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Lambert, A.

Publications and source records attributed to Lambert, A..

At least 37 records · Page 2Linked to original sources

Opening the species box: What microscopic models of neutral speciation have to say about macroevolution

In the last two decades, lineage-based models of diversification, where species are viewed as particles that can divide (speciate) or die (become extinct) at rates depending on some evolving trait, have been very popular tools to study macroevolutionary processes. Here, we argue that this approach cannot be used to break down the inner workings of species diversification and that "opening the species box" is necessary to understand the causes of macroevolution, but that too detailed speciation models also fail to make robust macroevolutionary predictions. We set up a general framework for parsimonious models of speciation that rely on a minimal number of mechanistic principles: (i) reproductive isolation is caused by excessive dissimilarity between genotypes; (ii) dissimilarity results from a balance between differentiation processes and homogenizing processes; and (iii) dissimilarity can feed back on these processes by decelerating homogenization. We classify such models according to the main homogenizing process : (1) clonal evolution models (ecological drift), (2) models of genetic isolation (gene flow) and (3) models of isolation by distance (spatial drift). We review these models and their specific predictions on macroscopic variables such as species abundances, speciation rates, interfertility relationships or phylogenetic tree structure. We propose new avenues of research by displaying conceptual questions remaining to be solved and new models to address them: the failure of speciation at secondary contact, the feedback of dissimilarity on homogenization, the emergence in space of breeding barriers.

evolutionary biology↗

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organisms metabolism using in silico Genome-Scale Metabolic Models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cells maintenance.

systems biology↗

The balance between shear flow and extracellular matrix in ovarian cancer-on-chip

Ovarian cancer is the most lethal gynecologic cancer in developed countries. Silent onset of the metastatic activity of ovarian tumor cells is factor for poor outcomes. In the tumor microenvironment, the extracellular matrix (ECM) and flow shear stress are known to play key roles in directing cell invasion. Hence, acute and tunable tools are critical to mimic scaffold and fluid for building clinically relevant in vitro models. We have built an ovarian tumor-on-chip where tunable ECM models are easily seeded with tumor spheroids and integrated within a microfluidic chip. This allows the investigation of the crosstalk between the characteristics of the ECM models and shear stress on the migratory behavior and cellular heterogeneity of ovarian tumor cells. We vary the composition of the ECM playing with type I and IV collagens and laminin, and control the shear stress in the chip. This work shows that in the shear stress regime of the peritoneal cavity, the ECM plays a major role in driving individual or collective modes of migration. In the presence of basement membrane proteins, migration is more collective that on type I collagen regardless of shear stress level. In addition, with increasing shear stress, individual cell migration was enhanced, while no significant impact on collective migration could be measured. This highlights our ability to discriminate relevant parameters for onset and shifts of cell behavior using our in vitro models. Furthermore, we described the ability to shift cells from an epithelial phenotype to a more mesenchymal phenotype, which could allow us to describe the role of these parameters during epithelial-to-mesenchymal (EMT) transition as a continuous process. Finally, we conclude that the ECM should hold a central position in in vitro cancer models, to understand cell response and develop platforms for therapeutic development.

bioengineering↗

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole genome sequencing with histopathological features

Extension of prostate cancer beyond the primary site into the surrounding organs by local invasion or nodal metastasis is associated with poor prognosis. The emergence and evolution of cancer clones at this early stage of expansion and spread has not been studied in detail. We performed whole genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and regional lymph nodes of five treatment-naive patients with locally advanced disease who underwent radical prostatectomy. Using cancer cell fractions computed from single nucleotide variants and copy number alterations, we reconstructed the tumour phylogenies, which in turn allowed us to infer key molecular steps in the progression of prostate cancer in these individuals. We mapped the clonal composition of cancer sampled across the prostate in each individual and inferred the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes. Based on these data, we delineated the route of tumour progression and metastasis following the transformation of adenocarcinoma to amphicrine morphology, the molecular events leading to whole genome duplication associated with a single clonal expansion and identified putative driver events associated with local invasion and lymph node metastasis. We also correlated genomic changes associated with differences in morphology and identified putative driver events associated with spread to seminal vesicle invasion and lymph node metastasis. Taken together, these findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes associated with morphological transformation. Our results demonstrate the value of integrating multi-region sequencing with histopathological data to study tumour evolution and identify mechanisms of prostate cancer spread.

