Open challenges for microbial network construction and analysis

Allelopathic Effect of Osmanthus fragrans Changes the Soil Microbial Community and Increases the Soil Nutrients and the Aroma Quality of Tea Leaves

Intercropping is a sustainable agricultural practice that promotes the growth of tea plants. However, research on the effects of intercropping with aromatic plants on the soil environment and tea aroma quality remains limited. In this study, we conducted both greenhouse and field experiments to investigate the allelopathic effect of Osmanthus fragrans on soil properties, the soil microbial community, and tea aroma quality. The treatment with O. fragrans leaf litter or intercropping significantly increased soil pH and ammonium nitrogen content and changed soil enzyme activities. Both treatments enhanced the bacterial richness in rhizosphere soil and altered microbial communities, particularly increasing the relative and absolute abundance of beneficial Bacillus. The allelopathic effect of O. fragrans improved tea aroma quality, with five key volatile compounds (linalool oxide II, linalool, nonanal, methyl salicylate, and geraniol) consistently identified in both greenhouse and field treatments. Network analysis demonstrated that our treatments enhanced the correlation between soil bacterial communities and five volatile compounds in tea leaves. These findings provide a theoretical foundation for improving soil health and tea aroma in tea plantations.

Optimization of compound bacterial inoculant enhances soil nutrients and bacterial community richness in alpine grassland

Grassland degradation results in nutrient loss and ecosystem function impairment, making it crucial to explore green and effective restoration technologies. In this study, three strains (Acinetobacter calcoaceticus J1, Pseudomonas piscium J5, Bacillus subtilis Y3) isolated from alpine grassland, and mixed in equal volume (VJ1:VJ5:VY3 = 1:1:1) to prepare the compound bacteria. The fermentation medium and conditions of the compound bacteria were optimized, then pot and field experiments were conducted to validate the application of the optimized compound bacterial inoculant. The optimal conditions were: soluble starch 2.62 g L-1, yeast extract 20.26 g L-1, KCl 5.01 g L-1, pH 6.0, incubated at 25 °C for 48 h, with 20 % liquid volume, 3 % inoculation ratio, and 200 r·min-1. Under these conditions, the number of viable bacteria was 128.47 times higher than before optimization. The obtained compound bacterial inoculant resulted in a significant increase of 34.24 %, 52.61 %, 57.74 %, 62.50 %, and 53.92 % in the height, total root length, root surface area, volume, and branch number of Elymus nutans, with better effects than the single bacterial. The field experiment revealed that the compound bacterial inoculant could significantly increase above-ground biomass (10.63 %), organic matter (16.99 %), available nitrogen (12.58 %), total nitrogen, and phosphorus (15.79 % and 11.54 %). This study has developed an environmentally friendly compound bacterial inoculant (G1), that accelerates soil nutrient cycling by reducing soil pH and increasing the richness of the bacterial community, thus restoring degraded grassland. This result realized the principle of "near-natural restoration" of degraded alpine grassland and provided a new biological restoration technology for the degraded alpine grassland.

Multi-Metagenome Analysis Unravels Community Collapse After Sampling and Hints the Cultivation Strategy of CPR Bacteria in Groundwater

Groundwater harbors phylogenetically diverse Candidate Phyla Radiation (CPR) bacteria, representing an ideal ecosystem for studying this microbial dark matter. However, no CPR strains have been successfully isolated from groundwater, severely limiting further research. This study employed a multi-metagenome approach, integrating time-resolved sampling, antibiotic/nutrient interventions, and microbial correlation networks to unravel CPR ecological roles in groundwater and provide insights into their subsequent cultivation. Through 36 metagenomes from a groundwater system containing at least 68 CPR phyla, we revealed the time-sensitive collapse of CPR communities: total abundance plummeted from 7.9% to 0.15% within 48 h post-sampling, driven by competition with rapidly dividing non-CPR bacteria, such as members of Pseudomonadota. Ampicillin (100 mg/L) stabilized CPR communities by suppressing competitors, whereas low-nutrient conditions paradoxically reversed this effect. Long-term enrichment (14 months) recovered 63 CPR phyla (0.35% abundance), revealing their survival resilience despite nutrient deprivation. Correlation networks prioritized Actinomyces, a novel Acidimicrobiaceae genus, Aestuariivirga, Baekduia and Caedimonadaceae as potential CPR partners, providing actionable targets for co-culture trials. Here, we propose actionable recommendations spanning groundwater sampling, activation status, identification of CPR symbiotic partners, and optimization of culture conditions, which bypass traditional blind cultivation and are critical for future efforts to cultivate CPR bacterial strains from groundwater. Cultivating CPR bacteria will contribute to clarifying their diversity, ecological roles, evolutionary mechanisms, metabolic pathways, and genetic potential.

The community succession mechanisms and interactive dynamics of microorganisms under high salinity and alkalinity conditions during composting

Microorganisms drive organic matter degradation and humification during composting. However, the mechanisms underlying microbial community succession and their interactions under saline-alkali stress are poorly understood. In this study, we investigated the microbial community assembly processes and microbial niche dynamics during composting in the high-saline-alkaline region. The niche breadth of the microbial community expanded from 5.8 to 15 and salt-alkali conditions alleviation prompted a shift in microbial community assembly towards stochastic processes. Alkalinity (R = 69.08%) and available phosphorus (AP) (R = 45.70%) are identified as the primary environmental stress factors. Salinity primarily impacted the niche breadth, while alkalinity predominantly determined the assembly processes of microorganisms. The degradation of organic matter in high-temperature environments enhanced the release of AP, altering the processes of microbial community assembly and driving niche differentiation within the microbial community. The abundant taxa actively responded to the changes in the environmental conditions, while the rare taxa maintained the community stability by expanding their ecological niches. The interactions between microorganisms are mainly based on synergism. The native microorganisms, such as Alcanivorax, Corynebacterium, and Rhodohalobacter, played a key role in promoting compost maturity. They tolerated the high-salinity and alkaline environments and also withstood high temperatures. This study revealed for the first time the succession mechanisms and interaction characteristics of microbial communities under salinity and temperature stress, providing theoretical guidance for microbial inoculation during the composting of high-saline and alkaline organic waste.

Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns

MOTIVATION

Inferring microbial interaction networks from microbiome data is a core task of computational ecology. An avenue of research to create reliable inference methods is based on a stylized view of microbiome data, starting from the assumption that the presences and absences of microbiomes, rather than the quantitative abundances, are informative about the underlying interaction network. With this starting point, inference algorithms can be based on the notion of attractors (asymptotic states) in Boolean networks. Boolean network framework offers a computationally efficient method to tackle this problem. However, often existing algorithms operating under a Boolean network assumption, fail to provide networks that can reproduce the complete set of initial attractors (abundance patterns). Therefore, there is a need for network inference algorithms capable of reproducing the initial stable states of the system.

RESULTS

We study the change of attractors in Boolean threshold dynamics on signed undirected graphs under small changes in network architecture and show, how to leverage these relationships to enhance network inference algorithms. As an illustration of this algorithmic approach, we analyse microbial abundance patterns from stool samples of humans with inflammatory bowel disease (IBD), with colorectal cancer and from healthy individuals to study differences between the interaction networks of the three conditions. The method reveals strong diversity in IBD interaction networks. The networks are first partially deduced by an earlier inference method called ESABO, then we apply the new algorithm developed here, EDAME, to this result to generate a network that comes nearest to satisfying the original attractors.

