Conceptual strategies for characterizing interactions in microbial communities

Spatial scale modulates stochastic and deterministic influence on biogeography of photosynthetic biofilms in Southeast Asian hot springs

Hot springs, with their well-characterized major abiotic variables and island-like habitats, are ideal systems for studying microbial biogeography. Photosynthetic biofilms are a major biological feature of hot springs but despite this large-scale studies are scarce, leaving critical questions about the drivers of spatial turnover unanswered. Here, we analysed 395 photosynthetic biofilms from neutral-alkaline hot springs (39-66 °C, pH 6.4-9.0) across a 2100 km latitudinal gradient in Southeast Asia. The Cyanobacteria-dominated communities were categorized into six biogeographic regions, each characterized by a distinct core microbiome and biotic interactions. We observed a significant decline in the explanatory power of major abiotic variables with increasing spatial scale, from 62.6% locally, 55% regionally, to 26.8% for the inter-regional meta-community. Statistical null models revealed that deterministic environmental filtering predominated at local and regional scales, whereas stochastic ecological drift was more influential at the inter-regional scale. These findings enhance our understanding of the differential contribution of ecological drivers and highlight the importance of spatial scale in shaping biogeographic distributions for microorganisms.

Ironing out the conflicts: iron supplementation reduces negatives bacterial interactions in the rhizosphere of an Atacama-endemic perennial grass

BACKGROUND

In plants, root exudates selectively influence the growth of bacteria that colonize the rhizosphere. Bacterial communities associated with root systems are involved in macro and micronutrients cycling and organic matter transformation. In particular, iron is an essential micronutrient required for the proper functioning of iron-containing enzymes in processes such as photosynthesis, respiration, biomolecule synthesis, redox homeostasis, and cell growth in plants. However, the impact of changes of iron availability on the structure and set of ecological interactions taking place in the rhizosphere remains poorly understood. In this study, field experiments were conducted to compare the effects of iron supplementation (0.1 and 0.5 mM of FeSO4) on the assembly of the bacterial community of rhizosphere soil and bulk soil in a perennial grass present in the Andes steppe of Atacama Desert.

RESULTS

The results indicated that the difference in beta diversity between bulk soil and rhizosphere soil detected before supplementation did not persist after iron supplementation, in addition, co-occurrence networks showed a significant reduction in negative interactions among soil bacteria, mainly in rare taxa (< 0.1% relative abundance).

CONCLUSIONS

These observations suggest that iron availability contributes to the differentiation between bulk soil and rhizosphere bacterial communities, a process that is linked to significant changes in the relative abundance of more abundant species (> 0.1% relative abundance) and with a decrease in the negative interactions in both compartments after metal exposure. The differential effect of iron on the competition/cooperation ratio between bulk soils and the rhizosphere microbiome supports the hypothesis that the host limits the degree of cooperation that can be achieved by the bacterial community associated with an organ dedicated to nutrient absorption.

PyBootNet: a python package for bootstrapping and network construction

Background

Network analysis has emerged as a tool for investigating interactions among species in a community, interactions among genes or proteins within cells, or interactions across different types of data (e.g., genes and metabolites). Two aspects of networks that are difficult to assess are the statistical robustness of the network and whether networks from two different biological systems or experimental conditions differ.

Methods

PyBootNet is a user-friendly Python package that integrates bootstrapping analysis and correlation network construction. The package offers functions for generating bootstrapped network metrics, statistically comparing network metrics among datasets, and visualizing bootstrapped networks. PyBootNet is designed to be accessible and efficient with minimal dependencies and straightforward input requirements. To demonstrate its functionality, we applied PyBootNet to compare correlation networks derived from study using a mouse model to investigate the impacts of Polycystic Ovary Syndrome (PCOS) on the gut microbiome. PyBootNet includes functions for data preprocessing, bootstrapping, correlation matrix calculation, network statistics computation, and network visualization.

Results

We show that PyBootNet generates robust bootstrapped network metrics and identifies significant differences in one or more network metrics between pairs of networks. Our analysis of the previously published PCOS gut microbiome data also showed that our network analysis uncovered patterns and treatment effects missed in the original study. PyBootNet provides a powerful and extendible Python bioinformatics solution for bootstrapping analysis and network construction that can be applied to microbes, genes, metabolites and other biological data appropriate for network correlation comparison and analysis.

