GEM-based computational modeling for exploring metabolic interactions in a microbial community.

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Deciphering the core microbes and their interactions in spontaneous Baijiu fermentation: A comprehensive review.

The spontaneous Baijiu fermentation system harbors a complex microbiome that is highly dynamic in time and space and varies depending on the Jiuqu starters and environmental factors. The intricate microbiota presents in the fermentation environment is responsible for carrying out various reactions. These reactions necessitate the interaction among the core microbes to influence the community function, ultimately shaping the distinct Baijiu styles through the process of spontaneous fermentation. Numerous studies have been conducted to enhance our understanding of the diversity, succession, and function of microbial communities with the aim of improving fermentation manipulation. However, a comprehensive and critical assessment of the core microbes and their interaction remains one of the significant challenges in the Baijiu fermentation industry. This paper focuses on the fermentation properties of core microbes. We discuss the state of the art of microbial traceability, highlighting the crucial role of environmental and starter microbiota in the Baijiu brewing microbiome. Also, we discuss the various interactions between microbes in the Baijiu production system and propose a potential conceptual framework that involves constructing predictive network models to simplify and quantify microbial interactions using co-culture models. This approach offers effective strategies for understanding the core microbes and their interactions, thus beneficial for the management of microbiota and the regulation of interactions in Baijiu fermentation processes.

Interactions between pili affect the outcome of bacterial competition driven by the type VI secretion system.

The bacterial type VI secretion system (T6SS) is a widespread, kin-discriminatory weapon capable of shaping microbial communities. Due to the system's dependency on contact, cellular interactions can lead to either competition or kin protection. Cell-to-cell contact is often accomplished via surface-exposed type IV pili (T4Ps). In Vibrio cholerae, these T4Ps facilitate specific interactions when the bacteria colonize natural chitinous surfaces. However, it has remained unclear whether and, if so, how these interactions affect the bacterium's T6SS-mediated killing. In this study, we demonstrate that pilus-mediated interactions can be harnessed by T6SS-equipped V. cholerae to kill non-kin cells under liquid growth conditions. We also show that the naturally occurring diversity of pili determines the likelihood of cell-to-cell contact and, consequently, the extent of T6SS-mediated competition. To determine the factors that enable or hinder the T6SS's targeted reduction of competitors carrying pili, we developed a physics-grounded computational model for autoaggregation. Collectively, our research demonstrates that T4Ps involved in cell-to-cell contact can impose a selective burden when V. cholerae encounters non-kin cells that possess an active T6SS. Additionally, our study underscores the significance of T4P diversity in protecting closely related individuals from T6SS attacks through autoaggregation and spatial segregation.

Arbuscular mycorrhizal fungi and Streptomyces: brothers in arms to shape the structure and function of the hyphosphere microbiome in the early stage of interaction.

BACKGROUND: Fungi and bacteria coexist in a wide variety of environments, and their interactions are now recognized as the norm in most agroecosystems. These microbial communities harbor keystone taxa, which facilitate connectivity between fungal and bacterial communities, influencing their composition and functions. The roots of most plants are associated with arbuscular mycorrhizal (AM) fungi, which develop dense networks of hyphae in the soil. The surface of these hyphae (called the hyphosphere) is the region where multiple interactions with microbial communities can occur, e.g., exchanging or responding to each other's metabolites. However, the presence and importance of keystone taxa in the AM fungal hyphosphere remain largely unknown. RESULTS: Here, we used in vitro and pot cultivation systems of AM fungi to investigate whether certain keystone bacteria were able to shape the microbial communities growing in the hyphosphere and potentially improved the fitness of the AM fungal host. Based on various AM fungi, soil leachates, and synthetic microbial communities, we found that under organic phosphorus (P) conditions, AM fungi could selectively recruit bacteria that enhanced their P nutrition and competed with less P-mobilizing bacteria. Specifically, we observed a privileged interaction between the isolate Streptomyces sp. D1 and AM fungi of the genus Rhizophagus, where (1) the carbon compounds exuded by the fungus were acquired by the bacterium which could mineralize organic P and (2) the in vitro culturable bacterial community residing on the surface of hyphae was in part regulated by Streptomyces sp. D1, primarily by inhibiting the bacteria with weak P-mineralizing ability, thereby enhancing AM fungi to acquire P. CONCLUSIONS: This work highlights the multi-functionality of the keystone bacteria Streptomyces sp. D1 in fungal-bacteria and bacterial-bacterial interactions at the hyphal surface of AM fungi. Video Abstract.

