Disentangling Interactions in the Microbiome: A Network Perspective

Differed biotic interactions influenced by anthropogenic disturbances among trophic levels in fragmented wetlands

Environmental changes derived from various human activities have largely disturbed the structure and functioning of various biological communities. However, little is known on how such disturbance impacts species interactions in biological communities. This study aims to elucidate the variation of species interactions across multiple trophic levels and further determine crucial factor(s) in regulating observed variation. We collected plankton samples from Sanjiang Wetlands in Northeastern China and used random matrix theory (MRT)-based approach to construct species interaction networks for bacterioplanktons, protozoans, and metazoans, respectively. We found that biotic interactions were more complex at lower trophic levels. Network key species (e.g., module hubs and connectors) were detected only in the bacterioplankton network. More inter- and intra-module connections, particularly negative connections, were detected in the bacterioplankton network. Across all three trophic levels, the element sodium (Na) was the most important factor influencing the network structure, while at each trophic level, physicochemical factors, nutrients, and organic pollutants were identified as crucial determinants but their relative importance differed. In particular, no correlation was detected between the metazoan network and any environmental factor. After separating protozoan and metazoan communities into subgroups in relatively poor and good water environments, we found community interaction networks were more complex in good conditions than in poor conditions. A simple network structure (e.g., no inter-module connectors or intra-module hubs, and less competitive links) and less association with environmental factors in the higher trophic levels clearly illustrate that metazoan and protozoan communities in the fragmented wetlands are unstable and vulnerable. Therefore, further environmental changes may greatly influence species interactions in these communities. Collectively, our findings provide new insights into dynamics of influence of environmental changes on biotic interactions in aquatic biological communities, highlighting the necessity to use a multi-trophic strategy when assessing negative effects of environmental changes in aquatic ecosystems.

A Probabilistic Structural Equation Model to Evaluate Links between Gut Microbiota and Body Weights of Chicken Fed or Not Fed Insect Larvae

Simple Summary

Feeding poultry with insects could reduce production costs, but the impact of this diet on their gut microbiota and growth is little known because the network of relationships between their weights, the composition of their microbiota and their diet is complex and potentially biased by confounding factors (such as the gut compartment, age and sex of the birds). In this study, we were able to unravel these relationships in local breed chickens fed or not fed with black soldier fly larvae thanks to a technique of artificial intelligence (the probabilistic structural equation model). Bacteria were grouped into few entities with distinctive metabolic attributes and were probably linked nutritionally. Birds’ age influenced body weights and bacterial composition. The proposed methodology was thus able to simplify the complex dependencies among bacteria present in the gut and to highlight links potentially important in the response of chicken to insect feed.

Abstract

Feeding chicken with black soldier fly larvae (BSF) may influence their rates of growth via effects on the composition of their gut microbiota. To verify this hypothesis, we aim to evaluate a probabilistic structural equation model because it can unravel the complex web of relationships that exist between the bacteria involved in digestion and evaluate whether these influence bird growth. We followed 90 chickens fed diets supplemented with 0%, 5% or 10% BSF and measured the strength of the relationship between their weight and the relative abundance of bacteria (OTU) present in their cecum or cloaca at 16, 28, 39, 67 or 73 days of age, while adjusting for potential confounding effects of their age and sex. Results showed that OTUs (62 genera) could be combined into ten latent constructs with distinctive metabolic attributes. Links were discovered between these constructs that suggest nutritional relationships. Age directly influenced weights and microbiotal composition, and three constructs indirectly influenced weights via their dependencies on age. The proposed methodology was able to simplify dependencies among OTUs into knowledgeable constructs and to highlight links potentially important to understand the role of insect feed and of microbiota in chicken growth.

Contrasting Effects of Local Environmental and Biogeographic Factors on the Composition and Structure of Bacterial Communities in Arid Monospecific Mangrove Soils

Mangrove forests are important biotic sinks of atmospheric CO2 and play an integral role in nutrient-cycling and decontamination of coastal waters, thereby mitigating climatic and anthropogenic stressors. These services are primarily regulated by the activity of the soil microbiome. To understand how environmental changes may affect this vital part of the ecosystem, it is key to understand the patterns that drive microbial community assembly in mangrove forest soils. High-throughput amplicon sequencing (16S rRNA) was applied on samples from arid Avicennia marina forests across different spatial scales from local to regional. Alongside conventional analyses of community ecology, microbial co-occurrence networks were assessed to investigate differences in composition and structure of the bacterial community. The bacterial community composition varied more strongly along an intertidal gradient within each mangrove forest, than between forests in different geographic regions (Australia/Saudi Arabia). In contrast, co-occurrence networks differed primarily between geographic regions, illustrating that the structure of the bacterial community is not necessarily linked to its composition. The local diversity in mangrove forest soils may have important implications for the quantification of biogeochemical processes and is important to consider when planning restoration activities. IMPORTANCE Mangrove ecosystems are increasingly being recognized for their potential to sequester atmospheric carbon, thereby mitigating the effects of anthropogenically driven greenhouse gas emissions. The bacterial community in the soils plays an important role in the breakdown and recycling of carbon and other nutrients. To assess and predict changes in carbon storage, it is important to understand how the bacterial community is shaped by its environment. Here, we compared the bacterial communities of mangrove forests on different spatial scales, from local within-forest to biogeographic comparisons. The bacterial community composition differed more between distinct intertidal zones of the same forest than between forests in distant geographic regions. The calculated network structure of theoretically interacting bacteria, however, differed most between the geographic regions. Our findings highlight the importance of local environmental factors in shaping the microbial soil community in mangroves and highlight a disconnect between community composition and structure in microbial soil assemblages.

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

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

Community Assembly and Co-Occurrence Patterns of Microeukaryotes in Thermokarst Lakes of the Yellow River Source Area

Thermokarst lakes are important aquatic ecosystems in cold regions, experiencing several changes due to global warming. However, the fundamental assembly mechanisms of microeukaryotic communities in thermokarst lakes are unknown. In this study, we examined the assembly processes and co-occurrence networks of microeukaryotic communities in sediment and water of thermokarst lakes in the Yellow River Source Area. Sediment microeukaryotic communities had a significantly lower α-diversity but higher β-diversity than water microeukaryotic communities. pH, sediment organic carbon, and total phosphorus significantly affected taxonomic and phylogenetic diversity of sediment communities, while conductivity was a significant driver for water communities. Both sediment and water microeukaryotic communities were strongly governed by dispersal limitation. However, deterministic processes, especially homogenous selection, were more relevant in structuring microeukaryotic communities in water than those in sediment. Changes in total nitrogen and phosphorus in sediment could contribute to shift its microeukaryotic communities from homogeneous selection to stochastic processes. Co-occurrence networks showed that water microeukaryotic communities are more complex and interconnected but have lower modularity than sediment microeukaryotic communities. The water microeukaryotic network had more modules than the sediment microeukaryotic network. These modules were dominated by different taxonomic groups and associated to different environmental variables.