AVAILABILITY AND IMPLEMENTATION

Implementation code is freely available at https://github.com/Jojo6297/edame.git.

Disruption and adaptation: infant gut microbiota's dynamic response to SARS-CoV-2 infection

BACKGROUND

The responses of the infant gut microbiota to infection significantly disrupt the natural intrahost evolutionary processes of the microbiome. Here, we collected a 16-month longitudinal cohort of infant gut microbiomes affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Then, we developed a multicriteria approach to identify core interaction network driving community dynamics under environmental disturbances, which we termed the Conserved Variated Interaction Group (CVIgroup).

RESULTS

The CVIgroup showed significant advantages on pinpointing a sparse set associated with the disturbances, as validated both our own and publicly available datasets. Leveraging the Oxford Nanopore Technology, we found this group facilitates the ecosystem's adaptation to environmental disruptions by enhancing the mobility of mobile genetic elements, including the reinforcement of the twin-arginine translocation pathway in response to increased virulence factors. Furthermore, the CVIgroup serves as an effective indicator of ecosystem health. The timescale for the gut microbiota's adaptation extends beyond 10 months. Members of the CVIgroup, such as Bacteroides thetaiotaomicron and Faecalibacterium, exhibit varying degrees of genomic structural variants, which contribute to guiding the community toward a new stable state rather than returning to its original configuration.

CONCLUSIONS

Collectively, the CVIgroup offers a snapshot of the gut microbiota's adaptive response to environmental disturbances. The disruption and subsequent adaptation of the gut microbiota in infants after COVID-19 infection underscores the necessity of re-evaluating reference standards in the context of the post-pandemic era. Video Abstract.

Long-term ammonium nitrate addition strengthens soil microbial cross-trophic interactions in a Tibetan alpine steppe

Global nitrogen (N) enrichment is modifying microbial interactions, which can be represented by network complexity. While a number of studies have explored how N addition influences the microbial intra-trophic network, its effects on the inter-trophic network have rarely been investigated. Here, we examined the effects of 8 years of multilevel N additions (i.e., 0, 1, 2, 4, 8, 16, 24 and 32 g N m-2 year-1) on inter-trophic interactions of soil microbial communities (i.e., protist-fungi, protist-prokaryote and fungi-prokaryote) in a Tibetan alpine steppe. Generally, there was a first increasing and then saturated trend of the complexity of inter-trophic networks along the N-addition gradient, which contrasts with the simplified or minimal response of intra-trophic network complexity reported previously. The intensified cross-trophic interactions were mainly explained by increased plant and litter biomass, which indicates that the N-induced increases in carbon supplies may have alleviated microbial energy limitations and thus resulted in more active metabolic processes, consequently stimulating various biotic interactions (e.g., predation, competition, and commensalism). Further, the enhanced inter-trophic network relationships were found to be associated with increased soil carbon and N mineralization processes. Overall, these findings highlight the importance of microbial cross-trophic interactions and indicate that they should be considered in predictions of ecosystem functioning under global N enrichment.

With a little help from my friends: importance of protist-protist interactions in structuring marine protistan communities in the San Pedro Channel

Marine protists form complex communities that are shaped by environmental and biological ecosystem properties, as well as ecological interactions between organisms. While all of these factors play a role in shaping protistan communities, the specific ways in which these properties and interactions influence protistan communities remain poorly understood. Fourteen years and 9 months of eukaryotic amplicon (18S-V4 rRNA gene) data collected monthly at the San Pedro Ocean Time-series (SPOT) station were used to evaluate the impacts that environmental and biological factors, and protist-protist interactions had on protistan community composition. Statistical analysis of the amplicon data revealed that seasonal patterns in protistan community composition were apparent, but that the environmental data collected through routine time-series sampling efforts could not explain most of the variability that was evident in the communities. To identify some of the protist-protist interactions that may have played a role in shaping protistan communities, ecological networks were constructed using the amplicon data and the network predictions were compared against a database of confirmed protist-protist interactions. The database comparisons revealed hundreds of established parasitic, predator-prey, photosymbiotic, and mutualistic relationships in the networks. Although many interactions were confirmed using the database, these confirmed interactions constituted only 2% of the interactions identified at the SPOT station, highlighting the need to better characterize protist-protist interactions in marine environments. Finally, the network-predicted interactions that were not found in the database were used to identify putative, novel protist-protist interactions that may have played a role in structuring the protistan communities at the SPOT station.

IMPORTANCE

Network analyses are commonly used to identify some of the ecological interactions that may be occurring between protists in the ocean; however, evaluating predictions obtained from these analyses remains difficult due to the large number of interactions that may be recovered and the limited amount of information available on protist-protist interactions in nature. In this study, ecological network analyses were conducted using data collected at the San Pedro Ocean Time-series (SPOT) station and the network predictions were compared against a database of established protist-protist interactions. These database comparisons revealed hundreds of confirmed protist-protist interactions, and thousands of putative, novel interactions that may be occurring at the SPOT station. The database comparisons carried out in this study provide a new way of evaluating network predictions and highlight the complex, yet critical role that ecological interactions play in shaping protistan community composition in marine ecosystems.

A novel robust network construction and analysis workflow for mining infant microbiota relationships

The gut microbiota plays a crucial role in infant health, with its development during the first 1,000 days influencing health outcomes. Understanding the relationships within the microbiota is essential to linking its maturation process to these outcomes. Several network-based methods have been developed to analyze the developing patterns of infant microbiota, but evaluating the reliability and effectiveness of these approaches remains a challenge. In this study, we created a test data pool using public infant microbiome data sets to assess the performance of four different network-based methods, employing repeated sampling strategies. We found that our proposed Probability-Based Co-Detection Model (PBCDM) demonstrated the best stability and robustness, particularly in network attributes such as node counts, average links per node, and the positive-to-negative link (P/N) ratios. Using the PBCDM, we constructed microbial co-existence networks for infants at various ages, identifying core genera networks through a novel network shearing method. Analysis revealed that core genera were more similar between adjacent age ranges, with increasing competitive relationships among microbiota as the infant microbiome matured. In conclusion, the PBCDM-based networks reflect known features of infant microbiota and offer a promising approach for investigating microbial relationships. This methodology could also be applied to future studies of genomic, metabolic, and proteomic data.

IMPORTANCE

As a research method and strategy, network analysis holds great potential for mining the relationships of bacteria. However, consistency and solid workflows to construct and evaluate the process of network analysis are lacking. Here, we provide a solid workflow to evaluate the performance of different microbial networks, and a novel probability-based co-existence network construction method used to decipher infant microbiota relationships. Besides, a network shearing strategy based on percolation theory is applied to find the core genera and connections in microbial networks at different age ranges. And the PBCDM method and the network shearing workflow hold potential for mining microbiota relationships, even possibly for the future deciphering of genome, metabolite, and protein data.

