Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data

Comammox Nitrospira among dominant ammonia oxidizers within aquarium biofilter microbial communities

Nitrification by aquarium biofilters transforms ammonia waste (NH3/NH4+) to less toxic nitrate (NO3-) via nitrite (NO2-). Prior to the discovery of complete ammonia-oxidizing ("comammox" or CMX) Nitrospira, previous research revealed that ammonia-oxidizing archaea (AOA) dominated over ammonia-oxidizing bacteria (AOB) in freshwater aquarium biofilters. Here, we profiled aquarium biofilter microbial communities and quantified the abundance of all three known ammonia oxidizers using 16S rRNA gene sequencing and quantitative PCR (qPCR), respectively. Biofilter and water samples were each collected from representative residential and commercial freshwater and saltwater aquaria. Distinct biofilter microbial communities were associated with freshwater and saltwater biofilters. Comammox Nitrospira amoA genes were detected in all 38 freshwater biofilter samples (average CMX amoA genes: 2.2 × 103 ± 1.5 × 103 copies/ng) and dominant in 30, whereas AOA were present in 35 freshwater biofilter samples (average AOA amoA genes: 1.1 × 103 ± 2.7 × 103 copies/ng) and only dominant in 7 of them. The AOB were at relatively low abundance within biofilters (average of 3.2 × 101 ± 1.1 × 102 copies of AOB amoA genes/ng of DNA), except for the aquarium with the highest ammonia concentration. For saltwater biofilters, AOA or AOB were differentially abundant, with no comammox Nitrospira detected. Additional sequencing of Nitrospira amoA genes revealed differential distributions, suggesting niche adaptation based on water chemistry (e.g., ammonia, carbonate hardness, and alkalinity). Network analysis of freshwater microbial communities demonstrated positive correlations between nitrifiers and heterotrophs, suggesting metabolic and ecological interactions within biofilters. These results demonstrate that comammox Nitrospira plays a previously overlooked, but important role in home aquarium biofilter nitrification.

IMPORTANCE

Nitrification is a crucial process that converts toxic ammonia waste into less harmful nitrate that occurs in aquarium biofilters. Prior research found that ammonia-oxidizing archaea (AOA) were dominant over ammonia-oxidizing bacteria (AOB) in freshwater aquarium biofilters. Our study profiled microbial communities of aquarium biofilters and quantified the abundance of all currently known groups of aerobic ammonia oxidizers. The findings reveal that complete ammonia-oxidizing (comammox) Nitrospira were present in all freshwater aquarium biofilter samples in high abundance, challenging our previous understanding of aquarium nitrification. We also highlight niche adaptation of ammonia oxidizers based on salinity. The network analysis of freshwater biofilter microbial communities revealed significant positive correlations among nitrifiers and other community members, suggesting intricate interactions within biofilter communities. Overall, this study expands our understanding of nitrification in aquarium biofilters, emphasizes the role of comammox Nitrospira, and highlights the value of aquaria as microcosms for studying nitrifier ecology.

Niche partitioning and plastisphere core microbiomes in the two most plastic polluted zones of the world ocean

Plastics are offering a new niche for microorganisms colonizing their surface, the so-called "plastisphere," in which diversity and community structure remain to be characterized and compared across ocean pelagic regions. Here, we compared the bacterial diversity of microorganisms living on plastic marine debris (PMD) and the surrounding free-living (FL) and organic particle-attached (PA) lifestyles sampled during the Tara expeditions in two of the most plastic polluted zones in the world ocean, i.e., the North Pacific gyre and the Mediterranean Sea. The 16S rRNA gene sequencing analysis confirmed that PMD are a new anthropogenic ocean habitat for marine microbes at the ocean-basin-scale, with clear niche partitioning compared to FL and PA lifestyles. At an ocean-basin-scale, the composition of the plastisphere communities was mainly driven by environmental selection, rather than polymer types or dispersal effect. A plastisphere "core microbiome" could be identified, mainly dominated by Rhodobacteraceae and Cyanobacteria. Predicted functions indicated the dominance of carbon, nitrogen and sulfur metabolisms on PMD that open new questions on the role of the plastisphere in a large number of important ecological processes in the marine ecosystem.

A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer

Horizontal gene transfer, the exchange of genetic material through means other than reproduction, is a fundamental force in prokaryotic genome evolution. Genomic persistence of horizontally transferred genes has been shown to be influenced by both ecological and evolutionary factors. However, there is limited availability of ecological information about species other than the habitats from which they were isolated, which has prevented a deeper exploration of ecological contributions to horizontal gene transfer. Here we focus on transfers detected through comparison of individual gene trees to the species tree, assessing the distribution of gene-exchanging prokaryotes across over a million environmental sequencing samples. By analysing detected horizontal gene transfer events, we show distinct functional profiles for recent versus old events. Although most genes transferred are part of the accessory genome, genes transferred earlier in evolution tend to be more ubiquitous within present-day species. We find that co-occurring, interacting and high-abundance species tend to exchange more genes. Finally, we show that host-associated specialist species are most likely to exchange genes with other host-associated specialist species, whereas species found across different habitats have similar gene exchange rates irrespective of their preferred habitat. Our study covers an unprecedented scale of integrated horizontal gene transfer and environmental information, highlighting broad eco-evolutionary trends.

Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities

Marine microorganisms form complex communities of interacting organisms that influence central ecosystem functions in the ocean such as primary production and nutrient cycling. Identifying the mechanisms controlling their assembly and activities is a major challenge in microbial ecology. Here, we integrated Tara Oceans meta-omics data to predict genome-scale community interactions within prokaryotic assemblages in the euphotic ocean. A global genome-resolved co-activity network revealed a significant number of inter-lineage associations across diverse phylogenetic distances. Identified co-active communities include species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling reveals a higher potential for interaction within co-active communities and points towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach suggests that genome streamlining and metabolic auxotrophies may act as joint mechanisms shaping bacterioplankton community assembly in the global ocean surface.

Revisiting the mercury cycle in marine sediments: A potential multifaceted role for Desulfobacterota

Marine sediments impacted by urban and industrial pollutants are typically exposed to reducing conditions and represent major reservoirs of toxic mercury species. Mercury methylation mediated by anaerobic microorganisms is favored under such conditions, yet little is known about potential microbial mechanisms for mercury detoxification. We used culture-independent (metagenomics, metabarcoding) and culture-dependent approaches in anoxic marine sediments to identify microbial indicators of mercury pollution and analyze the distribution of genes involved in mercury reduction (merA) and demethylation (merB). While none of the isolates featured merB genes, 52 isolates, predominantly affiliated with Gammaproteobacteria, were merA positive. In contrast, merA genes detected in metagenomes were assigned to different phyla, including Desulfobacterota, Actinomycetota, Gemmatimonadota, Nitrospirota, and Pseudomonadota. This indicates a widespread capacity for mercury reduction in anoxic sediment microbiomes. Notably, merA genes were predominately identified in Desulfobacterota, a phylum previously associated only with mercury methylation. Marker genes involved in the latter process (hgcAB) were also mainly assigned to Desulfobacterota, implying a potential central and multifaceted role of this phylum in the mercury cycle. Network analysis revealed that Desulfobacterota were associated with anaerobic fermenters, methanogens and sulfur-oxidizers, indicating potential interactions between key players of the carbon, sulfur and mercury cycling in anoxic marine sediments.

Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean

Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change.

Biotic interactions explain seasonal dynamics of the alpine soil microbiome

While it is acknowledged that alpine soil bacterial communities are primarily driven by season and elevation, there is no consensus on the factors influencing fungi and protists. Here we used a holistic approach of the microbiome to investigate the seasonal dynamics in alpine grasslands, focusing on soil food web interactions. We collected 158 soil samples along elevation transects from three mountains in the Alps, in spring during snowmelt and in the following summer. Using metatranscriptomics, we simultaneously assessed prokaryotic and eukaryotic communities, further classified into trophic guilds. Our findings reveal that the consumers' pressure increases from spring to summer, leading to more diverse and evenly distributed prey communities. Consequently, consumers effectively maintain the diverse soil bacterial and fungal communities essential for ecosystem functioning. Our research highlights the significance of biotic interactions in understanding the distribution and dynamics of alpine microbial communities.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Functional Redundancy Secures Resilience of Chain Elongation Communities upon pH Shifts in Closed Bioreactor Ecosystems

For anaerobic mixed cultures performing microbial chain elongation, it is unclear how pH alterations affect the abundance of key players, microbial interactions, and community functioning in terms of medium-chain carboxylate yields. We explored pH effects on mixed cultures enriched in continuous anaerobic bioreactors representing closed model ecosystems. Gradual pH increase from 5.5 to 6.5 induced dramatic shifts in community composition, whereas product range and yields returned to previous states after transient fluctuations. To understand community responses to pH perturbations over long-term reactor operation, we applied Aitchison PCA clustering, linear mixed-effects models, and random forest classification on 16S rRNA gene amplicon sequencing and process data. Different pH preferences of two key chain elongation species─one Clostridium IV species related to Ruminococcaceae bacterium CPB6 and one Clostridium sensu stricto species related to Clostridium luticellarii─were determined. Network analysis revealed positive correlations of Clostridium IV with lactic acid bacteria, which switched from Olsenella to Lactobacillus along the pH increase, illustrating the plasticity of the food web in chain elongation communities. Despite long-term cultivation in closed systems over the pH shift experiment, the communities retained functional redundancy in fermentation pathways, reflected by the emergence of rare species and concomitant recovery of chain elongation functions.

