Interactions-based classification of a single microbial sample

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

Microbial species pool-mediated diazotrophic community assembly in crop microbiomes during plant development

Plant-associated diazotrophs strongly relate to plant nitrogen (N) supply and growth. However, our knowledge of diazotrophic community assembly and microbial N metabolism in plant microbiomes is largely limited. Here we examined the assembly and temporal dynamics of diazotrophic communities across multiple compartments (soils, epiphytic and endophytic niches of root and leaf, and grain) of three cereal crops (maize, wheat, and barley) and identified the potential N-cycling pathways in phylloplane microbiomes. Our results demonstrated that the microbial species pool, influenced by site-specific environmental factors (e.g., edaphic factors), had a stronger effect than host selection (i.e., plant species and developmental stage) in shaping diazotrophic communities across the soil-plant continuum. Crop diazotrophic communities were dominated by a few taxa (~0.7% of diazotrophic phylotypes) which were mainly affiliated with Methylobacterium, Azospirillum, Bradyrhizobium, and Rhizobium. Furthermore, eight dominant taxa belonging to Azospirillum and Methylobacterium were identified as keystone diazotrophic taxa for three crops and were potentially associated with microbial network stability and crop yields. Metagenomic binning recovered 58 metagenome-assembled genomes (MAGs) from the phylloplane, and the majority of them were identified as novel species (37 MAGs) and harbored genes potentially related to multiple N metabolism processes (e.g., nitrate reduction). Notably, for the first time, a high-quality MAG harboring genes involved in the complete denitrification process was recovered in the phylloplane and showed high identity to Pseudomonas mendocina. Overall, these findings significantly expand our understanding of ecological drivers of crop diazotrophs and provide new insights into the potential microbial N metabolism in the phyllosphere.IMPORTANCEPlants harbor diverse nitrogen-fixing microorganisms (i.e., diazotrophic communities) in both belowground and aboveground tissues, which play a vital role in plant nitrogen supply and growth promotion. Understanding the assembly and temporal dynamics of crop diazotrophic communities is a prerequisite for harnessing them to promote plant growth. In this study, we show that the site-specific microbial species pool largely shapes the structure of diazotrophic communities in the leaves and roots of three cereal crops. We further identify keystone diazotrophic taxa in crop microbiomes and characterize potential microbial N metabolism pathways in the phyllosphere, which provides essential information for developing microbiome-based tools in future sustainable agricultural production.

Differentiated responses of the phyllosphere bacterial community of the yellowhorn tree to precipitation and temperature regimes across Northern China

Introduction

As an ephemeral and oligotrophic environment, the phyllosphere harbors many highly diverse microorganisms. Importantly, it is known that their colonization of plant leaf surfaces is considerably influenced by a few abiotic factors related to climatic conditions. Yet how the dynamics of phyllosphere bacterial community assembly are shaped by detailed climatological elements, such as various bioclimatic variables, remains poorly understood.

Methods

Using high-throughput 16S rRNA gene amplicon sequencing technology, we analyzed the bacterial communities inhabiting the leaf surfaces of an oilseed tree, yellowhorn (Xanthoceras sorbifolium), grown at four sites (Yinchuan, Otogqianqi, Tongliao, and Zhangwu) whose climatic status differs in northern China.

Results and Discussion

We found that the yellowhorn phyllosphere's bacterial community was generally dominated by four phyla: Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Nevertheless, bacterial community composition differed significantly among the four sampled site regions, indicating the possible impact of climatological factors upon the phyllosphere microbiome. Interestingly, we also noted that the α-diversities of phyllosphere microbiota showed strong positive or negative correlation with 13 bioclimatic factors (including 7 precipitation factors and 6 temperature factors). Furthermore, the relative abundances of 55 amplicon sequence variants (ASVs), including three ASVs representing two keystone taxa (the genera Curtobacterium and Streptomyces), exhibited significant yet contrary responses to the precipitation and temperature climatic variables. That pattern was consistent with all ASVs' trends of possessing opposite correlations to those two parameter classes. In addition, the total number of links and nodes, which conveys community network complexity, increased with rising values of most temperature variables. Besides that, remarkably positive relevance was found between average clustering coefficient and most precipitation variables. Altogether, these results suggest the yellowhorn phyllosphere bacterial community is capable of responding to variation in rainfall and temperature regimes in distinctive ways.

