Efficient statistical significance approximation for local similarity analysis of high-throughput time series data

Co-occurrence between key HAB species and particle-attached bacteria and substrate specificity of attached bacteria in the coastal ecosystem

The ecological dynamics of particle-attached bacteria (PAB) were observed through changes in the core phytoplankton phycosphere, and were associated with the dynamics of free-living bacteria (FLB) using metabarcoding and microscopic analyses over 210 days (with weekly sampling intervals) in the Jangmok coastal ecosystem, South Korea. Cluster analysis and non-metric multidimensional scaling classified the phytoplankton community into six groups comprising core phytoplankton species, including the harmful algal species Akashiwo sanguinea (dinoflagellate) in late autumn, Teleaulax amphioxeia (cryptomonads) in early winter and spring, Skeletonema marinoi-dohrnii complex (diatom) in winter, Pseudo-nitzschia delicatissima (diatom) in early spring, and diatom complexes such as Chaetoceros curvisetus and Leptocylindrus danicus in late spring. We identified 59 and 32 indicators in PAB and FLB, respectively, which rapidly changed with the succession of the six core phytoplankton species. The characteristics of PAB were mainly divided into "Random encounters" or "Attraction of motivation by chemotaxis." When Akashiwo sanguinea bloomed, bacteria of the genera Kordiimonas and Polaribacter, which are commonly observed in PAB and FLB, indicated "Random encounter" characteristics. In addition, Sedimenticola of PAB was uniquely presented in Akashiwo sanguinea, exhibiting characteristics of "Attraction of motivation by chemotaxis." In contrast, FLB followed the strategy of "Random encounters" because it was not affected by specific habitats and energy sources. Thus, many common bacteria were PAB and FLB, thereby dictating the bacteria's strategy of "Random encounters." "Attraction of motivation by chemotaxis" has characteristics of the species-specific interactions between PAB and specific harmful algal species, and is potentially influenced by organic matter of core phytoplankton cell surface and/or EPS released from phytoplankton.

Compositionally aware estimation of cross-correlations for microbiome data

In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables. In this paper we introduce the novel methods SparCEV (Sparse Correlations with External Variables) and SparXCC (Sparse Cross-Correlations between Compositional data) for quantifying correlations between OTU abundances and either continuous phenotypic variables or components of other compositional datasets, such as transcriptomic data. SparCEV and SparXCC both assume that the average correlation in the dataset is zero. Iterative versions of SparCEV and SparXCC are proposed to alleviate bias resulting from deviations from this assumption. We compare these new methods to empirical Pearson cross-correlations after applying naive transformations of the data (log and log-TSS). Additionally, we test the centered log ratio transformation (CLR) and the variance stabilising transformation (VST). We find that CLR and VST outperform naive transformations, except when the correlation matrix is dense. SparCEV and SparXCC outperform CLR and VST when the number of OTUs is small and perform similarly to CLR and VST for large numbers of OTUs. Adding the iterative procedure increases accuracy for SparCEV and SparXCC for all cases, except when the average correlation in the dataset is close to zero or the correlation matrix is dense. These results are consistent with our theoretical considerations.

Progress on network modeling and analysis of gut microecology: a review

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

Virus-prokaryote infection pairs associated with prokaryotic production in a freshwater lake

Viruses infect and kill prokaryotic populations in a density- or frequency-dependent manner and affect carbon cycling. However, the effects of the stratification transition, including the stratified and de-stratified periods, on the changes in prokaryotic and viral communities and their interactions remain unclear. We conducted a monthly survey of the surface and deep layers of a large and deep freshwater lake (Lake Biwa, Japan) for a year and analyzed the prokaryotic production and prokaryotic and viral community composition. Our analysis revealed that, in the surface layer, 19 prokaryotic species, accounting for approximately 40% of the total prokaryotic abundance, could potentially contribute to the majority of prokaryotic production, which is the highest during the summer and is suppressed by viruses. This suggests that a small fraction of prokaryotes and phages were the key infection pairs during the peak period of prokaryotic activity in the freshwater lake. We also found that approximately 50% of the dominant prokaryotic and viral species in the deep layer were present throughout the study period. This suggests that the "kill the winner" model could explain the viral impact on prokaryotes in the surface layer, but other dynamics may be at play in the deep layer. Furthermore, we found that annual vertical mixing could result in a similar rate of community change between the surface and deep layers. These findings may be valuable in understanding how communities and the interaction among them change when freshwater lake stratification is affected by global warming in the future.IMPORTANCEViral infection associated with prokaryotic production occurs in a density- or frequency-dependent manner and regulates the prokaryotic community. Stratification transition and annual vertical mixing in freshwater lakes are known to affect the prokaryotic community and the interaction between prokaryotes and viruses. By pairing measurements of virome analysis and prokaryotic production of a 1-year survey of the depths of surface and deep layers, we revealed (i) the prokaryotic infection pairs associated with prokaryotic production and (ii) the reset in prokaryotic and viral communities through annual vertical mixing in a freshwater lake. Our results provide a basis for future work into changes in stratification that may impact the biogeochemical cycling in freshwater lakes.

Using solid phase adsorption toxin tracking and extended local similarity analysis to monitor lipophilic shellfish toxins in a mussel culture ranch in the Yangtze River Estuary

Harmful algal blooms (HABs) and their associated phycotoxins are increasing globally, posing great threats to local coastal ecosystems and human health. Nutrients have been carried by the freshwater Yangtze River and have entered the estuary, which was reported to be a biodiversity-rich but HAB-frequent region. Here, in situ solid phase adsorption toxin tracking (SPATT) was used to monitor lipophilic shellfish toxins (LSTs) in seawaters, and extended local similarity analysis (eLSA) was conducted to trace the temporal and special regions of those LSTs in a one-year trail in a mussel culture ranch in the Yangtze River Estuary. Nine analogs of LSTs, including okadaic acid (OA), dinophysistoxin-1 (DTX1), yessotoxin (YTX), homoyessotoxin (homoYTX), 45-OH-homoYTX, pectenotoxin-2 (PTX2), 7-epi-PTX2 seco acid (7-epi-PTX2sa), gymnodimine (GYM) and azaspiracids-3 (AZA3), were detected in seawater (SPATT) or rope farmed mussels. The concentrations of OA + DTX1 and homoYTX in mussels were positively correlated with those in SPATT samplers (Pearson test, p < 0.05), indicating that SPATT (with resin HP20) would be a good monitoring tool and potential indicator for OA + DTX1 and homoYTX in mussel Mytilus coruscus. The eLSA results indicated that late summer and early autumn were the most phycotoxin-contaminated seasons in the Yangtze River Estuary. OA + DTX1, homoYTX, PTX2 and GYM were most likely driven by the local growing HAB species in spring and summer, while Yangtze River diluted water may impact the accumulation of HAB species, causing potential phycotoxin contamination in the Yangtze River Estuary in autumn and winter. Together, the results showed that the mussel harvesting season, late summer and early autumn, would be the season with the greatest phycotoxin risk and would be the most contaminated by local growing toxic algae. Routine monitoring sites should be set up close to the local seawaters.

