Biology subjects

Ambroise, C.

Publications and source records attributed to Ambroise, C..

8 recordsLinked to original sources

bioRxiv2026

Mosaic selection and gene flow at the onset of grain beetle domestication

Domestication is an evolutionary process that can begin before deliberate breeding, when association with humans reshapes gene flow and selection. The yellow mealworm, Tenebrio molitor, is undergoing this transition from a synanthropic pest of stored grain to farmed species. We asked whether gene flow between farmed populations contributes to adaptation to captivity and whether selection extends to production traits. We combined population genomics of 320 beetles from eight farmed and wild populations with genetic mapping of 15 production traits from 3,575 individuals. Farm stocks retained high diversity and weak differentiation, with Netherlands-related ancestry connecting populations across Europe and Canada. Farm-restricted sweep-like signatures were 2.06-fold enriched within regions of excess allele sharing, suggesting local selection on introgressed variation. Across 305 production-trait QTLs, overlaps with selection and introgression signals showed no significant enrichment or depletion, suggesting that adaptive gene flow may primarily contribute to adaptation to captivity rather than broadly shaping growth, development and fertility. Nevertheless, egg-hatching-rate and development-time QTLs overlapped regions of reduced nucleotide diversity and low Tajima's D. These findings support a model in which gene flow supplies variation for unintended selection under intensive rearing. Crowding and physiological stress may favour adaptation to captive environments before sustained breeding establishes a broad genomic response at production loci.

evolutionary biology↗

bioRxiv2021

How marine currents and environment shape plankton genomic differentiation: a mosaic view from Tara Oceans metagenomic data

Plankton seascape genomics show different trends from large-scale weak differentiation to micro-scale structures. Prior studies underlined the influence of environment and seascape on a few single species differentiation and adaptation. However, these works generally focused on few single species, sparse molecular markers, or local scales. Here, we investigate the genomic differentiation of plankton at macro-scale in a holistic approach using Tara Oceans metagenomic data together with a reference-free computational method to reconstruct the FST-based genomic differentiation of 113 marine planktonic species using metavariant species (MVS). These MVSs, modelling the species only by their polymorphism, include a wide range of taxonomic groups comprising notably 46 Maxillopoda/Copepoda, 24 Bacteria, 5 Dinoflagellates, 4 Haptophytes, 3 Cnidarians, 3 Mamiellales, 2 Ciliates, 1 Collodaria, 1 Echinoidea, 1 Pelagomonadaceae, 1 Cryptophyta and 1 Virus. The analyses showed that differentiation between populations was significantly lower within basins and higher in bacteria and unicellular eukaryotes compared to zooplantkon. By partitioning the variance of pairwise-FSTmatrices, we found that the main drivers of genomic differentiation were Lagrangian travel time, salinity and temperature. Furthermore, we classified MVSs into parameter-driven groups and showed that taxonomy poorly determines which environmental factor drives genomic differentiation. This holistic approach of plankton genomic differentiation for large geographic scales, a wide range of taxa and different oceanic basins, offers a systematic framework to analyse population genomics of non-model and undocumented marine organisms.

evolutionary biology↗

bioRxiv2020

The first AI-based mobile application for antibiotic resistance testing

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phones camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our applications reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The applications performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients access to AST worldwide.

microbiology↗

bioRxiv2020

Incorporating phylogenetic information in microbiome abundance studies has no effect on detection power and FDR control

We consider the problem of incorporating evolutionary information (e.g. taxonomic or phylogenic trees) in the context of metagenomics differential analysis. Recent results published in the literature propose different ways to leverage the tree structure to increase the detection rate of differentially abundant taxa. Here, we propose instead to use a different hierachical structure, in the form of a correlation-based tree, as it may capture the structure of the data better than the phylogeny. We first show that the correlation tree and the phylogeny are significantly different before turning to the impact of tree choice on detection rates. Using synthetic data, we show that the tree does have an impact: smoothing p-values according to the phylogeny leads to equal or inferior rates as smoothing according to the correlation tree. However, both trees are outperformed by the classical, non hierachical, Benjamini-Hochberg (BH) procedure in terms of detection rates. Other procedures may use the hierachical structure with profit but do not control the False Discovery Rate (FDR) a priori and remain inferior to a classical Benjamini-Hochberg procedure with the same nominal FDR. On real datasets, no hierarchical procedure had significantly higher detection rate that BH. Although intuition advocates the use of a hierachical structure, be it the phylogeny or the correlation tree, to increase the detection rate in microbiome studies, current hierachical procedures are still inferior to non hierachical ones and effective procedures remain to be invented.

