Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

Metagenomics analysis reveals effects of salinity fluctuation on diversity and ecological functions of high and low nucleic acid content bacteria

Salinity is a critical environmental factor in marine ecosystems and has complex and wide-ranging biological effects. However, the effects of changing salinity on diversity and ecological functions of high nucleic acid (HNA) and low nucleic acid (LNA) bacteria are not well understood. In this study, we used 16S rRNA sequencing and metagenomic sequencing analysis to reveal the response of HNA and LNA bacterial communities and their ecological functions to salinity, which was decreased from 26 ‰ to 16 ‰. The results showed that salinity changes had significant effects on the community composition of HNA and LNA bacteria. Among LNA bacteria, 14 classes showed a significant correlation between relative abundance and salinity. Salinity changes can lead to the transfer of some bacteria from HNA bacteria to LNA bacteria. In the network topology relationship, the complexity of the network between HNA and LNA bacterial communities gradually decreased with decreased salinity. The abundance of some carbon and nitrogen cycling genes in HNA and LNA bacteria varied with salinity. Overall, this study demonstrates the effects of salinity on diversity and ecological functions and suggests the importance of salinity in regulating HNA and LNA bacterial communities and functions.

Impact of soil physicochemical factors and heavy metals on co-occurrence pattern of bacterial in rural simple garbage dumping site

Rural waste accumulation leads to heavy metal soil pollution, impacting microbial communities. However, knowledge gaps exist regarding the distribution and occurrence patterns of bacterial communities in multi-metal contaminated soil profiles. In this study, high-throughput 16 S rRNA gene sequencing technology was used to explore the response of soil bacterial communities to various heavy metal pollution in rural simple waste dumps in karst areas of Southwest China. The study selected three habitats in the center, edge, and uncontaminated areas of the waste dump to evaluate the main factors driving the change in bacterial community composition. Pollution indices reveal severe contamination across all elements, except for moderately polluted lead (Pb); contamination severity ranks as follows: Mn > Cd > Zn > Cr > Sb > V > Cu > As > Pb. Proteobacteria, Actinobacteria, Chloroflexi, and Acidobacteriota predominate, collectively constituting over 60% of the relative abundance. Analysis of Chao and Shannon indices demonstrated that the waste dump center boasted the greatest bacterial richness and diversity. Correlation data indicated a predominant synergistic interaction among the landfill's bacterial community, with a higher number of positive associations (76.4%) compared to negative ones (26.3%). Network complexity was minimal at the dump's edge. RDA analysis showed that Pb(explained:46%) and Mn(explained:21%) were the key factors causing the difference in bacterial community composition in the edge area of the waste dump, and AK(explained:42.1%) and Cd(explained:35.2%) were the key factors in the center of the waste dump. This study provides important information for understanding the distribution patterns, co-occurrence networks, and environmental response mechanisms of bacterial communities in landfill soils under heavy metal stress, which helps guide the formulation of rural waste treatment and soil remediation strategies.

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

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

Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags

Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that require intrastrain resolution. Here we developed a genomic tagging system, termed wild-type isogenic standardized hybrid (WISH)-tags, that can be combined with quantitative polymerase chain reaction and next-generation sequencing for microbial strain enumeration. We experimentally validated the performance of 62 tags and showed that they can be differentiated with high precision. WISH-tags were introduced into model and non-model bacterial members of the mouse and plant microbiota. Intrastrain priority effects were tested using one species of isogenic barcoded bacteria in the murine gut and the Arabidopsis phyllosphere, both with and without microbiota context. We observed colonization resistance against late-arriving strains of Salmonella Typhimurium in the mouse gut, whereas the phyllosphere accommodated Sphingomonas latecomers in a manner proportional to their presence at the late inoculation timepoint. This demonstrates that WISH-tags are a resource for deciphering population dynamics underlying microbiome assembly across biological systems.

Effects of operational parameters on bacterial communities in Hong Kong and global wastewater treatment plants

Wastewater treatment plants (WWTPs) are indispensable biotechnology facilities for modern cities and play an essential role in modern urban infrastructure by employing microorganisms to remove pollutants in wastewater, thus protecting public health and the environment. This study conducted a 13-month bacterial community survey of six full-scale WWTPs in Hong Kong with samples of influent, activated sludge (AS), and effluent to explore their synchronism and asynchronism of bacterial community. Besides, we compared AS results of six Hong Kong WWTPs with data from 1,186 AS amplicon data in 269 global WWTPs and a 9-year metagenomic sequencing survey of a Hong Kong WWTP. Our results showed the compositions of bacterial communities varied and the bacterial community structure of AS had obvious differences across Hong Kong WWTPs. The co-occurrence analysis identified 40 pairs of relationships that existed among Hong Kong WWTPs to show solid associations between two species and stochastic processes took large proportions for the bacterial community assembly of six WWTPs. The abundance and distribution of the functional bacteria in worldwide and Hong Kong WWTPs were examined and compared, and we found that ammonia-oxidizing bacteria had more diversity than nitrite-oxidizing bacteria. Besides, Hong Kong WWTPs could make great contributions to the genome mining of microbial dark matter in the global "wanted list." Operational parameters had important effects on OTUs' abundance, such as the temperature to the genera of Tetrasphaera, Gordonia and Nitrospira. All these results obtained from this study can deepen our understanding of the microbial ecology in WWTPs and provide foundations for further studies.

