Biology subjects

Lambert, A.

Publications and source records attributed to Lambert, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Systems genetics identifies ETS1 as a stress-dependent regulator of adipocyte insulin action and heme-iron homeostasis

White adipose tissue plays a central role in systemic energy homeostasis by buffering nutrient excess through insulin-stimulated glucose uptake and triglyceride storage. Despite its importance, the genetic and molecular mechanisms governing adipose tissue insulin action remain poorly defined because tissue-specific insulin responsiveness has been difficult to quantify at the scale required for genetic discovery. Here, we developed the first scalable platform for high-throughput genetic mapping of tissue-specific insulin action in adipose tissue, enabling systems genetic analysis across 559 genetically diverse Diversity Outbred Australia (DOz) mice. Genetic analysis accounting for adiposity identified 39 loci associated with adipose tissue insulin action, demonstrating that adipose insulin responsiveness is a genetically encoded trait that captures a dimension of metabolic health beyond adiposity. Among these, a strong diet-dependent locus on chromosome 9 encompassed the transcription factor Ets1. Functional studies demonstrated that Ets1 silencing selectively restored insulin-stimulated glucose uptake in insulin-resistant adipocytes. Proteomic profiling revealed that ETS1 orchestrates a stress-responsive program involving heme metabolism, iron handling and redox homeostasis. Consistent with this, ETS1 knockdown reduced cellular heme and labile iron levels and attenuated oxidative stress under insulin-resistant conditions. Collectively, these findings demonstrate the power of systems genetics to identify previously unrecognised regulators of adipose insulin action and establish the heme-iron axis as a critical determinant of adipocyte insulin responsiveness.

systems biology↗

bioRxiv2026

Novel Methylation Markers in a Prostate Cancer Cohort are Associated with Disease Development and Relapse

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

cancer biology↗

bioRxiv2026

Blockbuster meets the theoretical limit of genome-wide SFS-based inference of recent demography

1The molecular diversity of a sample of DNA sequences from the same species is traditionally summarized by the so-called Site Frequency Spectrum (SFS). Past variations in population size leave a trace in the shape of this histogram that several popular programs leverage to infer demography. The ability of inference methods using genome-wide SFS to document recent demography for conservation purposes needs to be evaluated both theoretically and practically. Assuming population size is piecewise constant, we predict analytically that the date of the most recent demographic change that leaves a statistically detectable trace in the SFS is on the order of [Formula] generations, where n is the sample size and N is the effective population size before the change. We further show that robust parameter estimation is achieved at N3/4/n generations for both the date and intensity of the most recent change. We release Blockbuster, a deterministic program that reliably infers a demographic scenario with piecewise-constant population size through time from a genome-wide SFS. Blockbuster consistently outperforms recent similar programs in accuracy, robustness, and computational time. More importantly, it reaches the theoretical limit on the most recent demographic changes. Finally, we propose a simple yet efficient method to circumvent the presence of population structure, a well-known and pervasive complication that prevents reliable inference of demographic history.

genetics↗

bioRxiv2026

Stabilizing selection on a polygenic trait from the gene's-eye view.

We study a polygenic trait under stabilizing selection at statistical equilibrium, where genetic effect, mutation rate and mutational bias are heterogeneous across loci. The model assumes L biallelic sites subject to reversible mutations, each allele described by its frequency in the population. Using a diffusion approximation, a mean-field approximation and neglecting linkage disequilibrium, we predict consistent phenomena across several regimes of selection: (1) a small deviation {Delta}* of the trait mean from its optimal value appears and persists due to genetic mutations not aligned with selection; (2) while this deviation is often undetectable at the trait level, it leaves a substantial signature at the locus level by favoring alleles reducing it, resulting in genic selection with mean coefficient s* proportional to -{Delta}* acting pervasively; (3) with stronger selection on the trait, (3a) the value of {Delta}* is decreased but the intensity of genic selection is increased in inverse proportion, resulting in an essentially constant, non negligible value of s*. We show how the stationary distribution of allelic frequencies can be obtained from {Delta}*. The latter can then be characterized as the solution to a fixed-point equation. Finally, we quantify several macroscopic observables of interest (genetic variance, description of the fluctuations of the trait mean as an Ornstein-Uhlenbeck process). The orders of magnitude of the macroscopic observables can be derived on a wide region of the parameter space. The model shows good fit and can straightforwardly be extended to accommodate pleiotropy, dominance, and some forms of epistasis. We also discuss the different breakdown which may occur (Bulmer effect, Hill-Robertson effect, breakdown of the Ornstein-Uhlenbeck approximation for the dynamics of the trait mean, depletion of genetic variability due to low mutation rates).

