Interspecies interactions are an integral determinant of microbial community dynamics

Bioactive exometabolites drive maintenance competition in simple bacterial communities

During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532, and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure.

IMPORTANCE

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.

Modeling interactions of Clostridium cadaveris and Clostridium sporogenes in anaerobic acidogenesis of glucose and peptone

This study focuses on developing a mathematical model to assess interaction among acidogenic bacteria during the anaerobic degradation of two substrates. Clostridium cadaveris and Clostridium sporogenes were cultured in various combinations with glucose and peptone. Parameter estimates are given for both conventional Monod parameters from single substrate-single species cultures and sum kinetics with interaction parameters obtained from dual substrate-single species cultures. The presence of multiple substrates led to both inhibitory and enhancing effects on biodegradation rates for dual substrates compared to single substrate cultures. A new model of interspecies interaction was developed within the framework of Lotka-Volterra incorporating substrate interaction parameters, with a focus on accuracy, realism, simplicity, and biological significance. The model demonstrated competitive interaction for resource sharing and the additional non-linearity parameter eliminated the constraint of the linear relationship between growth rate and population density.

Stable States of a Microbial Community Are Formed by Dynamic Metabolic Networks with Members Functioning to Achieve Both Robustness and Plasticity

A more detailed understanding of the mechanisms underlying the formation of microbial communities is essential for the efficient management of microbial ecosystems. The stable states of microbial communities are commonly perceived as static and, thus, have not been extensively examined. The present study investigated stabilizing mechanisms, minority functions, and the reliability of quantitative ana-lyses, emphasizing a metabolic network perspective. A bacterial community, formed by batch transferred cultures supplied with phenol as the sole carbon and energy source and paddy soil as the inoculum, was analyzed using a principal coordinate ana-lysis (PCoA), mathematical models, and quantitative parameters defined as growth activity, community-changing activity, community-forming activity, vulnerable force, and resilience force depending on changes in the abundance of operational taxonomic units (OTUs) using 16S rRNA gene amplicon sequences. PCoA showed succession states until the 3rd transferred cultures and stable states from the 5th to 10th transferred cultures. Quantitative parameters indicated that the bacterial community was dynamic irrespective of the succession and stable states. Three activities fluctuated under stable states. Vulnerable and resilience forces were detected under the succession and stable states, respectively. Mathematical models indicated the construction of metabolic networks, suggesting the stabilizing mechanism of the community structure. Thirteen OTUs coexisted during stable states, and were recognized as core OTUs consisting of majorities, middle-class, and minorities. The abundance of the middle-class changed, whereas that of the others did not, which indicated that core OTUs maintained metabolic networks. Some extremely low abundance OTUs were consistently exchanged, suggesting a role for scavengers. These results indicate that stable states were formed by dynamic metabolic networks with members functioning to achieve robustness and plasticity.

Microbiome assembly in thawing permafrost and its feedbacks to climate

The physical and chemical changes that accompany permafrost thaw directly influence the microbial communities that mediate the decomposition of formerly frozen organic matter, leading to uncertainty in permafrost-climate feedbacks. Although changes to microbial metabolism and community structure are documented following thaw, the generality of post-thaw assembly patterns across permafrost soils of the world remains uncertain, limiting our ability to predict biogeochemistry and microbial community responses to climate change. Based on our review of the Arctic microbiome, permafrost microbiology, and community ecology, we propose that Assembly Theory provides a framework to better understand thaw-mediated microbiome changes and the implications for community function and climate feedbacks. This framework posits that the prevalence of deterministic or stochastic processes indicates whether the community is well-suited to thrive in changing environmental conditions. We predict that on a short timescale and following high-disturbance thaw (e.g., thermokarst), stochasticity dominates post-thaw microbiome assembly, suggesting that functional predictions will be aided by detailed information about the microbiome. At a longer timescale and lower-intensity disturbance (e.g., active layer deepening), deterministic processes likely dominate, making environmental parameters sufficient for predicting function. We propose that the contribution of stochastic and deterministic processes to post-thaw microbiome assembly depends on the characteristics of the thaw disturbance, as well as characteristics of the microbial community, such as the ecological and phylogenetic breadth of functional guilds, their functional redundancy, and biotic interactions. These propagate across space and time, potentially providing a means for predicting the microbial forcing of greenhouse gas feedbacks to global climate change.

Imbalance in Carbon and Nitrogen Metabolism in Comamonas testosteroni R2 Is Caused by Negative Feedback and Rescued by L-arginine