Soil nutrient management influences diversity, community association and functional structure of rhizosphere bacteriome under vegetable crop production

Introduction

Rhizosphere bacterial communities play a crucial role in promoting plant and soil ecosystem health and productivity. They also have great potential as key indicators of soil health in agroecosystems. Various environmental factors affect soil parameters, which have been demonstrated to influence soil microbial growth and activities. Thus, this study investigated how rhizosphere bacterial community structure and functions are affected by agronomic practices such as organic and conventional fertiliser application and plant species types.

Methods

Rhizosphere soil of vegetable crops cultivated under organic and conventional fertilisers in different farms was analysed using high-throughput sequencing of the 16S rRNA gene and co-occurrence network pattern among bacterial species. The functional structure was analysed with PICRUSt2 pipeline.

Results

Overall, rhizosphere bacterial communities varied in response to fertiliser type, with soil physicochemical parameters, including NH4, PO4, pH and moisture content largely driving the variations across the farms. Organic farms had a higher diversity richness and more unique amplicon sequence variants than conventional farms. Bacterial community structure in multivariate space was highly differentiated across the farms and between organic and conventional farms. Co-occurrence network patterns showed community segmentation for both farms, with keystone taxa more prevalent in organic than conventional farms.

Discussion

Module hub composition and identity varied, signifying differences in keystone taxa across the farms and positive correlations between changes in microbial composition and ecosystem functions. The organic farms comprised functionally versatile communities characterised by plant growth-promoting keystone genera, such as Agromyces, Bacillus and Nocardioides. The results revealed that organic fertilisers support high functional diversity and stronger interactions within the rhizosphere bacterial community. This study provided useful information about the overall changes in soil microbial dynamics and how the changes influence ecosystem functioning under different soil nutrient management and agronomic practices.

Diversity, composition, and networking of saliva microbiota distinguish the severity of COVID-19 episodes as revealed by an analysis of 16S rRNA variable V1-V3 region sequences

Studies on the role of the oral microbiome in SARS-CoV-2 infection and severity of the disease are limited. We aimed to characterize the bacterial communities present in the saliva of patients with varied COVID-19 severity to learn if there are differences in the characteristics of the microbiome among the clinical groups. We included 31 asymptomatic subjects with no previous COVID-19 infection or vaccination; 176 patients with mild respiratory symptoms, positive or negative for SARS-CoV-2 infection; 57 patients that required hospitalization because of severe COVID-19 with oxygen saturation below 92%, and 18 fatal cases of COVID-19. Saliva samples collected before any treatment were tested for SARS-CoV-2 by PCR. Oral microbiota in saliva was studied by amplification and sequencing of the V1-V3 variable regions of 16S gene using an Illumina MiSeq platform. We found significant changes in diversity, composition, and networking in saliva microbiota of patients with COVID-19, as well as patterns associated with severity of disease. The presence or abundance of several commensal species and opportunistic pathogens were associated with each clinical stage. Patterns of networking were also found associated with severity of disease: a highly regulated bacterial community (normonetting) was found in healthy people whereas poorly regulated populations (disnetting) were characteristic of severe cases. Characterization of microbiota in saliva may offer important clues in the pathogenesis of COVID-19 and may also identify potential markers for prognosis in the severity of the disease. IMPORTANCE SARS-CoV-2 infection is the most severe pandemic of humankind in the last hundred years. The outcome of the infection ranges from asymptomatic or mild to severe and even fatal cases, but reasons for this remain unknown. Microbes normally colonizing the respiratory tract form communities that may mitigate the transmission, symptoms, and severity of viral infections, but very little is known on the role of these microbial communities in the severity of COVID-19. We aimed to characterize the bacterial communities in saliva of patients with different severity of COVID-19 disease, from mild to fatal cases. Our results revealed clear differences in the composition and in the nature of interactions (networking) of the bacterial species present in the different clinical groups and show community-patterns associated with disease severity. Characterization of the microbial communities in saliva may offer important clues to learn ways COVID-19 patients may suffer from different disease severities.

