registered teams across five continents investigated a selected synthetic epidemic, producing probabilistic forecasts, inferring transmission drivers, and evaluating counterfactual control policies. Here the objectives, design choices, and data generation of the HPAI Modelling Challenge are described in full, covering transmission processes, surveillance system operations, and control strategy implementation, along with the selected epidemic trajectory, the narrative situation reports supplied to the teams at each phase, and the complete phase-specific datasets released to them. As the opening paper of this special issue, here is provided the foundation upon which all subsequent contributions, where teams document their approaches and analyses, are built.

ecology↗

Cnidarian oocytes reveal conserved actin-driven mechanisms of female meiosis

Oocyte meiosis is a specialized form of cell division adapted to produce a single haploid egg for fertilization. Diverse actin-driven mechanisms have essential roles in supporting these highly asymmetric divisions of the large oocyte. Whether these represent exotic adaptations in individual species or whether actin has broadly conserved functions in animal oocytes remains unclear. To address this, we established live-imaging assays, combined with targeted perturbations and biophysical modeling, in the non-bilaterian jellyfish Clytia hemisphaerica. We show that in Clytia, a nuclear F-actin network stabilizes the large oocyte nucleus. Upon meiotic entry, a transient F-actin shell forms to facilitate nuclear envelope rupture, followed by chromosome congression driven by the collapse of the nuclear F-actin network and capture by microtubules. Finally, the forming spindle is transported to the cell periphery by cytoplasmic flows produced by a wave of cortical contraction. Together, the presence of these actin-driven mechanisms in a basal metazoan evidence their ancient origin, establishing a metazoan complement of conserved molecular modules required for oocyte divisions.

cell biology↗

Interactions between single actin and vimentin filaments

The cytoskeleton determines cell shape, mechanical properties, and motility by interconnected networks of protein filaments - actin filaments, microtubules and intermediate filaments. Their collective function relies on crosstalk between these filament systems, yet the physical basis of interactions between the filaments remains insufficiently understood. Actin and vimentin filaments and networks frequently co-localize within cells and jointly regulate contractility, force transmission and mechanical resilience, indicating functional cooperation. However, it remains unclear whether these interactions arise from direct filament-filament interactions or are mediated exclusively by accessory crosslinking proteins. Studies of reconstituted composite networks probing direct interactions by rheology have yielded inconsistent results. Here, we show that single actin filaments and vimentin intermediate filaments do indeed interact directly, forming force-bearing contacts in the absence of crosslinking proteins, with interaction strengths comparable to other previously reported cytoskeletal filament pairs. Using quadruple optical tweezers combined with microfluidics and confocal microscopy, we systematically probe these interactions under controlled conditions across a range of ionic environments. We find that, in contrast to other filament pairs, variations in ionic strength do not appreciably affect the interaction breaking forces between actin and vimentin filaments. However, the interaction geometry determines the achievable interaction strengths, because the limited stretchability of the actin filaments sets an upper bound to the measurable force range. This limit also imposes a restriction on direct quantification of interaction parameters, which we circumvent by a Bayesian unmasking strategy that allows us to infer bond parameters despite the breaking of actin filaments. Furthermore, actin bundling enhances the stability against breaking, enabling the detection of higher interaction forces. These findings demonstrate that actin and vimentin form a mechanically interacting system through direct filament bonds, and our work establishes a minimal, protein-linker-independent physical basis for actin-vimentin crosstalk and quantifies the interaction forces between the two filament types.

biophysics↗

Bayesian Inference of Bond Parameters from Interactions between Single Filaments

We map interaction forces between two vimentin filaments (cytoskeletal components crucial for cell mechanics) using optical tweezers, while controlling the relative velocity. We introduce a powerful Bayesian inference framework to learn bond parameters directly from force trajectories. The information gained about the bond parameters is maximized by an optimal relative velocity and further by distributing measurements across multiple velocities. Our Bayesian framework is broadly applicable to a large range of biomolecular interactions and force spectroscopy techniques.

biophysics↗

Integrating multi-host modelling with empirical wildlife-livestock contacts reveals an essential population in a pathogen reservoir