IMPORTANCE

Wastewater treatment plants (WWTPs) are an indispensable component of modern cities, as they can remove pollutants in wastewater to prevent anthropogenic activities. Activated sludge (AS) is a fundamental wastewater treatment process and it harbors a highly complex microbial community that forms the main components and contains functional groups. Unveiling "who is there" is a long-term goal of the research on AS microbiology. High-throughput sequencing provides insights into the inventory diversity of microbial communities to an unprecedented level of detail. At present, the analysis of communities in WWTPs usually comes from a specific WWTP and lacks comparisons and verification among different WWTPs. The wide-scale and long-term sampling project and research in this study could help us evaluate the AS community more accurately to find the similarities and different results for different WWTPs in Hong Kong and other regions of the world.

Bioaerosols released from multistage biofilter for gaseous benzene removal: Escape behavior and pathogenicity

Biological deodorization systems are widely used to control odors and volatile organic compounds. However, the secondary contamination of bioaerosol emissions is a noteworthy issue in the operation of biofilters for off-gas purification. In this study, a multistage biofilter for benzene treatment was utilized to investigate the bioaerosol emissions under different flow rates and spray intervals. At the outlet of the biofilter, 99-7173 CFU/m3 of bioaerosols were detected, among which pathogens accounted for 8.93-98.73 %. Proteobacteria and Firmicutes dominated bioaerosols at the phylum level. The Mantel test based on the Bray-Curtis distance revealed strong influences of flow rate introduced to the biofilter and biomass colonized on the packing materials (PMs) on bioaerosol emissions. The non-metric multidimensional scaling results suggested a correlation between the bioaerosol community and bacteria on the PMs. Bacillus and Stenotrophomonas were the two main genera stripped from the biofilm on PMs to form the bioaerosols. SourceTracker analysis confirmed that microorganisms from the PMs near outlet contributed an average of 22.3 % to bioaerosols. Pathogenic bacteria carried by bioaerosols included Bacillus, Serratia, Stenotrophomonas, Achromobacter, Enterococcus, and Pseudomonas. Bioaerosols were predicted to cause human diseases, with antimicrobial drug resistance and bacterial infectious disease being the two main pathogenic pathways. Stenotrophomonas sp. LMG 19833, Pseudomonas sp., and Stenotrophomonas sp. were the keystone species in the bioaerosol co-occurrence network. Overall, results of present study promote the insight of bioaerosols, particularly pathogen emissions, and provide a basis for controlling bioaerosol contamination from biofilters.

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.

Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Quorum sensing bacteria in microplastics epiphytic biofilms and their biological characteristics which potentially impact marine ecosystem

Microplastics (MPs) have been shown to be a new type of pollutant in the oceans, with complex biofilms attached to their surfaces. Bacteria with quorum sensing (QS) systems are important participants in biofilms. Such bacteria can secrete and detect signal molecules. When a signal molecule reaches its threshold level, bacteria with QS systems can perform several biological functions, such as biofilm formation and antibiotic metabolite production. However, the ecological effects of QS bacteria in biofilm as MPs distribute globally with ocean currents are not to be elucidate yet. In this study, polypropylene and polyvinyl chloride were selected for on-site enrichment to acquire microplastics with biofilms. Eight culturable QS bacteria in the resulting biofilm were isolated by using biosensor assays, and their biodiversity was analyzed. The profiles of the N-acyl-homoserine lactones (AHLs) produced by these bacteria were analyzed by using thin-layer chromatography (TLC)-bioautography and gas chromatography and mass spectrometry (GC-MS). Biofilm-forming properties and several biological characteristics, such as bacteriostasis, algal inhibition, and dimethylsulfoniopropionate (DMSP) degradation, were explored along with QS quenching. Results showed that QS bacteria were mainly affiliated with class Alphaproteobacteria, particularly Rhodobacteraceae, followed by class Gammaproteobacteria. TLC-bioautography and GC-MS analyses revealed that seven AHLs, namely, C6-HSL, C8-HSL, 3-oxo-C6-HSL, 3-oxo-C8-HSL, 3-oxo-C10-HSL, and two unidentified AHLs were produced. The QS system equipped bacteria with strong biofilm-forming capacity and may contribute to the keystone roles of Rhodobacteraceae. In addition, QS bacteria may exacerbate the adverse environmental effects of MPs, such as inducing the misfeeding of planktons on MPs. This study elucidated the diversity of QS bacteria in MP-associated biofilms and provided a new perspective of the effect of key membrane-forming bacteria on the marine ecological environment.