Enhanced surface colonisation and competition during bacterial adaptation to a fungus.

Bacterial-fungal interactions influence microbial community performance of most ecosystems and elicit specific microbial behaviours, including stimulating specialised metabolite production. Here, we use a co-culture experimental evolution approach to investigate bacterial adaptation to the presence of a fungus, using a simple model of bacterial-fungal interactions encompassing the bacterium Bacillus subtilis and the fungus Aspergillus niger. We find in one evolving population that B. subtilis was selected for enhanced production of the lipopeptide surfactin and accelerated surface spreading ability, leading to inhibition of fungal expansion and acidification of the environment. These phenotypes were explained by specific mutations in the DegS-DegU two-component system. In the presence of surfactin, fungal hyphae exhibited bulging cells with delocalised secretory vesicles possibly provoking an RlmA-dependent cell wall stress. Thus, our results indicate that the presence of the fungus selects for increased surfactin production, which inhibits fungal growth and facilitates the competitive success of the bacterium.

Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.

Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Swimming towards each other: the role of chemotaxis in bacterial interactions.

Chemotaxis allows microorganisms to direct movement in response to chemical stimuli. Bacteria use this behaviour to develop spatial associations with animals and plants, and even larger microbes. However, current theory suggests that constraints imposed by the limits of chemotactic sensory systems will prevent sensing of chemical gradients emanating from cells smaller than a few micrometres, precluding the utility of chemotaxis in interactions between individual bacteria. Yet, recent evidence has revealed surprising levels of bacterial chemotactic precision, as well as a role for chemotaxis in metabolite exchange between bacterial cells. If indeed widespread, chemotactic sensing between bacteria could represent an important, but largely overlooked, phenotype within interbacterial interactions, and play a significant role in shaping cooperative and competitive relationships.

Minorities drive growth resumption in cross-feeding microbial communities.

Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent. Here, we hypothesize that such dependencies can be detrimental in situations where the external conditions change rapidly, as they often do in natural environments. After a shift in external conditions, microbes need to remodel their metabolism, but they can only resume growth once partners on which they depend have also adapted to the new conditions. It is currently not well understood how microbial communities resolve this dilemma and how metabolic interactions are reestablished after an environmental shift. To address this question, we investigated the dynamical responses to environmental perturbation by microbial consortia with distributed anabolic functions. By measuring the regrowth times at the single-cell level in spatially structured communities, we found that metabolic dependencies lead to a growth delay after an environmental shift. However, a minority of cells-those in the immediate neighborhood of their metabolic partners-can regrow quickly and come to numerically dominate the community after the shift. The spatial arrangement of a microbial community is thus a key factor in determining the communities' ability to maintain metabolic interactions and growth in fluctuating conditions. Our results suggest that environmental fluctuations can limit the emergence of metabolic dependencies between microorganisms.

Spatial-temporal dynamics of a microbial cooperative behavior resistant to cheating.

Much of our understanding of bacterial behavior stems from studies in liquid culture. In nature, however, bacteria frequently live in densely packed spatially-structured communities. How does spatial structure affect bacterial cooperative behaviors? In this work, we examine rhamnolipid production-a cooperative and virulent behavior of Pseudomonas aeruginosa. Here we show that, in striking contrast to well-mixed liquid culture, rhamnolipid gene expression in spatially-structured colonies is strongly associated with colony specific growth rate, and is impacted by perturbation with diffusible quorum signals. To interpret these findings, we construct a data-driven statistical inference model which captures a length-scale of bacterial interaction that develops over time. Finally, we find that perturbation of P. aeruginosa swarms with quorum signals preserves the cooperating genotype in competition, rather than creating opportunities for cheaters. Overall, our data demonstrate that the complex response to spatial localization is key to preserving bacterial cooperative behaviors.

A novel Trichoderma asperellum strain DQ-1 promotes tomato growth and induces resistance to gray mold caused by Botrytis cinerea.