Daily fluctuation of colonic microbiome in response to nutrient substrates in a pig model

Studies on rodents indicate the daily oscillations of the gut microbiota have biological implications for host. However, the responses of fluctuating gut microbes to the dynamic nutrient substrates are not fully clear. In the study, we found that the feed intake, nutrient substrates, microbiota and metabolites in the colon underwent asynchronous oscillation within a day. Short-chain fatty acids (SCFAs) including acetate, propionate, butyrate and valerate peaked during T24 ~ T27 (Timepoint 24, 12:00 pm, T27, 03:00 am) whereas branched SCFAs isobutyrate and isovalerate peaked during T09 ~ T12. Further extended local similarity analysis (eLSA) revealed that the fluctuation of feed intake dynamically correlated with the colonic carbon substrates which further influenced the oscillation of sugar metabolites and acetate, propionate, butyrate and valerate with a certain time shift. The relative abundance of primary degrader Ruminococcaceae taxa was highly related to the dynamics of the carbon substrates whereas the fluctuations of secondary degraders Lactobacillaceae and Streptococcaceae taxa were highly correlated with the sugar metabolites. Meanwhile, colonic nitrogen substrates were correlated with branched amino acids and the branched SCFAs. Furthermore, we validated the evolution of gut microbes under different carbohydrate and protein combinations by using an in vitro fermentation experiment. The study pictured the dynamics of the micro-ecological environment within a day which highlights the implications of the temporal dimension in studies related to the gut microbiota. Feed intake, more precisely substrate intake, is highly correlated with microbial evolution, which makes it possible to develop chronotherapies targeting the gut microbiota through nutrition intervention.

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.

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.

Symbiotic UCYN-A strains co-occurred with El Niño, relaxed upwelling, and varied eukaryotes over 10 years off Southern California

Biological nitrogen fixation, the conversion of N2 gas into a bioavailable form, is vital to sustaining marine primary production. Studies have shifted beyond traditionally studied tropical diazotrophs. Candidatus Atelocyanobacterium thalassa (or UCYN-A) has emerged as a focal point due to its streamlined metabolism, intimate partnership with a haptophyte host, and broad distribution. Here, we explore the environmental parameters that govern UCYN-A's presence at the San Pedro Ocean Time-series (SPOT), its host specificity, and statistically significant interactions with non-host eukaryotes from 2008-2018. 16S and 18S rRNA gene sequences were amplified by "universal primers" from monthly samples and resolved into Amplicon Sequence Variants, allowing us to observe multiple UCYN-A symbioses. UCYN-A1 relative abundances increased following the 2015-2016 El Niño event. This "open ocean ecotype" was present when coastal upwelling declined, and Ekman transport brought tropical waters into the region. Network analyses reveal all strains of UCYN-A co-occur with dinoflagellates including Lepidodinium, a potential predator, and parasitic Syndiniales. UCYN-A2 appeared to pair with multiple hosts and was not tightly coupled to its predominant host, while UCYN-A1 maintained a strong host-symbiont relationship. These biological relationships are particularly important to study in the context of climate change, which will alter UCYN-A distribution at regional and global scales.

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.

Prevalence of Viral Frequency-Dependent Infection in Coastal Marine Prokaryotes Revealed Using Monthly Time Series Virome Analysis

Viruses infecting marine prokaryotes have a large impact on the diversity and dynamics of their hosts. Model systems suggest that viral infection is frequency dependent and constrained by the virus-host encounter rate. However, it is unclear whether frequency-dependent infection is pervasive among the abundant prokaryotic populations with different temporal dynamics. To address this question, we performed a comparison of prokaryotic and viral communities using 16S rRNA amplicon and virome sequencing based on samples collected monthly for 2 years at a Japanese coastal site, Osaka Bay. Concurrent seasonal shifts observed in prokaryotic and viral community dynamics indicated that the abundance of viruses correlated with that of their predicted host phyla (or classes). Cooccurrence network analysis between abundant prokaryotes and viruses revealed 6,423 cooccurring pairs, suggesting a tight coupling of host and viral abundances and their "one-to-many" correspondence. Although stable dominant species, such as SAR11, showed few cooccurring viruses, a fast succession of their viruses suggests that viruses infecting these populations changed continuously. Our results suggest that frequency-dependent viral infection prevails in coastal marine prokaryotes regardless of host taxa and temporal dynamics. IMPORTANCE There is little room for doubt that viral infection is prevalent among abundant marine prokaryotes regardless of their taxa or growth strategy. However, comprehensive evaluations of viral infections in natural prokaryotic communities are still technically difficult. In this study, we examined viral infection in abundant prokaryotes by monitoring the monthly dynamics of prokaryotic and viral communities at a eutrophic coastal site, Osaka Bay. We compared the community dynamics of viruses with those of their putative hosts based on genome-based in silico host prediction. We observed frequent cooccurrence among the predicted virus-host pairs, suggesting that viral infection is prevalent in abundant prokaryotes regardless of their taxa or temporal dynamics. This likely indicates that frequent lysis of the abundant prokaryotes via viral infection has a considerable contribution to the biogeochemical cycling and maintenance of prokaryotic community diversity.

Co-variance between free-living bacteria and Cochlodinium polykrikoides (Dinophyta) harmful algal blooms, South Korea

To understand the co-variance between common free-living bacteria and Cochlodinium polykrikoides harmful algal blooms (HABs) and their metabolic functions, we investigated 110 sampling sites in the Southern Sea of South Korea. These sampling sites were divided into three groups based on environmental factors and phytoplankton data with a similarity of 85% using non-metric multidimensional scaling. One group represented high-severity C. polykrikoides blooms, while the other two represented low-severity or no blooms. In high-severity HABs, inorganic phosphorous and dissolved organic carbon concentrations were strongly correlated with C. polykrikoides density (p < 0.01). This may reflect the changes in biochemical cycling due to inorganic and organic substrates released by HAB cells (or by cell destruction). Furthermore, 88 common bacterial operational taxonomic units (OTUs, with mean relative abundance > 1%) were identified. These included Gammaproteobacteria (36 OTUs), Flavobacteriia (24), Alphaproteobacteria (18), and other taxa (11). When C. polykrikoides blooms intensified, the relative abundances of Gammaproteobacteria also increased. OTU #030 (Flavicella sp., Flavobacteria, 96%) was positively correlated with C. polykrikoides abundance (r = 0.77, p < 0.001). Functional analysis based on the dominant bacterial OTUs revealed that chemoheterotrophy-related functions were more common in high-severity sites of HABs than in other groups. Therefore, the occurrence of HABs highlighted their interactions with bacteria and affected the bacterial community structure and metabolic functions.