Long-Term Application of Bio-Compost Increased Soil Microbial Community Diversity and Altered Its Composition and Network

The influence of bio-compost on the diversity, composition and structure of soil microbial communities is less understood. Here, Illumina MiSeq sequencing and a network analysis were used to comprehensively characterize the effects of 25 years of bio-compost application on the microbial diversity of soil and community composition. High dosages of bio-compost significantly increased the bacterial and fungal richness. The compositions of bacterial and fungal communities were significantly altered by bio-compost addition. Bio-compost addition enriched the relative abundance of beneficial microorganisms (such as Sphingomonas, Acidibacter, Nocardioides, etc.) and reduced the relative abundance of harmful microorganisms (such as Stachybotrys and Aspergillus). Electrical conductivity, soil organic matter and total phosphorus were the key factors in shaping soil microbial community composition. The bacterial network was more complex than fungal network, and bacteria were more sensitive to changes in environmental factors than fungi. Positive interactions dominated both the bacterial and fungal networks, with stronger positive interactions found in the bacterial network. Functional prediction suggested that bio-composts altered the soil bacterial-community metabolic function with respect to carbon, nitrogen and phosphorus cycles and fungal community trophic modes. In conclusion, suitable bio-compost addition is beneficial to the improvement of soil health and crop quality and therefore the sustainability of agriculture.

Soil Microbiomes in Apple Orchards Are Influenced by the Type of Agricultural Management but Never Match the Complexity and Connectivity of a Semi-natural Benchmark

Conversion of natural ecosystems into agricultural land may strongly affect the soil microbiome and the functioning of the soil ecosystem. Alternative farming systems, such as organic farming, have therefore been advocated to reduce this impact, yet the outcomes of different agricultural management regimes often remain ambiguous and their evaluations mostly lack a proper more natural benchmark. We used high-throughput amplicon sequencing, linear models, redundancy analyses, and co-occurrence network analyses to investigate the effect of organic and integrated pest management (IPM) on soil fungal and bacterial communities in both the crop and drive rows of apple orchards in Belgium, and we included semi-natural grasslands as a benchmark. Fungi were strongly influenced by agricultural management, with lower diversity indices and distinct communities in IPM compared to organic orchards, whereas IPM orchards had a higher AMF abundance and the most complex and connected fungal communities. Bacterial diversity indices, community composition, and functional groups were less affected by management, with only a higher network connectivity and abundance of keystone taxa in organic drive rows. On the other hand, none of the agricultural soil microbiomes matched the complexity and connectedness of our semi-natural benchmark, demonstrating that even more nature-friendly agricultural management practices strongly affect the soil microbiome and highlighting the essential role of (semi-)natural systems as a harbor of robust and functionally diverse fungal and bacterial communities.

Soil pH Filters the Association Patterns of Aluminum-Tolerant Microorganisms in Rice Paddies

Soil microbes are considered the second genome of plants. Understanding the distribution and network of aluminum (Al)-tolerant microorganisms is helpful to alleviate Al toxicity to plants in acidic soils. Here, we examined soluble Al³⁺ and bacterial communities carrying Al resistance genes in paddy soils with a soil pH range of 3.6 to 8.7. In the acidic soil with pH <5.1, the content of Al³⁺ increased significantly. There were abundant and diverse Al-tolerant microorganisms in acidic soils, including Clostridium, Bacillus, Paenibacillus, Desulfitobacterium, and Desulfosporosinus, etc. Moreover, compared with neutral and alkaline soils, the network structure of Al-tolerant microorganisms was more complex. The potential roles of major Al-tolerant microbial taxa on each other in the ecological network were identified by a directed network along 0.01 pH steps. The influential taxa in the network had a broader niche and contained more antioxidant functional genes to resist Al stress, indicating their survival advantage over the sensitive taxa. Our study is the first to explore the distribution of Al-tolerant microorganisms in continental paddies and reveal their potential associations mediated by pH, which provides a basis for further utilization of microbial resources in acidic agricultural soils. IMPORTANCE Aluminum (Al) toxicity is the primary limiting factor of crop production in acidic soils with pH <5.0. Numerous studies have focused on the mechanism of Al toxicity and tolerance in plants; however, the effects of Al toxicity on soil microorganisms and their tolerance remain less studied. This study investigated the distribution and association patterns of Al-tolerant microorganisms across continental paddy fields with a soil pH range of 3.6 to 8.7. The results showed that soil pH filters exchangeable Al³⁺ content, diversity, and potential associations of Al-tolerant microbial community. The influential taxa in community network play an important role in Al tolerance and have potential applications in mitigating Al toxicity and promoting crop growth in acidic soils.

Changes in Wastewater Treatment Performance and the Microbial Community during the Bioaugmentation of a Denitrifying Pseudomonas Strain in the Low Carbon–Nitrogen Ratio Sequencing Batch Reactor

The low carbon–nitrogen ratio (C/N) of influent wastewater results in the insufficient carbon source for the process of denitrification in urban wastewater treatment plants (WWTPs). A denitrifying bacterial strain, Pseudomonas sp. JMSTP, was isolated and demonstrated effective denitrification ability under a low C/N ratio of 1.5–4 (w/w) in anoxic conditions. Sequencing batch reactor (SBR) studies were conducted to test the bioaugmentation of JMSTP on total nitrogen (TN) removal under the influent COD/N ratio of 3/1. After the second bioaugmentation, the TN of effluent in the bioaugmented SBR was significantly lower than that in the control SBR. Redundancy analysis results showed that there was a positive correlation between Pseudomonas sp. abundance and TN removal in the bioaugmented SBR. Microbial community analysis showed that, especially after the second bioaugmentation, the abundance of Pseudomonas sp. decreased rapidly, but it was still much higher than that in the control SBR. Correlation network analysis showed that after the addition, Pseudomonas sp. had no significant co-occurrence relationship with other native bacteria, owing to the quick increase and decrease. Our results suggest that JMSTP shows the potential to enhance TN removal through bioaugmentation. Since the effect of bioaugmentation gradually diminishes, further research is still needed to investigate its long-lasting applications.

Integrated microbiology and metabolomics analysis reveal plastic mulch film residue affects soil microorganisms and their metabolic functions

Research on microplastic pollution of terrestrial soils is catching up with the aquatic environment, especially agricultural soil systems. Plastic residues have caused various environmental problems in mulch film extensively used agricultural areas. However, studies focusing specifically on the potential influence of mulch film residues on the metabolic cycle of soil systems have yet to be conducted. Here, high-throughput sequencing combined with metabolomics were first used to study the effects of residual mulch on soil microbial communities and related metabolic functions. Plastic film treatment did not significantly affect soil physicochemical properties including pH, organic matter and nitrogen, etc in short term. However, it did significantly changed overall community structure of soil bacteria, and interfered with complexity of soil bacterial symbiosis networks; exposure time and concentration of residues were particularly important factors affecting community structure. Furthermore, metabolomics analysis showed that film residue significantly changed soil metabolite spectrum, and interfered with basic carbon and lipid metabolism, and also affected basic cellular processes such as membrane transport and, in particular, interfered with the biosynthesis of secondary metabolites, as well as, biodegradation and metabolism of xenobiotics. Additionally, through linear discriminant and collinear analysis, some new potential microplastic degrading bacteria including Nitrospira, Nocardioidaceae and Pseudonocardiaceae have been excavated.

