Characterizing microbial communities through space and time

Temporal Dynamics of Fungal Communities in Alkali-Treated Round Bamboo Deterioration under Natural Weathering

Microbes naturally inhabit bamboo-based materials in outdoor environments, sequentially contributing to their deterioration. Fungi play a significant role in deterioration, especially in environments with abundant water and favorable temperatures. Alkali treatment is often employed in the pretreatment of round bamboo to change its natural elastic and aesthetic behaviors. However, little research has investigated the structure and dynamics of fungal communities on alkali-treated round bamboo during natural deterioration. In this work, high-throughput sequencing and multiple characterization methods were used to disclose the fungal community succession and characteristic alterations of alkali-treated round bamboo in both roofed and unroofed habitats throughout a 13-week deterioration period. In total, 192 fungal amplicon sequence variants (ASVs) from six phyla were identified. The fungal community richness of roofed bamboo samples declined, whereas that of unroofed bamboo samples increased during deterioration. The phyla Ascomycota and Basidiomycota exhibited dominance during the entire deterioration process in two distinct environments, and the relative abundance of them combined was more than 99%. A distinct shift in fungal communities from Basidiomycota dominant in the early stage to Ascomycota dominant in the late stage was observed, which may be attributed to the increase of moisture and temperature during succession and the effect of alkali treatment. Among all environmental factors, temperature contributed most to the variation in the fungal community. The surface of round bamboo underwent continuous destruction from fungi and environmental factors. The total amount of cell wall components in bamboo epidermis in both roofed and unroofed conditions presented a descending trend. The content of hemicellulose declined sharply by 8.3% and 11.1% under roofed and unroofed environments after 9 weeks of deterioration. In addition, the contact angle was reduced throughout the deterioration process in both roofed and unroofed samples, which might be attributed to wax layer removal and lignin degradation. This study provides theoretical support for the protection of round bamboo under natural weathering.

The Vaginal Microbiota of Pregnant Women Varies with Gestational Age, Maternal Age, and Parity

The composition of the vaginal microbiota is heavily influenced by pregnancy and may factor into pregnancy complications, including spontaneous preterm birth. However, results among studies have been inconsistent due, in part, to variation in sample sizes and ethnicity. Thus, an association between the vaginal microbiota and preterm labor continues to be debated. Yet, before assessing associations between the composition of the vaginal microbiota and preterm labor, a robust and in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required. Here, we report a large longitudinal study (n = 474 women, 1,862 vaginal samples) of a predominantly African-American cohort-a population that experiences a relatively high rate of pregnancy complications-evaluating associations between individual identity, gestational age, and other maternal characteristics with the composition of the vaginal microbiota throughout gestation resulting in term delivery. The principal factors influencing the composition of the vaginal microbiota in pregnancy are individual identity and gestational age at sampling. Other factors are maternal age, parity, obesity, and self-reported Cannabis use. The general pattern across gestation is for the vaginal microbiota to remain or transition to a state of Lactobacillus dominance. This pattern can be modified by maternal parity and obesity. Regardless, network analyses reveal dynamic associations among specific bacterial taxa within the vaginal ecosystem, which shift throughout the course of pregnancy. This study provides a robust foundational understanding of the vaginal microbiota in pregnancy and sets the stage for further investigation of this microbiota in obstetrical disease. IMPORTANCE There is debate regarding links between the vaginal microbiota and pregnancy complications, especially spontaneous preterm birth. Inconsistencies in results among studies are likely due to differences in sample sizes and cohort ethnicity. Ethnicity is a complicating factor because, although all bacterial taxa commonly inhabiting the vagina are present among all ethnicities, the frequencies of these taxa vary among ethnicities. Therefore, an in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required prior to evaluating associations between the vaginal microbiota and obstetrical disease. This initial investigation is a large longitudinal study of the vaginal microbiota throughout gestation resulting in a term delivery in a predominantly African-American cohort, a population that experiences disproportionally negative maternal-fetal health outcomes. It establishes the magnitude of associations between maternal characteristics, such as age, parity, body mass index, and self-reported Cannabis use, on the vaginal microbiota in pregnancy.

Assessment of Vibrio populations in a transect of Rhizophora mangle in Punta Galeta, Panamá: culture-dependent analyses reveal biotechnological applications

Introduction

Rhizophora mangle is considered an ecological niche for microorganisms with potentially novel and complex degrading enzymes.

Objective

To characterize Vibrio populations using culture-dependent methods, using samples collected from sediments and water along a red mangrove transect composed of three sites.

Methods

Strains were characterized according to their distribution, capacity to degrade of organic matter and other environmental parameters. Additionally the sequence diversity was assessed using 16S rRNA sequencing.

Results

Bacterial densities were strongly associated with temperature and salinity. A total of 87 good-quality sequences representing the isolates from the three sites, were binned into eight OTUs (Operational taxonomic units). Taxonomic assignment indicated that the dominant members were Vibrionaceae. Beta diversity analyses showed that bacterial communities clustered by sample source rather than spatial distribution, and that alpha diversity was found to be higher in water than in sediment. Three percent of the strains from water samples could degrade carboxyl-methyl cellulose with the smallest enzymatic indexes compared to 4 % of the strains from sediment samples that showed the highest enzymatic indexes. Two strains identified as Vibrio agarivorans degraded cellulose and agarose, producing the highest enzymatic indexes.