Nesting Biology of the Potter Wasp Ancistrocerus flavomarginatus (Hymenoptera, Vespidae, Eumeninae) Revealed by Trap-Nest Experiments in Southern Brazil

This paper provides the first description of the nesting biology of Ancistrocerus flavomarginatus (Brèthes) (Hymenoptera, Vespidae, Eumeninae), the only species of the genus found in Brazil. Our trap-nest experiments were conducted in two Mixed Ombrophilous Forest fragments and two adjacent matrices in Guarapuava (Paraná state, Brazil) from August 2017 to July 2018. In each area, we set 192 trap nests divided into six groups of 32 units, totalling 768 trap nests. We obtained a total of 47 nests of A. flavomarginatus, the vast majority of them (43, 91.5%) founded in the forest fragments. Most nests were built in wooden traps with a bore diameter of either 5 or 7 mm (19 nests in each type, 80.8%). Nests comprised 1-12 subcylindrical brood cells arranged linearly and separated from one another by transverse partitions of soil mastic. Larvae consumed 6-10 lepidopteran caterpillars before spinning the cocoon. Ancistrocerus flavomarginatus produced up to 6 annual generations (multivoltinism) and its immature forms were parasitized by chrysidid and ichneumonid wasps. The calculated sex ratio (1.78:1) was statistically biased towards males, but since they (21.3 ± 2.0 mg) were significantly lighter than females (50.9 ± 4.0 mg), the resulting investment ratio (1.34:1) was female biased. Males emerged from more external cells and developed significantly faster (27.2 ± 0.46 days) than females (30.1 ± 0.66 days), hence a case of protandry. We demonstrated that A. flavomarginatus is largely dependent on the Atlantic Rainforest and thus that deforestation poses a critical threat to this important species.

Covariance of Marine Nucleocytoplasmic Large DNA Viruses with Eukaryotic Plankton Communities in the Sub-Arctic Kongsfjorden Ecosystem: A Metagenomic Analysis of Marine Microbial Ecosystems

Nucleocytoplasmic large DNA viruses (NCLDVs) infect various marine eukaryotes. However, little is known about NCLDV diversity and their relationships with eukaryotic hosts in marine environments, the elucidation of which will advance the current understanding of marine ecosystems. This study characterizes the interplay between NCLDVs and the eukaryotic plankton community (EPC) in the sub-Arctic area using metagenomics and metabarcoding to investigate NCLDVs and EPC, respectively, in the Kongsfjorden ecosystem of Svalbard (Norway) in April and June 2018. Gyrodinium helveticum (Dinophyceae) is the most prevalent eukaryotic taxon in the EPC in April, during which time Mimiviridae (31.8%), Poxviridae (25.1%), Phycodnaviridae (14.7%) and Pandoraviridae (13.1%) predominate. However, in June, the predominant taxon is Aureococcus anophagefferens (Pelagophyceae), and the NCLDVs, Poxviridae (32.9%), Mimiviridae (29.1%), and Phycodnaviridae (18.5%) appear in higher proportions with an increase in Pelagophyceae, Bacillariophyceae, and Chlorophyta groups. Thus, differences in NCLDVs may be caused by changes in EPC composition in response to environmental changes, such as increases in water temperature and light intensity. Taken together, these findings are particularly relevant considering the anticipated impact of NCLDV-induced EPC control mechanisms on polar regions and, therefore, improve the understanding of the Sub-Arctic Kongsfjorden ecosystem.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

Effective data filtering is prerequisite for robust microbial association network construction

Microorganisms do not exist as individual population in the environment. Rather, they form complex assemblages that perform essential ecosystem functions and maintain ecosystem stability. Besides the diversity and composition of microbial communities, deciphering their potential interactions in the form of association networks has attracted many microbiologists and ecologists. Much effort has been made toward the methodological development for constructing microbial association networks. However, microbial profiles suffer dramatically from zero values, which hamper accurate association network construction. In this study, we investigated the effects of zero-value issues associated with microbial association network construction. Using the TARA Oceans microbial profile as an example, different zero-value-treatment approaches were comparatively investigated using different correlation methods. The results suggested dramatic variations of correlation coefficient values for differently treated microbial profiles. Most specifically, correlation coefficients among less frequent microbial taxa were more affected, whichever method was used. Negative correlation coefficients were more problematic and sensitive to network construction, as many of them were inferred from low-overlapped microbial taxa. Consequently, microbial association networks were greatly differed. Among various approaches, we recommend sequential calculation of correlation coefficients for microbial taxa pairs by excluding paired zero values. Filling missing values with pseudo-values is not recommended. As microbial association network analyses have become a widely used technique in the field of microbial ecology and environmental science, we urge cautions be made to critically consider the zero-value issues in microbial data.

MicNet toolbox: Visualizing and unraveling a microbial network

Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code's functions with ease.

Long-term patterns of an interconnected core marine microbiota

BACKGROUND

Ocean microbes constitute ~ 70% of the marine biomass, are responsible for ~ 50% of the Earth's primary production and are crucial for global biogeochemical cycles. Marine microbiotas include core taxa that are usually key for ecosystem function. Despite their importance, core marine microbes are relatively unknown, which reflects the lack of consensus on how to identify them. So far, most core microbiotas have been defined based on species occurrence and abundance. Yet, species interactions are also important to identify core microbes, as communities include interacting species. Here, we investigate interconnected bacteria and small protists of the core pelagic microbiota populating a long-term marine-coastal observatory in the Mediterranean Sea over a decade.

