Exometabolomic Analysis of Cross-Feeding Metabolites

More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities

Microbial communities are not only shaped by the diversity of microorganisms and their individual metabolic potential, but also by the vast amount of intra- and interspecies interactions that can occur pairwise interactions among microorganisms, we suggest that more attention should be drawn towards the effects on the entire microbiome that emerge from individual interactions between community members. The production of certain metabolites that can be tied to a specific microbe-microbe interaction might subsequently influence the physicochemical parameters of the habitat, stimulate a change in the trophic network of the community or create new micro-habitats through the formation of biofilms, similar to the production of antimicrobial substances which might negatively affect only one microorganism but cause a ripple effect on the abundance of other community members. Here, we argue that combining established as well as innovative laboratory and computational methods is needed to predict novel interactions and assess their secondary effects. Such efforts will enable future microbiome studies to expand our knowledge on the dynamics of complex microbial communities.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Cross-Feeding of a Toxic Metabolite in a Synthetic Lignocellulose-Degrading Microbial Community

The recalcitrance of complex organic polymers such as lignocellulose is one of the major obstacles to sustainable energy production from plant biomass, and the generation of toxic intermediates can negatively impact the efficiency of microbial lignocellulose degradation. Here, we describe the development of a model microbial consortium for studying lignocellulose degradation, with the specific goal of mitigating the production of the toxin formaldehyde during the breakdown of methoxylated aromatic compounds. Included are Pseudomonas putida, a lignin degrader; Cellulomonas fimi, a cellulose degrader; and sometimes Yarrowia lipolytica, an oleaginous yeast. Unique to our system is the inclusion of Methylorubrum extorquens, a methylotroph capable of using formaldehyde for growth. We developed a defined minimal "Model Lignocellulose" growth medium for reproducible coculture experiments. We demonstrated that the formaldehyde produced by P. putida growing on vanillic acid can exceed the minimum inhibitory concentration for C. fimi, and, furthermore, that the presence of M. extorquens lowers those concentrations. We also uncovered unexpected ecological dynamics, including resource competition, and interspecies differences in growth requirements and toxin sensitivities. Finally, we introduced the possibility for a mutualistic interaction between C. fimi and M. extorquens through metabolite exchange. This study lays the foundation to enable future work incorporating metabolomic analysis and modeling, genetic engineering, and laboratory evolution, on a model system that is appropriate both for fundamental eco-evolutionary studies and for the optimization of efficiency and yield in microbially-mediated biomass transformation.

Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments

Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the microorganisms tend to interact with each other and perform complex actions. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia. Here, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named "Metabolic Support Index (MSI)", which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-cultures and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrated computational and experimental workflow.

From Microbiome to Traits: Designing Synthetic Microbial Communities for Improved Crop Resiliency

Plants teem with microorganisms, whose tremendous diversity and role in plant-microbe interactions are being increasingly explored. Microbial communities create a functional bond with their hosts and express beneficial traits capable of enhancing plant performance. Therefore, a significant task of microbiome research has been identifying novel beneficial microbial traits that can contribute to crop productivity, particularly under adverse environmental conditions. However, although knowledge has exponentially accumulated in recent years, few novel methods regarding the process of designing inoculants for agriculture have been presented. A recently introduced approach is the use of synthetic microbial communities (SynComs), which involves applying concepts from both microbial ecology and genetics to design inoculants. Here, we discuss how to translate this rationale for delivering stable and effective inoculants for agriculture by tailoring SynComs with microorganisms possessing traits for robust colonization, prevalence throughout plant development and specific beneficial functions for plants. Computational methods, including machine learning and artificial intelligence, will leverage the approaches of screening and identifying beneficial microbes while improving the process of determining the best combination of microbes for a desired plant phenotype. We focus on recent advances that deepen our knowledge of plant-microbe interactions and critically discuss the prospect of using microbes to create SynComs capable of enhancing crop resiliency against stressful conditions.