evolutionary biology↗

bioRxiv2025

MIMEco: Multi-objective metabolic modeling to predict and explain pairwise interactions

Genome-scale metabolic models (GEMs) provide a mechanistic framework for quantitatively exploring an organisms physiology and its interactions with the environment. However, their use in microbial community modeling has been limited since it usually relies on community-wide objective functions or the integration of community abundance data. Nevertheless, predicting microbial interactions is crucial for understanding ecosystem dynamics, and thus requires dedicated methods. Here, we introduce MIMEco (Metabolic Interaction Modeling in Ecosystems), an open-source Python package that predicts pairwise microbial interactions through multi-objective optimization. MIMEco does not require abundance data, provides a user-friendly workflow, and predicts interaction types, strengths, and exchanged metabolites directly from Pareto fronts. In this study, MIMEco is validated by replicating experimental results of an in vitro co-culture of two auxotrophic Escherichia coli strains. By combining mechanistic inference with usability, MIMEco makes metabolic interaction modeling accessible to non-specialists while remaining flexible for experts, establishing a versatile platform for advancing metabolic ecology across biomedical and environmental fields. IMPORTANCEDeciphering microbial interactions through in vitro experiments is often costly and limited in scope, while existing bioinformatics tools can be complex to use or limited by actual sample data. MIMEco offers an open-source and user-friendly multi-objective framework for predicting and quantifying pairwise microbial ecosystem interactions and nutrient exchanges. We demonstrate its reliability by replicating a well-characterized laboratory experiment, showing that MIMEco can accurately reflects real biological behavior. This tool improves the communitys ability to explore and understand metabolic interaction mechanisms underpinning microbial ecosystems.

bioinformatics↗

bioRxiv2025

Darwin's entangled bank through deep time: Structural stability of mutualistic networks over evolutionary timescales

Mutualistic interactions form species-rich, complex networks that play essential roles for ecosystem function. Over macroevolutionary time scales, global- and continental-level networks change as species emerge and go extinct, yet the stability of their structural organization remains poorly understood. Here, we develop ELEFANT, a novel method for reconstructing ancestral interaction networks based on the hypothesis that species interactions are shaped by unobserved traits that evolve over evolutionary time. We show that ancestral interaction networks can be reliably reconstructed from present-day phylogenetic and interaction data. We infer the ancestral networks of plant mutualisms involving arbuscular mycorrhizal fungi, bat pollinators, and bird seed dispersers at large biogeographic scales. We find that these mutualistic networks exhibit a modular structure that seems to have persisted for millions of years, in part maintained by the evolutionary conservatism of species interactions. As species diversify, they tend to show limited shifts in mutualistic partners, which results in a remarkable long-term stability of mutualistic network structure at large spatial scales.

evolutionary biology↗

bioRxiv2025

The extracellular matrix dictates ovarian cancer cell migration in an in vivo-derived circulating environment

Ovarian cancer (OC) disseminates via ascites and interaction with peritoneal extracellular matrix (ECM). To dissect the crosstalk between ECM and ascites in OC cell migration, we developed an ovarian tumor-on-chip integrating OC tumor spheroids, perfusion-induced shear stress of fluid supplemented with key components of ascites or patient-derived ascites, and biomimetic ECMs mimicking either early-stage basement membrane or late-stage connective tissues. We evaluated the individual and combined effects of two key ascitic components, fibronectin and TGF-{beta}, and compared these results with the perfusion of patient ascites. Results showed that cell migration, morphology, and epithelial-to-mesenchymal transition (EMT) markers such as the reorganization of vimentin cytoskeleton depend strongly on ECM composition, regardless of biochemical cues. Fibronectin and TGF-{beta} synergistically enhanced migration and EMT signatures, especially on basement membrane-rich ECM. Patient ascites further promoted migration but did not override ECM-driven migration patterns. Our findings show that clinical ascites perfusion exemplifies the ECM-dependence of cancer cell migration. This highlights that ECM protein composition is a dominant regulator of OC cell migration, providing key insights for in vitro tumor modeling and therapeutic strategies targeting the metastatic microenvironment.