RESULTS

Core microbes were defined as those present in > 30% of the monthly samples over 10 years, with the strongest associations. The core microbiota included 259 Operational Taxonomic Units (OTUs) including 182 bacteria, 77 protists, and 1411 strong and mostly positive (~ 95%) associations. Core bacteria tended to be associated with other bacteria, while core protists tended to be associated with bacteria. The richness and abundance of core OTUs varied annually, decreasing in stratified warmers waters and increasing in colder mixed waters. Most core OTUs had a preference for one season, mostly winter, which featured subnetworks with the highest connectivity. Groups of highly associated taxa tended to include protists and bacteria with predominance in the same season, particularly winter. A group of 13 highly-connected hub-OTUs, with potentially important ecological roles dominated in winter and spring. Similarly, 18 connector OTUs with a low degree but high centrality were mostly associated with summer or autumn and may represent transitions between seasonal communities.

CONCLUSIONS

We found a relatively small and dynamic interconnected core microbiota in a model temperate marine-coastal site, with potential interactions being more deterministic in winter than in other seasons. These core microbes would be essential for the functioning of this ecosystem over the year. Other non-core taxa may also carry out important functions but would be redundant and non-essential. Our work contributes to the understanding of the dynamics and potential interactions of core microbes possibly sustaining ocean ecosystem function.

Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data

Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.

Conceptual strategies for characterizing interactions in microbial communities

Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.

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

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

Modeling Microbial Community Networks: Methods and Tools

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

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.

Multivariate statistical monitoring system for microbial population dynamics

Microbiomes in their natural environments vary dynamically with changing environmental conditions. The detection of these dynamic changes in microbial populations is critical for understanding the impact of environmental changes on the microbial community. Here, we propose a novel method to detect time-series changes in the microbiome, based on multivariate statistical process control. By focusing on the interspecies structures, this approach enables the robust detection of time-series changes in a microbiome composed of a large number of microbial species. Applying this approach to empirical human gut microbiome data, we accurately traced time-series changes in microbiota composition induced by a dietary intervention trial. This method was also excellent for tracking the recovery process after the intervention. Our approach can be useful for monitoring dynamic changes in complex microbial communities.

Dynamic bacterial community response to Akashiwo sanguinea (Dinophyceae) bloom in indoor marine microcosms

We investigated the dynamics of the bacterial composition and metabolic function within Akashiwo sanguinea bloom using a 100-L indoor microcosm and metagenomic next-generation sequencing. We found that the bacterial community was classified into three groups at 54% similarity. Group I was associated with "during the A. sanguinea bloom stage" and mainly consisted of Alphaproteobacteria, Flavobacteriia and Gammaproteobacteria. Meanwhile, groups II and III were associated with the "late bloom/decline stage to post-bloom stage" with decreased Flavobacteriia and Gammaproteobacteria in these stages. Upon the termination of the A. sanguinea bloom, the concentrations of inorganic nutrients (particularly PO43-, NH4+ and dissolved organic carbon) increased rapidly and then decreased. From the network analysis, we found that the A. sanguinea node is associated with certain bacteria. After the bloom, the specific increases in NH4+ and PO43- nodes are associated with other bacterial taxa. The changes in the functional groups of the bacterial community from chemoheterotrophy to nitrogen association metabolisms were consistent with the environmental impacts during and after A. sanguinea bloom. Consequently, certain bacterial communities and the environments dynamically changed during and after harmful algal blooms and a rapid turnover within the bacterial community and their function can respond to ecological interactions.

Depth Profile of Nitrifying Archaeal and Bacterial Communities in the Remote Oligotrophic Waters of the North Pacific

Nitrification is a vital ecosystem function in the open ocean that regenerates inorganic nitrogen and promotes primary production. Recent studies have shown that the ecology and physiology of nitrifying organisms is more complex than previously postulated. The distribution of these organisms in the remote oligotrophic ocean and their interactions with the physicochemical environment are relatively understudied. In this work, we aimed to evaluate the depth profile of nitrifying archaea and bacteria in the Eastern North Pacific Subtropical Front, an area with limited biological surveys but with intense trophic transferences and physicochemical gradients. Furthermore, we investigated the dominant physicochemical and biological relationships within and between ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB) as well as with the overall prokaryotic community. We used a 16S rRNA gene sequencing approach to identify and characterize the nitrifying groups within the first 500 m of the water column and to analyze their abiotic and biotic interactions. The water column was characterized mainly by two contrasting environments, warm O₂-rich surface waters with low dissolved inorganic nitrogen (DIN) and a cold O₂-deficient mesopelagic layer with high concentrations of nitrate (NO₃^–). Thaumarcheotal AOA and bacterial NOB were highly abundant below the deep chlorophyll maximum (DCM) and in the mesopelagic. In the mesopelagic, AOA and NOB represented up to 25 and 3% of the total prokaryotic community, respectively. Interestingly, the AOA community in the mesopelagic was dominated by unclassified genera that may constitute a novel group of AOA highly adapted to the conditions observed at those depths. Several of these unclassified amplicon sequence variants (ASVs) were positively correlated with NO₃^– concentrations and negatively correlated with temperature and O₂, whereas known thaumarcheotal genera exhibited the opposite behavior. Additionally, we found a large network of positive interactions within and between putative nitrifying ASVs and other prokaryotic groups, including 13230 significant correlations and 23 sub-communities of AOA, AOB, NOB, irrespective of their taxonomic classification. This study provides new insights into our understanding of the roles that AOA may play in recycling inorganic nitrogen in the oligotrophic ocean, with potential consequences to primary production in these remote ecosystems.

Sludge digestibility and functionally active microorganisms in methanogenic sludge digesters revealed by E. coli-fed digestion and microbial source tracking