Habitat-specific regulation of microbiota in long-distance water diversion systems

Long-distance water diversion projects typically utilize various hydro-engineering facilities, creating complex and dynamic habitats. However, the microbial dynamics of multi-trophic microorganisms during water diversion and their responses to different hydro-engineering habitats remain poorly understood. In this study, we investigated bacteria, fungi, protists, and metazoa across tunnels, reservoirs, and inverted siphon piping along the main and northern branches of the Yellow River Diversion Project into Shanxi, during spring, summer, and autumn. Our results showed that both seasonal factors and hydro-engineering facilities significantly influenced the composition and diversity of microbiota. Bacterial community composition remained relatively stable during water transport, while fungi, protists, and metazoa exhibited greater spatial variability and habitat specificity. Stochastic processes predominantly governed the community assembly of all microbial groups across all hydro-engineering habitats. The structural features of the main network modules within the co-occurrence networks of multi-trophic species were highly consistent across different seasons within the same habitat, indicating the stable adaptation of microbiota interactions to the same habitat. Patterns of intra-kingdom (within bacteria, fungi, protists, or metazoa) and inter-kingdom (between bacteria, fungi, protists, and metazoa) associations of microbiota in different habitats varied, reflecting specific adaptations of microorganisms to particular habitats and suggesting an important role for environmental filtering. Variance partitioning analysis revealed that environmental factors accounted for 34.21 % to 45.19 % of the variation in the four microbial taxa. Our findings reveal the ecological processes of microbial assembly and adaptation in large-scale water diversion projects, providing insights for project management and risk control.

Enhancing microbial predator-prey detection with network and trait-based analyses

BACKGROUND

Network analyses are often applied to microbial communities using sequencing survey datasets. However, associations in such networks do not necessarily indicate actual biotic interactions, and even if they do, the nature of the interactions commonly remains unclear. While network analyses are valuable for generating hypotheses, the inferred hypotheses are rarely experimentally confirmed.

RESULTS

We employed cross-kingdom network analyses, applied trait-based functions to the microorganisms, and subsequently experimentally investigated the found putative predator-prey interactions to evaluate whether, and to what extent, correlations indicate actual predator-prey relationships. For this, we investigated algae and their protistan predators in biocrusts of three distinct polar regions, i.e., Svalbard, the Antarctic Peninsula, and Continental Antarctica. Network analyses using FlashWeave indicated that 89, 138, and 51 correlations occurred between predatory protists and algae, respectively. However, trait assignment revealed that only 4.7-9.3% of said correlations link predators to actually suitable prey. We further confirmed these results with HMSC modeling, which resulted in similar numbers of 7.5% and 4.8% linking predators to suitable prey for full co-occurrence and abundance models, respectively. The combination of network analyses and trait assignment increased confidence in the prediction of predator-prey interactions, as we show that 82% of all experimentally investigated correlations could be verified. Furthermore, we found that more vicious predators, i.e., predators with the highest growth rate in co-culture with their prey, exhibit higher stress and betweenness centrality - giving rise to the future possibility of determining important predators from their network statistics.

CONCLUSIONS

Our results support the idea of using network analyses for inferring predator-prey interactions, but at the same time call for cautionary consideration of the results, by combining them with trait-based approaches to increase confidence in the prediction of biological interactions. Video Abstract.

Adaptive Development of Soil Bacterial Communities to Ecological Processes Caused by Mining Subsidence

Subsidence from coal mining is a major environmental issue, causing significant damage to soil structure. Soil microorganisms, highly sensitive to environmental changes, adapt accordingly. This study focused on four areas of the Burdai coal mine: a non-subsidence area (CK), half-yearly (HY), 1-year (OY), and 2-year (TY) subsidence areas. Using high-throughput sequencing and molecular ecological network analysis, we examined soil microbial community diversity and structure across these zones, exploring microbial community assembly and functional predictions. Results showed that compared to the control, subsidence areas experienced reduced soil water content, organic matter, available phosphorus, and alkaline nitrogen, with the lowest levels observed at 1 year. These values began to rise after 1 year, suggesting natural recovery after subsidence stabilized. Microbial communities were closely related to soil organic matter, water content, and alkaline nitrogen. At the 1-year mark, soil property changes significantly reduced microbial diversity, which then began to recover after 2 years. The microbial network during 1-year subsidence was simpler, with 102 nodes, 179 edges, and an average degree of 3.51, indicating that early subsidence was unstable, and the microbial community was still adapting. By 1 year, community structure and interactions had begun to stabilize. Stochastic processes played a key role in microbial variability during short-term subsidence.

Crab bioturbation alters the community assemblies of abundant and rare bacteria on an intertidal wetland in the Yellow River estuary

Introduction

Revealing assembly patterns of abundant and rare bacteria is pivotal for comprehending the responses of soil bacterial community to environmental changes. Crabs exert significant impacts on soil environments through their frequent burrowing activities in intertidal wetlands. However, there remains a paucity of knowledge regarding the influencing mechanism of crab bioturbation on community assemblies of abundant and rare bacteria.

Methods

We delved into community structures, co-occurrence networks, and assembly processes of abundant and rare bacteria within crab-bioturbated soils (encompassing burrows and mounds) across an intertidal wetland.

Results and discussion

The compositions and diversities of abundant and rare subcommunities were notably altered in crab-bioturbated soils. Moreover, the co-occurrence network analysis unveiled that crab bioturbation substantially modified the interaction patterns of rare bacteria, whereas its influence on abundant bacteria was comparatively minor. Furthermore, we discovered that the assembly processes of abundant subcommunities were primarily influenced by stochastic processes, while rare subcommunity assemblies were collectively shaped by both stochastic and deterministic processes. In conclusion, our study elucidates the mechanism by which crab bioturbation mediates the distinct assembly processes of abundant and rare subcommunities, and underscores the importance of considering rare bacteria when evaluating the ecological functions of intertidal wetlands.

Increasing pesticide diversity impairs soil microbial functions

Pesticide application is essential for stabilizing agricultural production. However, the effects of increasing pesticide diversity on soil microbial functions remain unclear, particularly under varying nitrogen (N) fertilizer management practices. In this study, we investigated the stochasticity of soil microbes and multitrophic networks through amplicon sequencing, assessed soil community functions related to carbon (C), N, phosphorus (P), and sulfur (S) cycling, and characterized the dominant bacterial life history strategies via metagenomics along a gradient of increasing pesticide diversity under two N addition levels. Our findings show that higher pesticide diversity enriches the abundance of bacterial specialists and opportunists capable of degrading or resisting pesticides, reducing the proportion of bacterial generalists in the absence of N addition. These shifts can complicate multitrophic microbial networks. Under increased pesticide diversity, selective pressure may drive bacteria to streamline their average genome size to conserve energy while enhancing C, N, P, and S metabolic capacities, thus accelerating soil nutrient loss. In comparison, N addition was found to reduce bacterial niche differentiation at higher pesticide diversity, mitigating the impacts of network complexity and functional traits associated with pesticide diversity, ultimately alleviating soil nutrient loss. Our results reveal the contrasting impacts of pesticide diversity on microbial functions under different N input scenarios and emphasize that strategic N fertilizer management can mitigate the ecological effects of pesticide use in agricultural systems.