Linking differences in microbial network structure with changes in coral larval settlement

Coral cover and recruitment have decreased on reefs worldwide due to climate change-related disturbances. Achieving reliable coral larval settlement under aquaculture conditions is critical for reef restoration programmes; however, this can be challenging due to the lack of reliable and universal larval settlement cues. To investigate the role of microorganisms in coral larval settlement, we undertook a settlement choice experiment with larvae of the coral Acropora tenuis and microbial biofilms grown for different periods on the reef and in aquaria. Biofilm community composition across conditioning types and time was profiled using 16S and 18S rRNA gene sequencing. Co-occurrence networks revealed that strong larval settlement correlated with diverse biofilm communities, with specific nodes in the network facilitating connections between modules comprised of low- vs high-settlement communities. Taxa associated with high-settlement communities were identified as Myxoccales sp., Granulosicoccus sp., Alcanivoraceae sp., unassigned JTB23 sp. (Gammaproteobacteria), and Pseudovibrio denitrificans. Meanwhile, taxa closely related to Reichenbachiella agariperforans, Pleurocapsa sp., Alcanivorax sp., Sneathiella limmimaris, as well as several diatom and brown algae were associated with low settlement. Our results characterise high-settlement biofilm communities and identify transitionary taxa that may develop settlement-inducing biofilms to improve coral larval settlement in aquaculture.

Genomic adaptation of giant viruses in polar oceans

Despite being perennially frigid, polar oceans form an ecosystem hosting high and unique biodiversity. Various organisms show different adaptive strategies in this habitat, but how viruses adapt to this environment is largely unknown. Viruses of phyla Nucleocytoviricota and Mirusviricota are groups of eukaryote-infecting large and giant DNA viruses with genomes encoding a variety of functions. Here, by leveraging the Global Ocean Eukaryotic Viral database, we investigate the biogeography and functional repertoire of these viruses at a global scale. We first confirm the existence of an ecological barrier that clearly separates polar and nonpolar viral communities, and then demonstrate that temperature drives dramatic changes in the virus-host network at the polar-nonpolar boundary. Ancestral niche reconstruction suggests that adaptation of these viruses to polar conditions has occurred repeatedly over the course of evolution, with polar-adapted viruses in the modern ocean being scattered across their phylogeny. Numerous viral genes are specifically associated with polar adaptation, although most of their homologues are not identified as polar-adaptive genes in eukaryotes. These results suggest that giant viruses adapt to cold environments by changing their functional repertoire, and this viral evolutionary strategy is distinct from the polar adaptation strategy of their hosts.

Distinct patterns of distribution, community assembly and cross-domain co-occurrence of planktonic archaea in four major estuaries of China

BACKGROUND

Archaea are key mediators of estuarine biogeochemical cycles, but comprehensive studies comparing archaeal communities among multiple estuaries with unified experimental protocols during the same sampling periods are scarce. Here, we investigated the distribution, community assembly, and cross-domain microbial co-occurrence of archaea in surface waters across four major estuaries (Yellow River, Yangtze River, Qiantang River, and Pearl River) of China cross climatic zones (~ 1,800 km) during the winter and summer cruises.

RESULTS

The relative abundance of archaea in the prokaryotic community and archaeal community composition varied with estuaries, seasons, and stations (reflecting local environmental changes such as salinity). Archaeal communities in four estuaries were overall predominated by ammonia-oxidizing archaea (AOA) (aka. Marine Group (MG) I; primarily Nitrosopumilus), while the genus Poseidonia of Poseidoniales (aka. MGII) was occasionally predominant in Pearl River estuary. The cross-estuary dispersal of archaea was largely limited and the assembly mechanism of archaea varied with estuaries in the winter cruise, while selection governed archaeal assembly in all estuaries in the summer cruise. Although the majority of archaea taxa in microbial networks were peripherals and/or connectors, extensive and distinct cross-domain associations of archaea with bacteria were found across the estuaries, with AOA as the most crucial archaeal group. Furthermore, the expanded associations of MGII taxa with heterotrophic bacteria were observed, speculatively indicating the endogenous demand for co-processing high amount and diversity of organic matters in the estuarine ecosystem highly impacted by terrestrial/anthropogenic input, which is worthy of further study.

CONCLUSIONS

Our results highlight the lack of common patterns in the dynamics of estuarine archaeal communities along the geographic gradient, expanding the understanding of roles of archaea in microbial networks of this highly dynamic ecosystem.

Identifying local associations in biological time series: algorithms, statistical significance, and applications

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Predicting global distributions of eukaryotic plankton communities from satellite data

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.

Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria

Biotic and abiotic interactions shape natural microbial communities. The mechanisms behind microbe-microbe interactions, particularly those protein based, are not well understood. We hypothesize that released proteins with antimicrobial activity are a powerful and highly specific toolset to shape and defend plant niches. We have studied Albugo candida, an obligate plant parasite from the protist Oomycota phylum, for its potential to modulate the growth of bacteria through release of antimicrobial proteins into the apoplast. Amplicon sequencing and network analysis of Albugo-infected and uninfected wild Arabidopsis thaliana samples revealed an abundance of negative correlations between Albugo and other phyllosphere microbes. Analysis of the apoplastic proteome of Albugo-colonized leaves combined with machine learning predictors enabled the selection of antimicrobial candidates for heterologous expression and study of their inhibitory function. We found for three candidate proteins selective antimicrobial activity against Gram-positive bacteria isolated from A. thaliana and demonstrate that these inhibited bacteria are precisely important for the stability of the community structure. We could ascribe the antibacterial activity of the candidates to intrinsically disordered regions and positively correlate it with their net charge. This is the first report of protist proteins with antimicrobial activity under apoplastic conditions that therefore are potential biocontrol tools for targeted manipulation of the microbiome.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Ocean-wide comparisons of mesopelagic planktonic community structures

For decades, marine plankton have been investigated for their capacity to modulate biogeochemical cycles and provide fishery resources. Between the sunlit (epipelagic) layer and the deep dark waters, lies a vast and heterogeneous part of the ocean: the mesopelagic zone. How plankton composition is shaped by environment has been well-explored in the epipelagic but much less in the mesopelagic ocean. Here, we conducted comparative analyses of trans-kingdom community assemblages thriving in the mesopelagic oxygen minimum zone (OMZ), mesopelagic oxic, and their epipelagic counterparts. We identified nine distinct types of intermediate water masses that correlate with variation in mesopelagic community composition. Furthermore, oxygen, NO3- and particle flux together appeared as the main drivers governing these communities. Novel taxonomic signatures emerged from OMZ while a global co-occurrence network analysis showed that about 70% of the abundance of mesopelagic plankton groups is organized into three community modules. One module gathers prokaryotes, pico-eukaryotes and Nucleo-Cytoplasmic Large DNA Viruses (NCLDV) from oxic regions, and the two other modules are enriched in OMZ prokaryotes and OMZ pico-eukaryotes, respectively. We hypothesize that OMZ conditions led to a diversification of ecological niches, and thus communities, due to selective pressure from limited resources. Our study further clarifies the interplay between environmental factors in the mesopelagic oxic and OMZ, and the compositional features of communities.

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.

Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.

Connect the dots: sketching out microbiome interactions through networking approaches

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

Effects of global change on the ability of stream biofilm to dissipate the herbicide glyphosate

The herbicide glyphosate is contaminating a large number of freshwater ecosystems worldwide and its fate and effects remains uncertain in light of the effects of global change. The present study examines how variations in water temperature and light availability relative to global change affect the ability of stream biofilms to degrade the herbicide glyphosate. Biofilms were exposed in microcosms to two levels of water temperature simulating global warming (Ambient = 19-22 °C and Warm = 21-24 °C) and three levels of light representative of riparian habitat destruction due to land use change (Dark = 0, Intermediate = 600, High = 1200 μmol photons m^-2 s^-1). Biofilms were acclimated to six different experimental treatments, namely i) ambient temperature without light (AMB_D), ii) ambient temperature and intermediate light (AMB_IL), iii) ambient temperature and high light (AMB_HL), iv) warm temperature without light (WARM_D), v) warm temperature and intermediate light (WARM_IL) and vi) warm temperature and high light (WARM_HL). The ability of biofilms to degrade 50 μg L^-1 of glyphosate was tested. Results showed that water temperature increase, but not light availability increase, significantly increased aminomethyl phosphonic acid (AMPA) production by biofilms. However, the combined increase of temperature and light generated the shortest time to dissipate half of the glyphosate supplied and/or half of the maximum AMPA produced (6.4 and 5.4 days, respectively) by biofilms. Despite light had a major effect in modulating biofilm structural and functional descriptors, the response of certain descriptors (i. e. chlorophyll-a concentration, bacterial density and diversity, nutrient content and PHO activity) to light availability increase depended on water temperature. Specifically, the biofilms in the WARM_HL treatment displayed the highest Glucosidase: Peptidase and Glucosidase: Phosphatase enzyme activity ratios and the lowest biomass C: N molar ratios compared to the other treatments. According to these results, warmer temperatures and high light availability could have been exacerbating the decomposition of organic C compounds in biofilms, including the use of glyphosate as a C source for microbial heterotrophs. This study shows that ecoenzymatic stoichiometry and xenobiotic biodegradation approaches can be combined to better understand the functioning of biofilms in pesticide-polluted streams.

Disentangling temporal associations in marine microbial networks

BACKGROUND

Microbial interactions are fundamental for Earth's ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network.

RESULTS

A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions.

CONCLUSIONS

Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems. Video Abstract.

Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained debilitating fatigue, cognitive dysfunction, gastrointestinal disturbances, and orthostatic intolerance. Here, we report a multi-omic analysis of a geographically diverse cohort of 106 cases and 91 healthy controls that revealed differences in gut microbiome diversity, abundances, functional pathways, and interactions. Faecalibacterium prausnitzii and Eubacterium rectale, which are both recognized as abundant, health-promoting butyrate producers in the human gut, were reduced in ME/CFS. Functional metagenomics, qPCR, and metabolomics of fecal short-chain fatty acids confirmed a deficient microbial capacity for butyrate synthesis. Microbiome-based machine learning classifier models were robust to geographic variation and generalizable in a validation cohort. The abundance of Faecalibacterium prausnitzii was inversely associated with fatigue severity. These findings demonstrate the functional nature of gut dysbiosis and the underlying microbial network disturbance in ME/CFS, providing possible targets for disease classification and therapeutic trials.