Shifts in functional traits and interactions patterns of soil methane-cycling communities following forest-to-pasture conversion in the Amazon Basin

Deforestation threatens the integrity of the Amazon biome and the ecosystem services it provides, including greenhouse gas mitigation. Forest-to-pasture conversion has been shown to alter the flux of methane gas (CH₄ ) in Amazonian soils, driving a switch from acting as a sink to a source of atmospheric CH₄ . This study aimed to better understand this phenomenon by investigating soil microbial metagenomes, focusing on the taxonomic and functional structure of methane-cycling communities. Metagenomic data from forest and pasture soils were combined with measurements of in situ CH₄ fluxes and soil edaphic factors and analysed using multivariate statistical approaches. We found a significantly higher abundance and diversity of methanogens in pasture soils. As inferred by co-occurrence networks, these microorganisms seem to be less interconnected within the soil microbiota in pasture soils. Metabolic traits were also different between land uses, with increased hydrogenotrophic and methylotrophic pathways of methanogenesis in pasture soils. Land-use change also induced shifts in taxonomic and functional traits of methanotrophs, with bacteria harbouring genes encoding the soluble form of methane monooxygenase enzyme (sMMO) depleted in pasture soils. Redundancy analysis and multimodel inference revealed that the shift in methane-cycling communities was associated with high pH, organic matter, soil porosity and micronutrients in pasture soils. These results comprehensively characterize the effect of forest-to-pasture conversion on the microbial communities driving the methane-cycling microorganisms in the Amazon rainforest, which will contribute to the efforts to preserve this important biome.

Spatial co-occurrence patterns of benthic microbial assemblage in response to trace metals in the Atacama Desert Coastline

Taxonomic and functional microbial communities may respond differently to anthropogenic coastal impacts, but ecological quality monitoring assessments using environmental DNA and RNA (eDNA/eRNA) in response to pollution are poorly understood. In the present study, we investigated the utility of the co-occurrence network approach's to comprehensively explore both structure and potential functions of benthic marine microbial communities and their responses to Cu and Fe fractioning from two sediment deposition coastal zones of northern Chile via 16S rRNA gene metabarcoding. The results revealed substantial differences in the microbial communities, with the predominance of two distinct module hubs based on study zone. This indicates that habitat influences microbial co-occurrence networks. Indeed, the discriminant analysis allowed us to identify keystone taxa with significant differences in eDNA and eRNA comparison between sampled zones, revealing that Beggiatoaceae, Carnobacteriaceae, and Nitrosococcaceae were the primary representatives from Off Loa, whereas Enterobacteriaceae, Corynebacteriaceae, Latescibacteraceae, and Clostridiaceae were the families responsible for the observed changes in Mejillones Bay. The quantitative evidence from the multivariate analyses supports that the benthic microbial assemblages' features were linked to specific environments associated with Cu and Fe fractions, mainly in the Bay. Furthermore, the predicted functional microbial structure suggested that transporters and DNA repair allow the communities to respond to metals and endure the interacting variable environmental factors like dissolved oxygen, temperature, and salinity. Moreover, some active taxa recovered are associated with anthropogenic impact, potentially harboring antibiotic resistance and other threats in the coastal zone. Overall, the method of scoping eRNA in parallel with eDNA applied here has the capacity to significantly enhance the spatial and functional understanding of real-time microbial assemblages and, in turn, would have the potential to increase the acuity of biomonitoring programs key to responding to immediate management needs for the marine environment.

A shift from inorganic to organic nitrogen-dominance shapes soil microbiome composition and co-occurrence networks

Soil microbiomes are characterized by their composition and networks, which are linked to soil nitrogen (N) availability. In nature, inorganic N dominates at one end and organic N dominates at the other end along soil N gradients; however, little is known about how this shift influences soil microbiome composition and co-occurrence networks, as well as their controls. To this end, we conducted an experiment with the host plant Solidago canadensis, which was subject to three N regimes: inorganic N-dominated, co-dominated by inorganic and organic N (CIO), and organic N-dominated. Organic N dominance exhibited stronger effects on the composition and co-occurrence networks of soil microbiomes than inorganic N dominance. The predominant control was plant traits for bacterial and fungal richness, and soil pH for keystone species. Relative to the CIO regime, inorganic N dominance did not affect fungal richness and increased keystone species; organic N dominance decreased fungal richness and keystone species. Pathogenic fungi and arbuscular mycorrhizal fungi were suppressed by organic N dominance but not by inorganic N dominance. These findings suggest that the shift from soil inorganic N-dominance to soil organic N-dominance could strongly shape soil microbiome composition and co-occurrence networks by altering species diversity and topological properties.

