Use and abuse of correlation analyses in microbial ecology

Lactate-mediated medium-chain fatty acid production from expired dairy and beverage waste

Fruits, vegetables, and dairy products are typically the primary sources of household food waste. Currently, anaerobic digestion is the most used bioprocess for the treatment of food waste with concomitant generation of biogas. However, to achieve a circular carbon economy, the organics in food waste should be converted to new chemicals with higher value than energy. Here we demonstrate the feasibility of medium-chain carboxylic acid (MCCA) production from expired dairy and beverage waste via a chain elongation platform mediated by lactate. In a two-stage fermentation process, the first stage with optimized operational conditions, including varying temperatures and organic loading rates, transformed expired dairy and beverage waste into lactate at a concentration higher than 900 mM C at 43 °C. This lactate was then used to produce >500 mM C caproate and >300 mM C butyrate via microbial chain elongation. Predominantly, lactate-producing microbes such as Lactobacillus and Lacticaseibacillus were regulated by temperature and could be highly enriched under mesophilic conditions in the first-stage reactor. In the second-stage chain elongation reactor, the dominating microbes were primarily from the genera Megasphaera and Caproiciproducens, shaped by varying feed and inoculum sources. Co-occurrence network analysis revealed positive correlations among species from the genera Caproiciproducens, Ruminococcus, and CAG-352, as well as Megasphaera, Bacteroides, and Solobacterium, indicating strong microbial interactions that enhance caproate production. These findings suggest that producing MCCAs from expired dairy and beverage waste via lactate-mediated chain elongation is a viable method for sustainable waste management and could serve as a chemical production platform in the context of building a circular bioeconomy.

Quantifying microbial interactions: concepts, caveats, and applications

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

Genetic architecture and correlations between the gut microbiome and gut gene transcription in Chinook salmon (Oncorhynchus tshawytscha)

Population divergence through selection can drive local adaptation in natural populations which has implications for the effective restoration of declining and extirpated populations. However, adaptation to local environmental conditions is complicated when both the host and its associated microbiomes must respond via co-evolutionary change. Nevertheless, for adaptation to occur through selection, variation in both host and microbiome traits should include additive genetic effects. Here we focus on host immune function and quantify factors affecting variation in gut immune gene transcription and gut bacterial community composition in early life-stage Chinook salmon (Oncorhynchus tshawytscha). Specifically, we utilized a replicated factorial breeding design to determine the genetic architecture (sire, dam and sire-by-dam interaction) of gut immune gene transcription and microbiome composition. Furthermore, we explored correlations between host gut gene transcription and microbiota composition. Gene transcription was quantified using nanofluidic qPCR arrays (22 target genes) and microbiota composition using 16 S rRNA gene (V5-V6) amplicon sequencing. We discovered limited but significant genetic architecture in gut microbiota composition and transcriptional profiles. We also identified significant correlations between gut gene transcription and microbiota composition, highlighting potential mechanisms for functional interactions between the two. Overall, this study provides support for the co-evolution of host immune function and their gut microbiota in Chinook salmon, a species recognized as locally adapted. Thus, the inclusion of immune gene transcription profile and gut microbiome composition as factors in the development of conservation and commercial rearing practices may provide new and more effective approaches to captive rearing.

Distance-decay equations of antibiotic resistance genes across freshwater reservoirs

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR). This is because our model is based on the 'law of limiting factors', which reduces influence of unmeasured factors that reduce the efficacy of the LSR method. DD equations generated from the 99th QR model for ARGs were 'Sall = 90.03e-0.01Dall' in water and 'Sall = 92.31e-0.011Dall' in sediment. The 99th QR model was less impacted by uneven sample sizes, resulting in a better quantification of ARGs dissemination. Within an individual reservoir, the 99th QR model demonstrated that there is no dispersal limitation of ARGs at this smaller spatial scale. The QR method not only allows for construction of robust DD equations that better display dissemination of organisms and genes across ecosystems, but also provides new insights into the biogeography exhibited by key parameters, as well as the interactions between organisms and environment.

Antimicrobial resistance in rural rivers: Comparative study of the Coquet (Northumberland) and Eden (Cumbria) River catchments

Many studies have characterised resistomes in river microbial communities. However, few have compared resistomes in parallel rural catchments that have few point-source inputs of antimicrobial genes (ARGs) and organisms (i.e., AMR) - catchments where one can contrast more nebulous drivers of AMR in rural rivers. Here, we used quantitative microbial profiling (QMP) to compare resistomes and microbiomes in two rural river catchments in Northern England, the Coquet and Eden in Northumberland and Cumbria, respectively, with different hydrological and geographical conditions. The Eden has higher flow rates, higher annual surface runoff, and longer periods of soil saturation, whereas the Coquet is drier and has lower flowrates. QMP analysis showed the Eden contained significantly more abundant microbes associated with soil sources, animal faeces, and wastewater than the Coquet, which had microbiomes like less polluted rivers (Wilcoxon test, p < 0.01). The Eden also had greater ARG abundances and resistome diversity (Kruskal Wallis, p < 0.05), and higher levels of potentially clinically relevant ARGs. The Eden catchment had greater and flashier runoff and more extensive agricultural land use in its middle reach, which explains higher levels of AMR in the river. Hydrological and geographic factors drive AMR in rural rivers, which must be considered in environmental monitoring programmes.

The Co-occurrence Matrix and the Correlation Network of Phytophagous Insects Are Driven by Abiotic and Biotic Variables: the Case of Canola

Co-occurrence a correlation profiles are driven by different factors (exogenous and endogenous) and drawing a profile of association between species based on co-occurrence, without assessing how these species vary in terms of ecological niche can lead to wrong conclusions. The objective was to determine the co-occurrence and correlation patterns of phytophagous insects in canola crop and to evaluate how these patterns varied according to the crop stage (phenology-biotic) and sowing times (agricultural practice-abiotic). We found that the patterns of co-occurrence and correlation between species were reflections of population variations due to the phenology and sowing times of canola. Variations in the multi-species abundance matrix were influenced by mean air temperature and accumulated rainfall. The main species associated with canola in southern Brazil, in terms of abundance, were P. xylostella, D. speciosa, and N. viridula. These species were mostly negatively associated. When evaluating their population variations, we found that they explore different temporal niches, whether in terms of phenology or sowing times. Finally, we demonstrate empirically that despite being important, association patterns based on co-occurrence and correlation should be interpreted in light of the understanding of patterns of niche exploitation and temporal variation of species.