Infections at the animal-human or wildlife-livestock interfaces have severe health and socio-economic consequences. Combined with empirical data, mathematical models can contribute to a better understanding of the reservoirs of these infections, which is a priority for mitigating their impact by using appropriate management interventions. Taking brucellosis in the Bargy massif (French Alps) as an example of a zoonosis at the wildlife-livestock interface, we developed and calibrated a multi-host model integrating data on direct and environment-mediated cross-species contacts from field observations. Estimates of the basic reproduction number (R0) allowed to identify the population of Alpine ibex (Capra ibex) and its environment as an essential host in the reservoir, driving both pathogen maintenance (within-species R0[≥]1: 1.66, 95% credible interval: 1.42-2.03) and its transmission to livestock (between-species R0>0: 0.035, 0.01-0.05). Our approach can be adapted to other multi-host pathogens, which will contribute to improve the understanding and management of these complex systems.

ecology↗

Graph Neural Networks for Likelihood-Free Inference in Diversification Models

A common approach to infer the processes that gave rise to past speciation and extinction rates across taxa, space and time is to formulate hypotheses in the form of probabilistic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. A drawback of this approach is that likelihoods can easily become computationally intractable, limiting our ability to extend current diversification models with new hypothesized mechanisms. Neural networks have been proposed as a likelihood-free alternative for parameter inference of stochastic models, but so far there is little experience in using this method for diversification models, and the quality of the results is likely to depend on finding the right network architecture and data representation. As phylogenies are essentially graphs, graph neural networks (GNNs) appear to be the most natural architecture but previous results on their performance are conflicting, with some studies reporting poor accuracy of GNNs in practice. Here, we show that this underperformance was likely caused by optimization issues and inappropriate pooling operations that flatten the information along the phylogeny and make it harder to extract relevant information about the diversification parameters. When equipped with PhyloPool, a new time-informed pooling procedure, GNNs show similar or better performance compared to all other architectures and data representations (including Maximum Likelihood Estimation) that we tested for two common diversification models, the Constant Rate Birth-Death and the Binary State Speciation and Extinction. We conclude that GNNs could serve as a generic tool for estimating diversification parameters of complex diversification models with intractable likelihoods.

evolutionary biology↗

Highly pathogenic avian influenza management in high-density poultry farming areas

The continuous spread of highly pathogenic avian influenza H5 viruses poses significant challenges, particularly in regions with high poultry farm densities where conventional control measures are less effective. Using phylogeographic and phylodynamic tools, we analysed virus spread in Southwestern France in 2020-21, a region with recurrent outbreaks. Following a single introduction, the virus spread regionally, mostly affecting duck farms, peaking in mid-December with a velocity of 27.8 km/week and an effective reproduction number between farms (Re) of 3.8, suggesting the virus can spread beyond current control radii. Transmission declined after late December following preventive culling. Farm infectiousness was estimated around 9 days. Duck farm density was the main driver of virus spread and we identified farm density and proximity thresholds required to maintain effective control (Re < 1). These findings offer actionable guidance to support regional biosecurity and to improve the robustness of the poultry sector to mitigate future outbreaks.

evolutionary biology↗

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets - containing association data for all variants in the analysis - are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

genetics↗

How to barcode (almost all) freshwater biodiversity

Freshwater ecosystems are complex, diverse and face a variety of imminent threats that have led to changes in both ecosystem structure and function. It is urgent that we develop and standardize monitoring tools allowing for rapid and comprehensive assessment of freshwater communities to understand their changing dynamics and to inform conservation. Environmental DNA surveys offer a means to inventory and monitor aquatic diversity, yet most studies focus on one or a few taxonomic groups only. In this study, we sought to 1) identify thoroughly validated, cost-efficient primer pair combinations that maximize detection of broad swaths of freshwater diversity, and 2) facilitate future primer pair selection by creating a free online and user-friendly tool. We first evaluated the completeness of public reference sequence databases and the efficiency of 14 primer pairs using an in silico approach, and then performed eDNA surveys using five mock communities (mix of DNA from tissues), water samples from aquarium samples with known taxonomic composition, and finally water samples from freshwater systems in Eastern Canada. We highlight the power of eDNA-based metabarcoding for reconstructing freshwater communities, including prey, parasite, pathogen, invasive, and declining species. Our work reveals the importance of the marker choice on species resolution, as well as the importance of degenerate primers, the length of the target fragment and the filtering parameters on detection success in water eDNA samples. Our new online tool SNIPe revealed that 13 to 14 primer pairs are necessary to recover 100% of the species in water samples (aquarium and natural systems), but four primer pairs are sufficient to recover almost 75% of taxa with little overlap. These results highlight the usefulness of eDNA for freshwater monitoring and should prompt more studies on tools to survey all-inclusive communities.