Evidence of sewage discharge on the coalescence mechanism of aquatic microbial communities during high amplitude hydrological periods

Microbial community coalescence is a ubiquitous ecological process in various ecosystems. However, limited research has addressed the effects of the coalescence on microbial ecological processes and network structure, particularly in the context of sewage discharge during high amplitude hydrological periods. Employing 16S rRNA sequencing and species source tracking analysis, we investigated the coalescence pattern of bacterioplankton in the Chishui river and sewage across various hydrological periods. The results demonstrated that the downstream bacterioplankton mainly originated from the upstream water body, and the sewage discharge from the wastewater treatment plants (WWTPs) had less impact on the downstream bacterioplankton. In the low-water period, the bacterioplankton community showed significant coalescence, and the specialist species and functional taxa gathered in the downstream. Bacterioplankton displayed distinct ecological succession patterns after community coalescence, with notable variations in the abundance of dominant group. Bacterioplankton community assembly was dominated by stochastic processes in river and the sewage over different hydrological periods. The ecological networks exhibited the highest complexity in the high-water period, whereas their stability was most pronounced in the low-water period. Species diversity, as opposed to functional and phylogenetic diversity, might be a more accurate indicator to predict changes in microbial network structure. Our findings will provide new perspectives on the mechanisms of aquatic microbial community coalescence in natural environments.

Diversity, connectivity and negative interactions define robust microbiome networks across land, stream, and sea

In this era of rapid global change, factors influencing the stability of ecosystems and their functions have come into the spotlight. For decades the relationship between stability and complexity has been investigated in modeled and empirical systems, yet results remain largely context dependent. To overcome this we leverage a multiscale inventory of fungi and bacteria ranging from single sites along an environmental gradient, to habitats inclusive of land, sea and stream, to an entire watershed. We use networks to assess the relationship between microbiome complexity and robustness and identify fundamental principles of stability. We demonstrate that while some facets of complexity are positively associated with robustness, others are not. Beyond positive biodiversity x robustness relationships we find that the number of "gatekeeper" species or those that are highly connected and central within their networks, and the proportion of predicted negative interactions are universal indicators of robust microbiomes. With the potential promise of microbiome engineering to address global challenges ranging from human to ecosystem health we identify properties of microbiomes for future experimental studies that may enhance their stability. We emphasize that features beyond biodiversity and additional characteristics beyond stability such as adaptability should be considered in these efforts.

Planted Citrus Regulates the Community and Networks of phoD-Harboring Bacteria to Drive Phosphorus Availability Between Karst and Non-Karst Soils

The phosphorus (P) availability in soils is influenced by microbes, particularly those containing the gene responsible for phosphate solubilization. The present study investigated the community structure, diversity, and co-occurrence networks of phoD-harboring bacteria in karst and non-karst citrus orchard soils across a planting duration gradient, natural forests, and abandoned land, as well as the soil total P (TP) and available P (AP) contents and enzyme activities. The soil AP contents were lower in the karst regions than in the non-karst regions, while the soil organic carbon (C; SOC), exchangeable calcium, and microbial biomass nitrogen (N) contents; alkaline phosphatase (ALP) and β-Glucuronidase activities; and pH had the opposite trends. In addition, the soil AP and SOC contents and the ALP and acid phosphatase (ACP) activities in the karst regions decreased with an increase in the planting years, whereas the AP, TP, and microbial biomass P contents and ACP activities in the non-karst regions increased. The diversity indices and network complexity of phoD-harboring bacteria were higher in the karst regions than in the non-karst regions, with marked community differences between different planting years in the non-karst regions. The soil AP was significantly and positively correlated with the rare genera Pelagicola, Methylobacter, Streptomyces, and Micromonospora in the karst regions and Roseivivax, Collimonas, Methylobacterium, Ralstonia, and Phyllobacterium in the non-karst regions. Structural Equation Modeling showed that citrus cultivation altered the soil pH, SOC, and total N, and, in turn, the phoD-harboring bacterial community structure and diversity, which led to changes in the ALP activity and P availability. Thus, the rare genera of the phoD-harboring bacteria, influenced by the pH and SOC, highly regulated the availability of P in the karst and non-karst citrus orchard soils.

Evidence of habitat specificity in sponge microbiomes from Antarctica

BACKGROUND

Marine sponges and their microbiomes are ecosystem engineers distributed across the globe. However, most research has focused on tropical and temperate sponges, while polar regions like Antarctica have been largely neglected. Despite its harsh conditions and geographical isolation, Antarctica is densely populated by sponges. In this study, we explored the extent of habitat specificity in the diversity, community composition, and microbial co-occurrence within Antarctic sponge microbiomes, in comparison to those from other marine environments. We used massive sequencing of 16S rRNA genes and integrated multiple databases to incorporate Antarctic sponges as a habitat in global microbiome analyses.

RESULTS

Our study revealed significant differences in microbial diversity and community composition between Antarctic and non-Antarctic sponges. We found that most microorganisms present in Antarctic sponges are unique to the South Shetland Islands. Nitrosomonas oligotropha, Candidatus Nitrosopumilus, Polaribacter, SAR116 clade, and Low Salinity Nitrite-Oxidizing Bacteria (LS-NOB) are microbial members characterizing the Antarctic sponge microbiomes. Based on their exclusivity and presence across different sponges worldwide, we identified habitat-specific and habitat-generalist bacteria associated with each habitat. They are particularly abundant and connected within all the Antarctic sponges, suggesting that they may play a crucial role as keystone species within these sponge ecosystems.

CONCLUSIONS

This study provides significant insights into the microbial diversity and community composition of sponges in Antarctica and non-Antarctic ecoregions. Our findings provide evidence for habitat-specific patterns that differentiate the microbiomes of Antarctic sponges from elsewhere, indicating the strong influence of environmental selection and dispersal limitation wrapped into the Antarctic ecoregions to shape more similar microbial communities in distantly related sponges. This study contributes to understanding signatures of microbial community assembly in the Antarctic sponges and has important implications for the ecology and evolution of these unique marine environments.

Key bacterial taxa with specific metabolisms and life history strategies sustain soil microbial network stability exposed to carbendazim and deoxynivalenol

Co-contamination of carbendazim (CBD) and deoxynivalenol (DON) is common in agricultural soils, yet their ecological impact on soil microbiome remains poorly assessed. Here, we investigated the influence of CBD and DON on the structure, function, and co-occurrence networks of soil microbiome. The combined treatment of CBD and DON significantly exacerbated the negative impacts on soil microbial diversity, functional diversity, and microbial network stability compared to individual treatments. Specifically, Lysobacter, Gemmatimonas, Nitrospira, Massilia, and Bacillus were identified as indicator species for CBD and DON. Simultaneously, the abundance of genes involved in key ecological functions, such as nitrification (amoA) and organic phosphorus mineralization (phoAD), was significantly reduced. Notably, key bacterial taxa Nitrospira and Gemmatimonas, with K-life history strategy and capabilities for nitrification and organic nitrogen mineralization, played crucial roles in promoting positive interactions in networks. Furthermore, variance partitioning analysis (VPA) and structural equation modeling (SEM) demonstrated that the abundance and niche breadth of key bacterial taxa were the primary drivers of microbial network stability. In conclusion, our study provides new insights into how soil microbiomes and networks respond to pesticides and mycotoxins, aiding in a more comprehensive assessment of exposure risks.