Toward FAIR Representations of Microbial Interactions

Despite an ever-growing number of data sets that catalog and characterize interactions between microbes in different environments and conditions, many of these data are neither easily accessible nor intercompatible. These limitations present a major challenge to microbiome research by hindering the streamlined drawing of inferences across studies. Here, we propose guiding principles to make microbial interaction data more findable, accessible, interoperable, and reusable (FAIR). We outline specific use cases for interaction data that span the diverse space of microbiome research, and discuss the untapped potential for new insights that can be fulfilled through broader integration of microbial interaction data. These include, among others, the design of intercompatible synthetic communities for environmental, industrial, or medical applications, and the inference of novel interactions from disparate studies. Lastly, we envision potential trajectories for the deployment of FAIR microbial interaction data based on existing resources, reporting standards, and current momentum within the community.

Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping

The leaf microbiota plays a key role in plant development, but a detailed mechanism of microbe-plant relationships remains elusive. Many genome-wide association studies (GWAS) have begun to map leaf microbes, but few have systematically characterized the genetics of how microbes act and interact. Previously, we integrated behavioral ecology and game theory to define four types of microbial interactions - mutualism, antagonism, aggression, and altruism, in a microbial community assembly. Here, we apply network mapping to identify specific plant genes that mediate the topological architecture of microbial networks. Analyzing leaf microbiome data from an Arabidopsis GWAS, we identify several heritable hub microbes for leaf microbial communities and detect 140-728 SNPs (Single nucleotide polymorphisms) responsible for emergent properties of microbial network. We reconstruct Bayesian genetic networks from which to identify 22-43 hub genes found to code molecular pathways related to leaf growth, abiotic stress responses, disease resistance and nutrition uptake. A further path analysis visualizes how genetic variants of Arabidopsis affect its fecundity through the internal workings of the leaf microbiome. We find that microbial networks and their genetic control vary along spatiotemporal gradients. Our study provides a new avenue to reveal the "endophenotype" role of microbial networks in linking genotype to end-point phenotypes in plants. Our integrative theory model provides a powerful tool to understand the mechanistic basis of structural-functional relationships within the leaf microbiome and supports the need for future research on plant breeding and synthetic microbial consortia with a specific function.

The wilt pathogen induces different variations of root-associated microbiomes of plant

Root-associated compartments, including the rhizosphere, rhizoplane, and endosphere, live with diverse microbial communities which profoundly affect plant growth and health. However, a systematic understanding of the microbiome assembly across the rhizosphere, rhizoplane, and endosphere under pathogen invasion remains elusive. Using 16S high-throughput sequencing, we studied how bacterial wilt disease affected the variation and assembly of the three continuous root-associated microbiomes of tobacco. The results indicated that microorganisms were gradually filtered from the rhizosphere to the endosphere. With the pathogen invasion, the rhizosphere, rhizoplane and endosphere microbiomes selected and recruited different beneficial bacterial taxa. Some recruited bacteria were also identified as keystone members in networks (i.e., Bosea in the endosphere). The microbiomes of endosphere and rhizoplane were more sensitive to plant disease than the rhizosphere microbiome. Still, response strategies of the rhizoplane and endosphere to disease were obviously different. Microbial networks of the rhizoplane became complex in diseased samples and genes involved in sporulation formation and cell cycle were enriched. However, microbial networks of the diseased endosphere were disrupted, and functional genes related to nitrogen utilization and chemotaxis were significantly increased, indicating the importance of nitrogen resources supply of plants for the endosphere microbiome under pathogen invasion. Our results provide novel insights for understanding the different responses of the root-associated microbiomes to plant disease.