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.

Contrasting response of microeukaryotic and bacterial communities to the interplay of seasonality and local stressors in shallow soda lakes

Seasonal environmental variation is a leading driver of microbial planktonic community assembly and interactions. However, departures from usual seasonal trends are often reported. To understand the role of local stressors in modifying seasonal succession, we sampled fortnightly, throughout three seasons, five nearby shallow soda lakes exposed to identical seasonal and meteorological changes. We characterised their microeukaryotic and bacterial communities by amplicon sequencing of the 16S and 18S rRNA gene, respectively. Biological interactions were inferred by analyses of synchronous and time-shifted interaction networks, and the keystone taxa of the communities were topologically identified. The lakes showed similar succession patterns during the study period with spring being characterised by the relevance of trophic interactions and a certain level of community stability followed by a more dynamic and variable summer-autumn period. Adaptation to general seasonal changes happened through shared core microbiome of the lakes. Stochastic events such as desiccation disrupted common network attributes and introduced shifts from the prevalent seasonal trajectory. Our results demonstrated that, despite being extreme and highly variable habitats, shallow soda lakes exhibit certain similarities in the seasonality of their planktonic communities, yet local stressors such as droughts instigate deviations from prevalent trends to a greater extent for microeukaryotic than for bacterial communities.

Untangling microbiota diversity and assembly patterns in the world's longest underground culvert water diversion canal

The long-distance underground box culvert water transport system (LUBWT) is a crucial link between the source of drinking water and the consumers. It must ensure the stability of water quality during transportation. However, uncontrollable microbial growth can develop in the water delivery system during the long delivery process, posing a risk to health and safety. Therefore, we applied 16 s and 18 s gene sequence analysis in order to study microbial communities in box culvert waters sampled in 2021, as well as a molecular ecological network-based approach to decipher microbial interactions and stability. Our findings revealed that, in contrast to natural freshwater ecosystems, micro-eukaryotes in LUBWT have complex interactions such as predation, parasitism, and symbiosis due to their semi-enclosed box culvert environment. Total nitrogen may be the primary factor affecting bacterial community interactions in addition to temperature. Moreover, employing stability indicators such as robustness and vulnerability, we also found that microbial stability varied significantly from season to season, with summer having the higher stability of microbial communities. Not only that but also the stability of the micronuclei also varied greatly during water transport, which might also be related to the complex interactions among the micro-eukaryotes. To summarize, our study reveals the microbial interactions and stability in LUBWT, providing essential ecological knowledge to ensure the safety of LUBWT's water quality.

Univariate and Multivariate Statistical Analysis of Microbiome Data: An Overview

Microbiome data is high dimensional, sparse, compositional, and over-dispersed. Therefore, modeling microbiome data is very challenging and it is an active research area. Microbiome analysis has become a progressing area of research as microorganisms constitute a large part of life. Since many methods of microbiome data analysis have been presented, this review summarizes the challenges, methods used, and the advantages and disadvantages of those methods, to serve as an updated guide for those in the field. This review also compared different methods of analysis to progress the development of newer methods.

Kinetic modulation of bacterial hydrolases by microbial community structure in coastal waters

In this study, we hypothesized that shifts in the kinetic parameters of extracellular hydrolytic enzymes may occur as a consequence of seasonal environmental disturbances and would reflect the level of adaptation of the bacterial community to the organic matter of the ecosystem. We measured the activities of enzymes that play a key role in the bacterial growth (leucine aminopeptidase, β- and α-glucosidases) in surface coastal waters of the Eastern Cantabrian Sea and determined their kinetic parameters by computing kinetic models of distinct complexity. Our results revealed the existence of two clearly distinct enzymatic systems operating at different substrate concentrations: a high-affinity system prevailing at low substrate concentrations and a low-affinity system characteristic of high substrate concentrations. These findings could be the result of distinct functional bacterial assemblages growing concurrently under sharp gradients of high-molecular-weight compounds. We constructed an ecological network based on contemporaneous and time-delayed correlations to explore the associations between the kinetic parameters and the environmental variables. The analysis revealed that the recurring phytoplankton blooms registered throughout the seasonal cycle trigger the wax and wane of those members of the bacterial community able to synthesize and secrete specific enzymes.

Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models

Networks are ubiquitous throughout biology, spanning the entire range from molecules to food webs and global environmental systems. Yet, despite substantial efforts by the scientific community, the inference of these networks from data still presents a problem that is unsolved in general. One frequent strategy of addressing the structure of networks is the assumption that the interactions among molecular or organismal populations are static and correlative. While often successful, these static methods are no panacea. They usually ignore the asymmetry of relationships between two species and inferences become more challenging if the network nodes represent dynamically changing quantities. Overcoming these challenges, two very different network inference approaches have been proposed in the literature: Lotka-Volterra (LV) models and Multivariate Autoregressive (MAR) models. These models are computational frameworks with different mathematical structures which, nevertheless, have both been proposed for the same purpose of inferring the interactions within coexisting population networks from observed time-series data. Here, we assess these dynamic network inference methods for the first time in a side-by-side comparison, using both synthetically generated and ecological datasets. Multivariate Autoregressive and Lotka-Volterra models are mathematically equivalent at the steady state, but the results of our comparison suggest that Lotka-Volterra models are generally superior in capturing the dynamics of networks with non-linear dynamics, whereas Multivariate Autoregressive models are better suited for analyses of networks of populations with process noise and close-to linear behavior. To the best of our knowledge, this is the first study comparing LV and MAR approaches. Both frameworks are valuable tools that address slightly different aspects of dynamic networks.

Selection pressure on the rhizosphere microbiome can alter nitrogen use efficiency and seed yield in Brassica rapa

Microbial experimental systems provide a platform to observe how networks of groups emerge to impact plant development. We applied selection pressure for microbiome enhancement of Brassica rapa biomass to examine adaptive bacterial group dynamics under soil nitrogen limitation. In the 9th and final generation of the experiment, selection pressure enhanced B. rapa seed yield and nitrogen use efficiency compared to our control treatment, with no effect between the random selection and control treatments. Aboveground biomass increased for both the high biomass selection and random selection plants. Soil bacterial diversity declined under high B. rapa biomass selection, suggesting a possible ecological filtering mechanism to remove bacterial taxa. Distinct sub-groups of interactions emerged among bacterial phyla such as Proteobacteria and Bacteroidetes in response to selection. Extended Local Similarity Analysis and NetShift indicated greater connectivity of the bacterial community, with more edges, shorter path lengths, and altered modularity through the course of selection for enhanced plant biomass. In contrast, bacterial communities under random selection and no selection showed less complex interaction profiles of bacterial taxa. These results suggest that group-level bacterial interactions could be modified to collectively shift microbiome functions impacting the growth of the host plant under soil nitrogen limitation.

Data-driven causal analysis of observational biological time series

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

Time-lagged interspecies interactions prevail during biofilm development in moving bed biofilm reactor

Clarifying the essential succession dynamics of interspecies interactions during biofilm development is crucial for the regulation and application of biofilm-based processes. In this study, regular and time-series phylogenetic molecular ecological networks (pMENs) were constructed to investigate ordinary and time-lagged interspecies interactions during biofilm development in a moving bed biofilm reactor (MBBR). Positive interactions dominated both regular (89.78%) and time-series (77.04%) ecological networks, suggesting that extensive cooperative behaviors facilitated biofilm development. The pronounced directional interactions (72.52%) in the time-series network further indicated that time-lagged interspecies interactions prevailed in the biofilm development process. Specifically, the proportion of directional negative interactions was higher than that of positive interactions, implying that interspecific competition preferred to be time-lagged. The time-series network revealed that module hubs exhibited extensive time-lagged positive interactions with their neighbors, and most of them exhibited altruistic behaviors. Keystone species possessing more positive interactions were positively correlated with biofilm biomass, NO₃ ^- -N concentrations, and the removal efficiencies of NH₄ ⁺ -N and COD. However, keystone species and peripherals that were negatively targeted by their neighbors showed positive correlations with the concentrations of NO₂ ^- -N, polysaccharides, and proteins in the soluble microbial products. The data highlight that the time-series network can provide directional microbial interactions along with the biofilm development process, which would help to predict the tendency of community shifts and propose efficient strategies for the regulation of biofilm-based processes. This article is protected by copyright. All rights reserved.

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.

Efficient Approximation of Statistical Significance in Local Trend Analysis of Dependent Time Series

Biological time series data plays an important role in exploring the dynamic changes of biological systems, while the determinate patterns of association between various biological factors can further deepen the understanding of biological system functions and the interactions between them. At present, local trend analysis (LTA) has been commonly conducted in many biological fields, where the biological time series data can be the sequence at either the level of gene expression or OTU abundance, etc., A local trend score can be obtained by taking the similarity degree of the upward, constant or downward trend of time series data as an indicator of the correlation between different biological factors. However, a major limitation facing local trend analysis is that the permutation test conducted to calculate its statistical significance requires a time-consuming process. Therefore, the problem attracting much attention from bioinformatics scientists is to develop a method of evaluating the statistical significance of local trend scores quickly and effectively. In this paper, a new approach is proposed to evaluate the efficient approximation of statistical significance in the local trend analysis of dependent time series, and the effectiveness of the new method is demonstrated through simulation and real data set analysis.