High-throughput ecological interaction mapping of dairy microorganisms

To engineer efficient microbial management strategies in the food industry, a comprehensive understanding of microbial interactions is crucial. Microorganisms live in communities where they influence each other in several ways. Although much attention has been paid to the production of antagonistic metabolites in lactic acid bacteria (LAB), research that accounts for the complexity of their ecological interactions and their dynamics remains limited. This study explores binary interactions within a mock community of 94 strains, including 23 LAB from culture collections and 71 isolated from dairy products. Using a colony-picking robot and image analysis, bidirectional interactions were measured at high throughput on solid media, where one test strain was challenged against other mock community members as the target strains. Assays of 15 test strains (14 LAB and one yeast) yielded 1,142 bidirectionally mapped interactions, classified by ecological type over seven days. The results showed variation in the strength, directionality, and type of interactions depending on the origin of the target strains. Cooperation rates increased for strains isolated from raw milk to pasteurized milk and cheese, while exploitation was more common in raw milk strains. Cooperating strains also exhibited more similar ecological behaviors than competing strains, suggesting the presence of microbial cliques. Interestingly, Lactococcus cremoris ATCC 19257 developed pink-red pigmentation when co-cultured with Pseudomonas aeruginosa. Overall, these findings present an unprecedented exploratory data set that significantly improves our understanding of microbial interactions at the system level, with potential applications in strain selection for food processes such as fermentation, bioprotection, and probiotics.

Metagenome Analysis Identified Novel Microbial Diversity of Sandy Soils Surrounded by Natural Lakes and Artificial Water Points in King Salman Bin Abdulaziz Royal Natural Reserve, Saudi Arabia

BACKGROUND

Soil microbes play a vital role in the ecosystem as they are able to carry out a number of vital tasks. Additionally, metagenomic studies offer valuable insights into the composition and functional potential of soil microbial communities. Furthermore, analyzing the obtained data can improve agricultural restoration practices and aid in developing more effective environmental management strategies.

METHODOLOGY

In November 2023, sandy soil samples were collected from ten sites of different geographical areas surrounding natural lakes and artificial water points in the Tubaiq conservation area of King Salman Bin Abdulaziz Royal Natural Reserve (KSRNR), Saudi Arabia. In addition, genomic DNA was extracted from the collected soil samples, and 16S rRNA sequencing was conducted using high-throughput Illumina technology. Several computational analysis tools were used for gene prediction and taxonomic classification of the microbial groups.

RESULTS

In this study, sandy soil samples from the surroundings of natural and artificial water resources of two distinct natures were used. Based on 16S rRNA sequencing, a total of 24,563 OTUs were detected. The metagenomic information was then categorized into 446 orders, 1036 families, 4102 genera, 213 classes, and 181 phyla. Moreover, the phylum Pseudomonadota was the most dominant microbial community across all samples, representing an average relative abundance of 34%. In addition, Actinomycetes was the most abundant class (26%). The analysis of clustered proteins assigned to COG categories provides a detailed understanding of the functional capabilities and adaptation of microbial communities in soil samples. Amino acid metabolism and transport were the most abundant categories in the soil environment.

CONCLUSIONS

Metagenome analysis of sandy soils surrounding natural lakes and artificial water points in the Tubaiq conservation area of KSRNR (Saudi Arabia) has unveils rich microbial activity, highlighting the complex interactions and ecological roles of microbial communities in these environments.

Improving genome-scale metabolic models of incomplete genomes with deep learning

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Hypernetwork modeling and topology of high-order interactions for complex systems

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

Disentangling the factors defining Bacillus subtilis group species abundance in natural soils

Bacillus subtilis is ubiquitously and broadly distributed in various environments but is mostly isolated from soil. Given that B. subtilis is known as a plant growth-promoting rhizobacterium in agriculture, we aimed to describe the natural distribution of this species and uncover how biotic and abiotic factors affect its distribution. When comparing different soils, we discovered that B. subtilis group species are most abundant in grasslands but can rarely be isolated from forest soil, even if the soil sample sites are situated in proximity. Differential analysis revealed that spore-forming bacteria exhibited enrichments in the grassland, suggesting niche overlap or synergistic interactions leading to the proliferation of certain Bacillus species in grassland environments. Network analysis further revealed that Bacillus and other Bacillota established a densely interconnected hub module in the grassland, characterised by positive associations indicating co-occurrence, a pattern not observed in the forest soil. Speculating that this difference was driven by abiotic factors, we combined amplicon sequencing with physico-chemical analysis of soil samples and found multiple chemical variables, mainly pH, to affect microbial composition. Our study pinpoints the factors that influence B. subtilis abundance in natural soils and, therefore, offers insights for designing B. subtilis-based biocontrol products in agricultural settings.