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.

Microbial community assembly in engineered bioreactors

Microbial community assembly (MCA) processes that shape microbial communities in environments are being used to analyze engineered bioreactors such as activated sludge systems and anaerobic digesters. The goal of studying MCA is to be able to understand and predict the effect of design and operation procedures on bioreactor microbial composition and function. Ultimately, this can lead to bioreactors that are more efficient, resilient, or resistant to perturbations. This review summarizes the ecological theories underpinning MCA, evaluates MCA analysis methods, analyzes how these MCA-based methods are applied to engineered bioreactors, and extracts lessons from case studies. Furthermore, we suggest future directions in MCA research in engineered bioreactor systems. The review aims to provide insights and guidance to the growing number of environmental engineers who wish to design and understand bioreactors through the lens of MCA.

Phage predation, disease severity, and pathogen genetic diversity in cholera patients

Despite an increasingly detailed picture of the molecular mechanisms of bacteriophage (phage)-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. In this work, we report a year-long, nationwide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative polymerase chain reaction while accounting for antibiotic exposure using quantitative mass spectrometry. Virulent phage (ICP1) and antibiotics suppressed V. cholerae to varying degrees and were inversely associated with severe dehydration depending on resistance mechanisms. In the absence of antiphage defenses, predation was "effective," with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of antiphage defenses, predation was "ineffective," with a lower predator:prey ratio that correlated with increased genetic diversity among the predators. Phage-bacteria coevolution within patients should therefore be considered in the deployment of phage-based therapies and diagnostics.

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake

Cylindrospermopsis raciborskii-dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

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

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

The Potential of Co-Evolution and Interactions of Gut Bacteria-Phages in Bamboo-Eating Pandas: Insights from Dietary Preference-Based Metagenomic Analysis

Bacteria and phages are two of the most abundant biological entities in the gut microbiome, and diet and host phylogeny are two of the most critical factors influencing the gut microbiome. A stable gut bacterial community plays a pivotal role in the host's physiological development and immune health. A phage is a virus that directly infects bacteria, and phages' close associations and interactions with bacteria are essential for maintaining the stability of the gut bacterial community and the entire microbial ecosystem. Here, we utilized 99 published metagenomic datasets from 38 mammalian species to investigate the relationship (diversity and composition) and potential interactions between gut bacterial and phage communities and the impact of diet and phylogeny on these communities. Our results highlight the co-evolutionary potential of bacterial-phage interactions within the mammalian gut. We observed a higher alpha diversity in gut bacteria than in phages and identified positive correlations between bacterial and phage compositions. Furthermore, our study revealed the significant influence of diet and phylogeny on mammalian gut bacterial and phage communities. We discovered that the impact of dietary factors on these communities was more pronounced than that of phylogenetic factors at the order level. In contrast, phylogenetic characteristics had a more substantial influence at the family level. The similar omnivorous dietary preference and closer phylogenetic relationship (family Ursidae) may contribute to the similarity of gut bacterial and phage communities between captive giant panda populations (GPCD and GPYA) and omnivorous animals (OC; including Sun bear, brown bear, and Asian black bear). This study employed co-occurrence microbial network analysis to reveal the potential interaction patterns between bacteria and phages. Compared to other mammalian groups (carnivores, herbivores, and omnivores), the gut bacterial and phage communities of bamboo-eating species (giant pandas and red pandas) exhibited a higher level of interaction. Additionally, keystone species and modular analysis showed the potential role of phages in driving and maintaining the interaction patterns between bacteria and phages in captive giant pandas. In sum, gaining a comprehensive understanding of the interaction between the gut microbiota and phages in mammals is of great significance, which is of great value in promoting healthy and sustainable mammals and may provide valuable insights into the conservation of wildlife populations, especially endangered animal species.

Phage predation, disease severity and pathogen genetic diversity in cholera patients

Despite an increasingly detailed picture of the molecular mechanisms of phage-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. Here we report a year-long, nation-wide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative PCR, while accounting for antibiotic exposure using quantitative mass spectrometry. Virulent phage (ICP1) and antibiotics suppressed V. cholerae to varying degrees and were inversely associated with severe dehydration depending on resistance mechanisms. In the absence of anti-phage defenses, predation was 'effective,' with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of anti-phage defenses, predation was 'ineffective,' with a lower predator:prey ratio that correlated with increased genetic diversity among the predators. Phage-bacteria coevolution within patients should therefore be considered in the deployment of phage-based therapies and diagnostics.

Microbial community interactions determine the mineralization of soil organic phosphorus in subtropical forest ecosystems

