Phenotypic responses to interspecies competition and commensalism in a naturally-derived microbial co-culture

Vertical variations in microbial diversity, composition, and interactions in freshwater lake sediments on the Tibetan plateau

Microbial communities in freshwater lake sediments exhibit a distinct depth-dependent variability. Further exploration is required to understand their biodiversity pattern and microbial interactions in vertical sediments. In this study, sediment cores from two freshwater lakes, Mugecuo (MGC) and Cuopu (CP), on the Tibetan plateau were sampled and subsequently sliced into layers at a depth of every centimeter or half a centimeter. Amplicon sequencing was used to analyze the composition, diversity, and interaction of microbial communities. Results showed that sediment samples of both lakes could be clustered into two groups at a sediment depth of about 20 cm, with obvious shifts in microbial community compositions. In lake MGC, the richness component dominated β-diversity and increased with depth, indicating that the microbial communities in the deep layer of MGC was selected from the surface layer. Conversely, the replacement component dominated β-diversity in CP, implying a high turnover rate in the surface layer and inactive seed banks with a high variety in the deep layer. A co-occurrence network analysis showed that negative microbial interactions were prevalent in the surface layers with high nutrient concentrations, while positive microbial interactions were more common in the deep layers with low nutrient concentrations, suggesting that microbial interactions are influenced by nutrient conditions in the vertical sediments. Additionally, the results highlight the significant contributions of abundant and rare taxa to microbial interactions and vertical fluctuations of β-diversity, respectively. Overall, this work deepens our understanding of patterns of microbial interactions and vertical fluctuation in β-diversity in lake sediment columns, particularly in freshwater lake sediments from the Tibetan plateau.

Facing Resistant Bacteria with Plant Essential Oils: Reviewing the Oregano Case

Antibiotic resistance is a serious global threat, and the misuse of antibiotics is considered its main cause. It is characterized by the expression of bacterial defense mechanisms, e.g., β-lactamases, expulsion pumps, and biofilm development. Acinetobacter baumannii and Pseudomonas aeruginosa are antibiotic-resistant species that cause high morbidity and mortality. Several alternatives are proposed to defeat antibiotic resistance, including antimicrobial peptides, bacteriophages, and plant compounds. Terpenes from different plant essential oils have proven antimicrobial action against pathogenic bacteria, and evidence is being generated about their effect against antibiotic-resistant species. That is the case for oregano essential oil (Lippia graveolens), whose antibacterial effect is widely attributed to carvacrol, its main component; however, minor constituents could have an important contribution. The analyzed evidence reveals that most antibacterial evaluations have been performed on single species; however, it is necessary to analyze their activity against multispecies systems. Hence, another alternative is using plant compounds to inactivate hydrolytic enzymes and biofilms to potentiate antibiotics' effects. Despite the promising results of plant terpenes, more extensive and deep mechanistic studies are needed involving antibiotic-resistant multispecies to understand their full potential against this problem.

Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions

Fitness benefits from division of labor are well documented in microbial consortia, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a “push” interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a “pull” interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild-type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of by-product accumulation all relative to the WT. However, the LAE consortium had the trade-off of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered performance metric. Performance advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the performance advantages compared to WT.

IMPORTANCE Most naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence performance properties of consortia. Environmental context ultimately controlled the division of labor performance as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning, which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.

Snowstorm Enhanced the Deterministic Processes of the Microbial Community in Cryoconite at Laohugou Glacier, Tibetan Plateau

Cryoconites harbor diverse microbial communities and are the metabolic hotspot in the glacial ecosystem. Glacial ecosystems are subjected to frequent climate disturbances such as precipitation (snowing), but little is known about whether microbial communities in cryoconite can maintain stability under such disturbance. Here, we investigated the bacterial community in supraglacial cryoconite before and after a snowfall event on the Laohugou Glacier (Tibetan Plateau), based on Illumina MiSeq sequencing of the 16S rRNA gene. Our results showed that the diversity of the microbial community significantly decreased, and the structure of the microbial community changed significantly after the disturbance of snowfall. This was partly due to the relative abundance increased of cold-tolerant bacterial taxa, which turned from rare into abundant sub-communities. After snowfall disturbance, the contribution of the deterministic process increased from 38 to 67%, which is likely due to the enhancement of environmental filtering caused by nitrogen limitation. These findings enhanced our understanding of the distribution patterns and assembly mechanisms of cryoconite bacterial communities on mountain glaciers.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Cocultivation of Anaerobic Fungi with Rumen Bacteria Establishes an Antagonistic Relationship

Anaerobic gut fungi (Neocallimastigomycetes) live in the digestive tract of large herbivores, where they are vastly outnumbered by bacteria. It has been suggested that anaerobic fungi challenge growth of bacteria owing to the wealth of biosynthetic genes in fungal genomes, although this relationship has not been experimentally tested. Here, we cocultivated the rumen bacteria Fibrobacter succinogenes strain UWB7 with the anaerobic gut fungi Anaeromyces robustus or Caecomyces churrovis on a range of carbon substrates and quantified the bacterial and fungal transcriptomic response. Synthetic cocultures were established for at least 24 h, as verified by active fungal and bacterial transcription. A. robustus upregulated components of its secondary metabolism in the presence of Fibrobacter succinogenes strain UWB7, including six nonribosomal peptide synthetases, one polyketide synthase-like enzyme, and five polyketide synthesis O-type methyltransferases. Both A. robustus and C. churrovis cocultures upregulated S-adenosyl-l-methionine (SAM)-dependent methyltransferases, histone methyltransferases, and an acetyltransferase. Fungal histone 3 lysine 27 trimethylation marks were more abundant in coculture, and heterochromatin protein-1 was downregulated. Together, these findings suggest that fungal chromatin remodeling occurs when bacteria are present. F. succinogenes strain UWB7 upregulated four genes in coculture encoding drug efflux pumps, which likely protect the cell against toxins. Furthermore, untargeted nonpolar metabolomics data revealed at least one novel fungal metabolite enriched in coculture, which may be a defense compound. Taken together, these data suggest that A. robustus and C. churrovis produce antimicrobials when exposed to rumen bacteria and, more broadly, that anaerobic gut fungi are a source of novel antibiotics.

