Biology subjects

Mariadassou, M.

Publications and source records attributed to Mariadassou, M..

10 recordsLinked to original sources

bioRxiv2025

NeighborFinder: an R package inferring local microbial network around a species of interest

1MotivationUnderstanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of specific taxa of interest. ResultsWe introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with[l] 1 penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score [≥] 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being a biologically intuitive and computationally efficient. Availability and ImplementationThe R package is freely available on GitHub: https://github.com/metagenopolis/NeighborFinder. The data and source code used to calculate performances and produce the use case example in this paper can be found respectively at: https://doi.org/10.57745/UPITJ0 and https://doi.org/10.57745/HJLWW4. Supplementary informationSupplementary data are available

systems biology↗

bioRxiv2025

Atypical Replication Error Pattern and Limited Repair Efficiency Contribute to Elevated Mutation Rate in Phage lambda

Understanding the rate and nature of spontaneous mutations is crucial for understanding and modulating the pace and trajectory of evolution. Yet, both remain poorly characterized in double-stranded DNA (dsDNA) phages, despite their relevance for phage-based therapies. Here, we address this gap for the dsDNA phage lambda, using four complementary approaches: mutation accumulation assay combined with whole-genome sequencing, mutation visualization assay, duplex sequencing and fluctuation assay. We find that the mutation rate of wild-type phage lambda is 4.9 {+/-} 1.8 x10-9 per base per replication, approximately 15 times lower than previously estimated and about 20 times higher than that of its host, Escherichia coli (E. coli). Inactivation of Mismatch Repair (MMR), a major conserved cellular system for mutation avoidance, increases the lambda mutation rate by only 2-to 10-fold, in contrast to the approximately 150-fold increase in E. coli, however lambda does not exhibit the characteristic mutational bias associated with MMR deficiency. Interestingly replication of the lambda genome generates an error spectrum distinct from that of E. coli, characterized by a marked increase in transversions, poorly repaired by MMR. Together, these results reveal that lambda exhibits a replication error profile that is less amenable to repair, likely contributing to its elevated mutation rate.

microbiology↗

bioRxiv2023

Aggregating network inferences: towards useful networks

Modeling microbial interactions as sparse and reproducible networks is a major challenge in microbial ecology. Direct interactions between the microbial species of a biome can help to understand the mechanisms through which microbial communities influence the system. Most state-of-the art methods reconstruct networks from abundance data using Gaussian Graphical Models, for which several statistically grounded and computationnally efficient inference approaches are available. However, the multiplicity of existing methods, when applied to the same dataset, generates very different networks. In this article, we present OneNet, a consensus network inference method that combines seven methods based on stability selection. This resampling procedure is used to tune a regularization parameter by computing how often edges are selected in the networks. We modified the stability selection framework to use edge selection frequencies directly and combine them in the inferred network to ensure that only reproducible edges are included in the consensus. We demonstrated on synthetic data that our method generally led to slightly sparser networks while achieving much higher precision than any single method. We further applied the method to gut microbiome data from liver-cirrothic patients and demonstrated that the resulting network exhibited a microbial guild that was meaningful in terms of human health.

bioinformatics↗

bioRxiv2022

Viral DNA SIP: the isotopic composition of virions is correlated with that of the substrate used for host growth

DNA Stable Isotope Probing is emerging as a powerful tool to study host-virus interactions. Indeed, since all viruses depend on a host for virion production, a link between the isotopic compositions of hosts and the virions they produce is expected. However, stable isotope probing applied to viral DNA has never been evaluated on simple biological models. Here, this method was tested on the bacteriophage T4 and its host Escherichia coli. To validate that E. coli cells cultivated using a substrate enriched in 13C isotope were resulting on the production of 13C-labeled T4 DNA. T4 DNA buoyant density in CsCl gradient was overall higher than the values predicted by a previously established empirical model, highlighting the need to adapt this type of models when analysing modified viral DNA. Moreover, our results show a strong correlation between the proportion of 13C6-D-glucose in the substrate used for host growth and the buoyant density of T4 DNA, validating the use of DNA SIP in viral ecology, to identify viruses infecting hosts with a specific metabolism.

microbiology↗

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

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↗

bioRxiv2019

Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems

Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the seascape) by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton.

ecology↗

bioRxiv2019

Taxon appearance from extraction and amplification steps demonstrates the value of multiple controls in tick microbiome analysis

BackgroundThe development of high throughput sequencing technologies has substantially improved analysis of bacterial community diversity, composition, and functions. Over the last decade, high throughput sequencing has been used extensively to identify the diversity and composition of tick microbial communities. However, a growing number of studies are warning about the impact of contamination brought along the different steps of the analytical process, from DNA extraction to amplification. In low biomass samples, e.g. individual tick samples, these contaminants may represent a large part of the obtained sequences, and thus generate considerable errors in downstream analyses and in the interpretation of results. Most studies of tick microbiota either do not mention the inclusion of controls during the DNA extraction or amplification steps, or consider the lack of an electrophoresis signal as an absence of contamination. In this context, we aimed to assess the proportion of contaminant sequences resulting from these steps. We analyzed the microbiota of individual Ixodes ricinus ticks by including several categories of controls throughout the analytical process: crushing, DNA extraction, and DNA amplification.\n\nResultsControls yielded a significant number of sequences (1,126 to 13,198 mean sequences, depending on the control category). Some operational taxonomic units (OTUs) detected in these controls belong to genera reported in previous tick microbiota studies. In this study, these OTUs accounted for 50.9% of the total number of sequences in our samples, and were considered contaminants. Contamination levels (i.e. the percentage of sequences belonging to OTUs identified as contaminants) varied with tick stage and gender: 76.3% of nymphs and 75% of males demonstrated contamination over 50%, while most females (65.7%) had rates lower than 20%. Contamination mainly corresponded to OTUs detected in crushing and DNA extraction controls, highlighting the importance of carefully controlling these steps.\n\nConclusionHere, we showed that contaminant OTUs from extraction and amplification steps can represent more than half the total sequence yield in sequencing runs, and lead to unreliable results when characterizing tick microbial communities. We thus strongly advise the routine use of negative controls in tick microbiota studies, and more generally in studies involving low biomass samples.

microbiology↗

bioRxiv2017

Inference of Adaptive Shifts for Multivariate Correlated Traits

To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes preferred to the simple Brownian Motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence shifts, phylogenetic Principal Component Analysis (pPCA) fails to decorrelate traits efficiently, so that any method aiming at finding shift needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU (scOU), which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a re-scaled tree to our multivariate framework. We describe an Expectation Maximization algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method ({ell}1ou) on a wide range of simulated scenarios, and use this new framework to re-analyze recently gathered datasets on New World Monkeys and Anolis lizards.

evolutionary biology↗