Quantifying microbial interactions: concepts, caveats, and applications

Microbial communities are fundamental to every ecosystem on Earth and hold great potential for biotechnological applications. However, their complex nature hampers our ability to study and understand them. A common strategy to tackle this complexity is to abstract the community into a network of interactions between its members - a phenomenological description that captures the overall effects of various chemical and physical mechanisms that underpin these relationships. This approach has proven useful for numerous applications in microbial ecology, including predicting community dynamics and stability and understanding community assembly and evolution. However, care is required in quantifying and interpreting interactions. Here, we clarify the concept of an interaction and discuss when interaction measurements are useful despite their context-dependent nature. Furthermore, we categorize different approaches for quantifying interactions, highlighting the research objectives each approach is best suited for.

Comparison and analysis of soil microbial communities in organic and conventional paddy fields by farming season

Interest in soil health and biodiversity conservation has become increasingly important. Consequently, studies comparing the chemical and biological characteristics of organic and traditional paddy soils have been increasing. Soil microorganisms are essential in nutrient cycling; however, their diversity is challenging to ascertain because of their environmental sensitivity and complex interactions. Particularly, in domestic rice cultivation, the soil undergoes multiple irrigation and drainage processes during crop growth, providing a diverse ecological environment for soil microorganisms. The objective of this study is to compare the microbial community and diversity between paddy soils in two agricultural systems. We selected organic and conventional paddy fields in Yangpyeong, Gyeonggi Province, and collected monthly samples from August to November 2022 for analysis. Bacteria and fungi were amplified from the 16S rRNA V3V4 region, ITS 3-4 region respectively, For the comparison of microbial diversity, Alpha diversity indices (Chao1, Shannon, Gini-Simpson indices) were analyzed. The results indicated genus-level differences in microbial communities, with the genera Mucor and Sirastachys exclusively present in organic paddy soils, while the genus Ustilaginoidea was exclusively found in conventional paddy soils. Among them, Ustilaginoidea is reported to be a fungus causing false smut disease, causing damage to crop growth and quality. Additionally, the comparison of microbial diversity between the two farming showed no significant differences (p>0.05). In conclusion, When the microbial communities present in both farming systems were examined, organic farming appeared to be more advantageous than conventional farming regarding crop disease and health. This study provides essential soil chemical and microbiological data for understanding the fundamental characteristics of paddy soils in South Korea.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Modeling endosymbioses: Insights and hypotheses from theoretical approaches

Endosymbiotic relationships are pervasive across diverse taxa of life, offering key avenues for eco-evolutionary dynamics. Although a variety of experimental and empirical frameworks have shed light on critical aspects of endosymbiosis, theoretical frameworks (mathematical models) are especially well-suited for certain tasks. Mathematical models can integrate multiple factors to determine the net outcome of endosymbiotic relationships, identify broad patterns that connect endosymbioses with other systems, simplify biological complexity, generate hypotheses for underlying mechanisms, evaluate different hypotheses, identify constraints that limit certain biological interactions, and open new lines of inquiry. This Essay highlights the utility of mathematical models in endosymbiosis research, particularly in generating relevant hypotheses. Despite their limitations, mathematical models can be used to address known unknowns and discover unknown unknowns.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Body Size Poorly Predicts Host-Associated Microbial Diversity in Wild Birds

The composition and diversity of avian microbiota are shaped by many factors, including host ecologies and environmental variables. In this study, we examine microbial diversity across 214 bird species sampled in Malawi at five major body sites: blood, buccal cavity, gizzard, intestinal tract, and cloaca. Microbial community dissimilarity differed significantly across body sites. Ecological theory predicts that as area increases, so does diversity. We tested the hypothesis that avian microbiota diversity is correlated with body size, used as a proxy for area, using comparative phylogenetic methods. Using Pagel's lambda, we found that few microbial diversity metrics had significant phylogenetic signals. Phylogenetic generalized least squares identified a significant but weak negative correlation between host size and microbial diversity of the blood and a similarly significant but weakly positive correlation between the cloacal microbiota and host size among birds within the order Passeriformes. Phylosymbiosis, or a congruent branching pattern between host phylogeny and their associated microbiota similarity, was tested and found to be weak or not significant in four of the body sites with sufficient sample size (blood, buccal, cloaca, and intestines). Taken together, these results suggest that the avian microbiome is highly variable, with microbiota diversity demonstrating few clear associations with bird size. Finally, the blood microbiota have a unique relationship with host size. IMPORTANCE All animals coexist and interact with microorganisms, including bacteria, archaea, microscopic eukaryotes, and viruses. These microorganisms can have an enormous influence on the biology and health of macro-organisms. However, the general rules that govern these host-associated microbial communities are poorly described, especially in wild animals. In this paper, we investigate the microbial communities of over 200 species of birds from Malawi and characterize five body site bacterial microbiota in depth. Because the evolutionary relationships of the host underlie the relationship between any host-associated microbiota relationships, we use phylogenetic comparative methods to account for this relationship. We find that the size of a host (the bird) and the diversity and composition of the microbiota are largely uncorrelated. We also find that the general pattern of similarity between host phylogeny and microbiota similarity is weak. Together, we see that bird microbiota are not strongly tied to host size or evolutionary history.

