Restoring degraded microbiome function with self-assembled communities

Customized plant microbiome engineering for food security

Plant microbiomes play a vital role in promoting plant growth and resilience to cope with environmental stresses. Plant microbiome engineering holds significant promise to increase crop yields, but there is uncertainty about how this can best be achieved. We propose a step-by-step approach involving customized direct and indirect methods to condition soils and to match plants and microbiomes. Although three approaches, namely the development of (i) 'plant- and microbe-friendly' soils, (ii) 'microbe-friendly' plants, and (iii) 'plant-friendly' microbiomes, have been successfully tested in isolation, we propose that the combination of all three may lead to a step-change towards higher and more stable crop yields. This review aims to provide knowledge, future directions, and practical guidance to achieve this goal via customized plant microbiome engineering.

A study of microbial diversity in a biofertilizer consortium

Biofertilizers supply living microorganisms to help plants grow and keep their health. This study examines the microbiome composition of a commercial biofertilizer known for its plant growth-promoting activity. Using ITS and 16S rRNA gene sequence analyses, we describe the microbial communities of a biofertilizer, with 163 fungal species and 485 bacterial genera found. The biofertilizer contains a variety of microorganisms previously reported to enhance nutrient uptake, phytohormone production, stress tolerance, and pathogen resistance in plants. Plant roots created a microenvironment that boosted bacterial diversity but filtered fungal communities. Notably, preserving the fungal-inoculated substrate proves critical for keeping fungal diversity in the root fraction. We described that bacteria were more diverse in the rhizosphere than in the substrate. In contrast, root-associated fungi were less diverse than the substrate ones. We propose using plant roots as bioreactors to sustain dynamic environments that promote the proliferation of microorganisms with biofertilizer potential. The study suggests that bacteria grow close to plant roots, while root-associated fungi may be a subset of the substrate fungi. These findings show that the composition of the biofertilizer may be influenced by the selection of microorganisms associated with plant roots, which could have implications for the effectiveness of the biofertilizer in promoting plant growth. In conclusion, our study sheds light on the intricate interplay between plant roots and the biofertilizer's microbial communities. Understanding this relationship can aid in optimizing biofertilizer production and application, contributing to sustainable agricultural practices and improved crop yields.

TbasCO: trait-based comparative 'omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Exploiting the Potential of Bioreactors for Creating Spatial Organization in the Soil Microbiome: A Strategy for Increasing Sustainable Agricultural Practices

Industrial production of synthetic nitrogen fertilizers and their crop application have caused considerable environmental impacts. Some eco-friendly alternatives try to solve them but raise some restrictions. We tested a novel method to produce a nitrogen bioinoculant by enriching a soil microbial community in bioreactors supplying N2 by air pumping. The biomass enriched with diazotrophic bacteria was diluted and applied to N-depleted and sterilized soil of tomato plants. We estimated microbial composition and diversity by 16S rRNA metabarcoding from soil and bioreactors at different run times and during plant uprooting. Bioreactors promoted the N-fixing microbial community and revealed a hided diversity. One hundred twenty-four (124) operational taxonomic units (OTUs) were assigned to bacteria with a greater Shannon diversity during the reactor’s steady state. A total of 753 OTUs were found in the rhizospheres with higher biodiversity when the lowest concentration of bacteria was applied. The apparent bacterial abundance in the batch and continuous bioreactors suggested a more specific functional ecological organization. We demonstrate the usefulness of bioreactors to evidence hidden diversity in the soil when it passes through bioreactors. By obtaining the same growth of inoculated plants and the control with chemical synthesis fertilizers, we evidence the potential of the methodology that we have called directed bioprospecting to grow a complex nitrogen-fixing microbial community. The simplicity of the reactor’s operation makes its application promising for developing countries with low technological progress.

Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods

Eco-evolutionary forces are the key drivers of ecosystem biodiversity dynamics. This resulted in a large body of theory, which has partially been experimentally tested by mimicking evolutionary processes in the laboratory. In the first part of this perspective, we outline what model systems are used for experimental testing of eco-evolutionary processes, ranging from simple microbial combinations and, more recently, to complex natural communities. Microbial communities of spontaneous fermented foods are a promising model system to study eco-evolutionary dynamics. They combine the complexity of a natural community with extensive knowledge about community members and the ease of manipulating the system in a laboratory setup. Due to rapidly developing sequencing techniques and meta-omics approaches incorporating data in building ecosystem models, the diversity in these communities can be analysed with relative ease while hypotheses developed in simple systems can be tested. Here, we highlight several eco-evolutionary questions that are addressed using microbial communities from fermented foods. These questions relate to analysing species frequencies in space and time, the diversity-stability relationship, niche space and community coalescence. We provide several hypotheses of the influence of these factors on community evolution specifying the experimental setup of studies where microbial communities of spontaneous fermented food are used.

Extracting the GEMs: Genotype, Environment, and Microbiome Interactions Shaping Host Phenotypes

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E), and their interaction (GE). Quantitative phenotypes can then be modeled as Y = G + E + GE + e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. The importance and contribution of microbe interactions to a given host phenotype is largely unclear, nor how this relates to the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment, and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.