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.

Daily dynamics of contrasting spring algal blooms in Santa Monica Bay (central Southern California Bight)

Protistan algae (phytoplankton) dominate coastal upwelling ecosystems where they form massive blooms that support the world's most important fisheries and constitute an important sink for atmospheric CO₂ . Bloom initiation is well understood, but the biotic and abiotic forces that shape short-term dynamics in community composition are still poorly characterized. Here, high-frequency (daily) changes in relative abundance dynamics of the metabolically active protistan community were followed via expressed 18S V4 rRNA genes (RNA) throughout two algal blooms during the spring of 2018 and 2019 in Santa Monica Bay (central Southern California Bight). A diatom bloom formed after wind-driven, nutrient upwelling events in both years, but different taxa dominated each year. Whereas diatoms bloomed following elevated nutrients and declined after depletion each year, a massive dinoflagellate bloom manifested under relatively low inorganic nitrogen conditions following diatom bloom senescence in 2019 but not 2018. Network analysis revealed associations between diatoms and cercozoan putative parasitic taxa and syndinean parasites during 2019 that may have influenced the demise of the diatoms, and the transition to a dinoflagellate-dominated bloom.

Enteroendocrine peptides, growth, and the microbiome during the porcine weaning transition

Background

Growth rate in pigs can be affected by numerous factors that also affect feeding behavior and the microbiome. Recent studies report some communication between the microbiome and the enteroendocrine system. The present study examined if changes in the piglet microbiome between birth and during the weaning transition can be correlated either positively or negatively with growth rate and plasma concentrations of enteroendocrine peptides.

Results

During the post-weaning transition, a 49% reduction in average daily gain was observed at day 24 (P < 0.05) relative to day 21. Pigs recovered by day 28 with body weight and average daily gain increases of 17% and 175%, respectively relative to day 24 and the highest rate of gain was measured at day 35 (462 g/day). The time interval between day 21–24 had the highest number of correlations (n = 25) between the relative abundance differences in taxa over time and corresponding percent weight gain. Amplicon sequence variants with the greatest correlation with percent weight gain between day 21–24 belonged to families Prevotellaceae NK3B31 (ρ = 0.65, P < 0.001), Veillonellaceae (ρ = 0.63, P < 0.001) and Rikenellaceae RC9 (ρ = 0.62, P < 0.001). Seven taxa were positively correlated with percent weight gain between day 24–28. Eight taxa were positively correlated with percent weight gain between day 28–35, of which four were Clostridia. Only Lactobacillus reuteri was positively correlated across both day 24–28 and day 28–35 analyses. Insulin-like growth factor 1 (IGF-1; R2 = 0.61, P < 0.001), glucose-dependent insulinotropic polypeptide (GIP; R2 = 0.20, P < 0.001), glucagon-like peptide 1 (GLP-1; R2 = 0.51, P < 0.001), and glucagon-like peptide 2 (GLP-2; R2 = 0.21, P < 0.001) were significantly associated with the piglet fecal community NMDS, while serotonin showed no significant association (R2 = 0.03, P = 0.15). Higher concentrations of GLP-1 and GLP-2 characterized day 1 fecal communities, while GIP levels had the strongest relationship primarily with samples ordinated with the day 21 cluster.

Conclusions

Demonstration of an association of certain taxa with individual gut peptides at specific ages suggests the potential for the microbiome to elicit changes in the gut enteroendocrine system during early postnatal development in the pig.

Co-occurrences enhance our understanding of aquatic fungal metacommunity assembly and reveal potential host-parasite interactions

Our knowledge of aquatic fungal communities, their assembly, distributions and ecological roles in marine ecosystems is scarce. Hence, we aimed to investigate fungal metacommunities of coastal habitats in a subarctic zone (northern Baltic Sea, Sweden). Using a novel joint species distribution model and network approach, we quantified the importance of biotic associations contributing to the assembly of mycoplankton, further, detected potential biotic interactions between fungi-algae pairs, respectively. Our long-read metabarcoding approach identified 493 fungal taxa, of which a dominant fraction (44.4%) was assigned as early-diverging fungi (i.e. Cryptomycota and Chytridiomycota). Alpha diversity of mycoplankton declined and community compositions changed along inlet-bay-offshore transects. The distributions of most fungi were rather influenced by environmental factors than by spatial drivers, and the influence of biotic associations was pronounced when environmental filtering was weak. We found great number of co-occurrences (120) among the dominant fungal groups, and the 25 associations between fungal and algal OTUs suggested potential host-parasite and/or saprotroph links, supporting a Cryptomycota-based mycoloop pathway. We emphasize that the contribution of biotic associations to mycoplankton assembly are important to consider in future studies as it helps to improve predictions of species distributions in aquatic ecosystems.