Conclusions

We found higher bacterial densities and diversity in the bacterial communities of the water samples compared to the sediment, with different OTUs including those similar to Ferrimonas, Providencia, or Shewanella which were not isolated in the sediment. Vibrio OTUs were shown to degrade cellulose in both sample types. The results of this study highlight the importance of red mangroves as Vibrio habitats and as reservoirs of potential enzyme sources with biotechnological applications.

Differences in Soil Microbial Communities between Healthy and Diseased Lycium barbarum cv. Ningqi-5 Plants with Root Rot

For a long time, the development of the Lycium barbarum industry has been seriously restricted by root rot disease. In general, the occurrence of plant root rot is considered to be closely related to the composition and diversity of the soil microbial community. It is critical to understand the relationship between the occurrence of root rot in L. barbarum and the soil microbial composition. In this study, samples of the rhizosphere, rhizoplane, and root zone were collected from diseased and healthy plants. The V3–V4 region of bacterial 16S rDNA and the fungal ITS1 fragment of the collected samples were sequenced using Illumina MiSeq high-throughput sequencing technology. The sequencing results were first quality controlled and then aligned with the relevant databases for annotation and analysis. The richness of fungal communities in the rhizoplane and root zone of the healthy plants was significantly higher than that of the diseased plants (p < 0.05), and the community evenness and diversity of all the rhizoplane samples were significantly different from those of the rhizosphere and root zone. The richness of the bacterial communities in the rhizosphere and root zone of healthy plants was significantly greater than those of diseased plants (p < 0.05). The community composition of the rhizoplane was quite different from the other parts. The abundance of Fusarium in the rhizoplane and rhizosphere soil of diseased plants was higher than that in the corresponding parts of healthy plants. The abundances of Mortierella and Ilyonectria in the three parts of the healthy plants were correspondingly higher than those in the three parts of the diseased plants, and Plectosphaerella was the most abundant in the rhizoplane of diseased plants. There was little difference in the composition of the dominant bacteria at the phylum and genus levels between healthy plants and diseased plants, but the abundances of these dominant bacteria were different between healthy and diseased plants. Functional prediction showed that the bacterial community had the largest proportion of functional abundance belonging to metabolism. The functional abundances of the diseased plants, such as metabolism and genetic information processing, were lower than those of the healthy plants. The fungal community function prediction showed that the Animal Pathogen-Endophyte-Lichen Parasite-Plant Pathogen-Soil Saprotroph-Wood Saprotroph group had the largest functional abundance, and the corresponding fungi were Fusarium. In this study, we mainly discussed the differences in the soil microbial communities and their functions between the healthy and diseased L. barbarum cv. Ningqi-5, and predicted the functional composition of the microbial community, which is of great significance to understanding the root rot of L. barbarum.

The impact of genetic modified Ma bamboo on soil microbiome

Evaluating the potential alteration of microbial communities is a vital step for biosafety of genetic modified plants. Recently, we have produced genetic modified Ma bamboo with increased cold and drought tolerance by anthocyanin accumulation. In this work, we aim to study the potential effects on microbial communities in rhizosphere soils during the cultivation of genetic modified bamboo. Rhizosphere and surrounding soil were collected at 3-month post-transplant. The amplicon (16S rDNA and ITS1) were sequenced for analysis of bacterial and fungal communities. Multiple software and database (Picrust2, FAPROTAX and FUNGulid) were applied to predict and compare the microbial functions involving basic metabolisms, nitrogen usage and presence of plant pathogens. There were no substantial change of the structure and abundance of rhizosphere soil microbial communities between genetic modified and wild type bamboo. For the surrounding soil, the bacterial biota α-diversity increased (chao1: 1,001 ± 80-1,276 ± 84, observed species: 787 ± 52-1,194 ± 137, PD whole tree: 75 ± 4-117 ± 18) and fungal biota α-diversity decreased (chao1: 187 ± 18-145 ± 10) in samples of genetic modified bamboo compared to those of wild type bamboo. The microbiota predicted functions did not change or had no negative alteration between genetic modified and wild type bamboo, in both rhizosphere and surrounding soils. As a conclusion, the growth of genetic modified bamboo had no substantial change on rhizosphere soil microbial communities, while minor alteration on bamboo surrounding soil microbial communities with no harmful effects. Moreover, the genetic modified bamboo had no negative effect on the predicted functions of microbiota in soil.