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.

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.

Earthworms reduce the dissemination potential of antibiotic resistance genes by changing bacterial co-occurrence patterns in soil

Globally distributed earthworms affect compositions of soil compounds, microbial community structures, as well as antibiotic resistance genes (ARGs). Compared to their surroundings, earthworm gut is a simpler environment which could filter out microbes when soil passes through it. However, little is known about how earthworms affect the dissemination of ARGs in soil, and the understanding of the relationship between microbe-microbe interactions and ARGs is still lacking. Here, we designed a microcosm experiment with earthworm addition, and determined bacterial and fungal community compositions based on amplicon sequencing. We also examined mobile genetic elements (MGEs) and ARGs in earthworm gut and soils using high-throughput qPCR. The results showed significant differences of bacterial, fungal and ARG patterns between gut and soil. Earthworms indirectly impacted the patterns of ARGs in soils by affecting bacterial communities and soil properties, which play key roles in the distribution of ARGs and MGEs. The absolute abundances of MGEs in earthworm gut were significantly lower than those in soils, and earthworms reduce the absolute abundance of MGEs in soils. Earthworms changed the microbial co-occurrence patterns, and reduced bacterial connectivity, which were significantly and positively correlated with MGE abundance. These results highlight the importance of earthworm on the distribution and dissemination of ARGs in soils.

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.

Dynamic carbon flux network of a diverse marine microbial community

The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton-heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.

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.

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.

kLDM: Inferring Multiple Metagenomic Association Networks based on the Variation of Environmental Factors

Identification of significant biological relationships or patterns is central to many metagenomic studies. Methods that estimate association networks have been proposed for this purpose; however, they assume that associations are static, neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors (EFs), which can result in inaccurate estimations. Therefore, in this study, we propose a computational model, called the k-Lognormal-Dirichlet-Multinomial (kLDM) model, which estimates multiple association networks that correspond to specific environmental conditions, and simultaneously infers microbe–microbe and EF–microbe associations for each network. The effectiveness of the kLDM model was demonstrated on synthetic data, a colorectal cancer (CRC) dataset, the Tara Oceans dataset, and the American Gut Project dataset. The results revealed that the widely-used Spearman’s rank correlation coefficient method performed much worse than the other methods, indicating the importance of separating samples by environmental conditions. Cancer fecal samples were then compared with cancer-free samples, and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria, especially five CRC-associated operational taxonomic units, indicating gut microbe translocation in cancer patients. Some EF-dependent associations were then found within a marine eukaryotic community. Finally, the gut microbial heterogeneity of inflammatory bowel disease patients was detected. These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems. The kLDM program, R, and Python scripts, together with all experimental datasets, are accessible at https://github.com/tinglab/kLDM.git.

The sum is greater than the parts: exploiting microbial communities to achieve complex functions

Multi-species microbial communities are ubiquitous in nature. The widespread prevalence of these communities is due to highly elaborated interactions among their members thereby accomplishing metabolic functions that are unattainable by individual members alone. Harnessing these communal capabilities is an emerging field in biotechnology. The rational intervention of microbial communities for the purpose of improved function has been facilitated in part by developments in multi-omics approaches, synthetic biology, and computational methods. Recent studies have demonstrated the benefits of rational interventions to human and animal health as well as agricultural productivity. Emergent technologies, such as in situ modification of complex microbial community and community metabolic modeling, represent an avenue to engineer sustainable microbial communities. In this opinion, we review relevant computational and experimental approaches to study and engineer microbial communities and discuss their potential for biotechnological applications.

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.

Self-regulating microbiome networks ensure functional resilience of biofilms in sand biofilters during manganese load fluctuations

Sand biofilters (SBFs) are commonly used to remove manganese (Mn(II)) from drinking water. Mn(II) load variation frequently occurs in SBFs due to fluctuating influent Mn(II) concentrations or flow rates. Therefore, it is important to understand the responses of microbial biofilms in SBFs to environmental disturbances and how they affect Mn(II) oxidation efficiency. Here, the responses of microbial ecological networks and Mn(II) removal in SBFs to increasing Mn(II) load were investigated. The Mn(II) removal efficiency in two SBFs remained at 99.8% despite an increase in influent Mn(II) from 2 mg/L to 4 mg/L, but significantly deteriorated (50.1-58.5%) upon increasing the filtration rate. A canonical correlation analysis of the microbial communities indicated that the local Mn(II) concentration and biofilter depth impacted community compositions of biofilms. The dominant species within the biofilms exhibited clear stratification, with simple associations in the upper layer of the SBFs and more complex interspecies interactions in the bottom layers. Putative manganese-oxidizing bacteria Hyphomicrobium and Pedomicrobium dominated the microbiomes in different layers of SBFs, and changed relatively little in abundance when Mn(II) and filtration rate increased. The community networks showed that biofilm microbiomes in SBFs were resilient to the disturbance of Mn(II) load, primarily via regulating microbial interactions. High manganese loads negatively affected the functional modules for Mn(II) removal. Furthermore, the relatively rare species Candidatus Entotheonella palauensis was identified as a module hub, implying taxa with low abundances can have important roles in ecosystem function. These results shed new light on the ecological rules guiding responses of microbiomes in sand biofilters to environmental stress.