Metabolic modelling approaches for describing and engineering microbial communities

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.

Deep learning predicts microbial interactions from self-organized spatiotemporal patterns

Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Sustainable production of FAEE biodiesel using the oleaginous yeast Yarrowia lipolytica

Fatty acid ethyl esters (FAEEs) are fatty acid‐derived molecules and serve as an important form of biodiesel. The oleaginous yeast Yarrowia lipolytica is considered an ideal host platform for the production of fatty acid‐derived products due to its excellent lipid accumulation capacity. In this proof‐of‐principle study, several metabolic engineering strategies were applied for the overproduction of FAEE biodiesel in Y. lipolytica. Here, chromosome‐based co‐overexpression of two heterologous genes, namely, PDC1 (encoding pyruvate decarboxylase) and ADH1 (encoding alcohol dehydrogenase) from Saccharomyces cerevisiae, and the endogenous GAPDH (encoding glyceraldehyde‐3‐phosphate dehydrogenase) gene of Y. lipolytica resulted in successful biosynthesis of ethanol at 70.8 mg/L in Y. lipolytica. The engineered Y. lipolytica strain expressing the ethanol synthetic pathway together with a heterologous wax ester synthase (MhWS) exhibited the highest FAEE titer of 360.8 mg/L, which is 3.8‐fold higher than that of the control strain when 2% exogenous ethanol was added to the culture medium of Y. lipolytica. Furthermore, a synthetic microbial consortium comprising an engineered Y. lipolytica strain that heterologously expressed MhWS and a S. cerevisiae strain that could provide ethanol as a substrate for the production of the final product in the final engineered Y. lipolytica strain was created in this study. Finally, this synthetic consortium produced FAEE biodiesel at a titer of 4.8 mg/L under the optimum coculture conditions.

Do Organic Substrates Drive Microbial Community Interactions in Arctic Snow?

The effect of nutrients on microbial interactions, including competition and collaboration, has mainly been studied in laboratories, but their potential application to complex ecosystems is unknown. Here, we examined the effect of changes in organic acids among other parameters on snow microbial communities in situ over 2 months. We compared snow bacterial communities from a low organic acid content period to that from a higher organic acid period. We hypothesized that an increase in organic acids would shift the dominant microbial interaction from collaboration to competition. To evaluate microbial interactions, we built taxonomic co-variance networks from OTUs obtained from 16S rRNA gene sequencing. In addition, we tracked marker genes of microbial cooperation (plasmid backbone genes) and competition (antibiotic resistance genes) across both sampling periods in metagenomes and metatranscriptomes. Our results showed a decrease in the average connectivity of the network during late spring compared to the early spring that we interpreted as a decrease of cooperation. This observation was strengthened by the significantly more abundant plasmid backbone genes in the metagenomes from the early spring. The modularity of the network from the late spring was also found to be higher than the one from the early spring, which is another possible indicator of increased competition. Antibiotic resistance genes were significantly more abundant in the late spring metagenomes. In addition, antibiotic resistance genes were also positively correlated to the organic acid concentration of the snow across both seasons. Snow organic acid content might be responsible for this change in bacterial interactions in the Arctic snow community.

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities

An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables predictions for interspecies interactions affected by shifts in community composition and species populations. Our approach first identifies interspecies interactions in binary communities, which is subsequently used as a basis to infer modulation in more complex multi-species communities based on the assumption that microbes minimize adjustments of pairwise interactions in response to neighbor species. We termed this rule-based inference minimal interspecies interaction adjustment (MIIA). Our critical assessment of MIIA has produced reliable predictions of shifting interspecies interactions that are dependent on the functional role of neighbor organisms. We also show how MIIA has been applied to a microbial community composed of competing soil bacteria to elucidate a new finding that – in many cases – adding fewer competitors could impose more significant impact on binary interactions. The ability to predict membership-dependent community behavior is expected to help deepen our understanding of how microbiomes are organized in nature and how they may be designed and/or controlled in the future.

Reconciling Ecological and Engineering Design Principles for Building Microbiomes

Simplified microbial communities, or “benchtop microbiomes,” enable us to manage the profound complexity of microbial ecosystems. Widespread activities aiming to design and control communities result in novel resources for testing ecological theories and also for realizing new biotechnologies.

Simplified microbial communities, or “benchtop microbiomes,” enable us to manage the profound complexity of microbial ecosystems. Widespread activities aiming to design and control communities result in novel resources for testing ecological theories and also for realizing new biotechnologies. There is much to be gained by reconciling engineering design principles with ecological processes that shape microbiomes in nature. In this short Perspective, I will address how natural processes such as environmental filtering, the establishment of priority effects, and community “blending” (coalescence) can be harnessed for engineering microbiomes from complex starting materials. I will also discuss how future microbiome architects may draw inspiration from modern practices in synthetic biology. This topic is based on an important overarching research goal, which is to understand how natural forces shape microbial communities and interspecies interactions such that new engineering design principles can be extracted to promote human health or energy and environmental sustainability.