In subtropical forest ecosystems with few phosphorus (P) inputs, P availability and forest productivity depend on soil organic P (Po) mineralization. However, the mechanisms by which the microbial community determines the status and fate of soil Po mineralization remain unclear. In the present study, soils were collected from three typical forest types: secondary natural forest (SNF), mixed planting, and monoculture forest of Chinese fir. The P fractions, Po-mineralization ability, and microbial community in the soils of different forest types were characterized. In addition, we defined Po-mineralizing taxa with the potential to interact with the soil microbial community to regulate Po mineralization. We found that a higher labile P content persisted in SNF and was positively associated with the Po-mineralization capacity of the soil microbial community. In vitro cultures of soil suspensions revealed that soil Po mineralization of three forest types was distinguished by differences in the composition of fungal communities. We further identified broad phylogenetic lineages of Po-mineralizing fungi with a high intensity of positive interactions with the soil microbial community, implying that the facilitation of Po-mineralizing taxa is crucial for soil P availability. Our dilution experiments to weaken microbial interactions revealed that in SNF soil, which had the highest interaction intensity of Po-mineralizing taxa with the community, Po-mineralization capacity was irreversibly lost after dilution, highlighting the importance of microbial diversity protection in forest soils. In summary, this study demonstrates that the interactions of Po-mineralizing microorganisms with the soil microbial community are critical for P availability in subtropical forests.IMPORTANCEIn subtropical forest ecosystems with few phosphorus inputs, phosphorus availability and forest productivity depend on soil organic phosphorus mineralization. However, the mechanisms by which the microbial community interactions determine the mineralization of soil organic phosphorus remain unclear. In the present study, soils were collected from three typical forest types: secondary natural forest, mixed planting, and monoculture forest of Chinese fir. We found that a higher soil labile phosphorus content was positively associated with the organic phosphorus mineralization capacity of the soil microbial community. Soil organic phosphorus mineralization of three forest types was distinguished by the differences in the composition of fungal communities. The positive interactions between organic phosphorus-mineralizing fungi and the rest of the soil microbial community facilitated organic phosphorus mineralization. This study highlights the importance of microbial diversity protection in forest soils and reveals the microbial mechanism of phosphorus availability maintenance in subtropical forest ecosystems.

Microbiologic surveys for Baijiu fermentation are affected by experimental design

The traditional Chinese alcoholic beverage Baijiu is produced by spontaneous fermentation of grains under anaerobic conditions. While numerous studies have used metagenomic technology to investigate the microbiome of Baijiu brewing, the microbial succession mechanism of Baijiu brewing has not been fully clarified, and metagenomic strategies for microecology surveys have not been comprehensively evaluated. Using the fermentation process of strong-flavor Baijiu as a model, we compared the data for bacterial communities based on short read 16S rRNA variable regions, V3-V4, and full-length 16S regions, V1-V9, to whole metagenomic shotgun sequencing (WMS) to measure the effect of technology selection on phylogenetic and functional profiles. The results showed differences in bacterial compositions and their relation to volatiles and physicochemical variables between sequencing methods. Furthermore, the percentage of V3-V4 sequences assigned to species level was higher than the percentage of V1-V9 sequences according to SILVA taxonomy, but lower according to NCBI taxonomy (P < 0.05). In both SILVA and NCBI taxonomies, the relative abundances of bacterial communities at both the genus and family levels were more positively correlated with WMS data in the V3-V4 dataset than in the V1-V9 dataset. The WMS identified changes in abundances of multiple metabolic pathways during fermentation (P < 0.05), including "starch and sucrose metabolism," "galactose metabolism," and "fatty acid biosynthesis." Although functional predictions derived from 16S data show similar patterns to WMS, most metabolic pathway changes are uncorrelated (P > 0.05). This study provided new technical and biological insights into Baijiu production that may assist in selection of methodologies for studies of fermentation systems.

Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

On the De Novo Emergence of Ecological Interactions during Evolutionary Diversification: A Conceptual Framework and Experimental Test

AbstractEcological interactions are crucial to the structure and function of biological communities, but we lack a causal understanding of the forces shaping their emergence during evolutionary diversification. Here we provide a conceptual framework linking different modes of diversification (e.g., ecological diversification), which depend on environmental characteristics, to the evolution of different forms of ecological interactions (e.g., resource partitioning) in asexual lineages. We tested the framework by examining the net interactions in communities of Pseudomonas aeruginosa produced via experimental evolution in nutritionally simple (SIM) or complex (COM) environments by contrasting the productivity and competitive fitness of whole evolved communities relative to their component isolates. As expected, we found that nutritional complexity drove the evolution of communities with net positive interactions whereas SIM communities had similar performance as their component isolates. A follow-up experiment revealed that high fitness in two COM communities was driven by rare variants (frequency <0.1%) that antagonized PA14, the ancestral strain and common competitor used in fitness assays. Our study suggests that the evolution of de novo ecological interactions in asexual lineages is predictable at a broad scale from environmental conditions. Further, our work demonstrates that rare variants can disproportionately impact the function of relatively simple microbial communities.

Community assembly of the human piercing microbiome

Predicting how biological communities respond to disturbance requires understanding the forces that govern their assembly. We propose using human skin piercings as a model system for studying community assembly after rapid environmental change. Local skin sterilization provides a 'clean slate' within the novel ecological niche created by the piercing. Stochastic assembly processes can dominate skin microbiomes due to the influence of environmental exposure on local dispersal, but deterministic processes might play a greater role within occluded skin piercings if piercing habitats impose strong selection pressures on colonizing species. Here we explore the human ear-piercing microbiome and demonstrate that community assembly is predominantly stochastic but becomes significantly more deterministic with time, producing increasingly diverse and ecologically complex communities. We also observed changes in two dominant and medically relevant antagonists (Cutibacterium acnes and Staphylococcus epidermidis), consistent with competitive exclusion induced by a transition from sebaceous to moist environments. By exploiting this common yet uniquely human practice, we show that skin piercings are not just culturally significant but also represent ecosystem engineering on the human body. The novel habitats and communities that skin piercings produce may provide general insights into biological responses to environmental disturbances with implications for both ecosystem and human health.

Natural restoration exhibits better soil bacterial network complexity and stability than artificial restoration on the Loess Plateau, China

Natural restoration (NR, e.g., secondary succession) and artificial restoration (AR, e.g., afforestation) are key approaches for rehabilitating degraded land; however, a comparative assessment of microbial network between these approaches is lacking. We compared bacterial networks under NR and AR in two different watersheds on the Loess Plateau. Our findings revealed significantly heightened network complexity under NR compared to AR, including metrics such as node, edge, modularity, degree, centrality, and keystone nodes. NR's network robustness exceeded AR by 19.45-35.9% and 7.79-17.74% in the two watersheds, aligning with the ecological principle that complexity begets stability. The significantly higher negative/positive cohesion and natural connectivity under NR also support its better network stability than AR. Integrated analysis of paired sequencing data from five Loess Plateau studies conducted on the Loess Plateau further confirmed the higher complexity and stability of bacterial networks under NR. Further analysis unveiled "biological interactions" as primary drivers of bacterial co-occurrence (on average 84.21% of links), surpassing the influence of environmental filtering (5.17%) or dispersal limitation (4.2%). Importantly, networked communities under NR exhibited generally stronger linkages with various ecosystem function than AR. Overall, our study provides insights into vegetation restoration strategies from the perspective of microbial network, underscoring natural regeneration's potential as a superior remedy for degraded-land restoration.