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks

Co-occurrence networks inferred from the abundance data of microbial communities are widely applied to predict microbial interactions. However, the high workloads of bacterial isolation and the complexity of the networks themselves constrained experimental demonstrations of the predicted microbial associations and interactions. Here, we integrate droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from environmental samples, and experimentally investigate the relationships between taxon pairs inferred from microbial co-occurrence networks. We collected Potamogeton perfoliatus plants (including roots) and associated sediments from Beijing Olympic Park wetland. Droplets of series diluted homogenates of wetland samples were inoculated into 126 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates with > 30% wells showed microbial growth were selected for the inference of microbial co-occurrence networks. We cultivated 129 bacterial isolates belonging to 15 species that could represent the zero-level OTUs (Zotus) in the inferred co-occurrence networks. The co-cultivations of bacterial isolates corresponding to the prevalent Zotus pairs in networks were performed on agar plates and in broth. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism, depending on bacterial isolate combination and cultivation time.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Microbiome network in the pelagic and benthic offshore systems of the northern Adriatic Sea (Mediterranean Sea)

Because of their recognized global importance, there is now the urgent need to map diversity and distribution patterns of marine microbial communities. Even if available studies provided some advances in the understanding the biogeographical patterns of marine microbiomes at the global scale, their degree of plasticity at the local scale it is still underexplored, and functional implications still need to be dissected. In this scenario here we provide a synoptical study on the microbiomes of the water column and surface sediments from 19 sites in a 130 km2 area located 13.5 km afar from the coast in the North-Western Adriatic Sea (Italy), providing the finest-scale mapping of marine microbiomes in the Mediterranean Sea. Pelagic and benthic microbiomes in the study area showed sector specific-patterns and distinct assemblage structures, corresponding to specific variations in the microbiome network structure. While maintaining a balanced structure in terms of potential ecosystem services (e.g., hydrocarbon degradation and nutrient cycling), sector-specific patterns of over-abundant modules—and taxa—were defined, with the South sector (the closest to the coast) characterized by microbial groups of terrestrial origins, both in the pelagic and the benthic realms. By the granular assessment of the marine microbiome changes at the local scale, we have been able to describe, to our knowledge at the first time, the integration of terrestrial microorganisms in the marine microbiome networks, as a possible natural process characterizing eutrophic coastal area. This raises the question about the biological threshold for terrestrial microorganisms to be admitted in the marine microbiome networks, without altering the ecological balance.

Microbial response to multiple-level addition of grass organic matter in lake sediments with different salinity

Water surface expansion of saline lakes usually causes the inundation of surrounding grassland, leading to the increase of terrestrial grass organic matter (OM) input to the lakes and the decrease of lake salinity. However, the influence of terrestrial grass OM input increase and salinity decrease on organic carbon mineralization and microbial community composition remains unknown in saline lakes. Here, microbial mineralization of terrestrial grass (Achnatherum splendens) OM at different quantity levels in lake sediments with different salinity was investigated by performing microcosm experiments. The results showed that the CO2 production rates increased with the increase of grass OM supply in the studied sediments with different salinity, which may be driven by certain microbial groups (e.g., Bacteroidota, Firmicutes and Ascomycota). The increase of grass OM supply reduced the richness of prokaryotic community, which will decrease the size and complexity of the studied microbial networks, but increase the interaction between prokaryotic and fungal taxa. Taken together, our results suggest that the increase of terrestrial grass OM input caused by lake expansion would enhance the mineralization of organic carbon and affect the community composition and interactions of related microorganisms in lake sediments with different salinity.

The Self-Organization of Marine Microbial Networks under Evolutionary and Ecological Processes: Observations and Modeling

Simple Summary

The properties and structure of ecological networks in marine microbial communities determine ecosystem functions and stability; however, the principles of microbial network assemblages are poorly understood. In this study, we revealed the influences of species phylogeny and niches on the self-organization of marine microbial co-occurrence networks and provided a mathematical framework to simulate microbial network assemblages. Our results provide deep insights into network stability from the perspective of network assembly principles and not just network properties, such as complexity and modularity.

Abstract

Evolutionary and ecological processes are primary drivers of ecological network constrictions. However, the ways that these processes underpin self-organization and modularity in networks are poorly understood. Here, we performed network analyses to explore the evolutionary and ecological effects on global marine microbial co-occurrence networks across multiple network levels, including those of nodes, motifs, modules and whole networks. We found that both direct and indirect species interactions were evolutionarily and ecologically constrained across at least four network levels. Compared to ecological processes, evolutionary processes generally showed stronger long-lasting effects on indirect interactions and dominated the network assembly of particle-associated communities in spatially homogeneous environments. Regarding the large network path distance, the contributions of either processes to species interactions generally decrease and almost disappear when network path distance is larger than six. Accordingly, we developed a novel mathematical model based on scale-free networks by considering the joint effects of evolutionary and ecological processes. We simulated the self-organization of microbial co-occurrence networks and found that long-lasting effects increased network stability via decreasing link gain or loss. Overall, these results revealed that evolutionary and ecological processes played key roles in the self-organization and modularization of microbial co-occurrence networks.