Gray mold caused by Botrytis cinerea is a major cause of economic losses during tomato production. In this study, we obtained 23 Trichoderma strains from tomato rhizosphere soil and their inhibitory effects on B. cinerea and the promoting effects on tomato growth were determined. Among them, the inhibition rate of strain DQ-1 on B. cinerea was 88.56%; compared with the control group, after treatment with strain DQ-1, the seeds germination rate and root length of tomato increased by 5.55 and 37.86%. The induced disease resistance of strain DQ-1 was evaluated by pot experiments. The disease incidence (DI) and disease severity index (DSI) of tomato pre-inoculated with strain DQ-1 and then inoculated with B. cinerea were reduced by 38 and 64% compared with the control. Furthermore, we detected the expression levels of tomato disease resistance related genes PR2 and TPX, ethylene pathway related genes ETR1 and CTR1 and jasmonic acid pathway related genes LOX1 and PAL in challenging and non-challenging inoculation treatments. The results showed that the tomato treated with strain DQ-1 triggered the system acquired resistance (SAR) and induced systemic resistance (ISR) pathway, thereby enhancing the disease resistance of tomato. Then the strain DQ-1 was identified as Trichoderma asperellum based on morphological characteristics and phylogenetic information. This study suggests that the novel T. asperellum strain DQ-1 can be a potential candidate for the biological control of gray mold in tomato.

The role of competition versus cooperation in microbial community coalescence.

New microbial communities often arise through the mixing of two or more separately assembled parent communities, a phenomenon that has been termed "community coalescence". Understanding how the interaction structures of complex parent communities determine the outcomes of coalescence events is an important challenge. While recent work has begun to elucidate the role of competition in coalescence, that of cooperation, a key interaction type commonly seen in microbial communities, is still largely unknown. Here, using a general consumer-resource model, we study the combined effects of competitive and cooperative interactions on the outcomes of coalescence events. To do so, we simulate coalescence events between pairs of communities with different degrees of competition for shared carbon resources and cooperation through cross-feeding on leaked metabolic by-products (facilitation). We also study how structural and functional properties of post-coalescence communities evolve when they are subjected to repeated coalescence events. We find that in coalescence events, the less competitive and more cooperative parent communities contribute a higher proportion of species to the new community because of their superior ability to deplete resources and resist invasions. Consequently, when a community is subjected to repeated coalescence events, it gradually evolves towards being less competitive and more cooperative, as well as more speciose, robust and efficient in resource use. Encounters between microbial communities are becoming increasingly frequent as a result of anthropogenic environmental change, and there is great interest in how the coalescence of microbial communities affects environmental and human health. Our study provides new insights into the mechanisms behind microbial community coalescence, and a framework to predict outcomes based on the interaction structures of parent communities.

Oral Microbiome Alterations and SARS-CoV-2 Saliva Viral Load in Patients with COVID-19.

Bacterial-viral interactions in saliva have been associated with morbidity and mortality for respiratory viruses such as influenza and SARS-CoV. However, such transkingdom relationships during SARS-CoV-2 infection are currently unknown. Here, we aimed to elucidate the relationship between saliva microbiota and SARS-CoV-2 in a cohort of newly hospitalized COVID-19 patients and controls. We used 16S rRNA sequencing to compare microbiome diversity and taxonomic composition between COVID-19 patients (n = 53) and controls (n = 59) and based on saliva SARS-CoV-2 viral load as measured using reverse transcription PCR (RT-PCR). The saliva microbiome did not differ markedly between COVID-19 patients and controls. However, we identified significant differential abundance of numerous taxa based on saliva SARS-CoV-2 viral load, including multiple species within Streptococcus and Prevotella. IMPORTANCE Alterations to the saliva microbiome based on SARS-CoV-2 viral load indicate potential biologically relevant bacterial-viral relationships which may affect clinical outcomes in COVID-19 disease.

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia.

The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain's division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains-but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

Bacterial Community Analysis and Potential Functions of Core Taxa in Different Parts of the Fungus Cantharellus cibarius.