Methanogenic sludge digestion plays a pivotal role in attenuating and hygienizing the massively-produced waste activated sludge (WAS), which is predominantly composed of microbial cells and extracellular polymeric substances (EPS). The efficient sludge digestion requires a variety of functionally active microorganisms working together closely to convert sludge organic matter into biogas. Nonetheless, the digestion efficiency (or digestibility quantified as carbon removal efficiency) of major sludge constituents (i.e., microbial cells and EPS) and associated functionally active microorganisms in sludge digesters remain elusive. In this study, we identified the digestibility of sludge microbial cells and the associated functionally active microorganisms by using Escherichia coli (E. coli)-fed digestion and microbial source tracking. The average carbon removals in four digesters fed with fresh WAS (WAS-AD), thermal pretreated WAS (Thermal-WAS-AD), E. coli cells (E.coli-AD) and thermal pretreated E. coli cells (Thermal-E.coli-AD) were 30.6 ± 3.4%, 45.8 ± 2.9%, 69.0 ± 3.4% and 68.9 ± 4.6%, respectively. Compared to WAS-AD and Thermal-WAS-AD, the significantly higher carbon removals in E. coli-AD and Thermal-E. coli-AD suggested the remarkably higher digestibility of microbial cells than EPS, and releasing organic matter from EPS might be a rate-limiting step in sludge digestion. Functionally active microorganisms for microbial cell digestion predominantly included fermenters (e.g., Petrimonas and Lentimicrobium), syntrophic acetogens (e.g., Synergistaceae) and methanogens (e.g., Methanosaeta and Methanosarcina). Microbial source tracking estimation showed that the microbial cell-digesting populations accounted for 35.6 ± 9.1% and 70.3 ± 10.1% of total microbial communities in the WAS-AD and Thermal-WAS-AD, respectively. Accordingly, the functionally active microorganisms for digestion of both microbial cells and EPS accounted for 64.5 ± 12.1% and 97.3 ± 2.0% of total digestion sludge microbiome in WAS-AD and Thermal-WAS-AD, respectively. By contrast, feeding WAS-derived microorganisms accounted for 23.2 ± 4.4% and 2.3 ± 1.2% of total microbial communities in the WAS-AD and Thermal-WAS-AD, respectively.

Metal-induced bacterial interactions promote diversity in river-sediment microbiomes

Anthropogenic metal contamination results in long-term environmental selective pressure with unclear impacts on bacterial communities, which comprise key players in ecosystem functioning. Since metal contamination poses serious toxicity and bioaccumulation issues, assessing their impact on environmental microbiomes is important to respond to current environmental and health issues. Despite elevated metal concentrations, the river sedimentary microbiome near the MetalEurop foundry (France) shows unexpected higher diversity compared with the upstream control site. In this work, a follow-up of the microbial community assembly during a metal contamination event was performed in microcosms with periodic renewal of the supernatant river water. Sediments of the control site were gradually exposed to a mixture of metals (Cd, Cu, Pb and Zn) in order to reach similar concentrations to MetalEurop sediments. Illumina sequencing of 16S rRNA gene amplicons was performed. Metal-resistant genes, czcA and pbrA, as well as IncP plasmid content, were assessed by quantitative PCR. The outcomes of this study support previous in situ observations showing that metals act as community assembly managers, increasing diversity. This work revealed progressive adaptation of the sediment microbiome through the selection of different metal-resistant mechanisms and cross-species interactions involving public good-providing bacteria co-occurring with the rest of the community.

Grazing pressure-induced shift in planktonic bacterial communities with the dominance of acIII-A1 actinobacterial lineage in soda pans

Astatic soda pans of the Pannonian Steppe are unique environments with respect to their multiple extreme physical and chemical characteristics (high daily water temperature fluctuation, high turbidity, alkaline pH, salinity, polyhumic organic carbon concentration, hypertrophic state and special ionic composition). However, little is known about the seasonal dynamics of the bacterial communities inhabiting these lakes and the role of environmental factors that have the main impact on their structure. Therefore, two soda pans were sampled monthly between April 2013 and July 2014 to reveal changes in the planktonic community. By late spring in both years, a sudden shift in the community structure was observed, the previous algae-associated bacterial communities had collapsed, resulting the highest ratio of Actinobacteria within the bacterioplankton (89%, with the dominance of acIII-A1 lineage) ever reported in the literature. Before these peaks, an extremely high abundance (> 10,000 individuum l-1) of microcrustaceans (Moina brachiata and Arctodiaptomus spinosus) was observed. OTU-based statistical approaches showed that in addition to algal blooms and water-level fluctuations, zooplankton densities had the strongest effect on the composition of bacterial communities. In these extreme environments, this implies a surprisingly strong, community-shaping top-down role of microcrustacean grazers.

Zooming on dynamics of marine microbial communities in the phycosphere of Akashiwo sanguinea (Dinophyta) blooms

Characterizing ecological relationships between viruses, bacteria and phytoplankton in the ocean is critical to understanding the ecosystem; however, these relationships are infrequently investigated together. To understand the dynamics of microbial communities and environmental factors in harmful algal blooms (HABs), we examined the environmental factors and microbial communities during Akashiwo sanguinea HABs in the Jangmok coastal waters of South Korea by metagenomics. Specific bacterial species showed complex synergistic and antagonistic relationships with the A. sanguinea bloom. The endoparasitic dinoflagellate Amoebophrya sp. 1 controlled the bloom dynamics and correlated with HAB decline. Among nucleocytoplasmic large DNA viruses (NCLDVs), two Pandoraviruses and six Phycodnaviruses were strongly and positively correlated with the HABs. Operational taxonomic units of microbial communities and environmental factors associated with A. sanguinea were visualized by network analysis: A. sanguinea-Amoebophrya sp. 1 (r = .59, time lag: 2 days) and A. sanguinea-Ectocarpus siliculosus virus 1 in Phycodnaviridae (0.50, 4 days) relationships showed close associations. The relationship between A. sanguinea and dissolved inorganic phosphorus relationship also showed a very close correlation (0.74, 0 day). Microbial communities and the environment changed dynamically during the A. sanguinea bloom, and the rapid turnover of microorganisms responded to ecological interactions. A. sanguinea bloom dramatically changes the environments by exuding dissolved carbohydrates via autotrophic processes, followed by changes in microbial communities involving host-specific viruses, bacteria and parasitoids. Thus, the microbial communities in HAB are composed of various organisms that interact in a complex manner.

Impact of Terrestrial Input on Deep-Sea Benthic Archaeal Community Structure in South China Sea Sediments

Archaea are widespread in marine sediments and play important roles in the cycling of sedimentary organic carbon. However, factors controlling the distribution of archaea in marine sediments are not well understood. Here we investigated benthic archaeal communities over glacial-interglacial cycles in the northern South China Sea and evaluated their responses to sediment organic matter sources and inter-species interactions. Archaea in sediments deposited during the interglacial period Marine Isotope Stage (MIS) 1 (Holocene) were significantly different from those in sediments deposited in MIS 2 and MIS 3 of the Last Glacial Period when terrestrial input to the South China Sea was enhanced based on analysis of the long-chain n-alkane C₃₁. The absolute archaeal 16S rRNA gene abundance in subsurface sediments was highest in MIS 2, coincident with high sedimentation rates and high concentrations of total organic carbon. Soil Crenarchaeotic Group (SCG; Nitrososphaerales) species, the most abundant ammonia-oxidizing archaea in soils, increased dramatically during MIS 2, likely reflecting transport of terrestrial archaea during glacial periods with high sedimentation rates. Co-occurrence network analyses indicated significant association of SCG archaea with benthic deep-sea microbes such as Bathyarchaeota and Thermoprofundales in MIS 2 and MIS 3, suggesting potential interactions among these archaeal groups. Meanwhile, Thermoprofundales abundance was positively correlated with total organic carbon (TOC), along with n-alkane C₃₁ and sedimentation rate, indicating that Thermoprofundales may be particularly important in processing of organic carbon in deep-sea sediments. Collectively, these results demonstrate that the composition of heterotrophic benthic archaea in the South China Sea may be influenced by terrestrial organic input in tune with glacial-interglacial cycles, suggesting a plausible link between global climate change and microbial population dynamics in deep-sea marine sediments.