Network structure of resource use and niche overlap within the endophytic microbiome

Endophytes often have dramatic effects on their host plants. Characterizing the relationships among members of these communities has focused on identifying the effects of single microbes on their host, but has generally overlooked interactions among the myriad microbes in natural communities as well as potential higher-order interactions. Network analyses offer a powerful means for characterizing patterns of interaction among microbial members of the phytobiome that may be crucial to mediating its assembly and function. We sampled twelve endophytic communities, comparing patterns of niche overlap between coexisting bacteria and fungi to evaluate the effect of nutrient supplementation on local and global competitive network structure. We found that, despite differences in the degree distribution, there were few significant differences in the global network structure of niche-overlap networks following persistent nutrient amendment. Likewise, we found idiosyncratic and weak evidence for higher-order interactions regardless of nutrient treatment. This work provides a first-time characterization of niche-overlap network structure in endophytic communities and serves as a framework for higher-resolution analyses of microbial interaction networks as a consequence and a cause of ecological variation in microbiome function.

Governance using the water-food-energy nexus and human-factor measures

Household water food and energy (WFE) expenditures, reflect respective survival needs for which their resources and social welfare are inter-related. We developed a policy driven quantitative decision-making strategy (DMS) to address the domain geospatial entities' (nodes or administrative districts) of the WFE nexus, assumed to be information linked across the domain nodal-network. As investment in one of the inter-dependent nexus components may cause unexpected shock to the others, we refer to the WFE normalized expenditures product (Volume) as representing the nexus holistic measure. Volume rate conforms to Boltzman entropy suggesting directed information from high to low Volume nodes. Our hypothesis of causality-driven directional information is exemplified by a sharp price increase in wheat and rice, for U.S. and Thailand respectively, that manifests its impact on the temporal trend of Israel's administrative districts of the WFE expenditures. Welfare mass (WM) represents the node's Volume combined with its income and population density. Formulation is suggested for the nodal-network WM temporal balance where each node is scaled by a human-factor (HF) for subjective attitude and a superimposed nodal source/sink term manifesting policy decision. Our management tool is based on two sequential governance processes: one starting with historical data mapping the mean temporal nodal Volumes to single out extremes, and the second is followed by WM balance simulation predicting nodal-network outcome of policy driven targeting. In view of the proof of concept by model simulations in in our previous research, here HF extends the model and attention is devoted to emphasize how the current developed decision-making approach categorically differs from existing nexus related methods. The first governance process is exemplified demonstrating illustrations for Israel's districts. Findings show higher expenditures for water and lower for energy, and maps pointing to extremes in districts' mean temporal Volume. Illustrations of domain surfaces for that period enable assessment of relative inclination trends of the normalized Water, Food and Energy directions continuum assembled from time stations, and evolution trends for each of the WFE components.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

Network-based assessments are important for disentangling complex microbial and microbial-host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka-Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

A field study reveals links between hyperaccumulating Sedum plants-associated bacterial communities and Cd/Zn uptake and translocation

Hyperaccumulating ecotypes of Sedum plants are promising Cd/Zn phytoextractors, with potential for leveraging its rhizospheric or endophytic microbiomes to improve phytoremediation efficiency. However, research of bacteria associated with Sedum at field scale is still lacking. Here, we presented a detailed investigation of the bacterial microbiome of hyperaccumulating Sedum ecotypes (S. alfredii and S. plumbizincicola) and a non-hyperaccumulating S. alfredii ecotype, which grow at different soil environments. Moreover, we evaluated the heavy metal uptake and translocation potential of Sedum plants at different locations. The results showed that both HE ecotypes, contrary to the NHE, were efficient for phytoremediation in mine areas and farmlands. For NHE plants, rhizosphere co-occurrence networks were more complex than the networks of other compartments, while networks of HE plants were more complex in bulk soil and roots. The proportion of positive correlations within co-occurrence networks was higher for the HE plants, suggesting a greater potential for mutualistic interactions. Plant compartment and location predominantly shaped the microbiome assembly, and Proteobacteria, Actinobacteria and Acidobacteria dominated the bacterial communities of Sedum plants. Keystone taxa related to Zn hyperaccumulation are similar to those related to Cd hyperaccumulation, and nine bacterial genera had significantly positive correlation with Cd/Zn hyperaccumulation. Taxa, linked to phytoremediation in both mine and farmland (i.e. Actinospica and Streptomyces from Actinobacteria), could be targets for further investigation of their ability to promote metal phytoremediation of Sedum species.

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

It's the Economy, Stupid: Applying (Micro)economic Principles to Microbiome Science

A key challenge in microbiome science is the scale mismatch problem, which arises when the scale at which microbial communities are sampled, interrogated, and averaged is different from the scale at which individual microorganisms within those communities interact with each other and with their environment. Profiling the microbial communities in a teaspoon of soil, from a scoop of fecal matter, or along a plant leaf surface represents a scale mismatch of multiple orders of magnitude, which may limit our ability to interpret or predict species interactions and community assembly within such samples. In this Perspective, we explore how economists, who are historically and topically split along the lines of micro- and macroeconomics, deal with the scale mismatch problem, and how taking clues from (micro)economists could benefit the field of microbiomics.

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

Effect of treatments on skin microbiota in patients with atopic dermatitis: a protocol for systematic review

INTRODUCTION

Atopic dermatitis (AD) is a chronic inflammatory skin disease and skin microbiota dysbiosis shows an important role in the pathogenesis of AD. Effects of treatment on skin microbiota for patients with AD have been evaluated in recent years; however, the results remained controversial across studies. This systematic review will summarise studies evaluating the effect of treatments on skin microbiota among patients with AD.

METHODS AND ANALYSIS

We will search PubMed, EMBASE, Web of Science, ClinicalTrials.gov and Chinese Clinical Trial Registry in November 2021; other data sources will also be considered, including searching specific authors and screening references cited in the enrolled articles. Interventional studies, which enrolled patients with AD receiving treatments and reported treatment-related skin microbiota changes, will be included. Our primary outcomes include skin microbiota diversity and treatment-related differential microbes; the secondary outcomes include microbiota functions and microbial interactions. Risk of bias assessment will be performed using Cochrane risk-of-bias tool for randomised trials, risk of bias in non-randomised studies of interventions and methodological index for non-randomised studies. Two researchers will independently perform study selection, data extraction and risk of bias assessment, with disagreements resolved by group discussions. Subgroup analyses will be performed according to different types of treatment for AD.

ETHICS AND DISSEMINATION

Ethics approval is not required for this systematic review. Findings will be disseminated via peer-reviewed publication or conference proceedings.

PROSPERO REGISTRATION NUMBER

CRD42021246566.

Rhizosphere analysis of field-grown Panax ginseng with different degrees of red skin provides the basis for preventing red skin syndrome

Background

Ginseng red skin root syndrome (GRS) is one of the most common ginseng (Panax ginseng Meyer) diseases. It leads to a severe decline in P. ginseng quality and seriously affects the P. ginseng industry in China. However, as a root disease, the characteristics of the GRS rhizosphere microbiome are still unclear.

Methods

The amplicon bacterial 16 S rRNA genes and fungal ITS (Internal Transcribed Spacer) regions Illumina sequencing technology, combined with microbial diversity and composition analysis based on R software, was used to explore the relationship between soil ecological environment and GRS.