Competition-cooperation mechanism between Escherichia coli and Staphylococcus aureus based on systems mapping

Introduction

Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging.

Methods

In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions.

Results

By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1.

Discussion

By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

Severe Prolonged Drought Favours Stress-Tolerant Microbes in Australian Drylands

Drylands comprise one-third of Earth's terrestrial surface area and support over two billion people. Most drylands are projected to experience altered rainfall regimes, including changes in total amounts and fewer but larger rainfall events interspersed by longer periods without rain. This transition will have ecosystem-wide impacts but the long-term effects on microbial communities remain poorly quantified. We assessed belowground effects of altered rainfall regimes (+ 65% and -65% relative to ambient) at six sites in arid and semi-arid Australia over a period of three years (2016-2019) coinciding with a significant natural drought event (2017-2019). Microbial communities differed significantly among semi-arid and arid sites and across years associated with variation in abiotic factors, such as pH and carbon content, along with rainfall. Rainfall treatments induced shifts in microbial community composition only at a subset of the sites (Milparinka and Quilpie). However, differential abundance analyses revealed that several taxa, including Acidobacteria, TM7, Gemmatimonadates and Chytridiomycota, were more abundant in the wettest year (2016) and that their relative abundance decreased in drier years. By contrast, the relative abundance of oligotrophic taxa such as Actinobacteria, Alpha-proteobacteria, Planctomycetes, and Ascomycota and Basidiomycota, increased during the prolonged drought. Interestingly, fungi were shown to be more sensitive to the prolonged drought and to rainfall treatment than bacteria with Basidiomycota mostly dominant in the reduced rainfall treatment. Moreover, correlation network analyses showed more positive associations among stress-tolerant dominant taxa following the drought (i.e., 2019 compared with 2016). Our result indicates that such stress-tolerant taxa play an important role in how whole communities respond to changes in aridity. Such knowledge provides a better understanding of microbial responses to predicted increases in rainfall variability and the impact on the functioning of semi-arid and arid ecosystems.

Impact of intraspecific variation in insect microbiomes on host phenotype and evolution

Microbes can be an important source of phenotypic plasticity in insects. Insect physiology, behaviour, and ecology are influenced by individual variation in the microbial communities held within the insect gut, reproductive organs, bacteriome, and other tissues. It is becoming increasingly clear how important the insect microbiome is for insect fitness, expansion into novel ecological niches, and novel environments. These investigations have garnered heightened interest recently, yet a comprehensive understanding of how intraspecific variation in the assembly and function of these insect-associated microbial communities can shape the plasticity of insects is still lacking. Most research focuses on the core microbiome associated with a species of interest and ignores intraspecific variation. We argue that microbiome variation among insects can be an important driver of evolution, and we provide examples showing how such variation can influence fitness and health of insects, insect invasions, their persistence in new environments, and their responses to global environmental changes. A and B are two stages of an individual or a population of the same species. The drivers lead to a shift in the insect associated microbial community, which has consequences for the host. The complex interplay of those consequences affects insect adaptation and evolution and influences insect population resilience or invasion.

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

Relative abundance data can misrepresent heritability of the microbiome

BACKGROUND

Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample.

RESULTS

We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates.

CONCLUSIONS

We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract.

Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

Legionella relative abundance in shower hose biofilms is associated with specific microbiome members

Legionella are natural inhabitants of building plumbing biofilms, where interactions with other microorganisms influence their survival, proliferation, and death. Here, we investigated the associations of Legionella with bacterial and eukaryotic microbiomes in biofilm samples extracted from 85 shower hoses of a multiunit residential building. Legionella spp. relative abundance in the biofilms ranged between 0-7.8%, of which only 0-0.46% was L. pneumophila. Our data suggest that some microbiome members were associated with high (e.g. Chthonomonas, Vrihiamoeba) or low (e.g. Aquabacterium, Vannella) Legionella relative abundance. The correlations of the different Legionella variants (30 Zero-Radius OTUs detected) showed distinct patterns, suggesting separate ecological niches occupied by different Legionella species. This study provides insights into the ecology of Legionella with respect to: (i) the colonization of a high number of real shower hoses biofilm samples; (ii) the ecological meaning of associations between Legionella and co-occurring bacterial/eukaryotic organisms; (iii) critical points and future directions of microbial-interaction-based-ecological-investigations.

Nutritional and host environments determine community ecology and keystone species in a synthetic gut bacterial community

A challenging task to understand health and disease-related microbiome signatures is to move beyond descriptive community-level profiling towards disentangling microbial interaction networks. Using a synthetic gut bacterial community, we aimed to study the role of individual members in community assembly, identify putative keystone species and test their influence across different environments. Single-species dropout experiments reveal that bacterial strain relationships strongly vary not only in different regions of the murine gut, but also across several standard culture media. Mechanisms involved in environment-dependent keystone functions in vitro include exclusive access to polysaccharides as well as bacteriocin production. Further, Bacteroides caecimuris and Blautia coccoides are found to play keystone roles in gnotobiotic mice by impacting community composition, the metabolic landscape and inflammatory responses. In summary, the presented study highlights the strong interdependency between bacterial community ecology and the biotic and abiotic environment. These results question the concept of universally valid keystone species in the gastrointestinal ecosystem and underline the context-dependency of both, keystone functions and bacterial interaction networks.