cancer biology↗

bioRxiv2025

Liver FGF21 mediates sex-specific adaptation to juvenile protein malnutrition

Dietary protein sufficiency during childhood is essential for healthy growth and tissue development. Chronic protein deficiency leads to stunting, height-for-age more than two standard deviations below the median, and affects 149 million children under five (https://www.who.int/publications/i/item/9789240073791). Linear growth is governed by the somatotropic axis, wherein pituitary growth hormone (GH) stimulates hepatic insulin-like growth factor 1 (IGF-1) to promote bone elongation1. Although boys are consistently more stunted than girls across populations (ranging from 18% to 45%)2,3, mechanisms underlying this apparent resilience remain unclear. Rodent studies suggest that the protein-to-carbohydrate ratio modulates life-history traits such as metabolism, lifespan, and reproduction, with evidence of sex-dependent sensitivity4,5. Whether such dimorphism reflects coordinated developmental adaptations in response to protein scarcity, particularly in females, has not been fully explored. Here, we show that juvenile dietary protein restriction causes sexually dimorphic adaptations with pronounced stunting in males and delayed reproductive maturation in females. Using liver-specific deletion, we identify fibroblast growth factor 21 (FGF21) as a sex-specific hepatic regulator of female somatic growth and reproductive development, revealing a dimorphic endocrine adaptation to early-life protein scarcity. Our findings position FGF21 as a critical physiological checkpoint coordinating growth and reproductive timing in response to nutritional stress.

physiology↗

bioRxiv2025

Learning evolutionary parameters from genealogies using allelic trees

Cellular diversification in processes from development to cancer progression and affinity maturation is often linked to the appearance of new mutations, generating genetic heterogeneity. Describing the underlying coupled genetic and growth processes that result in the observed diversity in cell populations is informative about the timing, drivers and outcomes of cell fates. Current approaches based on phylogenetic methods do not cover the entire range of evolutionary rates, often making artificial assumptions about the timing of events. We introduce CBA, a probabilistic method that infers the division, degradation and mutation rates from the observed genetic diversity in a population of cells. It uses a summarized backbone tree, intermediary between the true cell tree and the allelic tree representing the ancestral relationships between types, called a monogram, which allows for efficient sampling of possible phylogenies consistent with the observed mutational signatures. We demonstrate the accuracy of our method on simulated data and compare its performance to standard phylogenetic approaches.

genetics↗

bioRxiv2025

High-Throughput Conjugation Reveals Strain Specific Recombination Patterns Enabling Precise Trait Mapping in Escherichia coli

Genetic exchange is a cornerstone of evolutionary biology and genomics, driving adaptation and enabling the identification of genetic determinants underlying phenotypic traits. In Escherichia coli, horizontal gene transfer via conjugation and transduction not only promotes diversification and adaptation but has also been instrumental in mapping genetic traits. However, the dynamics and variability of bacterial recombination remain poorly understood, particularly concerning the patterns of recombined DNA fragments. To elucidate these patterns and simultaneously develop a tool for trait mapping, we designed a high-throughput conjugation method to generate recombinant libraries. Recombination profiles were inferred through whole-genome sequencing of individual clones and populations after selection of a marker from the donor strain in the recipient. This analysis revealed an extraordinary range of recombined fragment sizes, spanning less than ten kilobases to over a megabase--a pattern that varied across the three tested strains. Mathematical modelling indicated that this diversity in recombined fragment size enables precise identification of selected loci following genetic crosses. Consistently, population sequencing pinpointed a selected marker at kilobase-scale accuracy, offering a robust tool for identifying subtle genetic determinants that could include point mutations in core genes. These findings challenge the conventional view that conjugation always transfers large fragments, suggesting that even short recombined segments, traditionally attributed to transduction, may originate from conjugation.

microbiology↗

bioRxiv2025

The Baldwin effect reloaded: Intermediate levels of phenotypic plasticity favor evolutionary rescue