Depth-dependent effects of forest diversification on soil functionality and microbial community characteristics in subtropical forests

Soil microbes are critical to the maintenance of forest ecosystem function and stability. Forest diversification, such as monocultures versus mixed forests stands, can strongly influence microbial community patterns and processes, as well as their role in soil ecosystem multifunctionality, such as in subtropical forest ecosystems. However, less is known about these patterns and processes vary with soil depth. Here, we investigated the results of an eight-year forest diversification field experiment comparing the soil ecosystem multifunctionality, bacterial and fungal community assembly, and network patterns in mixed versus monoculture plantations along vertical profiles (0-80 cm depth) in a subtropical region. We found that the introduction of broadleaf trees in coniferous monocultures led to enhanced synergies between multiple functions, thus improving soil multifunctionality. The effects of mixed plantations on the functional potential in top soils were greater than in deep soils, especially for carbon degradation genes (apu, xylA, cex, and glx). Microbial community assembly in the top layer, particularly in mixed plantations, was dominated by stochastic processes, whereas deterministic were more important in the deep layer. Soil microbial network complexity and stability were higher in the top layer of mixed plantations, but in the deep layer was monoculture. Interestingly, the changes in microbial communities and multifunctionality in the top layer were mainly related to variation in nutrients, whereas those in the deep were more influenced by soil moisture. Overall, we reveal positive effects of mixed forest stands on soil microbial characteristics and functionality compared to that of monocultures. Our findings highlighted the importance of enhancing functional diversity through the promotion of tree species diversity, and managers can better develop forest management strategies to promote soil health under global change scenarios.

OneNet-One network to rule them all: Consensus network inference from microbiome data

Modeling microbial interactions as sparse and reproducible networks is a major challenge in microbial ecology. Direct interactions between the microbial species of a biome can help to understand the mechanisms through which microbial communities influence the system. Most state-of-the art methods reconstruct networks from abundance data using Gaussian Graphical Models, for which several statistically grounded and computationnally efficient inference approaches are available. However, the multiplicity of existing methods, when applied to the same dataset, generates very different networks. In this article, we present OneNet, a consensus network inference method that combines seven methods based on stability selection. This resampling procedure is used to tune a regularization parameter by computing how often edges are selected in the networks. We modified the stability selection framework to use edge selection frequencies directly and combine them in the inferred network to ensure that only reproducible edges are included in the consensus. We demonstrated on synthetic data that our method generally led to slightly sparser networks while achieving much higher precision than any single method. We further applied the method to gut microbiome data from liver-cirrothic patients and demonstrated that the resulting network exhibited a microbial guild that was meaningful in terms of human health.

Biocrusts Mediate the Niche Distribution and Diversity of Ammonia-Oxidizing Microorganisms in the Gurbantunggut Desert, Northwestern China

Biological soil crusts (biocrusts) play pivotal ecological roles in regulating nitrogen cycling within desert ecosystems. While acknowledging the essential role played by ammonia-oxidizing microorganisms in nitrogen transformation, there remains a paucity of understanding concerning how disturbances to biocrusts impact the diversity and spatial distribution patterns among ammonia oxidizer communities within temperate deserts. This investigation delved into assessing how 4 years' worth of removing biocrust influenced niche differentiation between nitrifying archaea and bacteria while also examining its effects on shaping community structures of predominant ammonia-oxidizing archaea (AOA) within the Gurbantunggut Desert soils. Despite notable variations in abundance of ammonia-oxidizing microbes across distinct soil depths throughout different seasons, it became apparent that removing biocrust significantly altered both the abundance and niche pattern for AOA alongside their bacterial counterparts during winter and summer periods. Notably dominating over their bacterial counterparts within desert soils, AOA displayed their highest archaeal to bacterial amoA gene copy ratio (6549-fold higher) at a soil depth of 5-10 cm during summer. Moreover, substantial impacts were observed upon AOA diversity along with compositional changes following such perturbation events. The aftermath saw an emergence of more diffuse yet dynamic AOA communities, especially noticeable amidst winter when nitrogen and water limitations were relatively alleviated. In summary, our findings underscore how interactions between biocrust coverages alongside factors like soil temperature, total carbon content, or NO3-_N concentrations govern niches occupied by ammoxidation communities whilst influencing assemblage processes too. The sensitivity shown by dominant AOAs towards biocrust removal further underscores how biocrust coverage influences nitrogen transformation processes while potentially involving other communities and functions in desert ecosystems.

Characteristics of soil microbial community assembly patterns in fields with serious occurrence of tobacco Fusarium wilt disease

Introduction

Fusarium wilt disease (FWD) of tobacco is a destructive disease caused by Fusarium spp. in tobacco-growing regions worldwide. The Fusarium spp. infection may alter the composition and structure of the tobacco root microbial community; however, the relationship between these factors under large-scale geographical conditions in China remains underexplored.

Methods

In the context of this investigation, soil samples from the rhizosphere of tobacco plants were procured from fields afflicted with FWD and those devoid of the disease in the Hanzhong region of Shaanxi province, as well as in the Sanmenxia and Nanyang regions of Henan province. These regions are recognized for the commercial cultivation of tobacco. The examination focused on discerning the influence of tobacco FWD on the composition and configuration of the rhizosphere microbial community, along with their co-occurrence patterns. This scrutiny was underpinned by targeted PCR amplification and high-throughput sequencing (amplicon sequencing) of the 16S rRNA gene and the ITS1 region.

Results

The amplicon data analyses showed that FWD influenced the microbial structure and composition of the tobacco rhizosphere soil. FWD had a greater impact on the microbiome of the tobacco fungal community than on the microbiome of the bacterial community. Healthy plants had the ability to recruit potential beneficial bacteria. Diseased plants were more susceptible to colonization by other pathogenic fungi, but they still had the capacity to recruit potential beneficial bacteria. The analysis of microbial intra- and inter-kingdom networks further indicated that FWD destabilized microbial networks. In the overall microbial interaction, microorganisms primarily interacted within their boundaries, but FWD increased the proportion of interactions occurring across boundaries. In addition, FWD could disrupt the interactions within microbial network modules.

Discussion

This study provides evidence that FWD can cause changes in the composition and network of microbial communities, affecting the interactions among various microorganisms, including bacteria and fungi. These findings contribute to our understanding of how plant microbiomes change due to disease. Furthermore, they add to our knowledge of the mechanisms that govern the assembly and interactions of microbial communities under the influence of FWD.

An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields

Motivation

The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference.

Results

In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method's ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations.

Availability and implementation

The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.

SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning

Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.

iNAP 2.0: Harnessing metabolic complementarity in microbial network analysis

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Networks as tools for defining emergent properties of microbiomes and their stability

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data

Alterations in human microbial communities are intricately linked to the onset and progression of diseases. Identifying the key microbes driving these community changes is crucial, as they may serve as valuable biomarkers for disease prevention, diagnosis, and treatment. However, there remains a need for further research to develop effective methods for addressing this critical task. This is primarily because defining the driver microbe requires consideration not only of each microbe's individual contributions but also their interactions. This paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (i) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (ii) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (iii) quantification of each microbe's contribution to the community's steady state by manipulating the causal graph implied by gLV equations. The performance of nonparametric spline-based denoising and regularized least squares estimation is comprehensively evaluated on simulated datasets, demonstrating superiority over existing methods. Furthermore, the practical applicability and effectiveness of mbDriver are showcased using a dietary fiber intervention dataset and an ulcerative colitis dataset. Notably, driver microbes identified in the dietary fiber intervention dataset exhibit significant effects on the abundances of short-chain fatty acids, while those identified in the ulcerative colitis dataset show a significant correlation with metabolism-related pathways.

