A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros.

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e. all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

Effect of strigolactones on recruitment of the rice root-associated microbiome.

Strigolactones are endogenous plant hormones regulating plant development and are exuded into the rhizosphere when plants experience nutrient deficiency. There, they promote the mutualistic association of plants with arbuscular mycorrhizal fungi that help the plant with the uptake of nutrients from the soil. This shows that plants actively establish-through the exudation of strigolactones-mutualistic interactions with microbes to overcome inadequate nutrition. The signaling function of strigolactones could possibly extend to other microbial partners, but the effect of strigolactones on the global root and rhizosphere microbiome remains poorly understood. Therefore, we analyzed the bacterial and fungal microbial communities of 16 rice genotypes differing in their root strigolactone exudation. Using multivariate analyses, distinctive differences in the microbiome composition were uncovered depending on strigolactone exudation. Moreover, the results of regression modeling showed that structural differences in the exuded strigolactones affected different sets of microbes. In particular, orobanchol was linked to the relative abundance of Burkholderia-Caballeronia-Paraburkholderia and Acidobacteria that potentially solubilize phosphate, while 4-deoxyorobanchol was associated with the genera Dyella and Umbelopsis. With this research, we provide new insight into the role of strigolactones in the interplay between plants and microbes in the rhizosphere.

Integration of omics data to unravel root microbiome recruitment.

The plant microbiome plays an essential role in supporting plant growth and health, but plant molecular mechanisms underlying its recruitment are still unclear. Multi-omics data integration methods can be used to unravel new signalling relationships. Here, we review the effects of plant genetics and root exudates on root microbiome recruitment, and discuss methodological advances in data integration approaches that can help us to better understand and optimise the crop-microbiome interaction for a more sustainable agriculture.

The auxin-signaling pathway is required for the lateral root response of Arabidopsis to the rhizobacterium Phyllobacterium brassicacearum.

Plant root development is highly responsive both to changes in nitrate availability and beneficial microorganisms in the rhizosphere. We previously showed that Phyllobacterium brassicacearum STM196, a plant growth-promoting rhizobacteria strain isolated from rapeseed roots, alleviates the inhibition exerted by high nitrate supply on lateral root growth. Since soil-borne bacteria can produce IAA and since this plant hormone may be implicated in the high nitrate-dependent control of lateral root development, we investigated its role in the root development response of Arabidopsis thaliana to STM196. Inoculation with STM196 resulted in a 50% increase of lateral root growth in Arabidopsis wild-type seedlings. This effect was completely abolished in aux1 and axr1 mutants, altered in IAA transport and signaling, respectively, indicating that these pathways are required. The STM196 strain, however, appeared to be a very low IAA producer when compared with the high-IAA-producing Azospirillum brasilense sp245 strain and its low-IAA-producing ipdc mutant. Consistent with the hypothesis that STM196 does not release significant amounts of IAA to the host roots, inoculation with this strain failed to increase root IAA content. Inoculation with STM196 led to increased expression levels of several IAA biosynthesis genes in shoots, increased Trp concentration in shoots, and increased auxin-dependent GUS staining in the root apices of DR5::GUS transgenic plants. All together, our results suggest that STM196 inoculation triggers changes in IAA distribution and homeostasis independently from IAA release by the bacteria.

Fast computation of genome-metagenome interaction effects.

MOTIVATION: Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype. However, it is now generally accepted that genes and environment must be examined jointly when estimating phenotypic variance. In this work we consider two types of biological markers: genotypic markers, which characterize an observation in terms of inherited genetic information, and metagenomic marker which are related to the environment. Both types of markers are available in their millions and can be used to characterize any observation uniquely. OBJECTIVE: Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype. CONTRIBUTIONS: We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost. The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets. These supervariables are given by a weighted group structure defined on sets of variables at different scales. A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing. RESULTS: We compare SICOMORE with other approaches in simulations, with varying sample sizes, noise, and numbers of true interactions. SICOMORE exhibits convincing results in terms of recall, as well as competitive performances with respect to running time. The method is also used to detect interaction between genomic markers in Medicago truncatula and metagenomic markers in its rhizosphere bacterial community. SOFTWARE AVAILABILITY: An R package is available [4], along with its documentation and associated scripts, allowing the reader to reproduce the results presented in the paper.

Multiple processes acting from local to large geographical scales shape bacterial communities associated with Phormidium (cyanobacteria) biofilms in French and New Zealand rivers.