Cryptic and abundant marine viruses at the evolutionary origins of Earth's RNA virome

Whereas DNA viruses are known to be abundant, diverse, and commonly key ecosystem players, RNA viruses are insufficiently studied outside disease settings. In this study, we analyzed ≈28 terabases of Global Ocean RNA sequences to expand Earth's RNA virus catalogs and their taxonomy, investigate their evolutionary origins, and assess their marine biogeography from pole to pole. Using new approaches to optimize discovery and classification, we identified RNA viruses that necessitate substantive revisions of taxonomy (doubling phyla and adding >50% new classes) and evolutionary understanding. "Species"-rank abundance determination revealed that viruses of the new phyla "Taraviricota," a missing link in early RNA virus evolution, and "Arctiviricota" are widespread and dominant in the oceans. These efforts provide foundational knowledge critical to integrating RNA viruses into ecological and epidemiological models.

The Effect of ß-Glucan Prebiotic on Kidney Function, Uremic Toxins and Gut Microbiome in Stage 3 to 5 Chronic Kidney Disease (CKD) Predialysis Participants: A Randomized Controlled Trial

There is growing evidence that gut dysbiosis contributes to the progression of chronic kidney disease (CKD) owing to several mechanisms, including microbiota-derived uremic toxins, diet and immune-mediated factors. The aim of this study was to investigate the effect of a ß-glucan prebiotic on kidney function, uremic toxins and the gut microbiome in stage 3 to 5 CKD participants. Fifty-nine participants were randomized to either the ß-glucan prebiotic intervention group (n = 30) or the control group (n = 29). The primary outcomes were to assess kidney function (urea, creatinine and glomerular filtration rate), plasma levels of total and free levels of uremic toxins (p-cresyl sulfate (pCS), indoxyl-sulfate (IxS), p-cresyl glucuronide (pCG) and indoxyl 3-acetic acid (IAA) and gut microbiota using 16S rRNA sequencing at baseline, week 8 and week 14. The intervention group (age 40.6 ± 11.4 y) and the control group (age 41.3 ± 12.0 y) did not differ in age or any other socio-demographic variables at baseline. There were no significant changes in kidney function over 14 weeks. There was a significant reduction in uremic toxin levels at different time points, in free IxS at 8 weeks (p = 0.003) and 14 weeks (p < 0.001), free pCS (p = 0.006) at 14 weeks and total and free pCG (p < 0.001, p < 0.001, respectively) and at 14 weeks. There were no differences in relative abundances of genera between groups. Enterotyping revealed that the population consisted of only two of the four enterotypes: Bacteroides 2 and Prevotella. The redundancy analysis showed a few factors significantly affected the gut microbiome: these included triglyceride levels (p < 0.001), body mass index (p = 0.002), high- density lipoprotein (p < 0.001) and the prebiotic intervention (p = 0.002). The ß-glucan prebiotic significantly altered uremic toxin levels of intestinal origin and favorably affected the gut microbiome.

Meconium Microbiome of Very Preterm Infants across Germany

Meconium constitutes infants' first bowel movements postnatally. The consistency and microbial load of meconium are different from infant and adult stool. While recent evidence suggests that meconium is sterile in utero, rapid colonization occurs after birth. The meconium microbiome has been associated with negative health outcomes, but its composition is not well described, especially in preterm infants. Here, we characterized the meconium microbiomes from 330 very preterm infants (gestational ages 28 to 32 weeks) from 15 hospitals in Germany and in fecal samples from a subset of their mothers (N = 217). Microbiome profiles were compiled using 16S rRNA gene sequencing with negative and positive controls. The meconium microbiome was dominated by Bifidobacterium, Staphylococcus, and Enterococcus spp. and was associated with gestational age at birth and age at sample collection. Bifidobacterial abundance was negatively correlated with potentially pathogenic genera. The amount of bacterial DNA in meconium samples varied greatly across samples and was associated with the time since birth but not with gestational age or hospital site. In samples with low bacterial load, human mitochondrial sequences were highly amplified using commonly used, bacterial-targeted 16S rRNA primers. Only half of the meconium samples contained sufficient bacterial material to study the microbiome using a standard approach. To facilitate future meconium studies, we present a five-level scoring system (“MecBac”) that predicts the success of 16S rRNA bacterial sequencing for meconium samples. These findings provide a foundational characterization of an understudied portion of the human microbiome and will aid the design of future meconium microbiome studies.