Green manure (Ophiopogon japonicus) cover promotes tea plant growth by regulating soil carbon cycling

Introduction

In mountainous tea plantations, which are the primary mode of tea cultivation in China, issues such as soil erosion and declining soil fertility are particularly severe. Although green manure cover is an effective agricultural measure for restoring soil fertility, its application in mountainous tea plantations has been relatively understudied.

Methods

This study investigated the effects of continuous green manure cover using the slope-protecting plant Ophiopogon japonicus on tea plant growth and soil microbial community structure. We implemented three treatments: 1 year of green manure coverage, 2 years of coverage, and a control, to study their effects on tea plant growth, soil physicochemical properties, and soil bacterial and fungal communities.

Results

Results demonstrate that green manure coverage significantly promote the growth of tea plants, enhanced organic matter and pH levels in soil, and various enzyme activities, including peroxidases and cellulases. Further functional prediction results indicate that green manure coverage markedly promoted several carbon cycling functions in soil microbes, including xylanolysis, cellulolysis, degradation of aromatic compounds, and saprotrophic processes. LEfSe analysis indicated that under green manure cover, the soil tends to enrich more beneficial microbial communities with degradation functions, such as Sphingomonas, Sinomonas, and Haliangium (bacteria), and Penicillium, Apiotrichum, and Talaromyce (fungi). In addition. Random forest and structural equation models indicated that carbon cycling, as a significant differentiating factor, has a significant promoting effect on tea plant growth.

Discussion

In the management practices of mountainous tea plantations, further utilizing slope-protecting plants as green manure can significantly influence the soil microbial community structure and function, enriching microbes involved in the degradation of organic matter and aromatic compounds, thereby positively impacting tea tree growth and soil nutrient levels.

Metabolic interdependencies in thermophilic communities are revealed using co-occurrence and complementarity networks

Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.

Plant diversity drives positive microbial associations in the rhizosphere enhancing carbon use efficiency in agricultural soils

Expanding and intensifying agriculture has led to a loss of soil carbon. As agroecosystems cover over 40% of Earth's land surface, they must be part of the solution put in action to mitigate climate change. Development of efficient management practices to maximize soil carbon retention is currently limited, in part, by a poor understanding of how plants, which input carbon to soil, and microbes, which determine its fate there, interact. Here we implement a diversity gradient by intercropping undersown species with barley in a large field trial, ranging from one to eight undersown species. We find that increasing plant diversity strengthens positive associations within the rhizosphere soil microbial community in relation to negative associations. These associations, in turn, enhance community carbon use efficiency. Jointly, our results highlight how increasing plant diversity in agriculture can be used as a management strategy to enhance carbon retention potential in agricultural soils.

A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a "target of interest" within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray-Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.

From nets to networks: tools for deciphering phytoplankton metabolic interactions within communities and their global significance

Our oceans are populated with a wide diversity of planktonic organisms that form complex dynamic communities at the base of marine trophic networks. Within such communities are phytoplankton, unicellular photosynthetic taxa that provide an estimated half of global primary production and support biogeochemical cycles, along with other essential ecosystem services. One of the major challenges for microbial ecologists has been to try to make sense of this complexity. While phytoplankton distributions can be well explained by abiotic factors such as temperature and nutrient availability, there is increasing evidence that their ecological roles are tightly linked to their metabolic interactions with other plankton members through complex mechanisms (e.g. competition and symbiosis). Therefore, unravelling phytoplankton metabolic interactions is the key for inferring their dependency on, or antagonism with, other taxa and better integrating them into the context of carbon and nutrient fluxes in marine trophic networks. In this review, we attempt to summarize the current knowledge brought by ecophysiology, organismal imaging, in silico predictions and co-occurrence networks using 'omics data, highlighting successful combinations of approaches that may be helpful for future investigations of phytoplankton metabolic interactions within their complex communities.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Maize/soybean intercropping with nitrogen supply levels increases maize yield and nitrogen uptake by influencing the rhizosphere bacterial diversity of soil

Introduction

Intercropping practices play a crucial role in enhancing and maintaining the biodiversity and resiliency of agroecosystems, as well as promoting stable and high crop yields. Yet the relationships between soil nitrogen, microbes, and yield in maize cultivated under maize/soybean intercropping systems remain unclear.

Methods

To fill that knowledge gap, here we collected maize rhizosphere soil at the staminate stage after 6 consecutive years of maize/soybean intercropping, to investigate how intercropping and nitrogen application rates affected nitrogen utilization by crops and soil microbial community composition and function. We also examined correlations of those responses with yields, to clarify the main ways that yield is enhanced via intercropping and by nitrogenous fertilizer gradient changes generated by different nitrogen application rates.

Results

The amount of applied fertilizer was 240 kg N ha-1 was best for obtaining a high maize yield and also led to the greatest nitrogen-use efficiency and bacterial diversity. Under the same N application rate, intercropping increased the maize yield by 31.17% and soil nitrogen (total, ammonium and nitrate nitrogen) by 14.53%, on average, in comparison to monocropping. The enrichment of Gemmatimonas and Bradyrhizobium significantly increased the soil nitrogen content, and a greater relative abundance of Sphingomonas and Gemmatimonas increased the maize yield, whereas enrichment of Candidatus_Udaeobacter and Bradyrhizobium decreased it. The benefits of intercropping mainly arise from augmenting the abundance of beneficial microorganisms and enhancing the efficiency of N use by crop plants.

Discussion

This study's findings are of key importance to bolster the stability of agro-ecosystems, to guide the scientific rational use of nitrogen fertilizers, and to provide a sound theoretical basis for achieving the optimal management of intensive crop-planting patterns and green sustainable development.

Maize-Soybean Rotation and Intercropping Increase Maize Yield by Influencing the Structure and Function of Rhizosphere Soil Fungal Communities

Soil-borne diseases are exacerbated by continuous cropping and negatively impact maize health and yields. We conducted a long-term (11-year) field experiment in the black soil region of Northeast China to analyze the effects of different cropping systems on maize yield and rhizosphere soil fungal community structure and function. The experiment included three cropping systems: continuous maize cropping (CMC), maize-soybean rotation (MSR), and maize-soybean intercropping (MSI). MSI and MSR resulted in a 3.30-16.26% lower ear height coefficient and a 7.43-12.37% higher maize yield compared to CMC. The richness and diversity of rhizosphere soil fungi were 7.75-20.26% lower in MSI and MSR than in CMC. The relative abundances of Tausonia and Mortierella were associated with increased maize yield, whereas the relative abundance of Solicoccozyma was associated with decreased maize yield. MSI and MSR had higher proportions of wood saprotrophs and lower proportions of plant pathogens than CMC. Furthermore, our findings indicate that crop rotation is more effective than intercropping for enhancing maize yield and mitigating soil-borne diseases in the black soil zone of Northeast China. This study offers valuable insights for the development of sustainable agroecosystems.