Cantharellus cibarius is a widely distributed, popular, edible fungus with high nutritional and economic value. However, significant challenges persist in the microbial ecology and artificial cultivation of C. cibarius. Based on the 16S rRNA sequencing data, this study analyzed bacterial community structures and diversity of fruit bodies and rhizomorph parts of C. cibarius and mycosphere samples (collected in the Wudang District, Guiyang, Guizhou Province, China). It explored the composition and function of the core bacterial taxa. The analyzed results showed that the rhizomorph bacterial community structure was similar to mycosphere, but differed from the fruit bodies. Members of the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium complex had the highest abundance in the fruit bodies. However, they were either absent or low in abundance in the rhizomorphs and mycosphere. At the same time, members of the Burkholderia-Caballeronia-Paraburkholderia complex were abundant in the fruit bodies and rhizomorphs parts of C. cibarius, as well as mycosphere. Through functional annotation of core bacterial taxa, we found that there was an apparent trend of potential functional differentiation of related bacterial communities in the fruit body and rhizomorph: potential functional groups of core bacterial taxa in the fruit bodies centered on nitrogen fixation, nitrogen metabolism, and degradation of aromatic compounds, while those in rhizomorphs focused on aerobic chemoheterotrophy, chemoheterotrophy, defense against soil pathogens, decomposition of complex organic compounds, and uptake of insoluble inorganic compounds. The analysis of functional groups of bacteria with different structures is of great significance to understand that bacteria promote the growth and development of C. cibarius.

Latent Pathogenic Fungi in the Medicinal Plant Houttuynia cordata Thunb. Are Modulated by Secondary Metabolites and Colonizing Microbiota Originating from Soil.

Latent pathogenic fungi (LPFs) affect plant growth, but some of them may stably colonize plants. LPFs were isolated from healthy Houttuynia cordata rhizomes to reveal this mechanism and identified as Ilyonectria liriodendri, an unidentified fungal sp., and Penicillium citrinum. Sterile H. cordata seedlings were cultivated in sterile or non-sterile soils and inoculated with the LPFs, followed by the plants' analysis. The in vitro antifungal activity of H. cordata rhizome crude extracts on LPF were determined. The effect of inoculation of sterile seedlings by LPFs on the concentrations of rhizome phenolics was evaluated. The rates of in vitro growth inhibition amongst LPFs were determined. The LPFs had a strong negative effect on H. cordata in sterile soil; microbiota in non-sterile soil eliminated such influence. There was an interactive inhibition among LPFs; the secondary metabolites also regulated their colonization in H. cordata rhizomes. LPFs changed the accumulation of phenolics in H. cordata. The results provide that colonization of LPFs in rhizomes was regulated by the colonizing microbiota of H. cordata, the secondary metabolites in the H. cordata rhizomes, and the mutual inhibition and competition between the different latent pathogens.

Microbial Interactions Drive Distinct Taxonomic and Potential Metabolic Responses to Habitats in Karst Cave Ecosystem.

The geological role of microorganisms has been widely studied in the karst cave ecosystem. However, microbial interactions and ecological functions in such a dark, humid, and oligotrophic habitat have received far less attention, which is crucial to understanding cave biogeochemistry. Herein, microorganisms from weathered rock and sediment along the Heshang Cave depth were analyzed by random matrix theory-based network and Tax4Fun functional prediction. The results showed that although the cave microbial communities have spatial heterogeneity, differential habitats drove the community structure and diversity. Actinobacteria were predominant in weathered rock, whereas Proteobacteria dominated the sediment. The sediment communities presented significantly higher alpha diversities due to the relatively abundant nutrition from the outside by the intermittent stream. Consistently, microbial interactions in sediment were more complex, as visualized by more nodes and links. The abundant taxa presented more positive correlations with other community members in both of the two networks, indicating that they relied on promotion effects to adapt to the extreme environment. The keystones in weathered rock were mainly involved in the biodegradation of organic compounds, whereas the keystone Nitrospira in sediment contributed to carbon/nitrogen fixation. Collectively, these findings suggest that microbial interactions may lead to distinct taxonomic and functional communities in weathered rock and sediment in the subsurface Heshang Cave. IMPORTANCE In general, the constant physicochemical conditions and limited nutrient sources over long periods in the subsurface support a stable ecosystem in karst cave. Previous studies on cave microbial ecology were mostly focused on community composition, diversity, and the relationship with local environmental factors. There are still many unknowns about the microbial interactions and functions in such a dark environment with little human interference. Two representative habitats, including weathered rock and sediment in Heshang Cave, were selected to give an integrated insight into microbial interactions and potential functions. The cooccurrence network, especially the subnetwork, was used to characterize the cave microbial interactions in detail. We demonstrated that abundant taxa primarily relied on promotion effects rather than inhibition effects to survive in Heshang Cave. Keystone species may play important metabolic roles in sustaining ecological functions. Our study provides improved understanding of microbial interaction patterns and community ecological functions in the karst cave ecosystem.