Microbial dynamics and bioreactor performance are interlinked with organic matter removal from wastewater treatment plant effluent

Optimizing bioreactor performance for organic matter removal can achieve sustainable and energy-efficient micropollutant removal in subsequent tertiary treatment. Bioreactor performance heavily depends on its resident microbial community; hence, a deeper understanding of community dynamics is essential. The microbial communities of three different bioreactors (biological activated carbon, moving bed biofilm reactor, sand filter), used for organic matter removal from wastewater treatment effluent, were characterized by 16S rRNA gene amplicon sequence analysis. An interdependency between bioreactor performance and microbial community profile was observed. Overall, Proteobacteria was the most predominant phylum, and Comamonadaceae was the most predominant family in all bioreactors. The relative abundance of the genus Roseococcus was positively correlated with organic matter removal. A generalized Lotka-Volterra (gLV) model was established to understand the interactions in the microbial community. By identifying microbial dynamics and their role in bioreactors, a strategy can be developed to improve bioreactor performance.

Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades

BACKGROUND

Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention.

RESULTS

Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality.

CONCLUSIONS

This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

How nutrient loads influence microbial-derived carbon accumulation in wetlands: A new insight from microbial metabolic investment strategies

Excessive anthropogenic nutrient inputs often lead to the degradation of wetland ecosystems and a decrease in carbon sink capacity. Microbial-derived carbon is increasingly recognized as an important precursor for organic carbon formation, which is controlled by the balance between microbial anabolic and catabolic processes. Shifts in microbial metabolic investment under nutrient load disturbance are key, but understudied, components of microbial-derived carbon turnover. Here, the roles of the distinct life-history traits and cooperation degree of key microbial assemblies in regulating microbial-derived carbon accumulation in a wetland receiving treated wastewater were firstly assessed by combining microbial biomarkers and genomic approaches. It was found that microbial-derived carbon was an important source of organic carbon in wetlands, and strongly associated with several microbial assemblies with specific trait strategies. Further analysis demonstrated that high growth yield strategists were mainly associated with microbial necromass accrual, while microbial biomass was more dominated by resource acquisition strategies in nutrient-imbalanced wetlands. A significant positive relationship between positive cohesion and microbial-derived carbon indicated that cooperative behavior among taxa promoted the production and accumulation of microbial-derived carbon. Moreover, resource stoichiometric balance, including C:N and C:P, was identified as an important driver of shifts in microbial metabolic investment strategies. The decreased C:N ratio led to a shift from resource acquisition strategies to high growth yield strategies for the microbial community, which facilitated microbial necromass accrual along the N-limited wetland, while the increased C:P ratio caused by excessive P deposition in sediments limits microbial cooperative growth to some extent. This study highlighted the importance of stoichiometric balance in mediating microbial growth metabolism and, in turn, enhancing the carbon sink capacity of wetlands.

The Alteration of Akkermansiaceae/Lachnospiraceae Ratio Is a Microbial Feature of Antibiotic-Induced Microbiota Remodeling

Antibiotic treatment has been shown to cause gut microbiota dysbiosis. However, lacking critical features defining gut microbiota dysbiosis makes it challenging to prevent. By co-occurrence network analysis, we found that despite short antibiotic courses eliminating certain microbial taxa, the Akkermansia genus played the role of a high-centrality hub to maintain microbiota homeostasis. When the antibiotic courses continued, the elimination of Akkermansia induced a significant microbiota remodeling of the gut microbiota networks. Based on this finding, we found that under long-term antibiotic stress, the gut microbiota was rearranged into a stable network with a significantly lower Akkermansiaceae/Lachnospiraceae (A/L) ratio and no microbial hub. By functional prediction analysis, we confirmed that the gut microbiota with a low A/L ratio also had enhanced mobile elements and biofilm-formation functions that may be associated with antibiotic resistance. This study identified A/L ratio as an indicator of antibiotic-induced dysbiosis. This work reveals that besides the abundance of specific probiotics, the hierarchical structure also critically impacts the microbiome function. Co-occurrence analysis may help better monitor the microbiome dynamics than only comparing the differentially abundant bacteria between samples.