Plant myo-inositol transport influences bacterial colonization phenotypes

Plant microbiomes are assembled and modified through a complex milieu of biotic and abiotic factors. Despite dynamic and fluctuating contributing variables, specific host metabolites are consistently identified as important mediators of microbial interactions. We combine information from a large-scale metatranscriptomic dataset from natural poplar trees and experimental genetic manipulation assays in seedlings of the model plant Arabidopsis thaliana to converge on a conserved role for transport of the plant metabolite myo-inositol in mediating host-microbe interactions. While microbial catabolism of this compound has been linked to increased host colonization, we identify bacterial phenotypes that occur in both catabolism-dependent and -independent manners, suggesting that myo-inositol may additionally serve as a eukaryotic-derived signaling molecule to modulate microbial activities. Our data suggest host control of this compound and resulting microbial behavior are important mechanisms at play surrounding the host metabolite myo-inositol.

Exploration of potential driving mechanisms of Comamonas testosteroni in polycyclic aromatic hydrocarbons degradation and remodelled bacterial community during co-composting

Polycyclic aromatic hydrocarbons (PAHs) are a cluster of highly hazardous organic pollutants that are widespread in ecosystems and threaten human health. Composting has been shown to be an effective strategy for PAHs degredation. Here, we used Comamonas testosteroni as an inoculant in composting and investigated the potential mechanisms of PAHs degradation by co-occurrence network and structural equation modelling analysis. The results showed that more than 60% of PAHs were removed and the bacterial community responded to the negative effects of PAHs by upgrading the network. Inoculation with C. testosteroni altered bacterial community succession, intensified bacterial response to PAHs, improved metabolic activity, and promoted the degradation of PAHs during co-composting. The increased in the positive cohesion index of the community suggested that agents increased the cooperative behaviour between bacteria and led to changes in keystones of the bacterial network. However, the topological values of C. testosteroni in the network were not elevated, which confirmed that C. testosteroni altered communities by affecting other bacterial growth rather than its own colonisation. This study strengthens our comprehension of the potential mechanisms for the degradation of PAHs in inoculated composting.

Effect of microbial network complexity and stability on nitrogen and sulfur pollutant removal during sediment remediation in rivers affected by combined sewer overflows

Discovering the complexity and improving the stability of microbial networks in urban rivers affected by combined sewer overflows (CSOs) is essential for restoring the ecological functions of urban rivers, especially to improve their ability to resist CSO impacts. In this study, the effects of sediment remediation on the complexity and stability of microbial networks was investigated. The results revealed that the restored microbial community structure using different approaches in the river sediments differed significantly, and random matrix theory showed that sediment remediation significantly affected microbial networks and topological properties; the average path distance, average clustering coefficient, connectedness, and other network topological properties positively correlated with remediation time and weakened the small-world characteristics of the original microbial networks. Compared with other sediment remediation methods, regulating low dissolved oxygen (DO) shifts the microbial network module hubs from Actinobacteria and Bacteroidetes to Chloroflexi and Proteobacteria. This decreases the positive association of networks by 17%-18%, which intensifies the competitiveness among microorganisms, further weakening the influence and transmission of external pressure across the entire microbial network. Compared with that of the original sediment, the vulnerability of the restored network was reduced by more than 36%, while the compositional stability was improved by more than 12%, with reduced fluctuation in natural connectivity. This microbial network succession substantially increased the number of key enzyme-producing genes involved in nitrogen and sulfur metabolism, enhancing nitrification, denitrification, and assimilatory sulfate reduction, thereby increasing the removal rates of ammonia, nitrate, and acid volatile sulfide by 43.42%, 250.68% and 2.66%, respectively. This study comprehensively analyzed the succession patterns of microbial networks in urban rivers affected by CSOs before and after sediment remediation, which may provide a reference for reducing the impact of CSO pollution on urban rivers in the subsequent stages.

Decreased soil multifunctionality is associated with altered microbial network properties under precipitation reduction in a semiarid grassland

Our results reveal different responses of soil multifunctionality to increased and decreased precipitation. By linking microbial network properties to soil functions, we also show that network complexity and potentially competitive interactions are key drivers of soil multifunctionality.

Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth

Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R² of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.

From guest to host: parasite Cistanche deserticola shapes and dominates bacterial and fungal community structure and network complexity

BACKGROUND

Rhizosphere and plant microbiota are assumed to play an essential role in deciding the well-being of hosts, but effects of parasites on their host microbiota have been rarely studied. Also, the characteristics of the rhizosphere and root microbiota of parasites and hosts under parasitism is relatively unknown. In this study, we used Cistanche deserticola and Haloxylon ammodendron from cultivated populations as our model parasites and host plants, respectively. We collected samples from BULK soil (BULK), rhizosphere soil of H. ammodendron not parasitized (NCD) and parasitized (RHA) to study how the parasite influenced the rhizosphere microbiota of the host. We also collected samples from the rhizosphere soil and roots of C. deserticola (RCD and ECD) and Haloxylon ammodendron (RHA and EHA) to explore the difference between the microbiota of the parasite and its host under parasitism.

RESULTS

The parasite reduced the compositional and co-occurrence network complexities of bacterial and fungal microbiota of RHA. Additionally, the parasite increased the proportion of stochastic processes mainly belonging to dispersal limitation in the bacterial microbiota of RHA. Based on the PCoA ordinations and permutational multivariate analysis of variance, the dissimilarity between microbiota of C. deserticola and H. ammodendron were rarely evident (bacteria, R² = 0.29971; fungi, R² = 0.15631). Interestingly, four hub nodes of H. ammodendron in endosphere fungal microbiota were identified, while one hub node of C. deserticola in endosphere fungal microbiota was identified. It indicated that H. ammodendron played a predominant role in the co-occurrence network of endosphere fungal microbiota. Source model of plant microbiome suggested the potential source percentage from the parasite to the host (bacteria: 52.1%; fungi: 16.7%) was lower than host-to-parasite (bacteria: 76.5%; fungi: 34.3%), illustrating that microbial communication was bidirectional, mainly from the host to the parasite.