Corpse decay of wild animals leads to the divergent succession of nrfA-type microbial communities

Animal carcasses introduce large amounts of nitrates and ammonium into the soil ecosystem. Some of this ammonium is transformed from nitrite through the nrfA-type microbial community. However, it is unclear how nrfA-type microorganisms respond to the decomposition of corpses. This study applied high-throughput sequencing to characterize the ecological succession of nrfA-type microbial communities in grassland soil. Our results showed that Cyclobacterium and Trueperella were the predominant genera for nrfA-type communities in soil with a decomposing corpse (experimental group), while Cyclobacterium and Archangium were dominant in soil without a corpse (control group). The alpha diversity indexes and the resistance and resilience indexes of the microbial communities initially increased and then decreased during decomposition. Compared with the control group, nrfA-encoding community structure in the experimental group gradually became divergent with succession and temporal turnover accelerated. Network analysis revealed that the microbial communities of the experimental group had more complex interactions than those of the control groups. Moreover, the bacterial community assembly in the experimental group was governed by stochastic processes, and the communities of the experimental group had a weaker dispersal capacity than those of the control group. Our results reveal the succession patterns of the nrfA-type microbial communities during degradation of wild animal corpses, which can offer references for demonstrating the ecological mechanism underlying the changes in the nrfA-type microbial community during carcass decay. KEY POINTS: • Corpse decay accelerates the temporal turnover of the nrfA-type community in soil. • Corpse decay changes the ecological succession of the nrfA-type community in soil. • Corpse decay leads to a complex co-occurrence pattern of the nrfA-type community in soil.

Variation of bacterial community and alkane monooxygenase gene abundance in diesel n-alkane contaminated subsurface environment under seasonal water table fluctuation

n-Alkanes, the main component of diesel fuel, are common light non-aqueous phase liquids (LNAPLs) that threaten ecological security. The subsurface from vadose zone, through fluctuating zone, to saturated zone, is a critical multi-interface earth layer which significantly affects the biodegradation processes of n-alkanes. A pilot-scale diesel contaminated aquifer column experiment has been undertaken to investigate the variations of bacterial community and alkane monooxygenase (alkB) gene abundance in these zones due to water-table fluctuations. The n-alkanes formed a layer immediately above the water table, and when this was raised, they were carried upwards through the fluctuating zone into the vadose zone. Water content and n-alkanes component C10-C12 are main factors influencing bacterial community variation in the vadose zone, while C10-C12 is a key driving factor shaping bacterial community in the fluctuating zone. The most abundant bacterial phyla at all three zones were Proteobacteria, Firmicutes and Actinobacteria, but moisture-niche selection determined their relative abundance. The intermittent wetting cycle resulted in higher abundance of Proteobacteria, and lower abundance of Actinobacteria in the vadose and fluctuating zones in comparison to the control column with a static water-table. The abundances of the alkB gene variants were relatively uniform in different zones, probably because the bacterial populations harboring alkB gene are habituated to biogenic n-alkanes rather than responding to diesel fuel contamination. The variation in the bacterial populations with height due to moisture-niche selection had very little effect on the alkB gene abundance, possibly because numerous species in both phyla (Proteobacteria and Actinobacteria) carry an alkB gene variant. Nevertheless, the drop in the water table caused a short-term spike in alkB gene abundance in the saturated zone, which is most likely associated with transport of solutes or colloids from the fluctuating zone to bacteria species in the saturated zone, so a fluctuating water table could potentially increase n-alkane biodegradation function.

Bacterial response to spatial gradients of algal-derived nutrients in a porous microplate

Photosynthetic microalgae are responsible for 50% of the global atmospheric CO₂ fixation into organic matter and hold potential as a renewable bioenergy source. Their metabolic interactions with the surrounding microbial community (the algal microbiome) play critical roles in carbon cycling, but due to methodological limitations, it has been challenging to examine how community development is influenced by spatial proximity to their algal host. Here we introduce a copolymer-based porous microplate to co-culture algae and bacteria, where metabolites are constantly exchanged between the microorganisms while maintaining physical separation. In the microplate, we found that the diatom Phaeodactylum tricornutum accumulated to cell abundances ~20 fold higher than under normal batch conditions due to constant replenishment of nutrients through the porous structure. We also demonstrate that algal-associated bacteria, both single isolates and complex communities, responded to inorganic nutrients away from their host as well as organic nutrients originating from the algae in a spatially predictable manner. These experimental findings coupled with a mathematical model suggest that host proximity and algal culture growth phase impact bacterial community development in a taxon-specific manner through organic and inorganic nutrient availability. Our novel system presents a useful tool to investigate universal metabolic interactions between microbes in aquatic ecosystems.

Bacterioplankton Zonation Does Exist in High Elevation, Polymictic Lakes

The assessment of distribution patterns or zonation of planktonic microbes along the water column is a crucial step to interpret their function in the ecosystem. In lakes without seasonal thermal stratification or polymictic systems such as high elevation tropical lakes, planktonic bacterial taxa are probably homogeneously distributed in the water column in contrast to what is known for thermally stratified lakes. However, we know little about bacterial distribution patterns in polymictic lakes and their relation to environmental gradients other than temperature. Here we assessed the diversity, microdiversity, and bacterial community composition at different discrete depths in three high elevation lakes (4,400-4,550 m above sea level) from the Andean plateau to test whether bacterial zonation patterns exist along the water column. For this objective, we analyzed bulk DNA and the putatively active fraction (cDNA) of the 16S rRNA gene. Although a clear gradient of temperature and oxygen was not detected along the water column, a significant vertical spatial zonation of the bacterial communities was present in two out of the three lakes, with microdiversity contributing to such pattern. Our results provide a reference for understanding how changing environmental conditions could affect high elevation aquatic ecosystems, particularly when warming is amplified with elevation, accelerating changes in hydrological regimes and biodiversity. Finally, our results highlight the importance of incorporating the whole water column in ecological studies of aquatic ecosystems lacking temporal or permanent thermal stratification.