Comparison of the Endophytic Bacterial Microbiota of Asymptomatic and Symptomatic Ginger Rhizomes During the Activation of Adventitious Bud Development

Bacterial infections are the cause of rhizome rot in ginger (Zingiber officinale). Key members of the endophytic microbial community in ginger rhizomes have not been identified, and their impact on the decay of rhizomes during the activation of adventitious bud development has not been investigated. High-throughput, 16S rRNA amplicon sequencing and inoculation experiments were used to analyze the microbial diversity, community structure and composition, and pathogenicity of isolated bacteria. Our results indicated that the composition of the endophytic microbiota underwent a shift during the progression of rhizome rot disease. Enterobacteriaceae, Lachnospiraceae, and the bacterial genera Clostridium, Bacteroides, Acrobacter, Dysgonomonas, Anaerosinus, Pectobacterium, and Lactococcus were relatively abundant in the bacterial community of rhizomes exhibiting bacterial decay symptoms but were also present in asymptomatic rhizomes. The presence of Enterobacteriaceae and Pseudomonadaceae were positively correlated (ρ = 0.83) at the beginning of the sampling period in the symptomatic group, while a positive correlation (ρ = 0.89) was only observed after 20 days in the asymptomatic group. These data indicate that the co-occurrence of Enterobacteriaceae and Pseudomonadaceae may be associated with the development of ginger rot. Bacterial taxa isolated from ginger rhizomes, such as Enterobacter cloacae, E. hormaechei, and Pseudomonas putida, induced obvious rot symptoms when they were inoculated on ginger rhizomes. Notably, antibiotic-producing bacterial taxa in the Streptococcaceae and Flavobacteriaceae were also relatively abundant in rhizomes with rot and appeared to be linked to the onset of rhizome rot disease. Our results provide important information on the establishment and management of disease in ginger rhizomes.

The Leaf Microbiome of Arabidopsis Displays Reproducible Dynamics and Patterns throughout the Growing Season

Leaves are primarily responsible for the plant’s photosynthetic activity. Thus, changes in the leaf microbiota, which includes deleterious and beneficial microbes, can have far-reaching effects on plant fitness and productivity. Identifying the processes and microorganisms that drive these changes over a plant’s lifetime is, therefore, crucial. In this study, we analyzed the temporal dynamics in the leaf microbiome of Arabidopsis thaliana, integrating changes in both composition and microbe-microbe interactions via the study of microbial networks. Field-grown Arabidopsis were used to monitor leaf bacterial, fungal and oomycete communities throughout the plant’s natural growing season (extending from November to March) over three consecutive years. Our results revealed the existence of conserved temporal patterns, with microbial communities and networks going through a stabilization phase of decreased diversity and variability at the beginning of the plant’s growing season. Despite a high turnover in these communities, we identified 19 “core” taxa persisting on Arabidopsis leaves across time and plant generations. With the hypothesis these microbes could be playing key roles in the structuring of leaf microbial communities, we conducted a time-informed microbial network analysis which showed core taxa are not necessarily highly connected network “hubs,” and “hubs” alternate with time. Our study shows that leaf microbial communities exhibit reproducible dynamics and patterns, suggesting the potential of using our understanding of temporal trajectories in microbial community composition to design experiments aimed at driving these communities toward desired states.

Soil Water Content Shapes Microbial Community Along Gradients of Wetland Degradation on the Tibetan Plateau

Soil microbes are important components in element cycling and nutrient supply for the development of alpine ecosystems. However, the development of microbial community compositions and networks in the context of alpine wetland degradation is unclear. We applied high-throughput 16S rRNA gene amplicon sequencing to track changes in microbial communities along degradation gradients from typical alpine wetland (W), to wet meadow (WM), to typical meadow (M), to grassland (G), and to desert (D) in the Zoige alpine wetland region on the Tibetan Plateau. Soil water content (SWC) decreased as wetland degradation progressed (79.4 and 9.3% in W and D soils, respectively). Total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) increased in the soils of WM, and then decreased with alpine wetlands degradation from WM to the soils of M, G, and D, respectively. Wetland degradation did not affect microbial community richness and diversity from W soils to WM, M, and G soils, but did affect richness and diversity in D soils. Microbial community structure was strongly affected by wetland degradation, mainly due to changes in SWC, TOC, TN, and TP. SWC was the primary soil physicochemical property influencing microbial community compositions and networks. In wetland degradation areas, Actinobacteriota, Acidobacteriota, Cholorflexi, and Proteovacteria closely interacted in the microbial network. Compared to soils of W, WM, and M, Actinobacteriota played an important role in the microbial co-occurrence network of the G and D soils. This research contributes to our understanding of how microbial community composition and networks change with varied soil properties during degradation of different alpine wetlands.