The collapse of Comamonas testosteroni R2 under chemostat conditions and the aerobic growth of strain R2 under batch conditions with phenol as the sole carbon source were investigated using physiological and transcriptomic techniques. Phenol-/catechol-degrading activities under chemostat conditions gradually decreased, suggesting that metabolites produced from strain R2 accumulated in the culture, which caused negative feedback. The competitive inhibition of phenol hydroxylase and catechol dioxygenase was observed in a crude extract of the supernatant collected from the collapsed culture. Transcriptomic analyses showed that genes related to nitrogen transport were up-regulated; the ammonium transporter amtB was up-regulated approximately 190-fold in the collapsed status, suggesting an increase in the concentration of ammonium in cells. The transcriptional levels of most of the genes related to gluconeogenesis, glycolysis, the pentose phosphate pathway, and the TCA and urea cycles decreased by ~0.7-fold in the stable status, whereas the activities of glutamate synthase and glutamine synthetase increased by ~2-fold. These results suggest that ammonium was assimilated into glutamate and glutamine via 2-oxoglutarate under the limited supply of carbon skeletons, whereas the synthesis of other amino acids and nucleotides was repressed by 0.6-fold. Furthermore, negative feedback appeared to cause an imbalance between carbon and nitrogen metabolism, resulting in collapse. The effects of amino acids on negative feedback were investigated. L-arginine allowed strain R2 to grow normally, even under growth-inhibiting conditions, suggesting that the imbalance was corrected by the stimulation of the urea cycle, resulting in the rescue of strain R2.

The role of artificial intelligence in the battle against antimicrobial-resistant bacteria

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.

Coexisting mechanisms of bacterial community are changeable even under similar stable conditions in a chemostat culture

The coexisting mechanism of a synthetic bacterial community (SBC) was investigated to better understand how to manage microbial communities. The SBC was constructed with three kinds of phenol-utilizing bacteria, Pseudomonas sp. LAB-08, Comamonas testosteroni R2, and Cupriavidus sp. P-10, under chemostat conditions supplied with phenol as a sole carbon and energy source. Population densities of all strains were monitored by real-time quantitative PCR (qPCR) targeting the gene encoding the large subunit of phenol hydroxylase. Although the supply of phenol was stopped to allow perturbation in the SBC, all of the strains coexisted and the degradation of phenol was maintained for more than 800 days. The qPCR analyses showed that strains LAB-08 and R2 became dominant simultaneously, whereas strain P-10 was a minor population. This phenomenon was observed before and after the phenol-supply stoppage. The kinetic parameters for phenol of the SBC changed before and after the phenol-supply stoppage, which suggests a change in functional roles of strains in the SBC. Transcriptional levels of phenol hydroxylase and catechol dioxygenases of three strains were monitored by reverse-transcription qPCR (RT-qPCR). The RT-qPCR analyses revealed that all strains shared phenol and survived independently before the phenol-supply stoppage. After the stoppage, strain P-10 would incur the cost for degradation of phenol and catechol, whereas strains LAB-08 and R2 seemed to be cheaters using metabolites, indicating the development of the metabolic network. These results indicated that it is important for the management and redesign of microbial communities to understand the metabolism of bacterial communities.

Bacterial dominance is due to effective utilisation of secondary metabolites produced by competitors

Interactions between bacteria govern the progression of respiratory infections; however, the mechanisms underpinning these interactions are still unclear. Understanding how a bacterial species comes to dominate infectious communities associated with respiratory infections has direct relevance to treatment. In this study, Burkholderia, Pseudomonas, and Staphylococcus species were isolated from the sputum of an individual with Cystic Fibrosis and assembled in a fully factorial design to create simple microcosms. Measurements of growth and habitat modification were recorded over time, the later using proton Nuclear Magnetic Resonance spectra. The results showed interactions between the bacteria became increasingly neutral over time. Concurrently, the bacteria significantly altered their ability to modify the environment, with Pseudomonas able to utilise secondary metabolites produced by the other two isolates, whereas the reverse was not observed. This study indicates the importance of including data about the habitat modification of a community, to better elucidate the mechanisms of bacterial interactions.

Development of an interspecies interaction model: An experiment on Clostridium cadaveris and Clostridium sporogenes under anaerobic condition

The specific primer and probe sets for quantifying Clostridium cadaveris and Clostridium sporogenes using a quantitative real-time PCR were designed. Each primer and probe set detected only the target species very specifically. The two species were cultivated in pure and mixed culture in batch mode with glucose as the only carbon source. The designed QPCR sets were used successfully to estimate the biokinetic parameters of each species in pure culture: i.e., maximum specific growth rate μ_max, half saturation concentration K_s, growth yield Y, and decay coefficient K_d. of C. cadaveris and C. sporogenes were 0.311 ± 0.020 and 0.360 ± 0.019 h^-1, 4.241 ± 1.653 and 5.171 ± 1.097 g/L, 0.301 ± 0.065 and 0.199 ± 0.037 10¹¹ copies/g, 0.005 ± 0.043 and 0.009 ± 0.025 h^-1, respectively. The effect of interspecific interaction of on substrate consumption rate and microbial growth was evaluated using mixed culture; curve fitting and comparison of coefficients detected increase in substrate consumption rate but decrease in microbial growth rate; these results imply interspecific interaction effect. A new model was of the interspecific interaction was developed, with focus on accuracy, realism, simplicity and biological significance. This interspecific interaction model may be extended to more-complex bioprocesses such as biological wastewater treatment systems and anaerobic digestion.

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Draft Genome Sequence of Comamonas testosteroni R2, Consisting of Aromatic Compound Degradation Genes for Phenol Hydroxylase

Comamonas testosteroni strain R2 was isolated from a continuous culture enriched by a low concentration of phenol-oxygenating activities with low K_s values (below 1 μM). The draft genome sequence of C. testosteroni strain R2 reported here may contribute to determining the phenol degradation gene cluster.