Functional response regression model on correlated longitudinal microbiome sequencing data

Functional regression has been widely used on longitudinal data, but it is not clear how to apply functional regression to microbiome sequencing data. We propose a novel functional response regression model analyzing correlated longitudinal microbiome sequencing data, which extends the classic functional response regression model only working for independent functional responses. We derive the theory of generalized least squares estimators for predictors' effects when functional responses are correlated, and develop a data transformation technique to solve the computational challenge for analyzing correlated functional response data using existing functional regression method. We show by extensive simulations that our proposed method provides unbiased estimations for predictors' effect, and our model has accurate type I error and power performance for correlated functional response data, compared with classic functional response regression model. Finally we implement our method to a real infant gut microbiome study to evaluate the relationship of clinical factors to predominant taxa along time.

The Response of Extracellular Polymeric Substances Production by Phototrophic Biofilms to a Sequential Disturbance Strongly Depends on Environmental Conditions

Phototrophic biofilms are exposed to multiple stressors that can affect them both directly and indirectly. By modifying either the composition of the community or the physiology of the microorganisms, press stressors may indirectly impact the ability of the biofilms to cope with disturbances. Extracellular polymeric substances (EPS) produced by the biofilm are known to play an important role in its resilience to various stresses. The aim of this study was to decipher to what extent slight modifications of environmental conditions could alter the resilience of phototrophic biofilm EPS to a realistic sequential disturbance (4-day copper exposure followed by a 14-day dry period). By using very simplified biofilms with a single algal strain, we focused solely on physiological effects. The biofilms, composed by the non-axenic strains of a green alga (Uronema confervicolum) or a diatom (Nitzschia palea) were grown in artificial channels in six different conditions of light intensity, temperature and phosphorous concentration. EPS quantity (total organic carbon) and quality (ratio protein/polysaccharide, PN/PS) were measured before and at the end of the disturbance, and after a 14-day rewetting period. The diatom biofilm accumulated more biomass at the highest temperature, with lower EPS content and lower PN/PS ratio while green alga biofilm accumulated more biomass at the highest light condition with lower EPS content and lower PN/PS ratio. Temperature, light intensity, and P concentration significantly modified the resistance and/or recovery of EPS quality and quantity, differently for the two biofilms. An increase in light intensity, which had effect neither on the diatom biofilm growth nor on EPS production before disturbance, increased the resistance of EPS quantity and the resilience of EPS quality. These results emphasize the importance of considering the modulation of community resilience ability by environmental conditions, which remains scarce in the literature.

The effect of periodic disturbances and carrying capacity on the significance of selection and drift in complex bacterial communities

Understanding how periodical disturbances affect the community assembly processes is vital for predicting temporal dynamics in microbial communities. However, the effect of dilutions as disturbances are poorly understood. We used a marine bacterial community to investigate the effect of disturbance (+/-) and carrying capacity (high/low) over 50 days in a dispersal-limited 2 × 2 factorial study in triplicates, with a crossover in the disturbance regime between microcosms halfway in the experiment. We modelled the rate of change in community composition between replicates and used this rate to quantify selection and ecological drift. The disturbed communities increased in Bray-Curtis similarity with 0.011 ± 0.0045 (Period 1) and 0.0092 ± 0.0080 day-1 (Period 2), indicating that selection dominated community assembly. The undisturbed communities decreased in similarity at a rate of -0.015 ± 0.0038 day-1 in Period 1 and were stable in Period 2 at 0.00050 ± 0.0040 day-1, suggesting drift structured community assembly. Interestingly, carrying capacity had minor effects on community dynamics. This study is the first to show that stochastic effects are suppressed by periodical disturbances resulting in exponential growth periods due to density-independent biomass loss and resource input. The increased contribution of selection as a response to disturbances implies that ecosystem prediction is achievable.

phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data

MOTIVATION

Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this paper, we propose a novel deep learning framework "phyLoSTM", using a combination of Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) for feature extraction and analysis of temporal dependency in longitudinal microbiome sequencing data along with host's environmental factors for disease prediction. Additional novelty in terms of handling variable timepoints in subjects through LSTMs, as well as, weight balancing between imbalanced cases and controls is proposed.

RESULTS

We simulated 100 datasets across multiple time points for model testing. To demonstrate the model's effectiveness, we also implemented this novel method into two real longitudinal human microbiome studies: (i) DIABIMMUNE three country cohort with food allergy outcomes (Milk, Egg, Peanut and Overall) (ii) DiGiulio study with preterm delivery as outcome. Extensive analysis and comparison of our approach yields encouraging performance with an AUC of 0.897 (increased by 5%) on simulated studies and AUCs of 0.762 (increased by 19%) and 0.713 (increased by 8%) on the two real longitudinal microbiome studies respectively, as compared to the next best performing method, Random Forest. The proposed methodology improves predictive accuracy on longitudinal human microbiome studies containing spatially correlated data, and evaluates the change of microbiome composition contributing to outcome prediction.