cancer biology↗

Evo-Scope: Fully automated assessment of correlated evolution on phylogenetic trees

O_LICorrelated evolution describes how multiple biological traits evolve together. Recently developed methods provide increasingly detailed results of correlated evolution, sometimes at elevated computational costs. C_LIO_LIHere, we present evo-scope, a fast and fully-automated pipeline with minimal input requirements to compute correlation between discrete traits evolving on a phylogenetic tree. Notably, we improve two of our previously developed tools that efficiently compute statistics of correlated evolution to characterize the nature, such as synergy or antagonism, and the strength of the interdependence between the traits. C_LIO_LIFurthermore, we improved the running time and implemented several additional features, such as genetic mapping, Bayesian Markov Chain Monte Carlo estimation, consideration of missing data and phylogenetic uncertainty. C_LIO_LIAs an application, we scan a published penicillin resistance data set of Streptococcus pneumoniae and characterize genetic mutations that correlate with antibiotic resistance. The pipeline is accessible both as a self-contained github repository (https://github.com/Maxime5G/EvoScope) and through a graphical galaxy interface (https://galaxy.pasteur.fr/u/maximeg/w/evoscope). C_LI

evolutionary biology↗

Bottlenecks can constrain and channel evolutionary paths

Population bottlenecks are commonplace in experimental evolution, specifically in serial passaging experiments where microbial populations alternate between growth and dilution. Natural populations also experience such fluctuations caused by seasonality, resource limitation, or host-to-host transmission for pathogens. Yet, how unlimited growth with periodic bottlenecks influence the adaptation of populations is not fully understood. Here we study theoretically the effects of bottlenecks on the accessibility of evolutionary paths and on the rate of evolution. We model an asexual population evolving on a minimal fitness landscape consisting of two types of beneficial mutations with the empirically supported trade-off between mutation rate and fitness advantage, in the regime where multiple beneficial mutations may segregate simultaneously. In the limit of large population sizes and small mutation rates, we show the existence of a unique most likely evolutionary scenario, determined by the size of the wild-type population at the beginning and at the end of each cycle. These two key demographic parameters determine which adaptive paths may be taken by the evolving population by controlling the supply of mutants during growth and the loss of mutants at the bottleneck. We do not only show that bottlenecks act as a deterministic control of evolutionary paths but also that each possible evolutionary scenario can be forced to occur by tuning demographic parameters. This work unveils the effects of demography on adaptation of periodically bottlenecked populations and can guide the design of evolution experiments.

genetics↗

Interpreting the pervasive observation of U-shaped Site Frequency Spectra

The standard neutral model of molecular evolution has traditionally been used as the null model for population genomics. We gathered a collection of 45 genome-wide site frequency spectra from a diverse set of species, most of which display an excess of low and high frequency variants compared to the expectation of the standard neutral model, resulting in U-shaped spectra. We show that multiple merger coalescent models often provide a better fit to these observations than the standard Kingman coalescent. Hence, in many circumstances these under-utilized models may serve as the more appropriate reference for genomic analyses. We further discuss the underlying evolutionary processes that may result in the widespread U-shape of frequency spectra.

evolutionary biology↗

Topology of connective tissues: a key parameter in cellular heterogeneity, beyond composition and stiffness