Gut microbial co-abundance networks show specificity in inflammatory bowel disease and obesity

The gut microbiome is an ecosystem that involves complex interactions. Currently, our knowledge about the role of the gut microbiome in health and disease relies mainly on differential microbial abundance, and little is known about the role of microbial interactions in the context of human disease. Here, we construct and compare microbial co-abundance networks using 2,379 metagenomes from four human cohorts: an inflammatory bowel disease (IBD) cohort, an obese cohort and two population-based cohorts. We find that the strengths of 38.6% of species co-abundances and 64.3% of pathway co-abundances vary significantly between cohorts, with 113 species and 1,050 pathway co-abundances showing IBD-specific effects and 281 pathway co-abundances showing obesity-specific effects. We can also replicate these IBD microbial co-abundances in longitudinal data from the IBD cohort of the integrative human microbiome (iHMP-IBD) project. Our study identifies several key species and pathways in IBD and obesity and provides evidence that altered microbial abundances in disease can influence their co-abundance relationship, which expands our current knowledge regarding microbial dysbiosis in disease.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

manta: a Clustering Algorithm for Weighted Ecological Networks

Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks.IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.

Characterizing community dynamics and exploring bacterial assemblages in two activated sludge systems

Bacterial communities in the activated sludge (AS) determine the wastewater treatment performance in the municipal wastewater treatment plants (WWTPs). Aiming at identifying the affecting factors and the variation patterns of the bacterial assemblages in AS, a 2-year time-series AS samples were collected from two separated WWTPs and metagenomic sequencing was conducted. Obvious seasonal shift and succession of the bacterial community were observed in both WWTPs on the genus and species levels, especially for the persistent taxa, implying that temperature was a decisive factor for maintaining bacterial assemblage patterns in long-term period. Taxa abundance distribution (TAD) concerning occurrence frequency and average abundance were found fitting for exponential formulations, and the approximately equal total abundance of persistent taxa suggested that stable and high abundance (~ 90%) of core functional bacterial groups would help to maintain wastewater treatment performance. Drastic changes of environmental factors were found causing temporally significant bacterial structure variation, while the innate correlations between bacterial species could recover the community gradually and maintain relative stability of the AS system. Delayed correlations between environmental factors and abundant (persistent or intermittent) bacterial species were observed widely, while synchronous biotic interactions were identified more frequently. Besides, bacterial species with similar functions were prone to cluster together and shared the same seasonal variation pattern, implicating that the cooperation of functional correlated taxa played the most dominant role in shaping the bacterial assemblages. Furthermore, rare bacterial groups were to be explored for removing emerging pollutants with lower concentrations. The results of this study would assist dealing with operational defect and optimize the treatment system in WWTPs.

Distribution and co-occurrence of antibiotic and metal resistance genes in biofilms of an anthropogenically impacted stream

Urban stream biofilms are potential hotspots for resistomes and antibiotic resistance genes (ARGs). Biofilm communities that harbor resistance genes may be influenced by contaminant input (e.g., metals and antibiotics) from urban drainage (i.e., Wastewater Treatment Plant effluent and stormwater runoff); understanding the ecology of these communities and their resistome is needed. Given the potential importance of the co-occurrence of ARGs and metal resistance genes (MRGs), we investigated the spatial and temporal distribution of three ARGs (tetracycline [tetW] and sulfonamides [sulI and sulII]), four MRGs (lead [pbrT], copper [copA], and cadmium/cobalt/zinc [czcA and czcC]) via quantitative PCR and biofilm bacterial community composition via MiSeq 16S sequencing at four time points along an urbanization gradient (i.e., developed, agriculture, and forested sites) in a stream's watershed. Our results revealed that ARG and MRG abundances were significantly affected by land use-time interaction, with greater resistance abundances occurring in more urban locations during particular times of the year. It was also observed that changes in ARG and MRG profiles were influenced by differences in community composition among land use types, and that these differences were in response to changes in stream physicochemical parameters (pH, redox, temperature, nutrient availability, and metal concentration) that were driven by sub-watershed land use. Moreover, the dynamics between ARGs and MRGs within these communities correlated strongly and positively with one another. Taken altogether, our results demonstrate that changes in environmental properties due to human activity may drive the ARG-MRG profiles of biofilm communities by modulating community structure over time and space.

A hydrocarbon-contaminated aquifer reveals a Piggyback-the-Persistent viral strategy

Subsurface environments hold the largest reservoir of microbes in the biosphere. They play essential roles in transforming nutrients, degrading contaminants and recycling organic matter. Here, we propose a previously unrecognised fundamental microbial process that influences aquifer bioremediation dynamics and that applies to all microbial communities. In contrast to previous models, our proposed Piggyback-the-Persistent (PtP) mechanism occurs when viruses become more dominated by those exhibiting temperate rather than lytic lifestyles driven by persistent chemicals (in our case chlorinated-hydrocarbon pollutants) that provide long-term carbon sources and that refocus the aquifer carbon cycle, thus altering the microbial community. In this ultra-oligotrophic system, the virus:microbial ratio (VMR) ranges from below the detection limit of 0.0001 to 0.6, well below the common aquatic range of 3-10. Shortest-average-path network analysis revealed VMR and trichlorethene (TCE) as nodes through which ecosystem information and biomass most efficiently pass. Novel network rearrangement revealed a hierarchy of Kill-the-Winner (KtW), Piggyback-the-Winner (PtW) and PtP nodes. We propose that KtW, PtW and PtP occur simultaneously as competing strategies, with their relative importance depending on conditions at a particular time and location with unusual nutrient sources, such as TCE, appearing to contribute to a shift in this balance between viral mechanisms.

