Biology subjects

Chiquet, J.

Publications and source records attributed to Chiquet, J..

8 recordsLinked to original sources

bioRxiv2026

Catching the effects of biotic interactions on community data: partial correlations outperform marginal ones with proper abiotic modelling.

A major goal of community ecology lies in the deciphering of the processes underlying species distribution. A widespread approach to this question is to identify patterns in species community data and relate them to possible processes. Joint Species Distribution Models (JS-DMs) offer one way to do so through the infernece of association networks that describe patterns of statistical correlations and dependencies between species, but it is unclear what processes can explain the presence of such correlations. While it has now been established that there is no equivalence between JSDM-inferred associations and biotic interactions, the later remain one possible explanation, among others, for the former. However, to our knowledge, there is no specific study of the statistical patterns induced by different types of interactions or of the conditions under which they may or may not appear as statistical correlations / dependencies in species communities. To explore these questions, we propose a "virtual ecologist" approach that consists in simulating community data based on abiotic and biotic processes with the VirtualCom model that emulates the effects of environmental processes and of competition and facilitation interactions. Then, we study to what extent JSDMs retrieve correlations between species that match the simulated interactions. We show that these interactions are better identified when using JSDMs that model partial correlations between species rather than marginal ones. We further demonstrate how critical it is to correctly model abiotic effects in order to identify biotic ones and that the "correct modelling" of these effects depend on the type of interactions at stake.

ecology↗

bioRxiv2025

Deep Generative Models for Discrete Genotype Simulation

Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have primarily focused on generating gene expression or haplotype data, this study explores generating genotype data in both unconditioned and phenotype-conditioned settings, which is inherently more challenging due to the discrete nature of genotype data. In this work, we developed and evaluated commonly used generative models, including Variational Autoencoders (VAEs), Diffusion Models, and Generative Adversarial Networks (GANs), and proposed adaptation tailored to discrete genotype data. We conducted extensive experiments on large-scale datasets, including all chromosomes from cow and multiple chromosomes from human. Model performance was assessed using a well-established set of metrics drawn from both deep learning and quantitative genetics literature. Our results show that these models can effectively capture genetic patterns and preserve genotype-phenotype association. Our findings provide a comprehensive comparison of these models and offer practical guidelines for future research in genotype simulation. We have made our code publicly available at https://github.com/SihanXXX/DiscreteGenoGen.

bioinformatics↗

bioRxiv2025

A JSDM with zero-inflation to increase the ecological relevance of analyses of species distribution data.

1. A long-term goal of community ecology has been to decipher the mechanisms that shape the spatio-temporal organization of species communities. Understanding these processes is critical to predicting the responses of ecological communities to environmental change. To this end, Joint Species Distribution Models (JSDMs) offer statistical tools to analyze community data, identify the impact of abiotic factors on them and study inter-species correlations in their distributions. In particular, the JSDM-inferred partial-correlation networks allow one to identify direct links between species that can help decipher the mechanisms that shape their joint distribution. 2. Community data based on species counts often contains numerous zeros. However, not accounting for these zeros in a data set is known to hinder parameter inference. We investigate this issue in the context of JSDMs, and ask what impact it can have on the inference of partial-correlation networks. 3. We propose a novel JSDM, the ZIPLN-network model, based on the PLN-network (Poisson log-normal network) and ZIPLN (Zero-Inflated Poisson log-normal) model, which models count data while including zero-inflation and infers a partial-correlation network. Using simulated data, we compare the results obtained by this model with existing JSDMs in terms of association network inference from abundance data containing structural zeros. We then illustrate the ZIPLN-network approach using real data from tropical freshwater fish communities. 4. Simulations show that zero-inflation can significantly bias the inference of partial-correlation networks from community data and that the ZIPLN-network model efficiently counterbalances these effects. The ZIPLN-network approach is widely applicable to community data, delivers ecologically-insightful analyses, helps distinguish amongst potential mechanisms, and aids better understanding of community assembly rules. We provide guidance for getting started with our approach.

ecology↗

bioRxiv2023

Cell specialization and coordination in Arabidopsis leaves upon pathogenic attack revealed by scRNA-seq.

Plant defense responses involve several biological processes that allow plants to fight against pathogenic attacks. How these different processes are orchestrated within organs and depend on specific cell types is poorly known. Here, using scRNA-seq technology on three independent biological replicates, we identified 10 distinct cell populations in wild-type Arabidopsis leaves inoculated with the bacterial pathogen Pseudomonas syringae DC3000. Among those, we retrieved major cell types of the leaves (mesophyll, guard, epidermal, companion and vascular S cells) to which we could associate characteristic transcriptional reprogramming and regulators, thereby specifying different cell-type responses to the pathogen. Further analyses of transcriptional dynamics, based on inference of cell trajectories, indicated that the different cell types, in addition to their characteristic defense responses, can also share similar modules of gene reprogramming, allowing for instance vascular S cells, epidermal cells and mesophyll cells to converge towards an identical cell fate, mostly characterized by lignification and detoxification functions. Moreover, it appeared that the defense responses of these three cell types can evolve along a second separate path. As this divergence does not correspond to the differentiation between immune and susceptible cells, we speculate that this might reflect the discrimination between cell-autonomous and non-cell-autonomous responses. Altogether our data provide an upgraded framework to describe, explore and explain the specialization and the coordination of plant cell responses upon pathogenic challenge.

plant biology↗

bioRxiv2020

Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of community structure in fruit flies

AO_SCPLOWBSTRACTC_SCPLOWThe relative importance of ecological factors and species interactions for phytophagous insect species distributions has long been a controversial issue. Using field abundances of eight sympatric Tephritid fruit flies on 21 host plants, we inferred flies realized niches using joint species distribution modelling and network inference, on the community as a whole and separately on three groups of host plants. These inferences were then confronted to flies fundamental niches estimated through laboratory-measured fitnesses on host plants. Species abundances were mainly determined by host plants followed by climatic factors, with a minor role for competition between species sharing host plants. The relative importance of these factors mildly changed when we focused on particular host plant groups. Despite overlapping fundamental niches, specialists and generalists had almost distinct realized niches, with possible competitive exclusion of generalists by specialists on Cucurbitaceae, and different assembly rules: specialists were mainly influenced by their adaptation to host plants while generalist abundances varied regardless of their fundamental host use.

ecology↗

bioRxiv2020

The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances

Joint Species Abundance Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type or nutrient availability) and potential interactions between the species (competition, mutualism, parasitism, etc.), which is instrumental in disentangling meaningful ecological interactions from mere statistical associations. Modeling the dependency between the species is challenging because of the count-valued nature of abundance data and most JSDM rely on Gaussian latent layer to encode the dependencies between species in a covariance matrix. The multivariate Poisson-lognormal (PLN) model is one such model, which can be viewed as a multivariate mixed Poisson regression model. The inference of such models raises both statistical and computational issues, many of which were solved in recent contributions using variational techniques and convex optimization. The PLN model turns out to be a versatile framework, within which a variety of analyses can be performed, including multivariate sample comparison, clustering of sites or samples, dimension reduction (ordination) for visualization purposes, or inference of interaction networks. This paper presents the general PLN framework and illustrates its use on a series a typical experimental datasets. All the models and methods are implemented in the R package PLNmodels, available from cran.r-project.org.

ecology↗

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↗

bioRxiv2020

Microbial association networks give relevant insights into plant pathobiomes

Interactions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.

ecology↗