Biology subjects

Aubert, J.

Publications and source records attributed to Aubert, J..

10 recordsLinked to original sources

bioRxiv2026

CORE: Deterministic alignment for bias-free organoid histology

Histological analysis of 3D tissue models is limited by geometric sampling bias arising from inconsistent positioning during embedding. We present Coplanar Organoid Reproducible Embedding (CORE), which aligns heterogeneous specimens on a shared sectioning plane to enable reproducible, bias-free histology. By eliminating depth-dependent sampling artifacts, CORE reveals biological differences obscured by sectioning depth and integrates with automated liquid- handling workflows.

bioengineering↗

bioRxiv2026

Deciphering the microbial architecture of pesticide and antibiotics biodegradation

Understanding emerging functions at the scale of a bacterial community is a major challenge in microbial ecology and could lead up to promising tools for engineering microbial communities, for example in bioremediation. Here, through a top-down approach we obtained compositional variants of pesticide and antibiotics-degrading communities and further investigated communities features associated with their degradation abilities. We first tested whether diversity index or functional genes abundance could reliably be used as a proxy for this function, and obtained encouraging, albeit variable results. Further, through the use of statistical tools borrowed from the genomic selection literature, we were able to derive accurate prediction of the mineralisation potential of a bacterial community, based on its composition. However, the parallel between genotype-phenotype and community composition-mineralisation potential suffers a crucial caveat: bacterial abundances vary on a much wider scale than allele dosage at a given locus and are prone to change over time (particularly at the mineralisation scale). Here we observed that using presence/absence data instead of relative abundance can overcome these limitations and provide a clearer functional signal for mineralisation prediction through linear regression models. Random forest can also intrinsically deal with microbial data without transformation and select for significant predictors. We suggest drawing inspiration from the tools and concepts used in genotype-phenotype mapping to elucidate microbial functions at the community level while keeping in mind the significant differences between these two fields. This parallel is here exemplified by the concept of microbial architecture of degrading functions, akin to the genetic architecture of phenotypic traits.

microbiology↗

bioRxiv2025

Modeling host-microbe interactions in immunocompetent engineered human gut tissues

The intestinal mucosal barrier contains microbial organisms within the lumen while preserving the ability to absorb nutrients. Dietary, microbial, and other exposures shaped human barrier evolution and continue to impact disease susceptibility. Here, we established engineered barrier models of the human small intestine and colon composed of a multilineage epithelium, mucus layer, accessible microbial compartment and autologous tissue-resident immune cells. The epithelium has crypt- and villus-like topological domains, with stem cells differentiating into absorptive and secretory lineages with region-specific identities. Secreted mucins accumulate apically, forming a dense mucus layer separating the epithelium from colonizing commensal and pathogenic bacteria. Intestinal memory T cells integrate into and interact with the epithelium. We use the engineered intestinal tissues to identify an epithelial gene regulatory network underlying response to Salmonella Typhimurium infection, and uncover epithelial-immune-pathogen crosstalk coordinating cytokine release and epithelial damage. Overall, this work allows for the modular integration of epithelial, microbial, and immune compartments providing a versatile system for studying human intestinal physiology and pathologies.

bioengineering↗

bioRxiv2025

Ridge Redundancy Analysis for High-Dimensional Omics Data

MotivationRedundancy Analysis (RDA) is a popular reduced rank regression approach for modeling the relationships between two sets of variables. In omics studies, RDA provides a flexible framework for investigating associations between high-dimensional molecular data. However, omics datasets often exhibit strong multicollinearity and suffer from the "large p, small n" problem, where the number of predictor variables p (features) exceeds the number of samples n. Ridge RDA addresses multicollinearity by introducing a ridge penalty in addition to the rank restriction. Despite these advantages, its application to high-dimensional omics data remains challenging due to i) difficulties in manipulating the large coefficient matrix of dimensions p x q, where q represents the number of response variables, and ii) the need to jointly select the rank and the regularization parameter that both influence the performance of the method. ResultsWe propose an efficient computational framework for ridge RDA that overcomes these challenges by leveraging the Singular Value Decomposition of the predictor matrix X. This approach eliminates the need for direct covariance matrix inversion, improving computational efficiency. Furthermore, we introduce a novel strategy that reduces the memory burden associated with the storage of the coefficient matrix from pq to (p + q)r, with r the chosen reduced-rank dimensionality. Our method enables an efficient grid search to jointly select the ridge penalty{lambda} and r via cross-validation. The proposed framework is implemented in the R package rrda, providing a practical and scalable solution for the analysis of high-dimensional omics data. AvailabilityThe R package rrda is available on CRAN: https://CRAN.R-project.org/package=rrda. Scripts and data used for the analysis are available on GitHub: https://github.com/Yoska393/rrda.