CONCLUSIONS

Collectively, our results suggested that the parasite C. deserticola shaped the diversity, composition, co-occurrence network, and community assembly mechanisms of the rhizosphere microbiota of H. ammodendron. Additionally, the microbiota of C. deserticola and H. ammodendron were highly similar and shared. Our findings on parasite and host microbiota provided a novel line of evidence supporting the influence of parasites on the microbiota of their hosts.

Biogeographic Patterns and Community Assembly Processes of Bacterioplankton and Potential Pathogens in Subtropical Estuaries in China

Microbial communities in coastal waters are diverse and dynamic and play important roles in ecosystem functions and services. Despite the ecological impact of bacterioplankton or pathogens, little is known about whether bacterioplankton and pathogen communities exhibit similar patterns. Here, using 16S RNA gene amplicon sequencing, the geographic patterns and assembly processes of bacterioplankton and pathogen communities in 30 subtropical estuaries were studied. Results showed that the estuarine bacterioplankton communities mainly consisted of Proteobacteria (49.06%), Actinobacteria (17.62%), and Bacteroidetes (16.33%), among which 31 pathogen genera (186 amplicon sequence variants [ASVs]) were identified. Under the influence of salinity, bacterioplankton and pathogens showed similar biogeographic patterns. Redundancy and correlation analyses indicated that the bacterioplankton communities were strongly correlated with estuarine environmental factors, but potential pathogens were less influenced. Co-occurrence network analysis revealed a close relationship between bacterioplankton and potential pathogens, with two pathogens identified as connectors (i.e., ASV340 [Clostridium perfringens] and ASV1624 [Brevundimonas diminuta]), implying potential impacts of pathogens on structure, function, and stability of estuarine bacterioplankton communities. Null-model analysis revealed that deterministic processes (heterogeneous selection) dominated bacterioplankton community assembly, while stochastic processes (undominated effect) shaped the potential pathogen community. Our findings illustrate the biogeographic patterns and community assembly mechanisms of bacterioplankton and pathogens in estuaries, which should provide guidance and a reference for the control of potential pathogenic bacteria. IMPORTANCE Bacterioplankton play an important role in estuarine ecosystem functions and services; however, potentially pathogenic bacteria may exhibit infectivity and pose a serious threat to environmental and human health. In this study, geographic patterns and assembly processes of bacterioplankton communities in 30 subtropical estuaries were explored, and potential pathogenic bacteria in the estuaries were detected and profiled. Our results demonstrate here that bacterioplankton and pathogens show similar biogeographic patterns under the influence of salinity. Interestingly, heterogeneous selection dominated bacterioplankton assembly, while stochasticity dominated pathogen assembly. This study provides important information for future risk assessment of potential pathogenic bacteria as well as management in estuarine ecosystems.

The oropharynx of men using HIV pre-exposure prophylaxis is enriched with antibiotic resistance genes: A cross-sectional observational metagenomic study

BACKGROUND

Phenotypic studies have found high levels of antimicrobial resistance to cephalosporins, macrolides and fluoroquinolones in commensal Neisseria species in the oropharynx of men who have sex with men (MSM) using HIV pre-exposure prophylaxis (PrEP). These species include Neisseria subflava and Neisseria mucosa. This may represent a risk to pathogens like Neisseria gonorrhoeae which tend to take up antibiotic resistance genes (ARGs) from other bacteria. We aimed to explore to what extent the oropharyngeal resistome of MSM using PrEP differed from the general population.

METHODS

We collected oropharyngeal swabs from 32 individuals of the general population and from 64 MSM using PrEP. Thirty-two MSM had consumed antibiotics in the previous six months, whereas none of the other participants had. Samples underwent shotgun metagenomic sequencing. Sequencing reads were mapped against MEGARes 2.0 to estimate ARG abundance. ARG abundance was compared between groups by zero-inflated negative binomial regression.

FINDINGS

ARG abundance was significantly lower in the general population than in MSM (ratio 0.41, 95% CI 0.26-0.65). More specifically, this was the case for fluoroquinolones (0.33, 95% CI 0.15-0.69), macrolides (0.37, 95% CI 0.25-0.56), tetracyclines (0.41, 95% CI 0.25-0.69), and multidrug efflux pumps (0.11, 95% CI 0.03-0.33), but not for beta-lactams (1.38, 95% CI 0.73-2.61). There were no significant differences in ARG abundance between MSM who had used antibiotics and those that had not.

INTERPRETATION

The resistome of MSM using PrEP is enriched with ARGs, independent of recent antibiotic use. Stewardship campaigns should aim to reduce antibiotic consumption in populations at high risk for STIs.

Rhizosphere phage communities drive soil suppressiveness to bacterial wilt disease

BACKGROUND

Bacterial viruses, phages, play a key role in nutrient turnover and lysis of bacteria in terrestrial ecosystems. While phages are abundant in soils, their effects on plant pathogens and rhizosphere bacterial communities are poorly understood. Here, we used metagenomics and direct experiments to causally test if differences in rhizosphere phage communities could explain variation in soil suppressiveness and bacterial wilt plant disease outcomes by plant-pathogenic Ralstonia solanacearum bacterium. Specifically, we tested two hypotheses: (1) that healthy plants are associated with stronger top-down pathogen control by R. solanacearum-specific phages (i.e. 'primary phages') and (2) that 'secondary phages' that target pathogen-inhibiting bacteria play a stronger role in diseased plant rhizosphere microbiomes by indirectly 'helping' the pathogen.

RESULTS

Using a repeated sampling of tomato rhizosphere soil in the field, we show that healthy plants are associated with distinct phage communities that contain relatively higher abundances of R. solanacearum-specific phages that exert strong top-down pathogen density control. Moreover, 'secondary phages' that targeted pathogen-inhibiting bacteria were more abundant in the diseased plant microbiomes. The roles of R. solanacearum-specific and 'secondary phages' were directly validated in separate greenhouse experiments where we causally show that phages can reduce soil suppressiveness, both directly and indirectly, via top-down control of pathogen densities and by alleviating interference competition between pathogen-inhibiting bacteria and the pathogen.