Deciphering Metabolic Currencies That Support Marine Microbial Networks

Microbes are omnipresent in the biosphere and perform biological and chemical processes critical to ecosystem function, nutrient cycling, and global climate regulation. In the ocean, microbes constitute more than two-thirds of biomass with abundances reaching over one million microbial cells per milliliter of seawater. Our understanding of the marine microbial world has rapidly expanded with use of innovative molecular and chemical 'omics tools to uncover previously hidden taxonomic diversity, spatiotemporal distributions, and novel metabolic functions. Recognition that specific microbial taxa cooccur in consistent patterns in the ocean has implicated microbe-microbe interactions as important, but poorly constrained, regulators of microbial activity. Here, I examine cooperative interactions among marine plankton, with a focus on the metabolic "currencies" that establish microbial partnerships in the surface-ocean trade economy. I discuss current and future directions to study microbial metabolic interactions in order to strengthen our understanding of ecosystem interdependencies and their impact on ocean biogeochemistry.

Truffle Microbiome Is Driven by Fruit Body Compartmentalization Rather than Soils Conditioned by Different Host Trees

Truffles are among the most expensive edible mushrooms; their value is worth billions of U.S. dollars annually in international markets. They establish ectomycorrhizal symbiotic relationships with diverse host tree roots and produce hypogeous ascomata. Their whole life cycle is closely related to their associated microbiome. However, whether truffle-associated compartments or host tree rhizospheres are the vital driver for truffle ascomata microbiome is unclear. To identify and compare fungal and bacterial communities in four truffle-associated compartments (Tuber indicum: bulk soil, adhering soil to peridium, peridium, and gleba) from three host trees, we sequenced their ITS (fungal) and 16S (bacterial) ribosomal DNA using the Illumina MiSeq high-throughput platform. We further applied the amplicon data to analyze the core microbiome and microbial ecological networks. Tuber indicum microbiome composition was strongly driven by its associated compartments rather than by their symbiotic host trees. Truffle microbiome was bacteria dominated, and its bacterial community formed a substantially more complex interacting network compared to that of the fungal community. The core fungal community changed from Basidiomycota dominated (bulk soil) to Rozellomycota dominated (interphase soil); the core bacterial community shifted from Bacteroidetes to Proteobacteria dominance from truffle peridium to gleba tissue. Especially, at the truffle and soil interphase, the niche-based selection of truffle microbiome was verified by (i) a clear exclusion of four bacterial phyla (Rokubacteria, Nitrospirae, Chloroflexi, and Planctomycetes) in gleba; (ii) a significant decrease in alpha-diversity (as revealed by Chao 1, Shannon, and Simpson indices); and (iii) the complexity of the network substantially decreased from bulk soil to soil-truffle interphase and further to the peridium and gleba. The network analysis of microbiome showed that the microbial positive interactions were higher in truffle tissues than in both bulk soil and peridium-adhering soil and that Cupriavidus, Bradyrhizobium, Aminobacter, and Mesorhizobium spp. were the keystone network hubs in the truffle gleba. This study provides insights into the factors that drive the truffle microbiome dynamics and the recruitment and function of the microbiome components. IMPORTANCE Currently, the factors that drive the microbiome associated with truffles, the most highly prized fungi in the world, are largely unknown. We demonstrate for the first time here that truffle microbiome composition is strongly driven by associated compartments rather than by symbiotic host trees. The truffle microbiome was bacteria dominated, and its bacterial community formed a substantially more complex (with the higher numbers of nodes, links, and modules) interacting network compared to that of the fungal community. Network analysis showed a higher number of positive microbial interactions with each other in truffle tissues than in both bulk soil and peridium-adhering soil. For the first time, the fungal community structure associated with truffles using high-throughput sequencing, microbial networks, and keystone species analyses is presented. This study provides novel insights into the factors that drive the truffle microbiome dynamics and the recruitment and function of the microbiome components, showing that they are more complex than previously thought.