Results

There were significant differences in the diversity and richness of soil microorganisms between the rhizosphere with different degrees of disease, especially between healthy P. ginseng (HG) and heavily diseased groups. The variation characteristics of microbial abundance in different taxa levels were analyzed. The interaction network of rhizosphere microorganisms of P. ginseng under GRS background was established. We also found that different P. ginseng rhizosphere microbial communities have multiple changes in stability and complexity through the established interaction network. Microbes closely related to potential pathogenic fungi were also identified according to the interaction network, which provided clues for looking for biological control agents. Finally, the Distance-based redundancy analysis (dbRDA) results indicated that total phosphorus (TP), available potassium (AK), available phosphorus (AP), catalase (CAT), invertase (INV) are the key factors that influence the microbial communities. Moreover, the content of these key factors in the rhizosphere was negatively correlated with disease degrees.

Conclusions

In this study, we comprehensively analyzed the rhizosphere characteristics of P. ginseng with different levels of disease, and explored the interaction relationship among microorganisms. These results provide a basis for soil improvement and biological control of field-grown in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02430-9.

The Coexistence Relationship Between Plants and Soil Bacteria Based on Interdomain Ecological Network Analysis

The relationship between plants and their associated soil microbial communities plays a crucial role in maintaining ecosystem processes and function. However, identifying these complex relationships is challenging. In this study, we constructed an interdomain ecology network (IDEN) of plant–bacteria based on SparCC pairwise associations using synchronous aboveground plant surveys and belowground microbial 16S rRNA sequencing among four different natural forest types along the climate zones in China. The results found that a total of 48 plants were associated with soil bacteria among these four sites, and soil microbial group associations with specific plant species existed within the observed plant–bacteria coexistence network. Only 0.54% of operational taxonomy units (OTUs) was shared by the four sites, and the proportion of unique OTUs for each site ranged from 43.08 to 76.28%, which occupied a large proportion of soil bacterial community composition. The plant–bacteria network had a distinct modular structure (p < 0.001). The tree Acer tetramerum was identified as the network hubs in the warm temperate coniferous and broad-leaved mixed forests coexistence network and indicates that it may play a key role in stabilizing of the community structure of these forest ecosystems. Therefore, IDEN of plant–bacteria provides a novel perspective for exploring the relationships of interdomain species, and this study provides valuable insights into understanding coexistence between above-ground plants and below-ground microorganisms.

Sub-chronic low-dose arsenic in rice exposure induces gut microbiome perturbations in mice

Long-term consumption of arsenic-contaminated rice has become a public health issue that urgently needs to be addressed. In this study, mice were exposed to arsenic in rice (low dose, 0.91 mg/kg; medium dose, 9.1 mg/kg) for 30 days and 60 days, respectively, and the effects on pathological structures of spleen and skin, as well as the structure of the fecal microbiome were examined. The findings revealed dose/time cumulative effects on pathological changes, with even a low dose exposure for 30 days causing destruction of splenic follicular structure and thickening of dermal keratinized and epidermal layers. The Firmicutes/Bacteroidetes ratio in the community and the positive/negative ratio in network links were higher in arsenic groups, suggesting that arsenic resulted in a less healthy and unstable microbiome for the host. Thus lifetime consumption of arsenic in rice may have potential health effects on humans and must be carefully assessed to safeguard human health. Furthermore, in arsenic groups, arsenic-resistant bacteria or arsenic hazards remediation bacteria changed to be the dominant bacteria and acted as the core bacteria in the network modules. Some microbial arsenic transforming genes (arsC, arsR, arsA, ACR3, and aoxB) differed, indicating that the gut microbiome changed to withstand arsenic stress. Furthermore, Faecalibaculum, Lachnospiraceae_NK4A136_group, Angelakisella, Ruminiclostridium, and Desulfovibrionaceae are positively associated with arsenic dosage and may be useful in the early detection of arsenicals.

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.

Streptozotocin-induced hyperglycemia alters the cecal metabolome and exacerbates antibiotic-induced dysbiosis

It is well established in the microbiome field that antibiotic (ATB) use and metabolic disease both impact the structure and function of the gut microbiome. But how host and microbial metabolism interacts with ATB susceptibility to affect the resulting dysbiosis remains poorly understood. In a streptozotocin-induced model of hyperglycemia (HG), we use a combined metagenomic, metatranscriptomic, and metabolomic approach to profile changes in microbiome taxonomic composition, transcriptional activity, and metabolite abundance both pre- and post-ATB challenge. We find that HG impacts both microbiome structure and metabolism, ultimately increasing susceptibility to amoxicillin. HG exacerbates drug-induced dysbiosis and increases both phosphotransferase system activity and energy catabolism compared to controls. Finally, HG and ATB co-treatment increases pathogen susceptibility and reduces survival in a Salmonella enterica infection model. Our data demonstrate that induced HG is sufficient to modify the cecal metabolite pool, worsen the severity of ATB dysbiosis, and decrease colonization resistance.

The gut microbiota in pediatric multiple sclerosis and demyelinating syndromes

Objective

To examine the gut microbiota in individuals with and without pediatric‐onset multiple sclerosis (MS).

Methods

We compared stool‐derived microbiota of Canadian Pediatric Demyelinating Disease Network study participants ≤21 years old, with MS (disease‐modifying drug [DMD] exposed and naïve) or monophasic acquired demyelinating syndrome [monoADS] (symptom onset <18 years), and unaffected controls. All were ≥30 days without antibiotics or corticosteroids. V4 region 16S RNA gene‐derived amplicon sequence variants (Illumina MiSeq) were assessed using negative binomial regression and network analyses; rate ratios were age‐ and sex‐adjusted (aRR).

Results

Thirty‐two MS, 41 monoADS (symptom onset [mean] = 14.0 and 6.9 years) and 36 control participants were included; 75%/56%/58% were female, with mean ages at stool sample = 16.5/13.8/15.1 years, respectively. Nine MS cases (28%) were DMD‐naïve. Although microbiota diversity (alpha, beta) did not differ between participants (p > 0.1), taxa‐level and gut community networks did. MS (vs. monoADS) exhibited > fourfold higher relative abundance of the superphylum Patescibacteria (aRR = 4.2;95%CI:1.6–11.2, p = 0.004, Q = 0.01), and lower abundances of short‐chain fatty acid (SCFA)‐producing Lachnospiraceae (Anaerosporobacter) and Ruminococcaceae (p, Q < 0.05). DMD‐naïve MS cases were depleted for Clostridiales vadin‐BB60 (unnamed species) versus either DMD‐exposed, controls (p, Q < 0.01), or monoADS (p = 0.001, Q = 0.06) and exhibited altered community connectedness (p < 10⁻⁹ Kruskal–Wallis), with SCFA‐producing taxa underrepresented. Consistent taxa‐level findings from an independent US Network of Pediatric MS Centers case/control (n = 51/42) cohort included >eightfold higher abundance for Candidatus Stoquefichus and Tyzzerella (aRR = 8.8–12.8, p < 0.05) in MS cases and 72%–80% lower abundance of SCFA‐producing Ruminococcaceae‐NK4A214 (aRR = 0.38–0.2, p ≤ 0.01).

Interpretation

Gut microbiota community structure, function and connectivity, and not just individual taxa, are of likely importance in MS.

Robust bacterial co-occurence community structures are independent of r- and K-selection history

Selection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.