CONCLUSIONS

Together, our findings demonstrate that soil suppressiveness, which is most often attributed to bacteria, could be driven by rhizosphere phage communities that regulate R. solanacearum densities and strength of interference competition with pathogen-suppressing bacteria. Rhizosphere phage communities are hence likely to be important in determining bacterial wilt disease outcomes and soil suppressiveness in agricultural fields. Video Abstract.

A guide for comparing microbial co-occurrence networks

The article provides a pipeline for comparing microbial co-occurrence networks based on the R microeco package and meconetcomp package. It has high flexibility and expansibility and can help users efficiently compare networks built from different groups of samples or different construction approaches.

Riboflavin Supplementation Promotes Butyrate Production in the Absence of Gross Compositional Changes in the Gut Microbiota

Aims: We performed a randomized, placebo-controlled trial, RIBOGUT, to study the effect of 2 weeks supplementation with either 50 or 100 mg/d of riboflavin on (i) Faecalibacterium prausnitzii abundance, (ii) gut microbiota composition, (iii) short-chain fatty acid (SCFA) profiles, and (iv) the satiety and gut hormones. Results: Neither dose of riboflavin, analyzed separately, impacted the abundance of F. prausnitzii, and only minor differences in SCFA concentrations were observed. However, combining the results of the 50 and 100 mg/d groups showed a significant increase in butyrate production. While the gut bacterial diversity was not affected by riboflavin supplementation, the complexity and stability of the bacterial network were enhanced. Oral glucose tolerance tests showed a trend of increased plasma insulin concentration and GLP-1 after 100 mg/d supplementation. Innovation: Dietary supplements, such as vitamins, promote health by either directly targeting host physiology or indirectly via gut microbiota modulation. Here, we show for the first time that riboflavin intervention changes the activity of the microbiota. The butyrate production increased after intervention and although the composition did not change significantly, the network of microbial interactions was enforced. Conclusion: This RIBOGUT study suggests that oral riboflavin supplementation promotes butyrate production in the absence of major shifts in gut microbiota composition. ClinicalTrials.gov Identifier: NCT02929459.

Selective and differential feeding on marine prokaryotes by mucous mesh feeders

Microbial mortality impacts the structure of food webs, carbon flow, and the interactions that create dynamic patterns of abundance across gradients in space and time in diverse ecosystems. In the oceans, estimates of microbial mortality by viruses, protists, and small zooplankton do not account fully for observations of loss, suggesting the existence of underappreciated mortality sources. We examined how ubiquitous mucous mesh feeders (i.e. gelatinous zooplankton) could contribute to microbial mortality in the open ocean. We coupled capture of live animals by blue-water diving to sequence-based approaches to measure the enrichment and selectivity of feeding by two coexisting mucous grazer taxa (pteropods and salps) on numerically dominant marine prokaryotes. We show that mucous mesh grazers consume a variety of marine prokaryotes and select between coexisting lineages and similar cell sizes. We show that Prochlorococcus may evade filtration more than other cells and that planktonic archaea are consumed by macrozooplanktonic grazers. Discovery of these feeding relationships identifies a new source of mortality for Earth's dominant marine microbes and alters our understanding of how top-down processes shape microbial community and function.

Biotic interactions contribute more than environmental factors and geographic distance to biogeographic patterns of soil prokaryotic and fungal communities

Recent studies have shown distinct soil microbial assembly patterns across taxonomic types, habitat types and regions, but little is known about which factors play a dominant role in soil microbial communities. To bridge this gap, we compared the differences in microbial diversity and community composition across two taxonomic types (prokaryotes and fungi), two habitat types (Artemisia and Poaceae) and three geographic regions in the arid ecosystem of northwest China. To determine the main driving factors shaping the prokaryotic and fungal community assembly, we carried out diverse analyses including null model, partial mantel test and variance partitioning analysis etc. The findings suggested that the processes of community assembly were more diverse among taxonomic categories in comparison to habitats or geographical regions. The predominant driving factor of soil microbial community assembly in arid ecosystem was biotic interactions between microorganisms, followed by environmental filtering and dispersal limitation. Network vertex, positive cohesion and negative cohesion showed the most significant correlations with prokaryotic and fungal diversity and community dissimilarity. Salinity was the major environmental variable structuring the prokaryotic community. Although prokaryotic and fungal communities were jointly regulated by the three factors, the effects of biotic interactions and environmental variables (both are deterministic processes) on the community structure of prokaryotes were stronger than that of fungi. The null model revealed that prokaryotic community assembly was more deterministic, whereas fungal community assembly was structured by stochastic processes. Taken together, these findings unravel the predominant drivers governing microbial community assembly across taxonomic types, habitat types and geographic regions and highlight the impacts of biotic interactions on disentangling soil microbial assembly mechanisms.

Shared and contrasting associations in the dynamic nano- and picoplankton communities of two close but contrasting sites from the Bay of Biscay

Pico- and nanoplankton are key players in the marine ecosystems due to their implication in the biogeochemical cycles, nutrient recycling and the pelagic food webs. However, the specific dynamics and niches of most bacterial, archaeal and eukaryotic plankton remain unknown, as well as the interactions between them. Better characterization of these is critical for understanding and predicting ecosystem functioning under anthropogenic pressures. We used environmental DNA metabarcoding across a 6-year time series to explore the structure and seasonality of pico- and nanoplankton communities in two sites of the Bay of Biscay, one coastal and one offshore, and construct association networks to reveal potential keystone and connector taxa. Temporal trends in alpha diversity were similar between the two sites, and concurrent communities more similar than within the same site at different times. However, we found differences between the network topologies of the two sites, with both shared and site-specific keystones and connectors. For example, Micromonas, with lower abundance in the offshore site is a keystone here, indicating a stronger effect of associations such as resource competition. This study provides an example of how time series and association network analysis can reveal how similar communities may function differently despite being geographically close.