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.

Transformation of Microbial Negative Correlations into Positive Correlations by Saccharomyces cerevisiae Inoculation during Pomegranate Wine Fermentation

The application of starter is a common practice to accelerate and steer the pomegranate wine fermentation process. However, the use of starter needs a better understanding of the effect of the interaction between the starter and native microorganisms during alcoholic fermentation. In this study, high-throughput sequencing combined with metabolite analysis was applied to analyze the effect of commercial Saccharomyces cerevisiae inoculation on the native fungal community interaction and metabolism during pomegranate wine fermentation. Results showed that there were diverse native fungi in pomegranate juice, including Hanseniaspora uvarum, Hanseniaspora valbyensis, S. cerevisiae, Pichia terricola, and Candida diversa Based on ecological network analysis, we found that S. cerevisiae inoculation transformed the negative correlations into positive correlations among the native fungal communities and decreased the Granger causalities between native yeasts and volatile organic compounds. This might lead to decreased contents of isobutanol, isoamylol, octanoic acid, decanoic acid, ethyl laurate, ethyl acetate, ethyl hexadecanoate, phenethyl acetate, and 2-phenylethanol during fermentation. This study combined correlation and causality analysis to gain a more integrated understanding of microbial interaction and the fermentation process. It provided a new strategy to predict certain behaviors between inoculated and selected microorganisms and those coming directly from the fruit.IMPORTANCE Microbial interactions play an important role in flavor metabolism during traditional food and beverage fermentation. However, we understand little about how selected starters influence interactions among native microorganisms. In this study, we found that S. cerevisiae inoculation changed the interactions and metabolisms of native fungal communities during pomegranate wine fermentation. This study not only suggests that starter inoculation should take into account the positive features of starters but also characterizes the microbial interactions established among the starters and the native communities. It may be helpful to select appropriate starter cultures for winemakers to design different styles of wine.

Impact of a synthetic fungicide (fosetyl-Al and propamocarb-hydrochloride) and a biopesticide (Clonostachys rosea) on soil bacterial, fungal, and protist communities

The use of synthetic pesticides in agriculture is increasingly debated. However, few studies have compared the impact of synthetic pesticides and alternative biopesticides on non-target soil microorganisms playing a central role in soil functioning. We conducted a mesocosm experiment and used high-throughput amplicon sequencing to test the impact of a fungal biopesticide and a synthetic fungicide on the diversity, the taxonomic and functional compositions, and co-occurrence patterns of soil bacterial, fungal and protist communities. Neither the synthetic pesticide nor the biopesticide had a significant effect on microbial α-diversity. However, both types of pesticides decreased the complexity of the soil microbial network. The two pesticides had contrasting impacts on the composition of microbial communities and the identity of key taxa as revealed by microbial network analyses. The biopesticide impacted keystone taxa that structured the soil microbial network. The synthetic pesticide modified biotic interactions favouring taxa that are less efficient at degrading organic compounds. This suggests that the biopesticides and the synthetic pesticide have different impact on soil functioning. Altogether, our study shows that pest management products may have functionally significant impacts on the soil microbiome even if microbial α-diversity is unaffected. It also illustrates the potential of high-throughput sequencing analyses to improve the ecotoxicological risk assessment of pesticides on non-target soil microorganisms.