AVAILABILITY AND IMPLEMENTATION

https://github.com/divya031090/phyLoSTM.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

Observing Nutrient Gradients, Gene Expression and Growth Variation Using the "Yeast Machine" Microfluidic Device

The natural environment of microbial cells like bacteria and yeast is often a complex community in which growth and internal organization reflect morphogenetic processes and interactions that are dependent on spatial position and time. While most of research is performed in simple homogeneous environments (e.g., bulk liquid cultures), which cannot capture full spatiotemporal community dynamics, studying biofilms or colonies is complex and usually does not give access to the spatiotemporal dynamics at single cell level. Here, we detail a protocol for generation of a microfluidic device, the "yeast machine", with arrays of long monolayers of yeast colonies to advance the global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined and customizable spatial dimensions. With Saccharomyces cerevisiae as a model yeast system we used the "yeast machine" to demonstrate the emergence of glucose gradients by following expression of fluorescently labelled hexose transporters. We further quantified the expression spatial patterns with intra-colony growth rates and expression of other genes regulated by glucose availability. In addition to this, we showed that gradients of amino acids also form within a colony, potentially opening similar approaches to study spatiotemporal formation of gradients of many other nutrients and metabolic waste products. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development, and morphogenesis in other same species or more complex multi-species systems at single-cell resolution and timescales relevant to ecology and evolution.

Seasonal Variability of Conditionally Rare Taxa in the Water Column Bacterioplankton Community of Subtropical Reservoirs in China

Conditionally rare bacteria are ubiquitous and perhaps the most diverse of microbial lifeforms, but their temporal dynamics remain largely unknown. High-throughput and deep sequencing of the 16S rRNA gene has allowed us to identify and compare the conditionally rare taxa with other bacterioplankton subcommunities. In this study, we examined the effect of season, water depth, and ecological processes on the fluctuations of bacterial subcommunities (including abundant, conditionally rare, moderate, and rare taxa) from three subtropical reservoirs in China. We discovered that the conditionally rare taxa (CRT) made up 49.7 to 71.8% of the bacterioplankton community richness, and they accounted for 70.6 to 84.4% of the temporal changes in the community composition. Beta-diversity analysis revealed strong seasonal succession patterns among all bacterioplankton subcommunities, suggesting abundant, conditionally rare, moderate, and rare taxa subcommunities have comparable environmental sensitivity. The dominant phyla of CRT were Proteobacteria, Actinobacteria, and Bacteroidetes, whose variations were strongly correlated with environmental variables. Both deterministic and stochastic processes showed strong effect on bacterioplankton community assembly, with deterministic patterns more pronounced for CRT subcommunity. The difference in bacterial community composition was strongly linked with seasonal change rather than water depth. The seasonal patterns of CRT expand our understanding of underlying mechanisms for bacterial community structure and composition. This implies their importance in the function and stability of freshwater ecosystem after environmental disturbance.

Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities

Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.

We conducted a global characterization of the microbial communities of shipping ports to serve as a novel system to investigate microbial biogeography. The community structures of port microbes from marine and freshwater habitats house relatively similar phyla, despite spanning large spatial scales. As part of this project, we collected 1,218 surface water samples from 604 locations across eight countries and three continents to catalogue a total of 20 shipping ports distributed across the East and West Coast of the United States, Europe, and Asia to represent the largest study of port-associated microbial communities to date. Here, we demonstrated the utility of machine learning to leverage this robust system to characterize microbial biogeography by identifying trends in biodiversity across broad spatial scales. We found that for geographic locations sharing similar environmental conditions, subpopulations from the dominant phyla of these habitats (Actinobacteria, Bacteroidetes, Cyanobacteria, and Proteobacteria) can be used to differentiate 20 geographic locations distributed globally. These results suggest that despite the overwhelming diversity within microbial communities, members of the most abundant and ubiquitous microbial groups in the system can be used to differentiate a geospatial location across global spatial scales. Our study provides insight into how microbes are dispersed spatially and robust methods whereby we can interrogate microbial biogeography.

IMPORTANCE Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.

Impacts of Sampling Design on Estimates of Microbial Community Diversity and Composition in Agricultural Soils

Soil microbiota play important and diverse roles in agricultural crop nutrition and productivity. Yet, despite increasing efforts to characterize soil bacterial and fungal assemblages, it is challenging to disentangle the influences of sampling design on assessments of communities. Here, we sought to determine whether composite samples-often analyzed as a low cost and effort alternative to replicated individual samples-provide representative summary estimates of microbial communities. At three Minnesota agricultural research sites planted with an oat cover crop, we conducted amplicon sequencing for soil bacterial and fungal communities (16S_V4 and ITS2) of replicated individual or homogenized composite soil samples. We compared soil microbiota from within and among plots and then among agricultural sites using both sampling strategies. Results indicated that single or multiple replicated individual samples, or a composite sample from each plot, were sufficient for distinguishing broad site-level macroecological differences among bacterial and fungal communities. Analysis of a single sample per plot captured only a small fraction of the distinct OTUs, diversity, and compositional variability detected in the analysis of multiple individual samples or a single composite sample. Likewise, composite samples captured only a fraction of the diversity represented by the six individual samples from which they were formed, and, on average, analysis of two or three individual samples offered greater compositional coverage (i.e., greater number of OTUs) than a single composite sample. We conclude that sampling design significantly impacts estimates of bacterial and fungal communities even in homogeneously managed agricultural soils, and our findings indicate that while either strategy may be sufficient for broad macroecological investigations, composites may be a poor substitute for replicated samples at finer spatial scales.