Complex Microbial Communities Drive Iron and Sulfur Cycling in Arctic Fjord Sediments

Glacial retreat is changing biogeochemical cycling in the Arctic, where glacial runoff contributes iron for oceanic shelf primary production. We hypothesize that in Svalbard fjords, microbes catalyze intense iron and sulfur cycling in low-organic-matter sediments. This is because low organic matter limits sulfide generation, allowing iron mobility to the water column instead of precipitation as iron monosulfides. In this study, we tested this with high-depth-resolution 16S rRNA gene libraries in the upper 20 cm at two sites in Van Keulenfjorden, Svalbard. At the site closer to the glaciers, iron-reducing Desulfuromonadales, iron-oxidizing Gallionella and Mariprofundus, and sulfur-oxidizing Thiotrichales and Epsilonproteobacteria were abundant above a 12-cm depth. Below this depth, the relative abundances of sequences for sulfate-reducing Desulfobacteraceae and Desulfobulbaceae increased. At the outer station, the switch from iron-cycling clades to sulfate reducers occurred at shallower depths (∼5 cm), corresponding to higher sulfate reduction rates. Relatively labile organic matter (shown by δ¹³C and C/N ratios) was more abundant at this outer site, and ordination analysis suggested that this affected microbial community structure in surface sediments. Network analysis revealed more correlations between predicted iron- and sulfur-cycling taxa and with uncultured clades proximal to the glacier. Together, these results suggest that complex microbial communities catalyze redox cycling of iron and sulfur, especially closer to the glacier, where sulfate reduction is limited due to low availability of organic matter. Diminished sulfate reduction in upper sediments enables iron to flux into the overlying water, where it may be transported to the shelf.IMPORTANCE Glacial runoff is a key source of iron for primary production in the Arctic. In the fjords of the Svalbard archipelago, glacial retreat is predicted to stimulate phytoplankton blooms that were previously restricted to outer margins. Decreased sediment delivery and enhanced primary production have been hypothesized to alter sediment biogeochemistry, wherein any free reduced iron that could potentially be delivered to the shelf will instead become buried with sulfide generated through microbial sulfate reduction. We support this hypothesis with sequencing data that showed increases in the relative abundance of sulfate reducing taxa and sulfate reduction rates with increasing distance from the glaciers in Van Keulenfjorden, Svalbard. Community structure was driven by organic geochemistry, suggesting that enhanced input of organic material will stimulate sulfate reduction in interior fjord sediments as glaciers continue to recede.

Microbial Co-Occurrence Patterns and Keystone Species in the Gut Microbial Community of Mice in Response to Stress and Chondroitin Sulfate Disaccharide

Detecting microbial interactions is essential to the understanding of the structure and function of the gut microbiome. In this study, microbial co-occurrence patterns were inferred using a random matrix theory based approach in the gut microbiome of mice in response to chondroitin sulfate disaccharide (CSD) under healthy and stressed conditions. The exercise stress disrupted the network composition and microbial co-occurrence patterns. Thirty-four Operational Taxonomic Units (OTU) were identified as module hubs and connectors, likely acting as generalists in the microbial community. Mucispirillum schaedleri acted as a connector in the stressed network in response to CSD supplement and may play a key role in bridging intimate interactions between the host and its microbiome. Several modules correlated with physiological parameters were detected. For example, Modules M02 (under stress) and S05 (stress + CSD) were strongly correlated with blood urea nitrogen levels (r = 0.90 and -0.75, respectively). A positive correlation between node connectivity of the OTUs assigned to Proteobacteria with superoxide dismutase activities under stress (r = 0.57, p < 0.05) provided further evidence that Proteobacteria can be developed as a potential pathological marker. Our findings provided novel insights into gut microbial interactions and may facilitate future endeavor in microbial community engineering.

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

BACKGROUND

Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities.

METHODS

We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third "mediator" variable. These "mediator" variables appear to modulate the associations between pairs of bacteria.

RESULTS

Using this analysis, we were able to assess the OTUs' ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors.

CONCLUSIONS

By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download ( http://bitbucket.org/charade/elsa ).

Coupled virus - bacteria interactions and ecosystem function in an engineered microbial system

Viruses are thought to control bacterial abundance, affect community composition and influence ecosystem function in natural environments. Yet their dynamics have seldom been studied in engineered systems, or indeed in any system, for long periods of time. We measured virus abundance in a full-scale activated sludge plant every week for two years. Total bacteria and ammonia oxidising bacteria (AOB) abundances, bacterial community profiles, and a suite of environmental and operational parameters were also monitored. Mixed liquor virus abundance fluctuated over an order of magnitude (3.18 × 10⁸-3.41 × 10⁹ virus's mL^-1) and that variation was statistically significantly associated with total bacterial and AOB abundance, community composition, and effluent concentrations of COD and NH₄⁺- N and thus system function. This suggests viruses play a far more important role in the dynamics of activated sludge systems than previously realised and could be one of the key factors controlling bacterial abundance, community structure and functional stability and may cause reactors to fail. These findings are based on statistical associations, not mechanistic models. Nevertheless, viral associations with abiotic factors, such as pH, make physical sense, giving credence to these findings and highlighting the role that physical factors play in virus ecology. Further work is needed to identify and quantify specific bacteriophage and their hosts to enable us to develop mechanistic models of the ecology of viruses in wastewater treatment systems. However, since we have shown that viruses can be related to effluent quality and virus quantification is simple and cheap, practitioners would probably benefit from quantifying viruses now.

Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

IMPACT STATEMENT

This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

Statistical significance approximation for local similarity analysis of dependent time series data

Background

Local similarity analysis (LSA) of time series data has been extensively used to investigate the dynamics of biological systems in a wide range of environments. Recently, a theoretical method was proposed to approximately calculate the statistical significance of local similarity (LS) scores. However, the method assumes that the time series data are independent identically distributed, which can be violated in many problems.

Results

In this paper, we develop a novel approach to accurately approximate statistical significance of LSA for dependent time series data using nonparametric kernel estimated long-run variance. We also investigate an alternative method for LSA statistical significance approximation by computing the local similarity score of the residuals based on a predefined statistical model. We show by simulations that both methods have controllable type I errors for dependent time series, while other approaches for statistical significance can be grossly oversized. We apply both methods to human and marine microbial datasets, where most of possible significant associations are captured and false positives are efficiently controlled.

Conclusions

Our methods provide fast and effective approaches for evaluating statistical significance of dependent time series data with controllable type I error. They can be applied to a variety of time series data to reveal inherent relationships among the different factors.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2595-x) contains supplementary material, which is available to authorized users.