INFORMATION CONTENT OF HIGH-ORDER ASSOCIATIONS OF THE HUMAN GUT MICROBIOTA NETWORK

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

Bacterial community demonstrates stronger network connectivity than fungal community in desert-grassland salt marsh

The diversity of soil bacterial and fungal communities is closely related to the soil characteristics and vegetation types in salt marsh ecosystems, but the biogeographic patterns and driving factors in desert-grassland salt marsh (DGSM) are still unclear. In this study, we divided sample plots according to the dominant species in Jiantan Lake wetland of a typical DGSM in Northwestern China. The effects of different environmental factors and halophytes on the structure of soil bacterial and fungal communities were investigated using soil physicochemical characterization and high-throughput sequencing analysis. The diversity of bacterial communities in bulk soil and three dominant halophytes (Kalidium cuspidatum, Nitraria tangutorum and Sophora alopecuroides) were the main factors affecting soil physicochemical properties and halophyte vegetation coverage. Proteobacteria, Bacteroides and Gemmatimonadetes had the highest abundance in bulk soil and the lowest in Sophora alopecuroides sample soil; the opposite was true for Acidobacteria and Chloroflexi. The abundance of Ascomycota in bulk soil and Sophora alopecuroides sample soil was higher than Kalidium cuspidatum and Nitraria tangutorum sample soils, whereas the Mortierellomycota was the highest in Nitraria tangutorum sample soil. Co-occurrence network analysis showed that halophyte cover increased the connectivity and complexity of the bacterial-fungal interaction network, and the halophytic shrub sample soil had a more stable network relationship than the halophytic herb soil. The key taxa of each plot were identified through network relationships. It was found that the keystone taxa of Proteobacteria, Firmicutes, Ascomycota and Chytridiomycota played important roles in maintaining community functions, and most of them were not significantly influenced by soil physicochemical properties. The results of this study provide new insights for a deeper understanding of the halophytes that drive the multifunctionality and stability of soil ecosystems in DGSM.

Disentangling environmental effects in microbial association networks

BACKGROUND

Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.

RESULTS

We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges-87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors.

CONCLUSIONS

To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract.

Mutual Information Disentangles Interactions from Changing Environments

Real-world systems are characterized by complex interactions of their internal degrees of freedom, while living in ever-changing environments whose net effect is to act as additional couplings. Here, we introduce a paradigmatic interacting model in a switching, but unobserved, environment. We show that the limiting properties of the mutual information of the system allow for a disentangling of these two sources of couplings. Further, our approach might stand as a general method to discriminate complex internal interactions from equally complex changing environments.

Mono-specific algal diets shape microbial networking in the gut of the sea urchin Tripneustes gratilla elatensis

BACKGROUND

Algivorous sea urchins can obtain energy from a diet of a single algal species, which may result in consequent changes in their gut microbe assemblies and association networks.

METHODS

To ascertain whether such changes are led by specific microbes or limited to a specific region in the gut, we compared the microbial assembly in the three major gut regions of the sea urchin Tripneustes gratilla elatensis when fed a mono-specific algal diet of either Ulva fasciata or Gracilaria conferta, or an algal-free diet. DNA extracts from 5 to 7 individuals from each diet treatment were used for Illumina MiSeq based 16S rRNA gene sequencing (V3-V4 region). Niche breadth of each microbe in the assembly was calculated for identification of core, generalist, specialist, or unique microbes. Network analyzers were used to measure the connectivity of the entire assembly and of each of the microbes within it and whether it altered with a given diet or gut region. Lastly, the predicted metabolic functions of key microbes in the gut were analyzed to evaluate their potential contribution to decomposition of dietary algal polysaccharides.

RESULTS

Sea urchins fed with U. fasciata grew faster and their gut microbiome network was rich in bacterial associations (edges) and networking clusters. Bacteroidetes was the keystone microbe phylum in the gut, with core, generalist, and specialist representatives. A few microbes of this phylum were central hub nodes that maintained community connectivity, while others were driver microbes that led the rewiring of the assembly network based on diet type through changes in their associations and centrality. Niche breadth agreed with microbes' richness in genes for carbohydrate active enzymes and correlated Bacteroidetes specialists to decomposition of specific polysaccharides in the algal diets.

CONCLUSIONS

The dense and well-connected microbial network in the gut of Ulva-fed sea urchins, together with animal's rapid growth, may suggest that this alga was most nutritious among the experimental diets. Our findings expand the knowledge on the gut microbial assembly in T. gratilla elatensis and strengthen the correlation between microbes' generalism or specialism in terms of occurrence in different niches and their metabolic arsenal which may aid host nutrition.

Long-term dynamics of the human oral microbiome during clinical disease progression

BACKGROUND

Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition's stability that could adversely affect the host's health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis.

RESULTS

We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement.

CONCLUSIONS

Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.

Healthier Communities of Phytoplankton and Bacteria Achieved via the Application of Modified Clay in Shrimp Aquaculture Ponds

The composition and stability of microbial communities in aquaculture water are crucial for the healthy growth of shrimp and present considerable risk to aquatic ecosystems. The modified clay (MC) method has been proposed as an efficient and safe solution for the mitigation of harmful algal blooms (HABs). Currently, the effects of MC on microbial communities in aquaculture water remain unknown. Here, we adopted the MC method to regulate shrimp-culture water quality and evaluated the effects of MC on the composition and stability of phytoplankton together with bacteria communities through high-throughput sequencing. On the one hand, a prominent change in the composition of microbial community was observed, with green algae becoming the most abundant genera and pathogens being infrequent in the MC-treated pond, which was more conducive to the growth of shrimp than that in the control pond. Moreover, MC could increase the diversity and stability of the microbial community structure in the water column, which had a higher anti-interference ability, as demonstrated by the analysis of the diversity and molecular ecological network. Taken together, MC could reduce the possibility for the occurrence of HABs and maintain a stable microbial community, which is beneficial for the health and high yield of shrimp.

Supplemental L-Arginine Improves the Embryonic Intestine Development and Microbial Succession in a Chick Embryo Model

Early colonization of intestinal microbiota plays an important role in intestinal development. However, the microbial succession at an embryonic stage and its assembly patterns induced by prenatal nutrition are unknown. In the present study, we used a chick embryo model to investigate the effects of in ovo feeding (IOF) of L-arginine (Arg) on the intestinal development and microbial succession of embryos. A total of 216 fertile eggs were randomly distributed into 2 groups including the non-injected control group and IOF of Arg group with 7 mg/egg. The results showed that IOF Arg increased the intestinal index, absolute weight of jejunum, and improved jejunal morphology in terms of villus width and surface area (p < 0.05). The relative mRNA expressions of mTOR and 4E-BP1 were up-regulated and accompanied by higher contents of Mucin-2 in the Arg group (p < 0.05). There was a significant elevation in contents of serum glucose and high-density lipoprotein cholesterol, whereas there was a decreased low-density lipoprotein cholesterol in the Arg group (p < 0.05). Additionally, Proteobacteria and Firmicutes were major intestinal bacteria species at the embryonic stage. However, Arg supplementation targeted to shape assembly patterns of microbial succession and then changed microbial composition (p = 0.05). Meanwhile, several short-chain fatty acids (SCFAs)-producing bacteria, such as Roseburia, Blautia, and Ruminococcus were identified as biomarkers in the Arg group (LDA > 3, p < 0.05). Accordingly, significant elevated concentrations of SCFAs, including lactic acid and formic acid, were observed in the Arg group (p < 0.05), accompanied by the higher concentration of butyric acid (0.05 < p < 0.10). In conclusion, prenatal Arg supplementation improved embryonic intestine development by regulating glucose and lipid homeostasis to supply more energy for chick embryos. The possible mechanism could be the roles of Arg in shaping the microbial assembly pattern and succession of the embryonic intestine, particularly the enrichment of potential probiotics. These findings may contribute to exploring nutritional strategies to establish health-promoting microbiota by manipulating prenatal host-microbe interactions for the healthy development of neonates.

