Visualizing Microbial Community Dynamics via a Controllable Soil Environment

Exploring the potential of endophyte-plant interactions for improving crop sustainable yields in a changing climate

Climate change poses a major threat to global food security, significantly reducing crop yields as cause of abiotic stresses, and for boosting the spread of new and old pathogens and pests. Sustainable crop management as a route to mitigation poses the challenge of recruiting an array of solutions and tools for the new aims. Among these, the deployment of positive interactions between the micro-biotic components of agroecosystems and plants can play a highly significant role, as part of the agro-ecological revolution. Endophytic microorganisms have emerged as a promising solution to tackle this challenge. Among these, Arbuscular Mycorrhizal Fungi (AMF) and endophytic bacteria and fungi have demonstrated their potential to alleviate abiotic stresses such as drought and heat stress, as well as the impacts of biotic stresses. They can enhance crop yields in a sustainable way also by other mechanisms, such as improving the nutrient uptake, or by direct effects on plant physiology. In this review we summarize and update on the main types of endophytes, we highlight several studies that demonstrate their efficacy in improving sustainable yields and explore possible avenues for implementing crop-microbiota interactions. The mechanisms underlying these interactions are highly complex and require a comprehensive understanding. For this reason, omic technologies such as genomics, transcriptomics, proteomics, and metabolomics have been employed to unravel, by a higher level of information, the complex network of interactions between plants and microorganisms. Therefore, we also discuss the various omic approaches and techniques that have been used so far to study plant-endophyte interactions.

A Mineral-Doped Micromodel Platform Demonstrates Fungal Bridging of Carbon Hot Spots and Hyphal Transport of Mineral-Derived Nutrients

Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hot spots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood primarily due to the lack of a platform for spatially resolved analysis of biotic-driven mineral weathering. Here, we addressed this knowledge gap by demonstrating a mineral-doped soil micromodel platform where mineral weathering mechanisms can be studied. We directly visualize acquisition and transport of inorganic nutrients from minerals through fungal hyphae in the micromodel using a multimodal imaging approach. We found that Fusarium sp. strain DS 682, a representative of common saprotrophic soil fungus, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals. The fungus incorporated and translocated potassium (K) from K-rich mineral interfaces, as evidenced by visualization of mineral-derived nutrient transport and unique K chemical moieties following fungus-induced mineral weathering. Specific membrane transport proteins were expressed in the fungus in the presence of minerals, including those involved in oxidative phosphorylation pathways and the transmembrane transport of small-molecular-weight organic acids. This study establishes the significance of a spatial visualization platform for investigating microbial induced mineral weathering at microbially relevant scales. Moreover, we demonstrate the importance of fungal biology and nutrient translocation in maintaining fungal growth under water and carbon limitations in a reduced-complexity soil-like microenvironment. IMPORTANCE Fungal species are foundational members of soil microbiomes, where their contributions in accessing and transporting vital nutrients is key for community resilience. To date, the molecular mechanisms underlying fungal mineral weathering and nutrient translocation in low-nutrient environments remain poorly resolved due to the lack of a platform for spatial analysis of biotic weathering processes. Here, we addressed this knowledge gap by developing a mineral-doped soil micromodel platform. We demonstrate the function of this platform by directly probing fungal growth using spatially resolved optical and chemical imaging methodologies. We found the presence of minerals was required for fungal thigmotropism around obstacles and through soil-like pore spaces, and this was related to fungal transport of potassium (K) and corresponding K speciation from K-rich minerals. These findings provide new evidence and visualization into hyphal transport of mineral-derived nutrients under nutrient and water stresses.

Artificial Soils Reveal Individual Factor Controls on Microbial Processes

Soil matrix properties influence microbial behaviors that underlie nutrient cycling, greenhouse gas production, and soil formation. However, the dynamic and heterogeneous nature of soils makes it challenging to untangle the effects of different matrix properties on microbial behaviors. To address this challenge, we developed a tunable artificial soil recipe and used these materials to study the abiotic mechanisms driving soil microbial growth and communication. When we used standardized matrices with varying textures to culture gas-reporting biosensors, we found that a Gram-negative bacterium (Escherichia coli) grew best in synthetic silt soils, remaining active over a wide range of soil matric potentials, while a Gram-positive bacterium (Bacillus subtilis) preferred sandy soils, sporulating at low water potentials. Soil texture, mineralogy, and alkalinity all attenuated the bioavailability of an acyl-homoserine lactone (AHL) signaling molecule that controls community-level microbial behaviors. Texture controlled the timing of AHL sensing, while AHL bioavailability was decreased ~10⁵-fold by mineralogy and ~10³-fold by alkalinity. Finally, we built artificial soils with a range of complexities that converge on the properties of one Mollisol. As artificial soil complexity increased to more closely resemble the Mollisol, microbial behaviors approached those occurring in the natural soil, with the notable exception of organic matter. IMPORTANCE Understanding environmental controls on soil microbes is difficult because many abiotic parameters vary simultaneously and uncontrollably when different natural soils are compared, preventing mechanistic determination of any individual soil parameter's effect on microbial behaviors. We describe how soil texture, mineralogy, pH, and organic matter content can be varied individually within artificial soils to study their effects on soil microbes. Using microbial biosensors that report by producing a rare indicator gas, we identify soil properties that control microbial growth and attenuate the bioavailability of a diffusible chemical used to control community-level behaviors. We find that artificial soils differentially affect signal bioavailability and the growth of Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtilis) microbes. These artificial soils are useful for studying the mechanisms that underlie soil controls on microbial fitness, signaling, and gene transfer.