ecology↗

Deciphering the biology and chemistry of the mutualistic partnership between Bacillus velezensis and the arbuscular mycorrhizal fungus Rhizophagus irregularis

Arbuscular mycorrhizal (AM) fungi (e.g. Rhizophagus irregularis) recruit specific bacterial species in their hyphosphere. However, the chemical interplay and the mutual benefit of this intricate partnership have not yet been investigated especially as it involves bacteria known as strong producers of antifungal compounds such as Bacillus velezensis. Here, we show that the soil dwelling B. velezensis migrates along the hyphal network of the AM fungus R. irregularis, forming biofilms and inducing metabolic fluxes that contributes to host plant root colonization by the bacterium. During hyphosphere colonization, R. irregularis modulates the biosynthesis of specific lipopeptides and antimicrobial compounds in B. velezensis as a mechanism toward-off mycoparasitic fungi and bacteria to ensure stable coexistence. These mutual benefits are extended into a tripartite context via the provision of enhanced protection to the host plant through the induction of systemic resistance.

microbiology↗

A Comparison of Deep Learning Architectures for Inferring Parameters of Diversification Models from Extant Phylogenies

AO_SCPLOWBSTRACTC_SCPLOWTo infer the processes that gave rise to past speciation and extinction rates across taxa, space and time, we often formulate hypotheses in the form of stochastic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. Unfortunately, however, likelihoods can easily become intractable, limiting our ability to consider more complicated diversification processes. Recently, it has been proposed that deep learning (DL) could be used in this case as a likelihood-free inference technique. Here, we explore this idea in more detail, with a particular focus on understanding the ideal network architecture and data representation for using DL in phylogenetic inference. We evaluate the performance of different neural network architectures (DNN, CNN, RNN, GNN) and phylogeny representations (summary statistics, Lineage Through Time or LTT, phylogeny encoding and phylogeny graph) for inferring rates of the Constant Rate Birth-Death (CRBD) and the Binary State Speciation and Extinction (BISSE) models. We find that deep learning methods can reach similar or even higher accuracy than Maximum Likelihood Estimation, provided that network architectures and phylogeny representations are appropriately tuned to the respective model. For example, for the CRBD model we find that CNNs and RNNs fed with LTTs outperform other combinations of network architecture and phylogeny representation, presumably because the LTT is a sufficient and therefore less redundant statistic for homogenous BD models. For the more complex BiSSE model, however, it was necessary to feed the network with both topology and tip states information to reach acceptable performance. Overall, our results suggest that deep learning provides a promising alternative for phylogenetic inference, but that data representation and architecture have strong effects on the inferential performance.

ecology↗

Deep Learning from Phylogenies for Diversification Analyses

Birth-death models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models such formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time constant homogeneous birth-death model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for deployment of future models in the field.