A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies

Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labeled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, for example amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.

Managing Agroecosystems for Soil Microbial Carbon Use Efficiency: Ecological Unknowns, Potential Outcomes, and a Path Forward

Agricultural systems are increasingly managed for improving soil carbon (C) accumulation. However, there are limits to C returns in agricultural systems that constrain soil C accumulation capacity. Increasing the efficiency of how soil microbes process C is gaining interest as an important management strategy for increasing soil C and is a key feature of soil C dynamics in many new microbial-explicit models. A higher microbial C use efficiency (CUE) may increase C storage while reducing C system losses and is a fundamental trait affecting community assembly dynamics and nutrient cycling. However, the numerous ecological unknowns influencing CUE limit our ability to effectively manage CUE in agricultural soils for greater soil C storage. In this perspective, we consider three complex drivers of agroecosystem CUE that need to be resolved to develop effective C sequestration management practices in the future: (1) the environment as an individual trait moderator versus a filter, (2) microbial community competitive and faciliatory interactions, and (3) spatiotemporal dynamics through the soil profile and across the microbial lifecycle. We highlight ways that amendments, crop rotations, and tillage practices might affect microbial CUE conditions and the variable outcomes of these practices. We argue that to resolve some of the unknowns of CUE dynamics, we need to include more mechanistic, trait-based approaches that capitalize on advanced methods and innovative field research designs within an agroecosystem-specific context. By identifying the management-level determinants of CUE expression, we will be better positioned to optimize CUE to increase soil C storage in agricultural systems.

Adaptive shifts of bacterioplankton communities in response to nitrogen enrichment in a highly polluted river

Anthropogenic activity-mediated nutrient pollution, especially nitrogen enrichment, poses one of the major threats to river ecosystems. However, it remains unclear how and to which extent it affects aquatic microbial communities, especially in heavily polluted rivers. In this study, a significant environmental gradient, particularly nitrogen gradient, was observed along a wastewater receiving river, the North Canal River (NCR). The pollution level was highest, moderate, and lowest in the up-, middle, and down-streams, respectively. The community composition of bacterioplankton transitioned from being Betaproteobacteria-dominated upstream to Gammaproteobacteria-dominated downstream. Copiotrophic groups, such as Polynucleobacter (Betaproteobacteria) and Hydrogenophaga (Betaproteobacteria), were dominant in the upstream. Multiple statistical analyses indicated that total nitrogen (TN) was the most important factor driving the adaptive shifts of community structure. Analyses of co-occurrence networks showed that the complexity of networks was disrupted in the up- and middle streams, while enhanced in the downstream. Our findings here suggested that microbial interactions were reduced in response to the aggravation of nutrient pollution. Similar to these changes, we observed significant dissimilarity of composition of functional groups, with highest abundance of nitrogen metabolism members under the highest level of nitrogen enrichment. Further analyses indicated that most of these functional groups belonged to Betaproteobacteria, suggesting the potential coupling of community composition and function diversity. In summary, adaptive shifts of bacterioplankton community composition, as well as species interactions, occurred in response to nutrient pollution in highly polluted water bodies.

Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities

Microbiomes impact nearly every environment on Earth by modulating the molecular composition of the environment. Temporally changing environmental stimuli and spatial organization are major variables shaping the structure and function of microbiomes. The web of interactions among members of these communities and between the organisms and the environment dictates microbiome functions. Microbial interactions are major drivers of microbiomes and are modulated by spatiotemporal parameters. A mechanistic and quantitative understanding of ecological, molecular, and environmental forces shaping microbiomes could inform strategies to control microbiome dynamics and functions. Major challenges for harnessing the potential of microbiomes for diverse applications include the development of predictive modeling frameworks and tools for precise manipulation of microbiome behaviors.

Role of gut microbiota in intestinal wound healing and barrier function

The mammalian intestine harbors a highly complex and abundant ensemble of bacteria that flourish in a nutrient-rich environment while profoundly influencing many aspects of host biology. The intestine coevolved with its resident microbes in a manner where the mucosa developed a barrier function to segregate the resident microbes from the rest of the body, and yet paradoxically, allowing integration of microbial signals for the host benefit. In this review, we provided a comprehensive overview of why the gut microbiota is key to the efficient development and maintenance of the intestinal barrier. We also highlighted how a destabilized equilibrium between gut microbiota and the host may eventuate in a wide range of intestinal diseases characterized by the disrupted intestinal barrier. Finally, the review delineated how microenvironmental changes in the injured mucosa result in an enrichment of a pro-regenerating consortium of bacteria, which augments mucosal wound repair and restoration of barrier functions.