IMPORTANCE Meconium is present in the intestines of infants before and after birth and constitutes their first bowel movements postnatally. The consistency, composition and microbial load of meconium is largely different from infant and adult stool. While recent evidence suggests that meconium is sterile in utero, rapid colonization occurs after birth. The meconium microbiome has been associated with short-term and long-term negative health outcomes, but its composition is not yet well described, especially in preterm infants. We provide a characterization of the microbiome structure and composition of infant meconium and maternal feces from a large study cohort and propose a method to evaluate meconium samples for bacterial sequencing suitability. These findings provide a foundational characterization of an understudied portion of the human microbiome and will aid the design of future meconium microbiome studies.

Fast and flexible analysis of linked microbiome data with mako

Mako is a software tool that converts microbiome data and networks into a graph database and visualizes query results, thus allowing users without programming knowledge to carry out network-based queries. Mako is accompanied by a database compiled from 60 microbiome studies that is easily extended with the user's own data. We illustrate mako's strengths by enumerating association partners linked to propionate production and comparing frequencies of different network motifs across habitat types.

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.

Open challenges for microbial network construction and analysis

Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Environmental vulnerability of the global ocean epipelagic plankton community interactome

Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles and help regulate climate. Although global surveys are starting to reveal ecological drivers underlying planktonic community structure and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here, we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network-the community interactome-and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar) and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change and forecasted the most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios while identifying plausible plankton bioindicators for ocean monitoring of climate change.

NetCoMi: network construction and comparison for microbiome data in R

MOTIVATION

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.

RESULTS

Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).

AVAILABILITY

R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.

CONTACT

Tel:+49 89 3187 43258; stefanie.peschel@mail.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Null-model-based network comparison reveals core associations

Microbial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

A zero inflated log-normal model for inference of sparse microbial association networks

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Quantitative Assessment of Nucleocytoplasmic Large DNA Virus and Host Interactions Predicted by Co-occurrence Analyses

Nucleocytoplasmic large DNA viruses (NCLDVs) are highly diverse and abundant in marine environments. However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. Our results underpin the usage of co-occurrence approaches for the metagenomic exploration of the ecology of this diverse group of viruses.

microeco: an R package for data mining in microbial community ecology

A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-'microeco' as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).

Investigating the microbial ecology of coastal hotspots of marine nitrogen fixation in the western North Atlantic

Variation in the microbial cycling of nutrients and carbon in the ocean is an emergent property of complex planktonic communities. While recent findings have considerably expanded our understanding of the diversity and distribution of nitrogen (N₂) fixing marine diazotrophs, knowledge gaps remain regarding ecological interactions between diazotrophs and other community members. Using quantitative 16S and 18S V4 rDNA amplicon sequencing, we surveyed eukaryotic and prokaryotic microbial communities from samples collected in August 2016 and 2017 across the Western North Atlantic. Leveraging and significantly expanding an earlier published 2015 molecular dataset, we examined microbial community structure and ecological co-occurrence relationships associated with intense hotspots of N₂ fixation previously reported at sites off the Southern New England Shelf and Mid-Atlantic Bight. Overall, we observed a negative relationship between eukaryotic diversity and both N₂ fixation and net community production (NCP). Maximum N₂ fixation rates occurred at sites with high abundances of mixotrophic stramenopiles, notably Chrysophyceae. Network analysis revealed such stramenopiles to be keystone taxa alongside the haptophyte diazotroph host Braarudosphaera bigelowii and chlorophytes. Our findings highlight an intriguing relationship between marine stramenopiles and high N₂ fixation coastal sites.

Fermentation Ability of Gut Microbiota of Wild Japanese Macaques in the Highland and Lowland Yakushima: In Vitro Fermentation Assay and Genetic Analyses

Wild Japanese macaques (Macaca fuscata Blyth) living in the highland and lowland areas of Yakushima are known to have different diets, with highland individuals consuming more leaves. We aim to clarify whether and how these differences in diet are also reflected by gut microbial composition and fermentation ability. Therefore, we conduct an in vitro fermentation assay using fresh feces from macaques as inoculum and dry leaf powder of Eurya japonica Thunb. as a substrate. Fermentation activity was higher for feces collected in the highland, as evidenced by higher gas and butyric acid production and lower pH. Genetic analysis indicated separation of highland and lowland in terms of both community structure and function of the gut microbiota. Comparison of feces and suspension after fermentation indicated that the community structure changed during fermentation, and the change was larger for lowland samples. Analysis of the 16S rRNA V3-V4 barcoding region of the gut microbiota showed that community structure was clearly clustered between the two areas. Furthermore, metagenomic analysis indicated separation by gene and pathway abundance patterns. Two pathways (glycogen biosynthesis I and D-galacturonate degradation I) were enriched in lowland samples, possibly related to the fruit-eating lifestyle in the lowland. Overall, we demonstrated that the more leaf-eating highland Japanese macaques harbor gut microbiota with higher leaf fermentation ability compared with the more fruit-eating lowland ones. Broad, non-specific taxonomic and functional gut microbiome differences suggest that this pattern may be driven by a complex interplay between many taxa and pathways rather than single functional traits.