Since the late 1890s and until today, how phenotypic plasticity interacts with genetic adaptation is a debated issue. Proponents of a positive causal role of phenotypic plasticity -James M. Bald-win in the first place- supported the view that, in altered environmental conditions, phenotypic plasticity is a key factor allowing a population to avoid extinction and then genetic evolution to catch up ("Original Baldwin Effect", thereafter OBE). Opponents, like for instance Ernst Mayr, regularly pointed out that phenotypic plasticity, by masking genetic variation, slows gene-level evolution ("Mayr Effect", thereafter ME). For decades this opposition remained only verbal and qualitative. To resolve it, we propose here a stochastic model that, following Baldwins intuitive take, combines the minimal number of ingredients to account for extinction, selection, mutation and plasticity. We study evolutionary rescue of the population (arrival and invasion of an adaptive genetic mutant) in the altered environment for different values of phenotypic plasticity, here quantified as the probability p that the maladapted genotype develops into the adapted phenotype. Our claim is that OBE can be a genuine evolutionary mechanism, depending on the level of phenotypic plasticity with respect to a threshold value p[*]: when p < p[*], increasing p promotes evolutionary rescue by delaying extinction ("Strong" OBE); when p > p[*], plasticity sustains population survival and increasing p has two antagonistic effects: to accelerate adaptation by increasing the supply of adaptive mutants ("Weak" OBE, intermediate values of p), and to slow down adaptation by decreasing their fitness advantage (ME, high values of p).

evolutionary biology↗

bioRxiv2024

Biomimetic Virus-Like Particles to control cell functions

Biomimetic cues from the extracellular matrix (ECM) are essential for optimizing cell microenvironments and biomaterials. While native ECM proteins or synthetic peptides offer potential solutions, challenges such as production cost, solubility, and conformational stability limit their use. Here, we present the development of virus-like particles (VLPs) derived from the AP205 RNA phage displaying peptides from key ECM proteins and evaluate their biological activity in a variety of assays. We show that our engineered VLPs can effectively stimulate cell adhesion, migration, proliferation and differentiation. By comparing focal adhesions formed by RGD VLPs with their parent protein, fibronectin, we elucidate both similarities and differences in cell interactions. In addition, we construct heterodimeric particles co-expressing RGD with differentiation peptides and demonstrate retention of bioactivity in a multi-peptide context. This study establishes AP205 VLPs as versatile nanoscale platforms capable of tuning cell functions, with promising applications in nanomedicine and biomaterials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/612851v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@191b2c2org.highwire.dtl.DTLVardef@6edf5corg.highwire.dtl.DTLVardef@10a134aorg.highwire.dtl.DTLVardef@7864ef_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

bioengineering↗

bioRxiv2024

Protein restriction associated with high fat induces metabolic dysregulation without obesity in juvenile mice

Dysregulation of energy metabolism, including hyperglycemia, insulin resistance and fatty liver have been reported in a substantial proportion of lean children. However, non-obese murine models recapitulating these features are lacking to study the mechanisms underlying the development of metabolic dysregulations in lean children. Here, we develop a model of diet-induced metabolic dysfunction without obesity in juvenile mice by feeding male and female mice a diet reflecting Western nutritional intake combined with protein restriction (mWD) during 5 weeks after weaning. mWD-fed mice (33% fat, 8% protein) do not exhibit significant weight gain and have moderate increase in adiposity compared to control mice (16% fat, 20% protein). After 3 weeks of mWD, juvenile mice have impaired glucose metabolism including hyperglycemia, insulin resistance and glucose intolerance. mWD also triggers hepatic metabolism alterations, as shown by the development of simple liver steatosis. Both male and female mice fed with mWD displayed metabolic dysregulation, which a probiotic treatment with Lactiplantibacillus plantarum WJL failed to improve. Overall, mWD-fed mice appear to be a good preclinical model to study the development of diet-induced metabolic dysfunction without obesity in juveniles.

physiology↗

bioRxiv2024

Persistent, Private and Mobile genes: a model for gene dynamics in evolving pangenomes

The pangenome of a species is the set of all genes carried by at least one member of the species. In bacteria, pangenomes can be much larger than the set of genes carried by a single organism. Many questions remain unanswered regarding the evolutionary forces shaping the patterns of presence/absence of genes in pangenomes of a given species. We introduce a new model for bacterial pangenome evolution along a species phylogeny that explicitly describes the timing of appearance of each gene in the species and accounts for three generic types of gene evolutionary dynamics: persistent genes that are present in the ancestral genome, private genes that are specific to a given clade, and mobile genes that are imported once into the gene pool and then undergo frequent horizontal gene transfers. We call this model the Persistent-Private-Mobile (PPM) model. We develop an algorithm fitting the PPM model and apply it to a dataset of 902 Salmonella enterica genomes. We show that the best fitting model is able to reproduce the global pattern of some multivariate statistics like the gene frequency spectrum and the parsimony vs. frequency plot. Moreover, the gene classification induced by the PPM model allows us to study the position of accessory genes on the chromosome depending on their category, as well as the gene functions that are most present in each category. This work paves the way for a mechanistic understanding of pangenome evolution, and the PPM model developed here could be used for dynamics-aware gene classification.