Regularized S-Map Reveals Varying Bacterial Interactions

There is a growing awareness that bacterial interactions follow a highly nonlinear pattern in reality. However, it is challenging to track the varying bacterial interactions using pairwise correlation analysis, which fails to explore their potential effects on the behavior of microbes. Here, we utilized a regularized sequential locally weighted global linear map (S-map) to capture the varying interspecific interactions from the time series data of a bacterial community under exposure to nitrite. Our results show that bacterial interactions are highly variable and that asymmetric interactions dominate the interaction pattern in a community. Furthermore, we propose a Jacobian coefficient-based statistical method to predict the harmony level of a bacterial community at each successive ecosystem state. The results show that the bacterial community exhibits a higher harmony level in nitrite-treated samples than in the control group. We show that the community harmony level is positively associated with the specific endogenous respiration rates and biofilm formation of the culture. In addition, the community tends to process lower diversity and structural stability under zero- and high-nitrite stresses. We demonstrate that the harmony level, rather than structural stability, is a useful index for unveiling the underlying mechanism of bacterial performance. Overall, the regularized S-map can help us to understand bacterial interactions in ecosystems more accurately than previous approaches.IMPORTANCE It has long been acknowledged that bacterial interactions play important roles in community structure and function. Revealing the interaction variability can allow an understanding of how bacteria respond to perturbation and why bacterial community performance changes. Such information should improve our skills in engineering bacterial communities (e.g., in a wastewater treatment plant) and achieve better removal performance and lower energy consumption.

Seasonal dynamics of prokaryotes and their associations with diatoms in the Southern Ocean as revealed by an autonomous sampler

The Southern Ocean remains one of the least explored marine environments. The investigation of temporal microbial dynamics has thus far been hampered by the limited access to this remote ocean. We present here high-resolution seasonal observations of the prokaryotic community composition during phytoplankton blooms induced by natural iron fertilization. A total of 18 seawater samples were collected by a moored remote autonomous sampler over 4 months at 5-11 day intervals in offshore surface waters (central Kerguelen Plateau). Illumina sequencing of the 16S rRNA gene revealed that among the most abundant amplicon sequence variants, SAR92 and Aurantivirga were the first bloom responders, Pseudomonadaceae, Nitrincolaceae and Polaribacter had successive peaks during the spring bloom decline, and Amylibacter increased in relative abundance later in the season. SAR11 and SUP05 were abundant prior to and after the blooms. Using network analysis, we identified two groups of diatoms representative of the spring and summer bloom that had opposite correlation patterns with prokaryotic taxa. Our study provides the first seasonal picture of microbial community dynamics in the open Southern Ocean and thereby offers biological insights to the cycling of carbon and iron, and to an important puzzling issue that is the modest nitrate decrease associated to iron fertilization.

Bacterioplankton assembly and interspecies interactions follow trajectories of Gymnodinium-diatom bloom

The underlying mechanisms of bacterioplankton community assembly and interspecies interactions during harmful algal blooms remain largely unclear. Using 16S rRNA gene amplicon sequencing, we analyzed the bacterioplankton communities over the continuous course of saxitoxin-producing Gymnodinium catenatum blooms and two diatom (i.e., Skeletonema costatum and Chaetoceros curvisetus) blooms in an anthropogenically controlled and eutrophic bay, East China Sea. The succession of bacterioplankton communities correlated with changes in the dynamics of algal species. Deterministic versus stochastic bacterioplankton community assemblage processes were quantified, demonstrating that stochastic processes increased when algal blooms happened. The occurrence of algal blooms caused weaker bacterioplankton interspecies interactions and higher degrees of cooperative activities, changed keystone taxa and diminished the stability of bacterial communities. These findings consequently have important implications for our understanding of bacterioplankton community ecology during algal blooms.

Community composition and correlations between bacteria and algae within epiphytic biofilms on submerged macrophytes in a plateau lake, southwest China

Epiphytic biofilms are complex matrix-enclosed communities comprising large numbers of bacteria and algae, which play an important role in the biogeochemical cycles in aquatic systems. However, little is known about the correlations that occur between these communities or the relative impact of environmental factors on their composition. In this study, epiphytic biofilms on three different aquatic plants were sampled in a typical plateau lake (Caohai, southwest China) in July and November of 2018. Bacterial diversity was assessed using Miseq sequencing approaches and algal communities were assessed using light microscopy. Gammaproteobacteria (54.64%), Bacteroidetes (17.50%) and Firmicutes (13.99%) were the dominant bacterial taxa and Chlorophyta (47.62%), Bacillariophyta (28.57%) and Euglenophyta (19.05%) were the dominant algae. The alpha diversity values of the epiphytic bacterial and algal communities were greater during the macrophyte decline period (November) than during the growth period (July). Microbial community composition was significantly affected by abiotic factors (water temperature, NH₄⁺, pH or TP) and biotic factors (algae or bacteria). Interestingly, in July and November, the epiphytic algal community dissimilarity was stronger than that observed for bacterial community dissimilarity, suggesting that bacterial community dissimilarity may increase more slowly with environmental change than algal community dissimilarity. Furthermore, association network analysis revealed complex correlations between algal biomass and bacteria phylotype, and that 67.83% of correlations were positive and 32.17% were negative. This may indicate that facilitative correlations between algae and bacteria are predominant in epiphytic biofilms. These results provide new information on algal-bacterial correlations as well as the possible mechanisms that drive variations in the microbial community in epiphytic biofilms in freshwater lakes.