Cellular plasticity is essential in physiological contexts, including pathological ones. It is the basis of morphogenesis and organogenesis, as well as tumorigenesis and metastasis. The extracellular matrix (ECM) is a key player in the generation of cellular heterogeneity. Advances in our understanding of cell plasticity rely on our ability to provide relevant in vitro models. This requires to catch the characteristics of the tissues that are essential for controlling cell fate. To do this, we must consider the diversity of tissues, the diversity of physiological contexts, and the constant remodeling of ECM along these processes. To this aim, we have fabricated a library of ECM models for reproducing the scaffold of connective tissues and basement membrane with different biofabrication routes based on the electrospining and drop casting of biopolymers. Using a combination of multiphoton imaging and nanoindentation, we show that we can vary independently protein composition, topology of connective tissues and stiffness of ECM models. Reproducing the features of a tissue and physiological context in turns allows to generate the complexity of the phenotypic landscape associated with the epithelial-to-mesenchymal transition (EMT) in human ovarian cancer. We show that EMT shift cannot be directly correlated with a unique ECM feature, which reflects the multidimensionality of living environments. Very importantly, our combinatorial approach allows us to provide in vitro models, where the impact of the topological cues on cellular phenotypes can be revealed, beyond protein composition and stiffness of the ECM matrix. On this line, this work is a further step towards the development of ECM models recapitulating the constantly remodeled scaffolding environment that cells face and provides new insights for the development of cell-free matrices.

bioengineering↗

High-throughput tuning of ovarian cancer spheroids for on-chip invasion assays

We developed an invasion assay by using microfabricated culture devices. First, ovarian tumor spheroids were generated with a culture patch device consisting of an agarose membrane formed with a honeycomb microframe - the patch - and gelatin nanofiber backbone. By changing the dimensions of the honeycomb compartments we were able to control the number of cells and size of the spheroids. When the spheroids were placed on a patch coated with a thin membrane of fibrillary type I collagen, spheroid disruption was observed due to substrate induced cell migration. This process is straightforward and should be applicable to other cancer types, as well as assays under microfluidic conditions, thereby holding the potential for use in tumor modeling and anti-cancer drug development.

bioengineering↗

Deep-learning based retinal fluid segmentation in optical coherence tomography images using a cascade of ENets

Optical coherence tomography (OCT) is a non-invasive, painless and reproducible examination which allows ophthalmologists to visualize retinal layers. This imaging modality is useful to detect diseases such as diabetic macular edema (DME) or age related macular degeneration (AMD), which are associated with fluid accumulations. In this paper, a cascade of deep convolutional neural networks is proposed using ENets for the segmentation of fluid accumulations in OCT B-Scans. After denoising the B-Scans, a first ENet extracts the region of interest (ROI) between the inner limiting membrane (ILM) and the Bruchs membrane (BM), whereas the second ENet segments the fluid in the ROI. A random forest classifier was applied on the segmented fluid regions to reject false positive. Our framework was trained on three different datasets with several diseases such as diabetic retinopathy (DR) and AMD. Our method achieves an average Dice Score for fluid segmentation of 0.80, 0.83 and 0.83 on the UMN DME, UMN AMD and Kermany datasets respectively.

bioinformatics↗

EVI/WLS function is regulated by ubiquitination and linked to ER-associated degradation by ERLIN2

Wnt signalling is important for development in all metazoan animals and associated with various human diseases. The Ubiquitin-Proteasome System (UPS) and regulatory ER-associated degradation (ERAD) has been implicated in the secretion of WNT proteins. Here, we investigated how the WNT secretory factor EVI/WLS is ubiquitinated, recognised by ERAD components, and subsequently removed from the secretory pathway. We performed a focused, immunoblot-based RNAi screen for factors that influence EVI/WLS protein stability. We identified the VCP-binding proteins FAF2 and UBXN4 as novel interaction partners of EVI/WLS and showed that ERLIN2 links EVI/WLS to the ubiquitination machinery. Interestingly, we found in addition that EVI/WLS is ubiquitinated and degraded in cells irrespective of their level of WNT production. K11, K48, and K63-linked ubiquitination is mediated by the E2 ubiquitin conjugating enzymes UBE2J2, UBE2N, and UBE2K, but independent of the E3 ligases HRD1/SYVN. Taken together, our study identified factors that link UPS to the WNT secretory pathway and provides mechanistic details on the fate of an endogenous substrate of regulatory ERAD in mammalian cells.

cell biology↗

A minimal yet flexible likelihood framework to assess correlated evolution

An evolutionary process is reflected in the sequence of changes of any trait (e.g. morphological, molecular) through time. Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. Many previously developed parametric methods often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In its current implementation, the method takes as input a phylogenetic tree together with mapped discrete evolutionary events on it and then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power when compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 {gamma}-enterobacteria. We show that even with datasets of fewer than 100 species, the method performs well in parameter estimation and in the selection of evolutionary scenario.