evolutionary biology↗

bioRxiv2024

Hotwiring integrin endocytosis acutely modulates cell interactions

Integrins are heterodimeric cell surface receptors that govern cell-cell interactions, which in turn can influence multiscale processes: cell migration, extracellular matrix remodeling and tissue formation. These processes occur over timescales which range from milliseconds to days. While various strategies exist to study integrin function across biological scales from cell to tissue, they are often chronic and fail to target specific cell-cell interactions acutely. We engineered cells to rapidly alter cell behavior by downregulating the surface population of 5{beta}1 integrins through hot-wired clathrin-mediated endocytosis. This method allows for inducible, specific internalization of 5{beta}1 integrins, achieving acute downregulation across various cell lines in 5-30 minutes. We show that induced internalization of 5{beta}1 decreases the cell area, causes uptake of extracellular fibronectin, and decreases the rate of tumor spheroid compaction. This targeted control of multiscale processes by rapid downregulation of this important class of cell surface receptors demonstrates that hot-wired endocytosis is a useful tool to acutely modulate cell biology.

bioengineering↗

bioRxiv2024

Phenomics demonstrates cytokines additive induction of epithelial to mesenchymal transition

Epithelial to Mesenchymal Transition (EMT) is highly plastic with a program where cells lose adhesion and become more motile. EMT heterogeneity is one of the factors for disease progression and chemoresistance in cancer. Omics characterizations are costly and challenging to use. We developed single cell phenomics with easy to use wide-field fluorescence microscopy. We analyse over 70000 cells and combined 51 features. Our simplistic pipeline allows efficient tracking of EMT plasticity, with a single statistical metric. We discriminate four high EMT plasticity cancer cell lines along the EMT spectrum. We test two cytokines, inducing EMT in all cell lines, alone or in combination. The single cell EMT metrics demonstrate the additive effect of cytokines combination on EMT independently of cell line EMT spectrum. Single cell phenomics is uniquely suited to characterize the cellular heterogeneity in response to complex microenvironment, and show potential for drug testing assays.

cancer biology↗

bioRxiv2024

An unsupervised deep learning framework encodes super-resolved image features to decode bacterial cell cycle

Super-resolution microscopy can resolve cellular features at the nanoscale. However, increased spatial resolution comes with increased phototoxicity, and reduced temporal resolution. As a result, studies that require the highest spatial resolutions often rely on static or fixed images, lacking dynamic information. This is particularly true of bacteria, whose lateral dimensions approach the scale of the diffraction limit. In this work, we present Enso, a method based on unsupervised machine learning to recover bacterial cell cycle and cell type information from static single molecule localization microscopy (SMLM) images, whilst retaining nanoscale spatial resolution. Enso uses single-cell images as input, and orders cells according to their spatial pattern progression, ultimately linked to the cell cycle. Our method requires no a priori knowledge or categories, and is validated on both simulated and user-annotated experimental data.

bioinformatics↗

bioRxiv2023

How demography shapes linkage disequilibrium with or without recombination

Linkage Disequilibrium (LD) is a measure of the statistical association, within a population, between alleles present at different loci. Here, we produce detailed characterizations of LD statistics in different demographic scenarios, with simulations and with analytic results, focusing on several widely used measures of LD (D, |D'|, and r2), and introducing a fourth class of statistics: 2-site configuration probabilities, which correspond to the frequencies of different allelic association patterns. By studying these measures under no recombination, a single recombination event, and between fully independent loci, we are able to gain new insights to how these measures vary across recombination scales. We also classify recombination events into four types and rank them according to their respective frequencies and effects on LD measures. Using previously published human data, we show how tree-sequence information can be used to partition the genome and to analyze LD in relation to theoretical expectations. Finally, we provide recommendations for the use of the different statistics and suggest applications of LD measures in the absence of recombination.

evolutionary biology↗