Contribution of influent rivers affected by different types of pollution to the changes of benthic microbial community structure in a large lake

As a microbial group in watershed ecosystems, the bacterial community is a sensitive indicator of external environmental fluctuations. However, the effects of different sources of exogenous pollution on the diversity and structure of bacterial communities in inflow rivers and lakes have not been studied in depth. In this study, we used 16S rRNA gene sequencing technology to study the diversity and composition of bacterial communities in rivers affected by different types of pollution. The results showed that the composition of the bacterial communities in rivers with different exogenous pollution sources was different. For example, the genus Arenimonas, which belongs to the Gamma-proteobacteria, is extensively enriched in IDPR (industrially and domestically polluted rivers) and ADPR (agriculturally and domestically polluted rivers) (KW, p < 0.05), while the genus Micromonospora is a more unique genus found in APR (agriculturally polluted rivers). When exploring the topology and classification characteristics of river microbial symbiosis models, it was found that the bacterial community symbiosis network is divided into six modules under different exogenous pollution regimes, and the nodes in the different modules perform different functions, such as the IDPR-dominated module I. In the network, the relatively abundant the genus Flavobacterium and the genus Nitrospira are the key factors driving the nitrogen cycle in the watershed where the samples were collected. In addition, our research indicates that communities in lake environments may be more susceptible to disturbances of various physiological or functional redundancies, thus retaining their original community structure. Overall, this study emphasizes that adaptive changes in the bacterial community structure of the sediments in the catchment and the occurrence of interactions are responses to different exogenous pollution sources.

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.

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.

Longitudinal survey of microbiome associated with particulate matter in a megacity

BACKGROUND

While the physical and chemical properties of airborne particulate matter (PM) have been extensively studied, their associated microbiome remains largely unexplored. Here, we performed a longitudinal metagenomic survey of 106 samples of airborne PM_2.5 and PM₁₀ in Beijing over a period of 6 months in 2012 and 2013, including those from several historically severe smog events.

RESULTS

We observed that the microbiome composition and functional potential were conserved between PM_2.5 and PM₁₀, although considerable temporal variations existed. Among the airborne microorganisms, Propionibacterium acnes, Escherichia coli, Acinetobacter lwoffii, Lactobacillus amylovorus, and Lactobacillus reuteri dominated, along with several viral species. We further identified an extensive repertoire of genes involved in antibiotic resistance and detoxification, including transporters, transpeptidases, and thioredoxins. Sample stratification based on Air Quality Index (AQI) demonstrated that many microbial species, including those associated with human, dog, and mouse feces, exhibit AQI-dependent incidence dynamics. The phylogenetic and functional diversity of air microbiome is comparable to those of soil and water environments, as its composition likely derives from a wide variety of sources.

CONCLUSIONS

Airborne particulate matter accommodates rich and dynamic microbial communities, including a range of microbial elements that are associated with potential health consequences.

Coinfinder: detecting significant associations and dissociations in pangenomes

The accessory genes of prokaryote and eukaryote pangenomes accumulate by horizontal gene transfer, differential gene loss, and the effects of selection and drift. We have developed Coinfinder, a software program that assesses whether sets of homologous genes (gene families) in pangenomes associate or dissociate with each other (i.e. are 'coincident') more often than would be expected by chance. Coinfinder employs a user-supplied phylogenetic tree in order to assess the lineage-dependence (i.e. the phylogenetic distribution) of each accessory gene, allowing Coinfinder to focus on coincident gene pairs whose joint presence is not simply because they happened to appear in the same clade, but rather that they tend to appear together more often than expected across the phylogeny. Coinfinder is implemented in C++, Python3 and R and is freely available under the GNU license from https://github.com/fwhelan/coinfinder.

Model-based Comparisons of the Abundance Dynamics of Bacterial Communities in Two Lakes

Lake Lanier (Georgia, USA) is home to more than 11,000 microbial Operational Taxonomic Units (OTUs), many of which exhibit clear annual abundance patterns. To assess the dynamics of this microbial community, we collected time series data of 16S and 18S rRNA gene sequences, recovered from 29 planktonic shotgun metagenomic datasets. Based on these data, we constructed a dynamic mathematical model of bacterial interactions in the lake and used it to analyze changes in the abundances of OTUs. The model accounts for interactions among 14 sub-communities (SCs), which are composed of OTUs blooming at the same time of the year, and three environmental factors. It captures the seasonal variations in abundances of the SCs quite well. Simulation results suggest that changes in water temperature affect the various SCs differentially and that the timing of perturbations is critical. We compared the model results with published results from Lake Mendota (Wisconsin, USA). These comparative analyses between lakes in two very different geographical locations revealed substantially more cooperation and less competition among species in the warmer Lake Lanier than in Lake Mendota.