River biofilms dominated by Phormidium (cyanobacteria) are receiving increased attention worldwide because of a recent expansion in their distribution and their ability to produce neurotoxins leading to animal mortalities. Limited data are available on the composition and structure of bacterial communities (BCs) associated with Phormidium biofilms despite the important role they potentially play in biofilm functioning. By using a high-throughput sequencing approach, we compared the BCs associated with Phormidium biofilms in several sampling sites of the Tarn River (France) and in eight New Zealand rivers. The structure of the BCs from both countries displayed spatial and temporal variations but were well conserved at the order level and 28% of the OTUs containing 90% of the reads were shared by these BCs. This suggests that micro-environmental conditions occurring within thick Phormidium biofilms strongly shape the associated BCs. A strong and significant distance-decay relationship (rp = 0.7; P = 0.001) was found in BCs from New Zealand rivers but the Bray-Curtis dissimilarities between French and New Zealand BCs are in the same order of magnitude of those found between New Zealand BCs. All these findings suggest that local environmental conditions seem to have more impact on BCs than dispersal capacities of bacteria.

Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France.

Epilithic river biofilms are complex matrix-enclosed communities harboring a great diversity of prokaryotic and eukaryotic microorganisms. Interactions between these communities and the relative impacts of environmental factors on their compositions are poorly understood. In this study, we assessed the spatio-temporal variation in the diversity and composition of bacterial and microeukaryotic communities within biofilms in a French river. Significant changes were found in the composition of these microbial communities over the sampling period and between the upstream and downstream stations. In addition, the beta diversity of the bacterial community tended to decrease along the river, mostly as a result of turnover. These changes could be caused by the different water temperatures and geological and hydrological river contexts at the sampling sites (from karst landscape to river plain). Finally, our network analysis showed multiple correlations among dominant OTUs. Among them, negative correlations between Rhodobacteraceae and two other dominant groups of photosynthetic microorganisms (cyanobacteria and diatoms) were particularly interesting, which raises the question of what environmental factors trigger the changes occurring in benthic microbial photosynthetic communities.

Bacterial Communities Associated with Four Cyanobacterial Genera Display Structural and Functional Differences: Evidence from an Experimental Approach.

To overcome the limitations associated with studying the interactions between bacterial communities (BCs) and cyanobacteria in natural environments, we compared the structural and functional diversities of the BCs associated with 15 non-axenic cyanobacterial strains in culture and two natural BCs sampled during cyanobacterial blooms. No significant differences in richness and diversity were found between the natural and cultivated BCs, although some of the cyanobacterial strains had been isolated 11 years earlier. Moreover, these BCs shared some similar characteristics, such as a very low abundance of Actinobacteria, but they display significant differences at the operational taxonomic unit (OTU) level. Overall, our findings suggest that BCs associated with cyanobacteria in culture are good models to better understand the interactions between heterotrophic bacteria and cyanobacteria. Additionally, BCs associated with heterocystous cyanobacterial strains cultivated in Z8X culture medium without nitrate (Aphanizomenon-Dolichospermum) demonstrated significant differences compared to BCs associated with non-heterocystous strains cultivated in Z8 culture medium (Planktothrix-Microcystis) in terms of their composition and their ability to utilize different carbon sources, suggesting the potential influence of cyanobacterial metabolism and/or culture media on associated BCs. Finally, half of the dominant OTUs in these BCs were specifically associated with cyanobacteria or other phytoplankton, whereas the remaining OTUs were generally associated with ecosystems containing high organic matter content, such as sludge or intestines.

Soil nitrogen availability and plant genotype modify the nutrition strategies of M. truncatula and the associated rhizosphere microbial communities.

Plant and soil types are usually considered as the two main drivers of the rhizosphere microbial communities. The aim of this work was to study the effect of both N availability and plant genotype on the plant associated rhizosphere microbial communities, in relation to the nutritional strategies of the plant-microbe interactions, for six contrasted Medicago truncatula genotypes. The plants were provided with two different nutrient solutions varying in their nitrate concentrations (0 mM and 10 mM). First, the influence of both nitrogen availability and Medicago truncatula genotype on the genetic structure of the soil bacterial and fungal communities was determined by DNA fingerprint using Automated Ribosomal Intergenic Spacer Analysis (ARISA). Secondly, the different nutritional strategies of the plant-microbe interactions were evaluated using an ecophysiological framework. We observed that nitrogen availability affected rhizosphere bacterial communities only in presence of the plant. Furthermore, we showed that the influence of nitrogen availability on rhizosphere bacterial communities was dependent on the different genotypes of Medicago truncatula. Finally, the nutritional strategies of the plant varied greatly in response to a modification of nitrogen availability. A new conceptual framework was thus developed to study plant-microbe interactions. This framework led to the identification of three contrasted structural and functional adaptive responses of plant-microbe interactions to nitrogen availability.