bioinformatics↗

bioRxiv2025

High-throughput histopathology for complex in vitro models

Human complex in vitro models (CIVMs) have demonstrated remarkable potential to study tissue development, physiology and disease at high-throughput. To effectively employ these miniaturized systems in translational preclinical research, their in-depth benchmarking is pivotal. Histology has been the core of tissue characterization for centuries and the foundation of spatial phenotyping. However, standard histology workflows are inherently low-throughput and centered on large tissue pieces. This does not match the high sample volumes and small sample sizes in CIVM research. Here, we introduce a holistic histo-workflow, utilizing 3D-printed histomolds that facilitate co-planar embedding of CIVMs at high-throughput, resulting in up to 48 samples in one section. We developed a variety of model-specific histomold designs that enable spatially controlled histological sectioning and downstream analyses. We describe these workflows, including mold generation, highplex staining and image analysis, and exemplify their application to histological analyses of various CIVMs. Altogether, the histomolds introduced here afford opportunities for CIVM processing and analysis, while significantly reducing labor and reagent resources, thereby democratizing high-throughput CIVM in histopathology.

pathology↗

bioRxiv2025

Integration of Proxy Intermediate Omics traits into a Nonlinear Two-Step model for accurate phenotypic prediction

Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognised as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omicsomics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilises this "proxy" to enhance the accuracy of the phenotype prediction. We compared the linear model (Best Linear Unbiased Prediction, BLUP) and the nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.

bioinformatics↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2024

Engineering multi-degrading bacterial communities to bioremediate soils contaminated with pesticides residues

Parallel to the important use of pesticides in conventional agriculture there is a growing interest for green technologies to clear contaminated soil from pesticides and their degradation products. One specific technique, the inoculation of degrading micro-organisms in polluted soil, known as bioaugmentation, is a promising method still in needs of further developments. Specifically, improvements in the understanding of how degrading microorganisms must overcome abiotic filters and interact with the autochthonous microbial communities are needed in order to efficiently design bioremediation strategies. Here we designed a protocol aiming at studying the degradation of two herbicides, glyphosate (GLY) and isoproturon (IPU), via experimental modifications of two source bacterial communities. We used statistical methods stemming from genomic prediction to link community composition to herbicides degradation potentials. Our approach proved to be efficient with correlation estimates over 0.8 - between model predictions and measured pesticide degradation values. OTUs significantly associated with the degradation ability, and therefore identified as relevant by the models were confronted to the literature. Next, multi-degrading bacterial communities were obtained by coalescing bacterial communities with high GLY or IPU degradation ability based on their community-level properties. Finally, we evaluated the efficiency of constructed multi-degrading communities to remove pesticide contamination in a different soil. While results are less clear in the case of GLY, we showed an efficient transfer of degrading capacities towards the receiving soil even at relatively low inoculation levels in the case of IPU. Altogether, we developed an innovative protocol for building multi-degrading simplified bacterial communities with the help of genomic prediction tools and coalescence, and proved their efficiency in a contaminated soil.

microbiology↗

bioRxiv2023

A digital twin of bacterial metabolism during cheese production

Cheese organoleptic properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This digital twin accurately predicted the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration raised additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added-value of dynamic metabolic modeling for the comprehension of fermented food processes.

bioinformatics↗

bioRxiv2020

Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions

BackgroundTicks transmit pathogens of medical and veterinary importance, and represent an increasing threat for human and animal health. Important steps in assessing disease risk and developing possible new future control strategies involve identifying tick-borne microbes, their temporal dynamics and interactions. MethodsUsing high throughput sequencing, we studied the microbiota dynamics of Ixodes ricinus from 371 nymphs collected monthly over three consecutive years in a peri-urban forest. After adjusting a Poisson Log Normal model to our data set, the implementation of a principal component analysis as well as sparse network reconstruction and differential analysis allowed us to assess inter-annual, seasonal and monthly variability of I. ricinus microbial communities as well as their interactions. ResultsAround 75% of the detected sequences belonged to five genera known to be maternally inherited bacteria in arthropods and potentially circulating in ticks: Candidatus Midichloria, Rickettsia, Spiroplasma, Arsenophonus and Wolbachia. The structure of the I. ricinus microbiota was temporally variable with interannual recurrence and seemed to be mainly driven by OTUs belonging to environmental genera. The total network analysis revealed a majority of positive (partial) correlations. We identified strong relationships between OTUs belonging to Wolbachia and Arsenophonus, betraying the presence of the parasitoid wasp Ixodiphagus hookeri in ticks, and the well known arthropod symbiont Spiroplasma, previously documented to be involved in the defense against parasitoid wasp in Drosophila melanogaster. Other associations were observed between the tick symbiont Candidatus Midichloria and pathogens belonging to Rickettsia, probably Rickettsia helvetica. More specific network analysis finally suggested that the presence of pathogens belonging to genera Borrelia, Anaplasma and Rickettsia might disrupt microbial interactions in I. ricinus. ConclusionsHere, we identified the I. ricinus microbiota and documented for the first time the existence and recurrence of marked temporal shifts in the tick microbial community dynamics. We statistically showed strong relationships between the presence of some pathogens and the structure of the I. ricinus non-pathogenic microbes. We interestingly detected close links between some tick symbionts and the potential presence of either pathogenic Rickettsia or a parasitoid in ticks. All these new findings might be very promising for the future development of new control strategies of ticks and tick-borne diseases.

microbiology↗