evolutionary biology↗

Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020-2022

The spread of highly pathogenic avian influenza (HPAI) viruses worldwide has serious consequences for animal health and a major economic impact on the poultry production sector. Since 2014, Europe has been severely hit by several HPAI epidemics, with France being the most affected country. Most recently, France was again affected by two devastating highly pathogenic avian influenza epidemics in 2020-21 and 2021-22. We conducted a descriptive analysis of the 2020-21 and 2021-22 epidemics, in a first step towards identifying the poultry sectors remaining vulnerabilities regarding HPAI viruses in France. We examined the spatio-temporal distribution of outbreaks that occurred in France in 2020-21 and 2021-22, and we assessed the outbreaks spatial distribution in relation to two High-Risk Zones recently incorporated into French legislation to strengthen HPAI prevention and control. There were 468 reported outbreaks during the 2020-21 epidemic, and 1,223 outbreaks during the 2021-22 epidemic. In both epidemics, most outbreaks (80.6% and 74.0%) were located into the two High-Risk Zones. The southwestern High-Risk Zone was affected in both epidemics, while the western High-Risk Zone was affected for the first time in 2021-22, explaining the extremely high number of outbreaks reported. We showed that the spatial distribution model used to create the two High-Risk Zones was able to predict the location of outbreaks for the 2020-21 and 2021-22 epidemics. These zones were characterized by high poultry farm densities; future efforts should therefore focus on reducing the density of susceptible poultry in highly dense areas.

ecology↗

Effects of microplastics mixed with natural particles on Daphnia magna populations

The toxicity of microplastics on Daphnia magna as a key model for freshwater zooplankton is well described. While several studies predict population-level effects based on short-term, individual-level responses, only very few have validated these predictions experimentally. Thus, we exposed D. magna populations to irregular polystyrene microplastics and diatomite as natural particle (both [&le;] 63 m) over 50 days. We used mixtures of both particle types at fixed particle concentrations (50,000 particles mL-1) and recorded the effects on overall population size and structure, the size of the individual animals, and resting egg production. Particle exposure adversely affected the population size and structure and induced resting egg production. The terminal population size was 28-42% lower in exposed compared to control populations. Interestingly, mixtures containing diatomite induced stronger effects than microplastics alone, highlighting that natural particles are not per se less toxic than microplastics. Our results demonstrate that an exposure to synthetic and natural particles has negative population-level effects on zooplankton. Understanding the mixture toxicity of microplastics and natural particles is important given that aquatic organisms will experience exposure to both. Just as for chemical pollutants, better knowledge of such joint effects is essential to fully understand the environmental impacts of complex particle mixtures. Environmental ImplicationsWhile microplastics are commonly considered hazardous based on individual-level effects, there is a dearth of information on how they affect populations. Since the latter is key for understanding the environmental impacts of microplastics, we investigated how particle exposures affect the population size and structure of Daphnia magna. In addition, we used mixtures of microplastics and natural particles because neither occurs alone in nature and joint effects can be expected in an environmentally realistic scenario. We show that such mixtures adversely affect daphnid populations and highlight that population-level and mixture-toxicity designs are one important step towards more environmental realism in microplastics research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/490562v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@11a9358org.highwire.dtl.DTLVardef@1bfa315org.highwire.dtl.DTLVardef@6bc0cdorg.highwire.dtl.DTLVardef@3ef1b8_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDaphnia populations exposed to mixtures of microplastics and diatomite C_LIO_LIEffects on population size, structure, and resting egg production C_LIO_LIDiatomite as natural particle more toxic than microplastics C_LIO_LIParticle mixtures induce negative population-level effects C_LIO_LIParticle mixtures represent more realistic exposure scenario C_LI

ecology↗

A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex

Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.

neuroscience↗

A framework for evaluating edited cell libraries created by massively parallel genome engineering

Genome engineering methodologies are transforming biological research and discovery. Approaches based on CRISPR technology have been broadly adopted and there is growing interest in the generation of massively parallel edited cell libraries. Comparing the libraries generated by these varying approaches is challenging and researchers lack a common framework for defining and assessing the characteristics of these libraries. Here we describe a framework for evaluating massively parallel libraries of edited genomes based on established methods for sampling complex populations. We define specific attributes and metrics that are informative for describing a complex cell library and provide examples for estimating these values. We also connect this analysis to generic phenotyping approaches, using either pooled (typically via a selection assay) or isolate (often referred to as screening) phenotyping approaches. We approach this from the context of creating massively parallel, precisely edited libraries with one edit per cell, though the approach holds for other types of modifications, including libraries containing multiple edits per cell (combinatorial editing). This framework is a critical component for evaluating and comparing new technologies as well as understanding how a massively parallel edited cell library will perform in a given phenotyping approach.

genomics↗