Responses of rhizosphere bacterial communities, their functions and their network interactions to Cd stress under phytostabilization by Miscanthus spp

Miscanthus has good tolerance to heavy metals (HMs) and has received increasing attention in studies of HM-contaminated soil remediation. In this study, four Miscanthus cultivars (M. lutarioriparius Xiangnadi NO4, M. sinensis Xiangmang NO1, M. lutarioriparius × M. sinensis hybrid Xiangzamang NO1, and M. floridulus Wujiemang NO1) that grow in China were studied. Their tolerance and enrichment abilities in soils containing 50 mg kg^-1 cadmium (Cd) and the structure and function of their rhizosphere bacterial communities during the remediation process were analyzed. The results exhibiting a tolerance index (TI) higher than 75 in roots and the aboveground parts (TI > 60, indicating highly tolerant plants) indicated that all four Miscanthus cultivars were tolerant to high Cd concentrations. Moreover, Cd was mainly enriched in roots, the translocation ability from roots to aboveground parts was weak, and the four cultivars exhibited phytostabilization ability in Cd-contaminated soils. High-throughput sequencing (HTS) analysis showed that the Miscanthus rhizosphere bacterial community comprised 33 phyla and 446 genera, including plant growth-promoting rhizobacteria (PGPRs), such as Bacillus, Sphingomonas, and Mesorhizobium. The addition of Cd affected the Miscanthus rhizosphere bacterial community and reduced community diversity. Phylogenetic molecular ecological networks (pMENs) indicated that Cd addition reduced interactions between Miscanthus rhizosphere bacteria and thereby led to a simpler network structure, increased the number of negative-correlation links, enhanced the competition between rhizosphere bacterial species, reduced the number of key bacteria, and changed the composition of those bacteria. PICRUSt functional predictive analysis indicated that Cd stress reduced soil bacterial functions in the Miscanthus rhizosphere. The results of this study provide a basis for the remediation of Cd-contaminated soils by Miscanthus and provide a reference for the subsequent regulation of Miscanthus remediation efficiency by PGPRs or key bacteria.

Gut and Brain: Investigating Physiological and Pathological Interactions Between Microbiota and Brain to Gain New Therapeutic Avenues for Brain Diseases

Brain physiological functions or pathological dysfunctions do surely depend on the activity of both neuronal and non-neuronal populations. Nevertheless, over the last decades, compelling and fast accumulating evidence showed that the brain is not alone. Indeed, the so-called "gut brain," composed of the microbial populations living in the gut, forms a symbiotic superorganism weighing as the human brain and strongly communicating with the latter via the gut-brain axis. The gut brain does exert a control on brain (dys)functions and it will eventually become a promising valuable therapeutic target for a number of brain pathologies. In the present review, we will first describe the role of gut microbiota in normal brain physiology from neurodevelopment till adulthood, and thereafter we will discuss evidence from the literature showing how gut microbiota alterations are a signature in a number of brain pathologies ranging from neurodevelopmental to neurodegenerative disorders, and how pre/probiotic supplement interventions aimed to correct the altered dysbiosis in pathological conditions may represent a valuable future therapeutic strategy.

Preferences for core microbiome composition and function by different definition methods: Evidence for the core microbiome of Eucommia ulmoides bark

The core microbiome, as a unique group of microorganisms, is an emerging research hotspot that provides a new opportunity to improve growth and production of a host. However, the subjectivity associated with the concept of "core microbiome" means there is currently no uniform definition method for the core microbiome. In this study, the strengths and limitations of four commonly used definition methods for the core microbiome were explored from composition to function based on the 16S rRNA gene dataset of Eucommia ulmoides bark from 25 different biogeographical regions in China. There were differences in the composition of the core microbiomes defined by the different methods. The four definition methods of phylogeny, membership, composition, and network connection contained 274, 10, 5, and 5 core OTUs (operational taxonomic units), respectively. In contrast, the core microbiomes defined by different methods displayed similarities in function. In addition, different definition methods showed varying preferences for abundant taxa, intermediate taxa, and rare taxa. Some core taxa defined by the definition method of phylogeny were significantly associated with pharmacologically active ingredients of E. ulmoides bark. The findings of this study suggest that although the core microbiomes defined by different methods have preferences in composition and function, the term refers to a group of microbes that are particularly notable and important for host-associated microbiomes. Therefore, we propose: (I) The definition method of the core microbiome should be selected according to the ecological problems faced; (II) A combination of multiple methods may comprehensively reveal the core microbiome at different levels of the host, and may also facilitate understanding of the ecological and evolutionary processes that govern host-microbe interactions.

Microbial co-occurrence network and its key microorganisms in soil with permanent application of composted tannery sludge

Soil microbial communities act on important environmental processes, being sensitive to the application of wastes, mainly those potential contaminants, such as tannery sludge. Due to the microbiome complexity, graph-theoretical approaches have been applied to represent model microbial communities interactions and identify important taxa, mainly in contaminated soils. Herein, we performed network and statistical analyses into microbial 16S rRNA gene sequencing data from soil samples with the application of different levels of composted tannery sludge (CTS) to assess the most connected nodes and the nodes that act as bridges to identify key microbes within each community. The network analysis revealed hubs belonging to Proteobacteria in soil with lower CTS rates, while active degraders of recalcitrant and pollutant chemical hubs belonging to Proteobacteria and Actinobacteria were found in soils under the highest CTS rates. The majority of classified connectors belonged to Actinobacteria, but similarly to hubs taxa, they shifted from metabolic functional profile to taxa with abilities to degrade toxic compounds, revealing a soil perturbation with the CTS application on community organization, which also impacted the community modularity. Members of Actinobacteria and Acidobacteria were identified as both hub and connector suggesting their role as keystone groups. Thus, these results offered us interesting insights about crucial taxa, their response to environmental alterations, and possible implications for the ecosystem.

Fungi-Bacteria Associations in Wilt Diseased Rhizosphere and Endosphere by Interdomain Ecological Network Analysis

In the plant rhizosphere and endosphere, some fungal and bacterial species regularly co-exist, however, our knowledge about their co-existence patterns is quite limited, especially during invasion by bacterial wilt pathogens. In this study, the fungal communities from soil to endophytic compartments were surveyed during an outbreak of tobacco wilt disease caused by Ralstonia solanacearum. It was found that the stem endophytic fungal community was significantly altered by pathogen invasion in terms of community diversity, structure, and composition. The associations among fungal species in the rhizosphere and endosphere infected by R. solanacearum showed more complex network structures than those of healthy plants. By integrating the bacterial dataset, associations between fungi and bacteria were inferred by Inter-Domain Ecological Network (IDEN) approach. It also revealed that infected samples, including both the rhizosphere and endosphere, had more complex interdomain networks than the corresponding healthy samples. Additionally, the bacterial wilt pathogenic Ralstonia members were identified as the keystone genus within the IDENs of both root and stem endophytic compartments. Ralstonia members was negatively correlated with the fungal genera Phoma, Gibberella, and Alternaria in infected roots, as well as Phoma, Gibberella, and Diaporthe in infected stems. This suggested that those endophytic fungi may play an important role in resisting the invasion of R. solanacearum.