evolutionary biology↗

Biochemical and mathematical lessons from the evolution of the SARS-CoV-2 virus: paths for novel antiviral warfare

In the fight against the spread of COVID-19 the emphasis is on vaccination or on reactivating existing drugs used for other purposes. The tight links that necessarily exist between the virus as it multiplies and the metabolism of its host are systematically ignored. Here we show that the metabolism of all cells is coordinated by the availability of a core building block of the cells genome, cytidine triphosphate (CTP). This metabolite is also the key to the synthesis of the viral envelope and to the translation of its genome into proteins. This unique role explains why evolution has led to the early emergence in animals of an antiviral immunity enzyme, viperin, that synthesizes a toxic analogue of CTP. The constraints arising from this dependency guide the evolution of the virus. With this in mind, we explored the real-time experiment taking place before our eyes using probabilistic modelling approaches to the molecular evolution of the virus. We have thus followed, almost on a daily basis, the evolution of the composition of the viral genome to link it to the progeny produced over time, particularly in the form of blooms that sparked a firework of viral mutations. Some of those certainly increase the propagation of the virus. This led us to make out the critical role in this evolution of several proteins of the virus, such as its nucleocapsid N, and more generally to begin to understand how the virus ties up the host metabolism to its own benefit. A way for the virus to escape CTP-dependent control in cells would be to infect cells that are not expected to grow, such as neurons. This may account for unexpected body sites of viral development in the present epidemic.

microbiology↗

Comparison of developmental genome expression in rodent molars reveals extensive developmental system drift.

Serial appendages are similar organs found at different places in the body, such as fore/hindlimbs or different teeth. They are bound to develop with the same pleiotropic genes, apart from identity genes. These identity genes have logically been implicated in cases where a single appendage evolved a drastically new shape while the other retained an ancestral shape, by enabling developmental changes specifically in one organ. Here, we showed that independent evolution involved developmental changes happening in both organs, in two well characterized model systems. Mouse upper molars evolved a new dental plan with two more cusps on the lingual side, while the lower molar kept a much more ancestral morphology, as did the molars of hamster, our control species. We obtained quantitative timelines of cusp formation and corresponding transcriptomic timeseries in the 4 molars. We found that a molecular and morphogenetic identity of lower and upper molars predated the mouse and hamster divergence and likely facilitated the independent evolution of molars lingual side in the mouse lineage. We found 3 morphogenetic changes which could combine to cause the supplementary cusps in the upper molar and a candidate gene, Bmper. Unexpectedly given its milder morphological divergence, we observed extensive changes in mouse lower molar development. Its transcriptomic profiles diverged as much as, and co-evolved extensively with, those of the upper molar. Consistent with the transcriptomic quantifications, two out of the three morphogenetic changes also impacted lower molar development. Moving to limbs, we show the drastic evolution of the bat wing also involved gene expression co-evolution and a combination of specific and pleiotropic changes. Independent morphological innovation in one organ therefore involves concerted developmental evolution of the other organ. This is facilitated by evolutionary flexibility of its development, a phenomenon known as Developmental System Drift. AUTHOR SUMMARYSerial organs, such as the different wings of an insect or the different limbs or teeth of a vertebrate, can develop into drastically different shapes due to the position-specific expression of so-called "identity" genes. Often during evolution, one organ evolves a new shape while another retains a conserved shape. It was thought that identity genes were responsible for these cases of independent evolution, by enabling developmental changes specifically in one organ. Here, we showed that developmental changes evolved in both organs to enable the independent evolution of the upper molar in mice and the wing in bats. In the organ with the new shape, several developmental changes combine. In the organ with the conserved shape, part of these developmental changes are seen as well. This modifies the development but is not sufficient to drastically change the phenotype, a phenomenon known as "Developmental System Drift", DSD. Thus, the independent evolution of one organ relies on concerted molecular changes, which will contribute to adaptation in one organ and be no more than DSD in another organ. This concerted evolution could apply more generally to very different body parts and explain previous observations on gene expression evolution.