Microbial Ecology: Functional 'Modules' Drive Assembly of Polysaccharide-Degrading Marine Microbial Communities

Although ecological principles governing the competition of microbes for simple substrates are well-understood, less is known about how complex, structured substrates influence ecological outcomes in microbial communities. A new study sheds light on how marine microbial communities assemble on polysaccharide particles modeling marine snow.

Benzo(a)pyrene degradation by cytochrome P450 hydroxylase and the functional metabolism network of Bacillus thuringiensis

The relationship between benzo(a)pyrene biodegradation and certain target biomolecules has been investigated. To regulate the degradation process, the associated metabolism network must be clarified. To this end, benzo(a)pyrene degradation, carbon substrate metabolism and exometabolomic mechanism of Bacillus thuringiensis were analyzed. Benzo(a)pyrene was degraded through hydroxylation catalyzed by cytochrome P450 hydroxylase. After the treatment of 0.5 mg L^-1 of benzo(a)pyrene by 0.2 g L^-1 of cells for 9 d, biosorption and degradation efficiencies were measured at approximately 90% and 80%, respectively. During this process, phospholipid synthesis, glycogen, asparagine, arginine, itaconate and xylose metabolism were significantly downregulated, while glycolysis, pentose phosphate pathway, citrate cycle, amino sugar and nucleotide sugar metabolism were significantly upregulated. These findings offer insight into the biotransformation regulation of polycyclic aromatic hydrocarbons.

Metabolic niches in the rhizosphere microbiome: new tools and approaches to analyse metabolic mechanisms of plant-microbe nutrient exchange

Plants nourish rhizospheric microbes via provision of carbon substrates, and the composition of the microbiome is strongly influenced by metabolic phenomena such as niche differentiation, competitive exclusion, and cross-feeding. Despite intensive investigations of the taxonomic structure in root microbiomes, there is relatively little biochemical knowledge of the metabolic niches occupied by microbial strains in the rhizosphere. Here, we review new tools and approaches that are boosting our knowledge of the metabolic mechanisms that shape the composition of the root microbiome. New studies have elucidated biochemical pathways that mediate root colonisation and pathogen suppression, and synthetic communities are emerging as a powerful tool to understand microbe-microbe interactions. Knowledge of root exudate composition is being advanced by new metabolomics methodologies, which have highlighted that specific exudate components can inhibit pathogen growth, and that certain metabolites can recruit mutualistic strains according to substrate uptake preferences. Microbial genomics is rapidly advancing, with large collections of isolated rhizosphere strains and mutant libraries giving new insights into the metabolic mechanisms of root colonisation. Exometabolomics is emerging as a powerful methodology for directly observing microbial uptake of root metabolites, and also for profiling microbial cross-feeding. Integrative studies using these resources should enable rapid advances, particularly when applied to standardised experimental set-ups and model synthetic communities.

Production and Synthetic Modifications of Shikimic Acid

Shikimic acid is a natural product of industrial importance utilized as a precursor of the antiviral Tamiflu. It is nowadays produced in multihundred ton amounts from the extraction of star anise ( Illicium verum) or by fermentation processes. Apart from the production of Tamiflu, shikimic acid has gathered particular notoriety as its useful carbon backbone and inherent chirality provide extensive use as a versatile chiral precursor in organic synthesis. This review provides an overview of the main synthetic and microbial methods for production of shikimic acid and highlights selected methods for isolation from available plant sources. Furthermore, we have attempted to demonstrate the synthetic utility of shikimic acid by covering the most important synthetic modifications and related applications, namely, synthesis of Tamiflu and derivatives, synthetic manipulations of the main functional groups, and its use as biorenewable material and in total synthesis. Given its rich chemistry and availability, shikimic acid is undoubtedly a promising platform molecule for further exploration. Therefore, in the end, we outline some challenges and promising future directions.