Metagenomic Analysis of Cyanobacteria in an Oligotrophic Tropical Estuary, South Atlantic

This study assessed the species composition, distribution, and functional profiles of cyanobacteria in Camamu Bay, a tropical oligotrophic estuarine system on the northeast coast of Brazil, using shotgun metagenomic sequencing. Surface-water samples were evaluated in two different rainfall periods (rainy and dry seasons), at nine stations in the three hydrodynamic regions of the bay. At a fixed sampling station, on each season, samples were taken over a tidal cycle at 3-h intervals over 12 h. A total of 219 cyanobacterial taxa were identified, demonstrating a diverse community of freshwater, euryhaline, and marine cyanobacteria. The genera of greater relative abundance, Synechococcus and Prochlorococcus, corresponded to the picoplankton fraction. Although Camamu Bay has conspicuous marine characteristics, the contribution of freshwater during the rainy season caused variation in cyanobacteria community, with an increase in species richness. Due the high prevalence of Synechococcus (90% of the sequences), the functional analysis revealed only minor differences in gene content between the dry and rainy seasons. In both rainy and dry seasons, an increase in Prochlorococcus relative abundance occurred during high tide, demonstrating the tidal influence in the bay. The environmental characteristics of the bay provide niche conditions for a wide variety of cyanobacteria, including freshwater, euryhaline, and marine strains.

Environmental parameters altered by climate change affect the activity of soil microorganisms involved in bioremediation

Bioremediation, based on the use of microorganisms to break down pollutants, can be very effective at reducing soil pollution. But the climate change we are now experiencing is bound to have an impact on bioremediation performance, since the activity and degrading abilities of soil microorganisms are dependent on a series of environmental parameters that are themselves being altered by climate change, such as soil temperature, moisture, amount of root exudates, etc. Many climate-induced effects on soil microorganisms occur indirectly through changes in plant growth and physiology derived from increased atmospheric CO2 concentrations and temperatures, the alteration of precipitation patterns, etc., with a concomitant effect on rhizoremediation performance (i.e. the plant-assisted microbial degradation of pollutants in the rhizosphere). But these effects are extremely complex and mediated by processes such as acclimation and adaptation. Besides, soil microorganisms form complex networks of interactions with a myriad of organisms from many taxonomic groups that will also be affected by climate change, further complicating data interpretation.

SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies

Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies.

Application of Microfluidics in Experimental Ecology: The Importance of Being Spatial

Microfluidics is an emerging technology that is used more and more in biology experiments. Its capabilities of creating precisely controlled conditions in cellular dimensions make it ideal to explore cell-cell and cell-environment interactions. Thus, a wide spectrum of problems in microbial ecology can be studied using engineered microbial habitats. Moreover, artificial microfluidic ecosystems can serve as model systems to test ecology theories and principles that apply on a higher level in the hierarchy of biological organization. In this mini review we aim to demonstrate the versatility of microfluidics and the diversity of its applications that help the advance of microbiology, and in more general, experimental ecology.

Temporal patterns of phyto- and bacterioplankton and their relationships with environmental factors in Lake Taihu, China

Phytoplankton and bacterioplankton are integral components of aquatic food webs and play essential roles in the structure and function of freshwater ecosystems. However, little is known about how phyto- and bacterioplankton may respond synchronously to changing environmental conditions. Thus, we analyzed simultaneously the composition and structure of phyto- and bacterioplankton on a monthly basis over 12 months in cyanobacteria-dominated areas of Lake Taihu and compared their responses to changes in environmental factors. Metric multi-dimensional scaling (mMDS) revealed that the temporal variations of phyto- and bacterioplankton were significant. Time lag analysis (TLA) indicated that the temporal pattern of phytoplankton tended to exhibit convergent dynamics while bacterioplankton showed highly stable or stochastic variation. A significant directional change was found for bacterioplankton at the genus level and the slopes (rate of change) and regression R² (low stochasticity or stability) were greater if Cyanobacteria were included, suggesting a higher level of instability in the bacterial community at lower taxonomy level. Consequently, phytoplankton responded more rapidly to the change in environmental conditions than bacterioplankton when analyzed at the phylum level, while bacterioplankton were more sensitive at the finer taxonomic resolution in Lake Taihu. Redundancy analysis (RDA) results showed that environmental variables collectively explained 51.0% variance of phytoplankton and 46.7% variance of bacterioplankton, suggesting that environmental conditions have a significant influence on the temporal variations of phyto- and bacterioplankton. Furthermore, variance partitioning indicated that the bacterial community structure was largely explained by water temperature and nitrogen, suggesting that these factors were the primary drivers shaping bacterioplankton.

Response of bacterial communities to cyanobacterial harmful algal blooms in Lake Taihu, China

Cyanobacterial harmful algal blooms are prevalent around the world, influencing aquatic organisms and altering the physico-chemical properties in freshwater systems. However, the response of bacterial communities to toxic cyanobacterial blooms and associated microcystins (MC) remain poorly understood even though global concentrations of MC have increased dramatically in the past few decades. To address this issue, the dynamics of bacterial community composition (BCC) in the water column and how BCC is influenced by both harmful cyanobacterial blooms and environmental factors were investigated on a monthly basis from August 2013 to July 2014 in Lake Taihu, China. Non-metric multidimensional scaling (NMDS) revealed that seasonal variation in BCC was significant, and that the succession of BCC greatly depends on changes in environmental conditions. Redundancy analysis (RDA) results showed that the overall variation of BCC was explained mainly by dissolved oxygen (DO), nitrate nitrogen (NO₃^--N), and Microcystis. The alpha biodiversity of the bacterial community was different among months with the highest diversity in February and the lowest diversity in October. Furthermore, significant negative relationships were found between alpha biodiversity indices and Microcystis abundance as well as with intracellular MC concentrations, indicating that Microcystis and associated MC may influence the bacterial community structure by reducing its biodiversity. This study shows that potential associations exist between toxic cyanobacterial blooms and bacterial communities but more investigations are needed to obtain a mechanistic understanding of their complex relationships.