evolutionary biology↗

Model-based inference of punctuated molecular evolution

In standard models of molecular evolution, DNA sequences evolve through asynchronous substitutions according to Poisson processes with a constant rate (called the molecular clock) or a time-varying rate (relaxed clock). However, DNA sequences can also undergo episodes of fast divergence that will appear as synchronous substitutions affecting several sites simultaneously at the macroevolutionary time scale. Here, we develop a model combining basal, clock-like molecular evolution with episodes of fast divergence called spikes arising at speciation events. Given a multiple sequence alignment and its time-calibrated species phylogeny, our model is able to detect speciation events (including hidden ones) co-occurring with spike events and to estimate the probability and amplitude of these spikes on the phylogeny. We identify the conditions under which spikes can be distinguished from the natural variance of the clock-like component of molecular evolution and from temporal variations of the clock. We apply the method to genes underlying snake venom proteins and identify several spikes at gene-specific locations in the phylogeny. This work should pave the way for analyses relying on whole genomes to inform on modes of species diversification.

evolutionary biology↗

Eco-evolutionary dynamics of nested Darwinian populations and the emergence of community-level heredity

Interactions among microbial cells can generate new chemistries and functions, but exploitation requires establishment of communities that reliably recapitulate community-level phenotypes. Using mechanistic mathematical models, we show how simple manipulations to population structure can exogenously impose Darwinian-like properties on communities. Such scaffolding causes communities to participate directly in the process of evolution by natural selection and drives the evolution of cell-level interactions to the point where, despite underlying stochasticity, derived communities give rise to offspring communities that faithfully re-establish parental phenotype. The mechanism is akin to a developmental process (developmental correction) that arises from density dependent interactions among cells. Knowledge of ecological factors affecting evolution of developmental correction has implications for understanding the evolutionary origin of major egalitarian transitions, symbioses, and for top-down engineering of microbial communities.

evolutionary biology↗

Statistical inference for the evolutionary history of cancer genomes

Recent years have produced a large amount of work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the linear birth-death process (lbdp), a binary fission Markov age-dependent branching process. A genealogical view of such models is also available. The two approaches lead to similar but not identical results. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a sequencing experiment with a known number of sequences, we can estimate for each site at which a novel somatic mutation has arisen, the number of cells that carry that mutation. These numbers are then grouped into sites which have the same number of copies of the mutant. SFS can be computed from the statistics of mutations in a sample of cells, in which DNA has been sequenced. In this paper, examine how the SFS based on birth-death processes differ from those based on the coalescent model. This may stem from the different sampling mechanisms in the two approaches. However, we also show mathematically and computationally that despite this, they can be made quantitatively comparable at least for the range of parameters typical for tumor cell populations. We also present a model of tumor evolution with selective sweeps, based on coalescence, and demonstrate how it may help in understanding the past history of tumor as well the influence of data pre-processing. We illustrate the theory with applications to several examples of The Cancer Genome Atlas tumors.

cancer biology↗

Testing for population decline using maximal linkage disequilibrium blocks

Only 6% of known species have a conservation status. Methods that assess conservation statuses are often based on individual counts and are thus too laborious to be generalized to all species. Population genomics methods that infer past variations in population size are easy to use but limited to the relatively distant past. Here we propose a population genomics approach that tests for recent population decline and may be used to assess species conservation statuses. More specifically, we study Maximal Recombination Free (MRF) blocks, that are segments of a sequence alignment inherited from a common ancestor without recombination. MRF blocks are relatively longer in small than in large populations. We use the distribution of MRF block lengths rescaled by their mean to test for recent population decline. However, because MRF blocks are difficult to detect, we also consider Maximal Linkage Disequilibrium (MLD) blocks, which are runs of single nucleotide polymorphisms compatible with a single tree. We develop a new method capable of inferring a very recent decline (e.g. with a detection power of 50% for populations which size was halved to N, 0.05 xN generations ago) from rescaled MLD block lengths. Our framework could serve as a basis for quantitative tools to assess conservation status in a wide range of species.

evolutionary biology↗