Geography, Host Genetics, and Cross-Domain Microbial Networks Structure the Skin Microbiota of Fragmented Brazilian Atlantic Forest Frog Populations

The host-associated microbiome plays a significant role in health. However, the roles of factors such as host genetics and microbial interactions in determining microbiome diversity remain unclear. We examined these factors using amplicon-based sequencing of 175 Thoropa taophora frog skin swabs collected from a naturally fragmented landscape in southeastern Brazil. Specifically, we examined (1) the effects of geography and host genetics on microbiome diversity and structure; (2) the structure of microbial eukaryotic and bacterial co-occurrence networks; and (3) co-occurrence between microeukaryotes with bacterial OTUs known to affect growth of the fungal pathogen Batrachochytrium dendrobatidis (Bd). While bacterial alpha diversity varied by both site type and host MHC IIB genotype, microeukaryotic alpha diversity varied only by site type. However, bacteria and microeukaryote composition showed variation according to both site type and host MHC IIB genotype. Our network analysis showed the highest connectivity when both eukaryotes and bacteria were included, implying that ecological interactions may occur among domains. Lastly, anti-Bd bacteria were not broadly negatively co-associated with the fungal microbiome and were positively associated with potential amphibian parasites. Our findings emphasize the importance of considering both domains in microbiome research and suggest that for effective probiotic strategies for amphibian disease management, considering potential interactions among all members of the microbiome is crucial.

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next-generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network-level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA-based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community- and network-level properties for monitoring change. We selected crop-associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network-level properties, especially β-properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community-level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network-level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

Effects of Sphagnum Leachate on Competitive Sphagnum Microbiome Depend on Species and Time

Plant specialized metabolites play an important role in soil carbon (C) and nutrient fluxes. Through anti-microbial effects, they can modulate microbial assemblages and associated microbial-driven processes, such as nutrient cycling, so to positively or negatively cascade on plant fitness. As such, plant specialized metabolites can be used as a tool to supplant competitors. These compounds are little studied in bryophytes. This is especially notable in peatlands where Sphagnum mosses can dominate the vegetation and show strong interspecific competition. Sphagnum mosses form carpets where diverse microbial communities live and play a crucial role in Sphagnum fitness by regulating C and nutrient cycling. Here, by means of a microcosm experiment, we assessed to what extent moss metabolites of two Sphagnum species (S. fallax and S. divinum) modulate the competitive Sphagnum microbiome, with particular focus on microbial respiration. Using a reciprocal leachate experiment, we found that interactions between Sphagnum leachates and microbiome are species-specific. We show that both Sphagnum leachates differed in compound richness and compound relative abundance, especially sphagnum acid derivates, and that they include microbial-related metabolites. The addition of S. divinum leachate on the S. fallax microbiome immediately reduced microbial respiration (−95%). Prolonged exposition of S. fallax microbiome to S. divinum leachate destabilized the food web structure due to a modulation of microbial abundance. In particular, leachate addition decreased the biomass of testate amoebae and rotifers but increased that of ciliates. These changes did not influence microbial CO₂ respiration, suggesting that the structural plasticity of the food web leads to its functional resistance through the replacement of species that are functionally redundant. In contrast, S. fallax leachate neither affected S. divinum microbial respiration, nor microbial biomass. We, however, found that S. fallax leachate addition stabilized the food web structure associated to S. divinum by changing trophic interactions among species. The differences in allelopathic effects between both Sphagnum leachates might impact their competitiveness and affect species distribution at local scale. Our study further paves the way to better understand the role of moss and microbial specialized metabolites in peatland C dynamics.