The maximum entropy principle for compositional data

BACKGROUND

Compositional systems, represented as parts of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations.

RESULTS

To resolve both of these issues, we provide a general and data-driven modeling tool for compositional systems called Compositional Maximum Entropy (CME). By integrating the prior geometric structure of compositions with sample-specific information, CME infers the underlying multivariate relationships between the constituent components. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.

CONCLUSIONS

CME provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

BACKGROUND

Diarrhea is a major cause of morbidity and mortality in young calves, resulting in considerable economic loss for dairy farms. To determine if some gut microbes might have resistance to dysbiotic process with calf diarrhea by dictating the microbial co-occurrence patterns from birth to post-weaning, we examined the dynamic development of the gut microbiota and diarrhea status using two animal trials, with the first trial having 14 Holstein dairy calves whose fecal samples were collected 18 times over 78 d from birth to 15 d post-weaning and the second trial having 43 Holstein dairy calves whose fecal samples were collected daily from 8 to 18 days of age corresponding to the first diarrhea peak of trial 1.

RESULTS

Metataxonomic analysis of the fecal microbiota showed that the development of gut microbiota had three age periods with birth and weaning as the separatrices. Two diarrhea peaks were observed during the transition of the three age periods. Fusobacteriaceae was identified as a diarrhea-associated taxon both in the early stage and during weaning, and Clostridium_sensu_stricto_1 was another increased genus among diarrheic calves in the early stage. In the neonatal calves, Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), and Alloprevotella (ASV14) were negatively associated with Clostridium_sensu_stricto_1 (ASV48), the keystone taxa of the diarrhea-phase module. During weaning, unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and Ruminococcaceae_UCG-005 (ASV254) were identified being associated with non-diarrheic status, and they aggregated in the non-diarrhea module of co-occurrence patterns wherein unclassified Muribaculaceae (ASV28) and Barnesiella (ASV497) had a direct negative relationship with the members of the diarrhea module.

CONCLUSIONS

Taken together, our results suggest that the dynamic successions of calf gut microbiota and the interactions among some bacteria could influence calf diarrhea, and some species of Prevotella might be the core microbiota in both neonatal and weaning calves, while species of Muribaculaceae might be the core microbiota in weaning calves for preventing calf diarrhea. Some ASVs affiliated with Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella (ASV497) might be proper probiotics for preventing calf diarrhea whereas Clostridium_sensu_stricto_1 (ASV48) might be the biomarker for diarrhea risk in specific commercial farms.

Toward FAIR Representations of Microbial Interactions

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

The Stability of Phyto-Zooplanktonic Networks Varied with Zooplanktonic Sizes in Chinese Coastal Ecosystem

The linkages between phytoplankton and zooplankton are crucial for the stability of complex food webs and the flow of energy within the marine ecosystem. Despite body size exhibiting multiple effects on the planktonic community assembly and the dispersal scale, its role in determining the stability of phyto-zooplanktonic co-occurrence patterns remains unclear. Here, we focused on more than 13,000 kilometers of the Chinese coast to study the diatom-dominated plankton ecosystem and to report the significant negative effects of zooplanktonic body sizes on the topological properties of phyto-zooplanktonic networks (PZNs) by using more than 500 species from 251 samples taken along the coastline. PZNs tended to be more complex and stable when phytoplankton associated with smaller zooplankton. Particularly, the subnetworks of dominant phytoplankton displayed differences with different zooplanktonic body sizes. The zooplankton with larger and smaller body sizes tended to interact with dinoflagellates and diatoms, respectively. Additionally, abiotic factors (i.e., water temperature, pH, salinity, and metal concentrations) displayed significant effects on PZNs via the shifting of zooplanktonic composition, and the zooplanktonic body sizes altered the network modules' associations with different environmental factors. Our study elucidated the general relationship between zooplanktonic body sizes and the stability of PZNs, which provides new insights into marine food webs. IMPORTANCE Body size is a key life trait of aquatic plankton that affects organisms' metabolic rates and ecological functions; however, its specific effects on interactions between phytoplankton and zooplankton are poorly understood. We collected planktonic species and their body size data along more than 13,000 kilometers of coastline to explore the role of zooplanktonic body size in maintaining the stability of phyto-zooplanktonic networks (PZNs). We found that zooplankton play a more important role in maintaining PZN stability than do phytoplankton as well as that the PZN would be more complex and stable with smaller zooplankton. Furthermore, this work revealed that body size significantly determined the relationships between environmental factors and network structure. Overall, these findings lay a general relationship between zooplanktonic body sizes and the stability of PZNs, which helps us further explore the micro food web of coastal ecosystems.

Association of food insecurity on gut microbiome and metabolome profiles in a diverse college-based sample

Voluntary caloric restriction (e.g., eating disorders) often results in alterations in the gut microbiota composition and function. However, these findings may not translate to food insecurity, where an individual experiences inconsistent access to healthy food options. In this study we compared the fecal microbiome and metabolome of racially and ethnically diverse first year college students (n = 60) experiencing different levels of food access. Students were dichotomized into food secure (FS) and food insecure (FI) groups using a validated, 2-question screener assessing food security status over the previous 30 days. Fecal samples were collected up to 5 days post survey-completion. Gut microbiome and metabolome were established using 16S rRNA amplicon sequencing, targeted liquid chromatography-tandem mass spectrometry, and gas chromatography-mass spectrometry. FI students experienced significantly greater microbial diversity with increased abundance of Enterobacteriaceae and Eisenbergiella, while FS students had greater abundance of Megasphaera and Holdemanella. Metabolites related to energy transfer and gut–brain-axis communication (picolinic acid, phosphocreatine, 2-pyrrolidinone) were elevated in FI students (q < 0.05). These findings suggest that food insecurity is associated with differential gut microbial and metabolite composition for which the future implications are unknown. Further work is needed to elucidate the longitudinal metabolic effects of food insecurity and how gut microbes influence metabolic outcomes.

Data-driven causal analysis of observational biological time series

Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8).

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.