Fungi in Permafrost-Affected Soils of the Canadian Arctic: Horizon- and Site-Specific Keystone Taxa Revealed by Co-Occurrence Network

Permafrost-affected soil stores a significant amount of organic carbon. Identifying the biological constraints of soil organic matter transformation, e.g., the interaction of major soil microbial soil organic matter decomposers, is crucial for predicting carbon vulnerability in permafrost-affected soil. Fungi are important players in the decomposition of soil organic matter and often interact in various mutualistic relationships during this process. We investigated four different soil horizon types (including specific horizons of cryoturbated soil organic matter (cryoOM)) across different types of permafrost-affected soil in the Western Canadian Arctic, determined the composition of fungal communities by sequencing (Illumina MPS) the fungal internal transcribed spacer region, assigned fungal lifestyles, and by determining the co-occurrence of fungal network properties, identified the topological role of keystone fungal taxa. Compositional analysis revealed a significantly higher relative proportion of the litter saprotroph Lachnum and root-associated saprotroph Phialocephala in the topsoil and the ectomycorrhizal close-contact exploring Russula in cryoOM, whereas Sites 1 and 2 had a significantly higher mean proportion of plant pathogens and lichenized trophic modes. Co-occurrence network analysis revealed the lowest modularity and average path length, and highest clustering coefficient in cryoOM, which suggested a lower network resistance to environmental perturbation. Zi-Pi plot analysis suggested that some keystone taxa changed their role from generalist to specialist, depending on the specific horizon concerned, Cladophialophora in topsoil, saprotrophic Mortierella in cryoOM, and Penicillium in subsoil were classified as generalists for the respective horizons but specialists elsewhere. The litter saprotrophic taxon Cadophora finlandica played a role as a generalist in Site 1 and specialist in the rest of the sites. Overall, these results suggested that fungal communities within cryoOM were more susceptible to environmental change and some taxa may shift their role, which may lead to changes in carbon storage in permafrost-affected soil.

Network mapping of root-microbe interactions in Arabidopsis thaliana

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

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

The community of the diverse microorganisms residing in the gastrointestinal tract, known as the gut microbiota, is exceedingly being studied for its impact on health and disease. This community plays a major role in nutrient metabolism, maintenance of the intestinal epithelial barrier but also in local and systemic immunomodulation. A dysbiosis of the gut microbiota, characterized by an unbalanced microbial ecology, often leads to a loss of essential functions that may be associated with proinflammatory conditions. Specifically, some key microbes that are depleted in dysbiotic ecosystems, called keystone species, carry unique functions that are essential for the balance of the microbiota. In this review, we discuss current understanding of reported keystone species and their proposed functions in health. We also elaborate on current and future bioinformatics tools needed to identify missing functions in the gut carried by keystone species. We propose that the identification of such keystone species functions is a major step for the understanding of microbiome dynamics in disease and toward the development of microbiome-based therapeutics.

Investigating dairy microbiome: an opportunity to ensure quality, safety and typicity

A detailed understanding of the microbiome of cheese and dairy products is key to the optimization of flavour, appearance, overall quality and safety. Microorganisms (including bacteria, yeasts, moulds and viruses, especially bacteriophages) from the environment can enter the dairy supply chain at multiple stages with several implications. The ability to track these microorganisms and to understand their function and interaction can be greatly enhanced by the use of high-throughput sequencing. Depending on the specific production technology, dairy products can harbor several strains and antibiotic-resistance genes that can potentially interact with the gut microbiome, once the product is ingested. Milk-associated or cheese-associated microbial communities with their interaction, function and diversity are a key factor for the dairy industry. Multi-omics approaches have been seldom utilized in literature and they need to be further considered. Studying the role, origin, diversity and function of the microbial species involved in the complex system of dairy production can help improve processes in several fields of application. Integrating an extensive sampling procedure with an extensive culture based methodology is necessary. To this end, local producers, and in general stakeholders, should be guided to discover and maintain their microbial diversity. A better management of microbial resources through precision fermentation processes will in turn reduce overall food losses and increase the possibility to use the microbiome in order to increase the local producers' income.

Dispersal Limitation Plays Stronger Role in the Community Assembly of Fungi Relative to Bacteria in Rhizosphere Across the Arable Area of Medicinal Plant

Understanding the ecological patterns of rhizosphere microbial communities is critical for propelling sustainable agriculture and managing ecosystem functions by exploiting microorganisms. However, this knowledge is still unclear, especially under host-associated large-scale and regarding the comparison between bacteria and fungi. We examined community assembly processes and community characters including environmental thresholds and co-occurrence patterns across the cultivatable area of Panax notoginseng for bacteria and fungi. Both are vital members of the rhizosphere but differ considerably in their life history and dispersal potentiality. Edaphic factors drove the parallel variations of bacterial and fungal communities. Although bacterial and fungal communities exhibited similar biogeographic patterns, the assembly of fungi was more driven by dispersal limitation than selection compared with bacteria. This finding supported the 'size-dispersal' hypothesis. pH and total nitrogen respectively mediated the relative importance of deterministic and stochastic processes in shaping bacterial and fungal communities. In addition, fungal communities exhibited potentially broader environmental thresholds and more modular co-occurrence patterns than bacteria (bacteria: 0.67; fungi: 0.78). These results emphasized the importance of dispersal limitation in structuring rhizosphere microbiota and shaping community features of ecologically distinct microorganisms. This study provides insights into the improved prediction and management of the key functions of rhizosphere microbiota.

Mesosulfuron-methyl influenced biodegradability potential and N transformation of soil

The wide application of mesosulfuron-methyl (MS) in soil may affect soil microbial community, yet the information is limited. In this work, two distinct soil types from Anyang (AY) and Nanjing (NJ) were spiked with MS (0, 0.006, 0.06, or 0.6 mg kg^-1) and incubated for 90 days. MS decreased bacterial and fungal (except the last sampling) abundance and altered their diversity and community. Five biomarkers of bacterial species may help MS degradation and more increased xenobiotics biodegradation pathways were also observed in 0.6 mg kg^-1 treatment in AY soil. A co-occurrence network revealed the biomarkers grouped in one module in all AY soils, suggesting these biomarkers act in concert to degrade MS. MS impacted soil N transformation with increasing N₂-fixing bacteria in both soils and ammonia-oxidising bacteria (AOB) in NJ and decreasing ammonia-oxidizing archaea (AOA) in AY. The contents of NO₃^--N and NH₄⁺-N were increased by MS. Structural equation models revealed that the abundance of bacteria and fungi was responsible for the NO₃^--N and NH₄⁺-N contents. In conclusion, this work aids safety assessments and degradation-related research of MS in soil.