Maize, Peanut, and Millet Rotations Improve Crop Yields by Altering the Microbial Community and Chemistry of Sandy Saline-Alkaline Soils

Crop rotation increases crop yield, improves soil health, and reduces plant disease. However, few studies were conducted on the use of intensive cropping patterns to improve the microenvironment of saline soils. The present study thoroughly evaluated the impact of a three-year maize-peanut-millet crop rotation pattern on the crop yield. The rhizosphere soil of the crop was collected at maturity to assess the effects of crop rotation on the composition and function of microbial communities in different tillage layers (0-20 cm and 20-40 cm) of sandy saline-alkaline soils. After three years of crop rotation, the maize yield and economic benefits rose by an average of 32.07% and 22.25%, respectively, while output/input grew by 10.26%. The pH of the 0-40 cm tillage layer of saline-alkaline soils decreased by 2.36%, organic matter rose by 13.44%-15.84%, and soil-available nutrients of the 0-20 cm tillage layer increased by 11.94%-69.14%. As compared to continuous cropping, crop rotation boosted soil nitrogen and phosphorus metabolism capacity by 8.61%-88.65%. Enrichment of Actinobacteria and Basidiomycota increased crop yield. Crop rotation increases microbial community richness while decreasing diversity. The increase in abundance can diminish competitive relationships between species, boost synergistic capabilities, alter bacterial and fungal community structure, and enhance microbial community function, all of which elevate crop yields. The obtained insights can contribute to achieving optimal management of intensive cultivation patterns and green sustainable development.

Impact of wildfire ash on skin and gut microbiomes and survival of Rana dybowskii

Climate change and human activities escalate the frequency and intensity of wildfires, threatening amphibian habitats and survival; yet, research on these impacts remains limited. Wildfire ash alters water quality, introduces contaminants, and may disrupt microbial communities, impacting gut and skin microbiota; however, the effects on gut and skin microbiota remain unclear. Rana dybowskii were exposed to five concentrations (0 g L-1, 1.25 g L-1, 2.5 g L-1, 5 g L-1, and 10 g L-1) of aqueous extracts of wildfire ashes (AEAs) for 30 days to assess AEAs' metal content, survival, and microbiota diversity via Illumina sequencing. Our results showed that the major elements in ash were Ca > K > Mg > Al > Fe > Na > Mn, while in AEA they were K > Ca > Na > Mg > As > Al > Cu. A significant decrease in amphibian survival rates with increased AEA concentration was shown. The beta diversity analysis revealed distinct shifts in microbiota composition. Notably, bacterial genera associated with potential health risks showed increased abundance in skin microbiota, emphasising the potential for ash exposure to affect amphibian health. Functional prediction analyses revealed significant shifts in metabolic pathways related to health and disease, indicating that wildfire ash exposure may influence amphibian health through changes in microbial functions. This study highlights the urgent need for strategies to mitigate wildfire ash impacts on amphibians, as it significantly alters microbiota and affects their survival and health.

Non-flooding conditions caused by water table drawdown alter microbial network complexity and decrease multifunctionality in alpine wetland soils

Several soil functions of alpine wetland depend on microbial communities, including carbon storage and nutrient cycling, and soil microbes are highly sensitive to hydrological conditions. Wetland degradation is often accompanied by a decline in water table. With the water table drawdown, the effects of microbial network complexity on various soil functions remain insufficiently understood. In this research, we quantified soil multifunctionality of flooded and non-flooded sites in the Lalu Wetland on the Tibetan Plateau. We employed high-throughput sequencing to investigate the microbial community responses to water table depth changes, as well as the relationships between microbial network properties and soil multifunctionality. Our findings revealed a substantial reduction in soil multifunctionality at both surface and subsurface soil layers (0-20 cm and 20-40 cm) in non-flooded sites compared to flooded sites. The α-diversity of bacteria in the surface soil of non-flooded sites was significantly lower than that in flooded sites. Microbial network properties (including the number of nodes, number of edges, average degree, density, and modularity of co-occurrence networks) exhibited significant correlations with soil multifunctionality. This study underscores the adverse impact of non-flooded conditions resulting from water table drawdown on soil multifunctionality in alpine wetland soils, driven by alterations in microbial community structure. Additionally, we identified soil pH and moisture content as pivotal abiotic factors influencing soil multifunctionality, with microbial network complexity emerging as a valuable predictor of multifunctionality.

Body size: A hidden trait of the organisms that influences the distribution of antibiotic resistance genes in soil

Body size is a key life-history trait of organisms, which has important ecological functions. However, the relationship between soil antibiotic resistance gene (ARG) distribution and organisms' body size has not been systematically reported so far. Herein, the impact of organic fertilizer on the soil ARGs and organisms (bacteria, fungi, and nematode) at the aggregate level was analyzed. The results showed that the smaller the soil aggregate size, the greater the abundance of ARGs, and the larger the body size of bacteria and nematodes. Further analysis revealed significant positive correlations of ARG abundance with the body sizes of bacteria, fungi, and nematodes, respectively. Additionally, the structural equation model demonstrated that changes in soil fertility mainly regulate the ARG abundance by affecting bacterial body size. The random forest model revealed that total phosphorus was the primary soil fertility factor influencing the body size of organisms. Therefore, these findings proposed that excessive application of phosphate fertilizers could increase the risk of soil ARG transmission by increasing the body size of soil organisms. This study highlights the significance of organisms' body size in determining the distribution of soil ARGs and proposes a new disadvantage of excessive fertilization from the perspective of ARGs.

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.

Macrogenomics-Based Analysis of the Effects of Intercropped Soybean Photosynthetic Characteristics and Nitrogen-Assimilating Enzyme Activities on Yield at Different Nitrogen Levels

Currently, China's soybean self-sufficiency rate is only 15%, highlighting the soybean crisis and the supply chain risks that pose a major threat to China's food security. Thus, it has become imperative to step up efforts to boost soybean production capacity while promoting the green and sustainable development of regional farmland ecosystems. In this context, the present study comprehensively investigated the effects of intercropping and nitrogen application rate on soybean yield, as well as the changes in gradients generated by different levels of nitrogen application. Based on six consecutive years of maize-soybean intercropping planting patterns, the inter-root soils of soybeans were collected at the flowering stage and evaluated for soil nitrogen content, nitrogen-assimilating enzyme activities, and microbial community composition of soybean, which were correlated with yield, to clarify the main pathways and modes of intercropping effects. The N2 level (80 kg·ha-1) was favourable for higher yield. In comparison to monocropping, the intercropping reduced yield by 9.65-13.01%, photosynthetic characteristics by 1.33-7.31%, and plant nitrogen-assimilating enzyme activities by 8.08-32.01% at the same level of N application. Likewise, soil urease and catalase activities were reduced by 9.22 and 1.80%, while soil nitrogen content declined by an average of 6.38%. Gemmatimonas and Bradyrhizobium enrichment significantly increased soil nitrogen content, photosynthetic characteristics, and soybean yield, while it was reduced by Candidatus_Udaeobacter and Candidatus_Solibacte enrichment. The results of this study provide a theoretical basis for further optimising maize-soybean intercropping, which is crucial for enhancing the agricultural production structure and improving the overall soybean production capacity.