Network mapping of root-microbe interactions in Arabidopsis thaliana.

Understanding how plants interact with their colonizing microbiota to determine plant phenotypes is a fundamental question in modern plant science. Existing approaches for genome-wide association studies (GWAS) are often focused on the association analysis between host genes and the abundance of individual microbes, failing to characterize the genetic bases of microbial interactions that are thought to be important for microbiota structure, organization, and function. Here, we implement a behavioral model to quantify various patterns of microbe-microbe interactions, i.e., mutualism, antagonism, aggression, and altruism, and map host genes that modulate microbial networks constituted by these interaction types. We reanalyze a root-microbiome data involving 179 accessions of Arabidopsis thaliana and find that the four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity. We identify several fungus and bacterial hubs that play a central role in mediating microbial community assembly surrounding A. thaliana root systems. We detect 1142 significant host genetic variants throughout the plant genome and then implement Bayesian networks (BN) to reconstruct epistatic networks involving all significant SNPs, of which 91 are identified as hub QTLs. Results from gene annotation analysis suggest that most of the hub QTLs detected are in proximity to candidate genes, executing a variety of biological functions in plant growth and development, resilience against pathogens, root development, and abiotic stress resistance. This study provides a new gateway to understand how genetic variation in host plants influences microbial communities and our results could help improve crops by harnessing soil microbes.

Fungal social influencers: secondary metabolites as a platform for shaping the plant-associated community.

Fungal secondary metabolites (FSMs) are capable of manipulating plant community dynamics by inhibiting or facilitating the establishment of co-habitating organisms. Although production of FSMs is not crucial for survival of the producer, their absence can indirectly impair growth and/or niche competition of these fungi on the plant. The presence of FSMs with no obvious consequence on the fitness of the producer leaves questions regarding ecological impact. This review investigates how fungi employ FSMs as a platform to mediate fungal-fungal, fungal-bacterial and fungal-animal interactions associated with the plant community. We discuss how the biological function of FSMs may indirectly benefit the producer by altering the dynamics of surrounding organisms. We introduce several instances where FSMs influence antagonistic- or alliance-driven interactions. Part of our aim is to decipher the meaning of the FSM 'language' as it is widely noted to impact the surrounding community. Here, we highlight the contribution of FSMs to plant-associated interaction networks that affect the host either broadly or in ways that may have previously been unclear.

Molecular trafficking between bacteria determines the shape of gut microbial community.

Complex inter-bacterial interactions largely influence the structure and function of the gut microbial community. Though several host-associated phenomena have often been shown to be involved in the stability, structure, and function of the gut microbial community, the implication of contact-dependent and contact-independent inter-bacterial interactions has been overlooked. Such interactions are tightly governed at multiple layers through several extracellular organelles, including contact-dependent inhibition (CDI), nanotubes, type VI secretion system (T6SS), and membrane vesicles (MVs). Recent advancements in molecular techniques have revealed that such extracellular organelles function beyond exhibiting competitive behavior and are also involved in manifesting cooperative behaviors. Cooperation between bacteria occurs through the sharing of several beneficial molecules including nucleic acids, proteins, metabolites, and nutrients among the members of the community, while competition occurs by means of multiple toxins. Intrinsic coordination between contact-dependent and contact-independent mechanisms collectively provides a fitness advantage and increased colonization resistance to the gut microbiota, where molecular trafficking plays a key role. This review is intended to provide a comprehensive view of the salient features of the different bacterial interactions and to highlight how microbiota deploy multifaceted organelles, for exerting both cooperative and competitive behaviors. We discuss the current knowledge of bacterial molecular trafficking and its impact on shaping the gut microbial community.