Bacterial diversity in petroleum coke based biofilters treating oil sands process water

Adopting nature-based solutions for the bioremediation of oil sands process water (OSPW) is of significant interest, which requires a thorough understanding of how bacterial communities behave within treatment systems operated under natural conditions. This study investigates the OSPW remediation potential of delayed petroleum-coke (PC), which is a byproduct of bitumen upgrading process and is readily available at oil refining sites, in fixed-bed biofilters particularly for the degradation of naphthenic acids (NAs) and aromatics. The biofilters were operated continuously and total and active bacterial communities were studied by DNA and RNA-based amplicon sequencing in a metataxonomic fashion to extrapolate the underlying degradation mechanisms. The results of total community structure indicated a high abundance of aerobic bacteria at all depths of the biofilter, e.g., Porphyrobacter, Legionella, Pseudomonas, Planctomyces. However, redox conditions within the biofilters were anoxic (-153 to -182 mV) that selected anaerobic bacteria to actively participate in the remediation of OSPW, i.e., Ruminicoccus, Eubacterium, Faecalibacterium, Dorea. After 15 days of operation, the removal of classical NAs was recorded up to 20% whereas oxidized NAs species were poorly removed, i.e., O₃-NAs: 4.8%, O₄-NAs: 1.2%, O₅-NAs: 1.7%, and O₆-NAs: 0.5%. Accordingly, monoaromatics, diaromatics, and triaromatics were removed up to 16%, 22%, and 15%, respectively. The physiology of the identified genera suggested that the degradation in the PC-based biofilters was most likely proceeded in a scheme similar to beta-oxidation during anaerobic digestion process. The presence of hydrogenotrophic methanogens namely Methanobrevibacter and Methanomassiliicoccus and quantification of mcrA gene (2.4 × 10² to 8.7 × 10² copies/mg of PC) revealed that methane production was likely occurring in a syntrophic mechanism during the OSPW remediation. A slight reduction in toxicity was also observed. This study suggests that PC-based biofilters may offer some advantages in the remediation of OSPW; however, the production of methane could be of future concerns if operated at field-scale.

Microbial community and abiotic effects on aquatic bacterial communities in north temperate lakes

The aquatic bacterial community (BC) plays a vital role in determining the nature and rate of ecosystem function. However, the biotic and abiotic factors influencing BC structure and function are largely unknown. Hence, the current study characterizes the impact of biotic and abiotic factors on aquatic bacterial biodiversity to determine whether the dominant effects are biotic or abiotic by partitioning their relative effects across temperate Canadian lakes. We collected water samples from sixty southern Ontario lakes and characterized their BC and microbial eukaryotic community (MEC) compositions using high throughput metabarcode sequencing of 16S and 18S rRNA gene fragments. The diversity and richness of aquatic BCs differed considerably among our study lakes, and those differences were explained by environmental, spatial, and biotic (MEC) factors (31%, 23%, and 23% of variance explained, respectively). The relatively large contribution from biotic and abiotic factors (54%), relative to spatial effects, shows deterministic processes prevail in shaping BC assembly in freshwater lakes. However, spatial effects also contributed significantly, highlighting the role of stochastic processes (ecological drift and coupled with limited dispersal) in shaping BC structure. Furthermore, our co-occurrence network analysis showed strong positive and negative interactions within and between the BCs and MECs, indicating mutualistic or antagonistic co-occurrence patterns relationships play important roles in driving the variation in BC composition among our sampled lakes. Considered together, our community analyses show that deterministic and stochastic processes combined contribute to determining the aquatic BC composition, and hence likely function as well, across a broad array of temperate freshwater lakes.

Using Functional Annotations to Study Pairwise Interactions in Urinary Tract Infection Communities

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities.

Spatial and Temporal Distribution of Bacterioplankton Molecular Ecological Networks in the Yuan River under Different Human Activity Intensity

Bacterioplankton communities play a crucial role in freshwater ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to human activity disturbances. This represents an important knowledge gap because changes in microbial networks could have implications for their functionality and vulnerability to future disturbances. Here, we compare the spatiotemporal and biogeographical patterns of bacterioplankton molecular ecological networks using high-throughput sequencing of Illumina HiSeq and multivariate statistical analyses from a subtropical river during wet and dry seasons. Results demonstrated that the lower reaches (high human activity intensity) network had less of an average degree (10.568/18.363), especially during the dry season, when compared with the upper reaches (low human activity intensity) network (10.685/37.552) during the wet and dry seasons, respectively. The latter formed more complexity networks with more modularity (0.622/0.556) than the lower reaches (high human activity intensity) network (0.505/0.41) during the wet and dry seasons, respectively. Bacterioplankton molecular ecological network under high human activity intensity became significantly less robust, which is mainly caused by altering of the environmental conditions and keystone species. Human activity altered the composition of modules but preserved their ecological roles in the network and environmental factors (dissolved organic carbon, temperature, arsenic, oxidation-reduction potential and Chao1 index) were the best parameters for explaining the variations in bacterioplankton molecular ecological network structure and modules. Proteobacteria, Actinobacteria and Bacteroidetes were the keystone phylum in shaping the structure and niche differentiations in the network. In addition, the lower reaches (high human activity intensity) reduce the bacterioplankton diversity and ecological niche differentiation, which deterministic processes become more important with increased farmland and constructed land area (especially farmland) with only 35% and 40% of the community variation explained by the neutral community model during the wet season and dry season, respectively. Keystone species in high human activity intensity stress habitats yield intense functional potentials and Bacterioplankton communities harbor keystone taxa in different human activity intensity stress habitats, which may exert their influence on microbiome network composition regardless of abundance. Therefore, human activity plays a crucial role in shaping the structure and function of bacterioplankton molecular ecological networks in subtropical rivers and understanding the mechanisms of this process can provide important information about human-water interaction processes, sustainable uses of freshwater as well as watershed management and conservation.

Endophytic Fungal Community of Tobacco Leaves and Their Potential Role in the Formation of "Cherry-Red" Tobacco

"Cherry-red" tobacco is the superior variant of tobacco, appearing with the apperance of red dapples on cured leaves due to the demethylation of nicotine to nornicotine during maturation and curing. Fungi are known to have the capacity to convert nicotine to nornicotine. However, an endophytic fungal community of "cherry-red" tobacco has never been reported to our best knowledge. Here, we sampled mature leaves from both "cherry-red" and ordinary tobacco at lower, center, and upper plant sections, and we analyzed the ITS diversity using high-throughput sequencing. Results revealed a significantly different fungal community of foliar endophyte in "cherry-red" and ordinary tobacco. In comparison to the ordinary control, higher diversity and a co-occurrence network complex were found in "cherry-red" samples, especially in the center and upper leaves, where the red dapples mainly emerged. More taxa were enriched in the "cherry-red" than ordinary tobacco leaves at all plant sections. In particular, Aspergillus, some strains of which are reported capable of converting nicotine to nornicotine, was specifically enriched in upper "cherry-red" tobacco leaves, which showed most red dapples after curing. A less robust network structure was detected in the "cherry-red" tobacco compared to ordinary tobacco. The nearest taxon index (NTI) and β NTI indicated that the local community structuration of tobacco endophytic fungi mainly driven by deterministic process, while the community turnover among plant sections was stochastic. In conclusion, our study provides the earliest information of endophytic fungal community in "cherry-red" tobacco leaf, and the community diversity, composition, and network features are synchronously varied with the appearance of red dapples, which is suggestive of their relationship to the formation of "cherry-red" tobacco.