Generalized correlation measure using count statistics for gene expression data with ordered samples

Motivation

Capturing association patterns in gene expression levels under different conditions or time points is important for inferring gene regulatory interactions. In practice, temporal changes in gene expression may result in complex association patterns that require more sophisticated detection methods than simple correlation measures. For instance, the effect of regulation may lead to time-lagged associations and interactions local to a subset of samples. Furthermore, expression profiles of interest may not be aligned or directly comparable (e.g. gene expression profiles from two species).

Results

We propose a count statistic for measuring association between pairs of gene expression profiles consisting of ordered samples (e.g. time-course), where correlation may only exist locally in subsequences separated by a position shift. The statistic is simple and fast to compute, and we illustrate its use in two applications. In a cross-species comparison of developmental gene expression levels, we show our method not only measures association of gene expressions between the two species, but also provides alignment between different developmental stages. In the second application, we applied our statistic to expression profiles from two distinct phenotypic conditions, where the samples in each profile are ordered by the associated phenotypic values. The detected associations can be useful in building correspondence between gene association networks under different phenotypes. On the theoretical side, we provide asymptotic distributions of the statistic for different regions of the parameter space and test its power on simulated data.

Availability and implementation

The code used to perform the analysis is available as part of the Supplementary Material.

Contact

msw@usc.edu or hhuang@stat.berkeley.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparative Metagenomics

Thanks in large part to newer, better, and cheaper DNA sequencing technologies, an enormous number of metagenomic sequence datasets have been and continue to be generated, covering a huge variety of environmental niches, including several different human body sites. Comparing these metagenomes and identifying their commonalities and differences is a challenging task, due not only to the large amounts of data, but also because there are several methodological considerations that need to be taken into account to ensure an appropriate and sound comparison between datasets. In this chapter, we describe current techniques aimed at comparing metagenomes generated by 16S ribosomal RNA and shotgun DNA sequencing, emphasizing methodological issues that arise in these comparative studies. We provide a detailed case study to illustrate some of these techniques using data from the Human Microbiome Project comparing the microbial communities from ten buccal mucosa samples with ten tongue dorsum samples in terms of alpha diversity, beta diversity, and their taxonomic and functional profiles.

A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series

In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.

Inference of Significant Microbial Interactions From Longitudinal Metagenomics Data

Data of next-generation sequencing (NGS) and their analysis have been facilitating advances in our understanding of microbial ecosystems such as human gut microbiota. However, inference of microbial interactions occurring within an ecosystem is still a challenge mainly due to sequencing data (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe growtth dynamics of microbial communities and estimate the involved microbial interactions, we introduce a procedure by integrating generalized Lotka-Volterra equations, forward stepwise regression and bootstrap aggregation. First, we successfully identify experimentally confirmed microbial interactions based on relative abundance data of a cheese microbial community. Then, we apply the procedure to time-series of 16S rDNA sequences of gut microbiomes of children who were progressing to Type 1 diabetes (T1D progressors), and compare their gut microbial interactions to a healthy control group. Our results suggest that the number of inferred microbial interactions increased over time during the first 3 years of life. More microbial interactions are found in the gut flora of healthy children than that of T1D progressors. The inhibitory effects from Actinobacteria and Bacilli to Bacteroidia, from Bacteroidia to Clostridia, and the beneficial effect from Clostridia to Bacteroidia are shared between healthy children and T1D progressors. An inhibition of Clostridia by Gammaproteobacteria is found in healthy children that maintains through their first 3 years of life. This suppression appears in T1D progressors during the first year of life, which transforms to a commensalism relationship at the age of 3 years old. Gammaproteobacteria is found exerting an inhibition on Bacteroidia in the T1D progressors, which is not identified in the healthy controls.

Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling

Short timescale observations are valuable for understanding microbial ecological processes. We assessed dynamics in relative abundance and potential activities by sequencing the small sub-unit ribosomal RNA gene (rRNA gene) and rRNA molecules (rRNA) of Bacteria, Archaea, and Eukaryota once to twice daily between March 2014 and May 2014 from the surface ocean off Catalina Island, California. Typically Ostreococcus, Braarudosphaera, Teleaulax, and Synechococcus dominated phytoplankton sequences (including chloroplasts) while SAR11, Sulfitobacter, and Fluviicola dominated non-phytoplankton Bacteria and Archaea. We observed short-lived increases of diatoms, mostly Pseudo-nitzschia and Chaetoceros, with quickly responding Bacteria and Archaea including Flavobacteriaceae (Polaribacter & Formosa), Roseovarius, and Euryarchaeota (MGII), notably the exact amplicon sequence variants we observed responding similarly to another diatom bloom nearby, 3 years prior. We observed correlations representing known interactions among abundant phytoplankton rRNA sequences, demonstrating the biogeochemical and ecological relevance of such interactions: (1) The kleptochloroplastidic ciliate Mesodinium 18S rRNA gene sequences and a single Teleaulax taxon (via 16S rRNA gene sequences) were correlated (Spearman r = 0.83) yet uncorrelated to a Teleaulax 18S rRNA gene OTU, or any other taxon (consistent with a kleptochloroplastidic or karyokleptic relationship) and (2) the photosynthetic prymnesiophyte Braarudosphaera bigelowii and two strains of diazotrophic cyanobacterium UCYN-A were correlated and each taxon was also correlated to other taxa, including B. bigelowii to a verrucomicrobium and a dictyochophyte phytoplankter (all r > 0.8). We also report strong correlations (r > 0.7) between various ciliates, bacteria, and phytoplankton, suggesting interactions via currently unknown mechanisms. These data reiterate the utility of high-frequency time series to show rapid microbial reactions to stimuli, and provide new information about in situ dynamics of previously recognized and hypothesized interactions.

Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities

Inferring interactions from population time series is an active and ongoing area of research. It is relevant across many biological systems—particularly in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference—using correlations to predict interactions—is widespread. However, it is well-known that “correlation does not imply causation.” Despite this, many studies apply correlation-based inference methods to experimental time series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silico virus-microbe communities, raising questions on their relevance when applied in situ.

Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding 1 million per ml. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods that leverage product-moment, time-lagged, and relative-abundance-based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.

IMPORTANCE Inferring interactions from population time series is an active and ongoing area of research. It is relevant across many biological systems—particularly in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference—using correlations to predict interactions—is widespread. However, it is well-known that “correlation does not imply causation.” Despite this, many studies apply correlation-based inference methods to experimental time series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silico virus-microbe communities, raising questions on their relevance when applied in situ.

GEsture: an online hand-drawing tool for gene expression pattern search

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.