A Round Trip to the Desert: In situ Nanopore Sequencing Informs Targeted Bioprospecting

Bioprospecting expeditions are often performed in remote locations, in order to access previously unexplored samples. Nevertheless, the actual potential of those samples is only assessed once scientists are back in the laboratory, where a time-consuming screening must take place. This work evaluates the suitability of using Nanopore sequencing during a journey to the Tabernas Desert (Spain) for forecasting the potential of specific samples in terms of bacterial diversity and prevalence of radiation- and desiccation-resistant taxa, which were the target of the bioprospecting activities. Samples collected during the first day were analyzed through 16S rRNA gene sequencing using a mobile laboratory. Results enabled the identification of locations showing the greatest and the least potential, and a second, informed sampling was performed focusing on those sites. After finishing the expedition, a culture collection of 166 strains belonging to 50 different genera was established. Overall, Nanopore and culturing data correlated well, since samples holding a greater potential at the microbiome level also yielded a more interesting set of microbial isolates, whereas samples showing less biodiversity resulted in a reduced (and redundant) set of culturable bacteria. Thus, we anticipate that portable sequencers hold potential as key, easy-to-use tools for in situ-informed bioprospecting strategies.

Pollution impact on microbial communities composition in natural and anthropogenically modified soils of Southern Russia

Metagenomic studies of soil microbocenoses are extremely relevant nowadays. The study of pollution impact on soil microbiomes is of particular interest. The structure of microbial communities in soils with different levels of pollution by polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) was studied. High bacterial biodiversity was found in all the studied soil samples, but its lowest values are found in soil samples taken on the territory of technogenically polluted Lake Atamanskoye. Assessment of soil pollution showed the highest content of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) for the soils Lake Atamanskoye. The high content of pollutants negatively affects the abundance of representatives of the phyla Actinobacteria, Planctomycetes, Verrucomicrobia, and Nitrospirae. Such phyla as Proteobacteria, Candidate Divisions TM7, OD1, WPS-2, Chlamydiae, Cyanobacteria are characterized by positive direct correlation with the content of pollutants, especially with PAHs. A cooperative effect of decrease in the number of Actinobacteria and Proteobacteria with an increase in Armatimonadetes probably corresponds to PTEs contamination. The proportion of Candidate Division OD1, Chlamydiae, Cyanobacteria, and Candidate Division WPS-2 was increased in the soil microbiome under the influence of severe combined pollution. Pollutants negatively affect the abundance of dominant unclassified_o__Gaiellales and unclassified_o__WD2101 genera. Iamia, Salinibacterium, Arthrobacter, Kaistobacter, Thiobacillus genera are characterized by a low abundance, but they are presumably the most resistant to soil pollution. It was revealed that the level of soil pollution largely determines the composition and diversity of bacterial communities in the soils of the studied territories. Operating taxonomic units have been established that have prognostic value for assessing the state, level of soil pollution, and their biological safety.

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Expanding Molecular Coverage in Mass Spectrometry Imaging of Microbial Systems Using Metal-Assisted Laser Desorption/Ionization

Mass spectrometry imaging (MSI) is becoming an increasingly popular analytical technique to investigate microbial systems. However, differences in the ionization efficiencies of distinct MSI methods lead to biases in terms of what types and classes of molecules can be detected. Here, we sought to increase the molecular coverage of microbial colonies by employing metal-assisted laser desorption/ionization (MetA-LDI) MSI, and we compared our results to more commonly utilized matrix-assisted laser desorption/ionization MALDI MSI. We found substantial (∼67%) overlap in the molecules detected in our analysis of Bacillus subtilis colony biofilms using both methods, but each ionization technique did lead to the identification of a unique subset of molecular species. MetA-LDI MSI tended to identify more small molecules and neutral lipids, whereas MALDI MSI more readily detected other lipids and surfactin species. Putative annotations were made using METASPACE, Metlin, and the BsubCyc database. These annotations were then confirmed from analyses of replicate bacterial colonies using liquid extraction surface analysis tandem mass spectrometry. Additionally, we analyzed B. subtilis biofilms in a polymer-based emulated soil micromodel using MetA-LDI MSI to better understand bacterial processes and metabolism in a native, soil-like environment. We were able to detect different molecular signatures within the micropore regions of the micromodel. We also show that MetA-LDI MSI can be used to analyze microbial biofilms from electrically insulating material. Overall, this study expands the molecular universe of microbial metabolism that can be visualized by MSI.

IMPORTANCE Matrix-assisted laser desorption/ionization mass spectrometry imaging is becoming an important technique to investigate molecular processes within microbial colonies and microbiomes under different environmental conditions. However, this method is limited in terms of the types and classes of molecules that can be detected. In this study, we utilized metal-assisted laser desorption/ionization mass spectrometry imaging, which expanded the range of molecules that could be imaged from microbial samples. One advantage of this technique is that the addition of a metal helps facilitate ionization from electrically nonconductive substrates, which allows for the investigation of biofilms grown in polymer-based devices, like soil-emulating micromodels.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.