Microbial Network and Soil Properties Are Changed in Bacterial Wilt-Susceptible Soil

Bacterial wilt disease is a devastating disease of crops, which leads to huge economic loss worldwide. It is hypothesized that the occurrence of bacterial wilt may be related to changes in soil chemical properties and microbial interactions. In this study, we compared the soil chemical properties and microbial network structures of a healthy soil (HS) and a bacterial wilt-susceptible soil (BWS). The contents of available nitrogen, potassium, and phosphorus and the soil pH in the BWS were significantly lower than those in the HS. BWS showed nutrient deficiency and acidification in comparison with the HS. The structure and composition of the BWS network were quite different from those of the HS network. The BWS network had fewer modules and edges and lower connectivity than the HS network. The HS network contained more interacting species, more key microorganisms, and better high-order organization and thus was more complex and stable than the BWS network. Most nodes and module memberships were unshared by the two networks, while the ones that were shared showed different topological roles. Some generalists in the HS network became specialists in the BWS network, indicating that the topological roles of microbes were changed and key microorganisms were shifted in the BWS. In summary, the composition and structure of the microbial network of the BWS were different from that of the HS. Many microbial network connections were missing in the BWS, which most likely provided conditions leading to higher rates of bacterial wilt disease.IMPORTANCE Bacterial wilt disease is caused by the pathogen Ralstonia solanacearum and is a widespread devastating soilborne disease leading to huge economic losses worldwide. The soil microbial community is crucial to the capacity of soils to suppress soilborne diseases through complex interactions. Network analysis can effectively explore these complex interactions. In this study, we used a random matrix theory (RMT)-based network approach to investigate the changes in microbial network and associated microbial interactions in a bacterial wilt-susceptible soil (BWS) in comparison to a healthy soil (HS). We found that the structure and composition of the microbial network in BWSs were quite different from those of the HS. The BWS network had fewer modules, edges, and key microorganisms and lower connectivity than the HS network. In the BWSs, apparently the topological role of microbes was changed and key microorganisms were shifted to specialists.

Linking microbial co-occurrences to soil ecological processes across a woodland-grassland ecotone

Ecotones between distinct ecosystems have been the focus of many studies as they offer valuable insights into key drivers of community structure and ecological processes that underpin function. While previous studies have examined a wide range of above-ground parameters in ecotones, soil microbial communities have received little attention. Here we investigated spatial patterns, composition, and co-occurrences of archaea, bacteria, and fungi, and their relationships with soil ecological processes across a woodland-grassland ecotone. Geostatistical kriging and network analysis revealed that the community structure and spatial patterns of soil microbiota varied considerably between three habitat components across the ecotone. Woodland samples had significantly higher diversity of archaea while the grassland samples had significantly higher diversity of bacteria. Microbial co-occurrences reflected differences in soil properties and ecological processes. While microbial networks were dominated by bacterial nodes, different ecological processes were linked to specific microbial guilds. For example, soil phosphorus and phosphatase activity formed the largest clusters in their respective networks, and two lignolytic enzymes formed joined clusters. Bacterial ammonia oxidizers were dominant over archaeal oxidizers and showed a significant association (p < 0.001) with potential nitrification (PNR), with the PNR subnetwork being dominated by Betaproteobacteria. The top ten keystone taxa comprised six bacterial and four fungal OTUs, with Random Forest Analysis revealing soil carbon and nitrogen as the determinants of the abundance of keystone taxa. Our results highlight the importance of assessing interkingdom associations in soil microbial networks. Overall, this study shows how ecotones can be used as a model to delineate microbial structural patterns and ecological processes across adjoining land-uses within a landscape.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

www.aquaticmicrobial.net

Despite recent advances and new applications of molecular and biogeochemical methodology in aquatic microbial ecology, our perception of the aquatic microbial world remains one dominated by "free-living" bacteria that account for most of the microbial activities in the pelagic zone. Recent research has, however, shown that there exist vast and hidden "microbial networks" within the water column, connected via various microhabitats such as aggregates, fecal pellets and higher organisms. Bacterial abundance within these networks may rival or exceed that of the "free-living" bacteria. Hence, what we have learned in traditional aquatic microbial ecology represents merely a fraction of the microbial world. Within these networks a bacterium can travel long distances, communicate and closely interact with other bacteria and efficiently exchange genetic information with one another. The presence of microbial networks within the water column demands better sampling strategies and a new way to understand bacterial ecology, evolution and functions within the broader context of systems biology.