microbiology↗

bioRxiv2020

metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics

MotivationThe availability of large metagenomic data offers great opportunities for the population genomic analysis of uncultured organisms, especially for small eukaryotes that represent an important part of the unexplored biosphere while playing a key ecological role. However, the majority of these species lacks reference genome or transcriptome which constitutes a technical barrier for classical population genomic analyses. ResultsWe introduce the metavariant species (MVS) model, a representation of the species only by intra-species nucleotide polymorphism. We designed a method combining reference-free variant calling, multiple density-based clustering and maximum weighted independent set algorithms to cluster intra-species variant into MVS directly from multisample metagenomic raw reads without reference genome or reads assembly. The frequencies of the MVS variants are then used to compute population genomic statistics such as FST in order to estimate genomic differentiation between populations and to identify loci under natural selection. The MVSs construction was tested on simulated and real metagenomic data. MVs showed the required quality for robust population genomics and allowed an accurate estimation of genomic differentiation ({Delta}FST < 0.0001 and < 0.03 on simulated and real data respectively). Loci predicted under natural selection on real data were all found by MVSs. MVSs represent a new paradigm that may simplify and enhance holistic approaches for population genomics and evolution of microorganisms. AvailabilityThe method was implemented in a R package, metaVaR. https://github.com/madoui/MetaVaR Contactamadoui@genoscope.cns.fr

bioinformatics↗

bioRxiv2019

PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph

The use of comparative genomics for functional, evolutionary, and epidemiological studies requires methods to classify gene families in terms of occurrence in a given species. These methods usually lack multivariate statistical models to infer the partitions and the optimal number of classes and dont account for genome organization. We introduce a graph structure to model pangenomes in which nodes represent gene families and edges represent genomic neighborhood. Our method, named PPanGGOLiN, partitions nodes using an Expectation-Maximization algorithm based on multivariate Bernoulli Mixture Model coupled with a Markov Random Field. This approach takes into account the topology of the graph and the presence/absence of genes in pangenomes to classify gene families into persistent, cloud, and one or several shell partitions. By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github.com/labgem/PPanGGOLiN. Author summaryMicroorganisms have the greatest biodiversity and evolutionary history on earth. At the genomic level, it is reflected by a highly variable gene content even among organisms from the same species which explains the ability of microbes to be pathogenic or to grow in specific environments. We developed a new method called PPanGGOLiN which accurately represent the genomic diversity of a species (i.e. its pangenome) using a compact graph structure. Based on this pangenome graph, we classify genes by a statistical method according to their occurrence in the genomes. This method allowed us to build pangenomes even for uncultivated species at an unprecedented scale. We applied our method on all available genomes in databanks in order to depict the overall diversity of hundreds of species. Overall, our work enables microbiologists to explore and visualize pangenomes alike a subway map.

bioinformatics↗

bioRxiv2019

Linking Allele-Specific Expression And Natural Selection In Wild Populations

Allele-specific expression (ASE) is a widely studied molecular mechanism at cell, tissue and organism levels. Here, we extrapolated the concept of ASE to the population-scale (psASE), aggregating ASEs detected at smaller scales. We developed a novel approach to detect psASE based on metagenomic and metatranscriptomic data of environmental samples containing communities of organisms. This approach which measures the deviation between the frequency and the relative expression of biallelic loci, was applied on samples collected during the Tara Oceans expedition (2009-2013), in combination to new Oithona similis transcriptomes, a widespread marine copepod. Among a total of 25,768 single nucleotide variants (SNVs) of O. similis, 587 (2.3%) were targeted by psASE in at least one population. The distribution of SNVs targeted by psASE in different populations is significantly shaped by population genomic differentiation (p-value = 9.3x10-9), supporting a partial genetic control of psASE. To investigate the link between evolution and psASE, loci under selection were compared to loci under psASE. A significant amount of SNVs (0.6%) were targeted by both selection and psASE (p-values < 9.89x10-3), supporting the hypothesis that natural selection and ASE may lead to the same phenotype. Population-scale ASE offers new insights into the gene regulation control in populations and its link with natural selection.

genomics↗

bioRxiv2019

Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we comprehensively test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human reference tissue preparations, using standard (1g), low (100 and 10 ng) and ultra-low (< 1 ng) input amounts, and for mRNA and total RNA, stranded or unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows degraded performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts < 1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of a RNA-seq library preparation kit.

genomics↗