Biology subjects

Brisson, V.

Publications and source records attributed to Brisson, V..

5 recordsLinked to original sources

bioRxiv2025

Breeding of microbiomes conferring salt tolerance to plants

Microbiome breeding by host-mediated selection is a technique to artificially select for microbiomes conferring benefits to plants. Here, we describe leaf ionomics, microbial community composition, and network analyses of a microbiome-breeding experiment to generate microbiomes conferring salt tolerance to Brachypodium distachyon, a model for cereal crops. Plants receiving microbiomes selected to confer tolerance to either sodium- or aluminium-stress produced 69-198% higher total seed weight than plants receiving control microbiomes. Sodium-selected microbiomes reduced leaf-sodium concentration by 50%, whereas aluminium-selected microbiomes had no effect on leaf-tissue nutrient concentration, suggesting different mechanisms underlying microbiome-mediated salt tolerance. By testing these selected microbiomes in a cross-fostering experiment, we show that artificially-selected microbiomes attain (a) ecological robustness contributing to transplantability (inheritance) of microbiome-encoded effects between plants; and (b) network features identifying key bacteria promoting stress tolerance. Combined, these findings elucidate critical mechanisms underlying host-mediated selection as a tool to breed beneficial microbiomes in an agricultural context.

evolutionary biology↗

bioRxiv2024

Spatially structured competition and cooperation alters algal carbon flow to bacteria

Microbial communities regulate the transformations of carbon in aquatic systems through metabolic interactions and food-web dynamics that can alter the balance of photosynthesis and respiration. Direct competition for resources is thought to drive microbial community assembly in algal systems, but other interaction modes that may shape communities are more challenging to isolate. Through untargeted metabolomics and metabolic modeling, we predicted the degree of resource competition between bacterial pairs when growing on model diatom Phaeodactylum tricornutum-derived substrates. In a subsequent sequential media experiment, we found that pairwise interactions were consistently more cooperative than predicted based on resource competition alone, indicating an unexpected role for cooperation in algal carbon processing. To link this directly to algal carbon fate, we chose a representative cooperative and competitive influencer isolate and a model recipient and applied single-cell isotope tracing in a custom porous microplate cultivation system. In the presence of live algae, the recipient drew down more algal carbon in the presence of the cooperative influencer compared to the competitive influencer, supporting the sequential experiment results. We also found that total carbon assimilation into bacterial biomass, integrated over influencer and recipient, was significantly higher for the cooperative interaction. Our findings support the notion that non-competitive interactions are critical for predicting algal carbon fate. Significance StatementMicrobial interactions have widely been studied in the context of host resources but testing and measuring direct interactions in a lab has been particularly challenging. By combining untargeted metabolomics, sequential/(co-)culture, and metabolic modeling, we demonstrate that the presence of an unexpected interaction mode in a live system and show how it impacts the flow of host-derived resources. This top-down approach can help identify novel bacterial interactions that play a crucial role in microbial community-host ecosystems, which may have an impact in holobiont phenotypes including alga, fungal, or plant systems.

microbiology↗

bioRxiv2022

Dynamic Phaeodactylum tricornutum Exometabolites Shape Surrounding Bacterial Communities

The roles of exometabolites in mediating algal-bacterial interactions and regulating microbial community composition are not well understood. Here, we identified specific exometabolites from the model diatom Phaeodactylum tricornutum affecting abundance of specific bacterial taxa in isolation and in a community setting. We examined the response of a P. tricornutum-adapted enrichment community and found that both algal exudates and algal presence drove similar changes in community composition compared to controls. Using LC-MS/MS, we identified 50 metabolites produced by axenic P. tricornutum and found that different exometabolites accumulated during different algal growth phases. Profiling growth of 12 bacterial isolates representative of the enrichment community uncovered two algal exometabolites (out of 12 tested) which supported growth of a subset of isolates as a primary carbon source. We compared enrichment community response with and without the addition of two contrasting metabolites: 4-hydroxybenzoic acid, which supported isolate growth, and lumichrome, which did not. Exogenous metabolite additions did promote increased abundances of taxa that were able to utilize the metabolite in the isolate study, but also revealed the importance of factors relating to algal presence in regulating community composition. Collectively, this work demonstrates the influence of specific algal exometabolites in driving microbial community composition.

microbiology↗

bioRxiv2021

Identification of effector metabolites using exometabolite profiling of diverse microalgae

Dissolved exometabolites mediate algal interactions in aquatic ecosystems, but microalgal exometabolomes remain understudied. We conducted an untargeted metabolomic analysis of non-polar exometabolites exuded from four phylogenetically and ecologically diverse eukaryotic microalgal strains grown in the laboratory: freshwater Chlamydomonas reinhardtii, brackish Desmodesmus sp., marine Phaeodactylum tricornutum, and marine Microchloropsis salina, to identify released metabolites based on relative enrichment in the exometabolomes compared to cell pellet metabolomes. Exudates from the different taxa were distinct, but we did not observe clear phylogenetic patterns. We used feature based molecular networking to explore the identities of these metabolites, revealing several distinct di- and tripeptides secreted by each of the algae, lumichrome, a compound that is known to be involved in plant growth and bacterial quorum sensing, and novel prostaglandin-like compounds. We further investigated the impacts of exogenous additions of eight compounds selected based on exometabolome enrichment on algal growth. Of the these, five (lumichrome, 5-S-methyl-5-thioadenosine, 17-phenyl trinor prostaglandin A2, dodecanedioic acid, and aleuritic acid) impacted growth in at least one of the algal cultures. Two of these (dodecanedioic acid and aleuritic acid) produced contrasting results, increasing growth in some algae and decreasing growth in others. Together, our results reveal new groups of microalgal exometabolites, some of which could alter algal growth when provided exogenously, suggesting potential roles in allelopathy and algal interactions. IMPORTANCEMicroalgae are responsible for nearly half of primary production on earth and play an important role in global biogeochemical cycling as well as in a range of industrial applications. Algal exometabolites are important mediators of algal-algal and algal-bacterial interactions that ultimately affect algal growth and physiology. In this study we characterize exometabolomes across marine and freshwater algae to gain insights into the diverse metabolites they release into their environments ("exudates"). We observe that while phylogeny can play a role in exometabolome content, environmental conditions or habitat origin (freshwater vs marine) are also important. We also find that several of these compounds can influence algal growth (as measured by chlorophyll production) when provided exogenously, highlighting the importance of characterization of these novel compounds and their role in microalgal ecophysiology.

microbiology↗

bioRxiv2018

A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a real data set of 16S-V4 amplicon sequences that was expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

bioinformatics↗