Mycobiome mediates the interaction between environmental factors and mycotoxin contamination in wheat grains

Environmental factors significantly impact grain mycobiome assembly and mycotoxin contamination. However, there is still a lack of understanding regarding the wheat mycobiome and the role of fungal communities in the interaction between environmental factors and mycotoxins. In this study, we collected wheat grain samples from 12 major wheat-producing provinces in China during both the harvest and storage periods. Our aim was to evaluate the mycobiomes in wheat samples with varying deoxynivalenol (DON) contamination levels and to confirm the correlation between environmental factors, the wheat mycobiome, and mycotoxins. The results revealed significant differences in the wheat mycobiome and co-occurrence network between contaminated and uncontaminated wheat samples. Fusarium was identified as the main differential taxon responsible for inducing DON contamination in wheat. Correlation analysis identified key factors affecting mycotoxin contamination. The results indicate that both environmental factors and the wheat mycobiome play significant roles in the production and accumulation of DON. Environmental factors can affect the wheat mycobiome assembly, and wheat mycobiome mediates the interaction between environmental factors and mycotoxin contamination. Furthermore, a random forest (RF) model was developed using key biological indicators and environmental features to predict DON contamination in wheat with accuracies exceeding 90 %. The findings provide data support for the accurate prediction of mycotoxin contamination and lay the foundation for the research on biological control technologies of mycotoxin through the assembly of synthetic microbial communities.

Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables

The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the "stochastic block model" method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages.

IMPORTANCE

Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales. These findings could be applied to the design of new fermented products.

The development of the biological soil crust regulates the fungal distribution and the stability of fungal networks

The heterogeneous composition of fungi plays an indispensable role in the foundation of the multifunctionalities of ecosystems within drylands. The precise mechanisms that govern fluctuations in soil fungal assemblages in dryland ecosystems remain incompletely elucidated. In this study, biological soil crusts (biocrusts) at different successional stages in the Gurbantunggut Desert were used as substrates to examine the characteristics and driving factors that influence fungal abundance and community dynamics during biocrust development using qPCR and high-throughput sequencing of the ITS2 region. The findings showed that the physicochemical properties changed significantly with the development of biocrusts. In particular, total nitrogen increased 4.8 times, along with notable increases in ammonium, total phosphorus (2.1 times) and soil organic carbon (6.5 times). Initially, there was a rise in fungal abundance, which was subsequently followed by a decline as the biocrust developed, with the highest abundance detected in lichen crust (2.66 × 107 copies/g soil) and the lowest in bare sand (7.98 × 106 copies/g soil). Ascomycetes and Basidiomycetes emerged as dominant phyla, collectively forming 85% of the fungal community. As the biocrust developed, noticeable alterations occurred in fungal community compositions, resulting from changes in the relative proportions of Dothideomycetes, Lecanoromycetes and unclassified ascomycetes. Nitrogen, phosphorus, organic carbon content, and pH of biocrusts were identified as direct or indirect regulators of fungal abundance and community structure. The complexity of fungal networks increased as biocrusts developed as revealed by network analysis, but reduced in the stability of fungal communities within algal and lichen crusts. Keystone species within the fungal community also underwent changes as biocrust developed. These results suggested that shifts in interspecies relationships among fungi could further contribute to the variation in fungal communities during the development of biocrusts.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Ecological degradation of a fragile semi-arid wetland and the implications in its microbial community: The case study of Las Tablas de Daimiel National Park (Spain)

Las Tablas de Daimiel National Park (TDNP, Iberian Peninsula) is a semi-arid wetland of international significance for waterfowl and serves as a migratory route for various bird species. However, TDNP presents strong anthropization and fluctuating water levels, making it a highly fragile ecosystem. Water physico-chemical parameters and microbial diversity of the three domains (Bacteria-Archaea- Eukarya) were analysed in Zone A and Zone B of the wetland (a total of eight stations) during spring and summer, aiming to determine how seasonal changes influence the water quality, trophic status and ultimately, the microbial community composition. Additionally, Photosynthetically Active Radiation (PAR) was used to determine the trophic status instead of transparency using Secchi disk, setting the threshold to 20-40 μmol/sm2 for benthic vegetation growth. In spring, both zones of the wetland were considered eutrophic, and physico-chemical parameters as well as microbial diversity were similar to other wetlands, with most abundant bacteria affiliated to Actinobacteriota, Cyanobacteria, Bacteroidota, Gammaproteobacteria and Verrumicrobiota. Methane-related taxa like Methanosarcinales and photosynthetic Chlorophyta were respectively the most representative archaeal and eukaryotic groups. In summer, phytoplankton bloom led by an unclassified Cyanobacteria and mainly alga Hydrodictyon was observed in Zone A, resulting in an increase of turbidity, pH, phosphorus, nitrogen, chlorophyll-a and phycocyanin indicating the change to hypertrophic state. Microbial community composition was geographical and seasonal shaped within the wetland as response to changes in trophic status. Archaeal diversity decreases and methane-related species increase due to sediment disturbance driven by fish activity, wind, and substantial water depth reduction. Zone B in summer suffers less seasonal changes, maintaining the eutrophic state and still detecting macrophyte growth in some stations. This study provides a new understanding of the interdomain microbial adaptation following the ecological evolution of the wetland, which is crucial to knowing these systems that are ecological niches with high environmental value.

The MTIST platform: a microbiome time series inference standardized test

The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.

Enhancing soil health and nutrient cycling through soil amendments: Improving the synergy of bacteria and fungi

The synergy between bacteria and fungi is a key determinant of soil health and have a positive effect on plant development under drought conditions, with the potentially enhancing the sustainability of amending soil with natural materials. However, identifying how soil amendments influence plant growth is often difficult due to the complexity of microorganisms and their links with different soil amendment types and environmental factors. To address this, we conducted a field experiment to examine the impact of soil amendments (biochar, Bacillus mucilaginosus, Bacillus subtilis and super absorbent polymer) on plant growth. We also assessed variations in microbial community, links between fungi and bacteria, and soil available nutrients, while exploring how the synergistic effects between fungus and bacteria influenced the response of soil amendments to plant growth. This study revealed that soil amendments reduced soil bacterial diversity but increased the proportion of the family Enterobacteriaceae, Nitrosomonadaceae, and also increased soil fungal diversity and the proportion of the sum of the family Lasiosphaeriaceae, Chaetomiaceae, Pleosporaceae. Changes in soil microbial communities lead to increase the complexity of microbial co-occurrence networks. Furthermore, this heightened network complexity enhanced the synergy of soil bacteria and fungi, supporting bacterial functions related to soil nutrient cycling, such as metabolic functions and genetic, environmental, and cellular processes. Hence, the BC and BS had 3.0-fold and 0.5-fold greater root length densities than CK and apple tree shoot growth were increased by 62.14 %,50.53 % relative to CK, respectively. In sum, our results suggest that the synergistic effect of bacteria and fungi impacted apple tree growth indirectly by modulating soil nutrient cycling. These findings offer a new strategy for enhancing the quality of arable land in arid and semi-arid regions.