Variation between the oral and faecal microbiota in a free-living passerine bird, the great tit (Parus major)

The gastrointestinal tract of vertebrates is inhabited by diverse bacterial communities that induce marked effects on the host physiology and health status. The composition of the gastrointestinal microbiota is characterized by pronounced taxonomic and functional variability among different regions of the vertebrate gastrointestinal tract. Despite the relatively solid knowledge on the among-region variations of the gastrointestinal microbiota in model mammalian species, there are only a few studies concerning among-region variations of the gastrointestinal microbiota in free-living non-mammalian vertebrate taxa. We used Illumina MiSeq sequencing of bacterial 16S rRNA amplicons to compare the diversity as well as taxonomic composition of bacterial communities in proximal vs. distal parts of the gastrointestinal tract (represented by oral swabs and faecal samples, respectively) in a wild passerine bird, the great tit (Parus major). The diversity of the oral microbiota was significantly higher compared to the faecal microbiota, whereas interindividual variation was higher in faecal than in oral samples. We also observed a pronounced difference in taxonomic content between the oral and faecal microbiota. Bacteria belonging to the phyla Proteobacteria, Firmicutes and Actinobacteria typically dominated in both oral and faecal samples. A high abundance of bacteria belonging to Tenericutes was observed only in faecal samples. Surprisingly, we found only a slight correlation between the faecal and oral microbiota at the within-individual level, suggesting that the microbial composition in these body sites is shaped by independent regulatory processes. Given the independence of these two communities at the individual level, we propose that simultaneous sampling of the faecal and oral microbiota will extend our understanding of host vs. microbiota interactions in wild populations.

Distinct succession patterns of abundant and rare bacteria in temporal microcosms with pollutants

Elucidating the driving forces behind the temporal dynamics of abundant and rare microbes is essential for understanding the assembly and succession of microbial communities. Here, we explored the successional trajectories and mechanisms of abundant and rare bacteria via soil-enrichment subcultures in response to various pollutants (phenanthrene, n-octadecane, and CdCl₂) using time-series Illumina sequencing datasets. The results reveal different successional patterns of abundant and rare sub-communities in eighty pollutant-degrading consortia and two original soil samples. A temporal decrease in α-diversity and high turnover rate for β-diversity indicate that deterministic processes are the main drivers of the succession of the abundant sub-community; however, the high cumulative species richness indicates that stochastic processes drive the succession of the rare sub-community. A functional prediction showed that abundant bacteria contribute primary functions to the pollutant-degrading consortia, such as amino acid metabolism, cellular responses to stress, and hydrocarbon degradation. Meanwhile, rare bacteria contribute a substantial fraction of auxiliary functions, such as carbohydrate-active enzymes, fermentation, and homoacetogenesis, which indicates their roles as a source of functional diversity. Our study suggests that the temporal succession of microbes in polluted microcosms is mainly associated with abundant bacteria rather than the high proportion of rare taxa. The major forces (i.e., stochastic or deterministic processes) driving microbial succession could be dependent on the low- or high-abundance community members in temporal microcosms with pollutants.

Temporal dynamics of microbial communities in microcosms in response to pollutants

Elucidating the mechanisms underlying microbial succession is a major goal of microbial ecology research. Given the increasing human pressure on the environment and natural resources, responses to the repeated introduction of organic and inorganic pollutants are of particular interest. To investigate the temporal dynamics of microbial communities in response to pollutants, we analysed the microbial community structure in batch microcosms that were inoculated with soil bacteria following exposure to individual or combined pollutants (phenanthrene, n-octadecane, phenanthrene + n-octadecane and phenanthrene + n-octadecane + CdCl₂ ). Subculturing was performed at 10-day intervals, followed by high-throughput sequencing of 16S rRNA genes. The dynamics of microbial communities in response to different pollutants alone and in combination displayed similar patterns during enrichment. Specifically, the repression and induction of microbial taxa were dominant, and the fluctuation was not significant. The rate of appearance for new taxa and the temporal turnover within microbial communities were higher than the rates reported in other studies of microbial communities in air, water and soil samples. In addition, conditionally rare taxa that were specific to the treatments exhibited higher betweenness centrality values in the co-occurrence network, indicating a strong influence on other interactions in the community. These results suggest that the repeated introduction of pollutants could accelerate microbial succession in microcosms, resulting in the rapid re-equilibration of microbial communities.

Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior

Animals host multi-species microbial communities (microbiomes) whose properties may result from inter-species interactions; however, current understanding of host-microbiome interactions derives mostly from studies in which elucidation of microbe-microbe interactions is difficult. In exploring how Drosophila melanogaster acquires its microbiome, we found that a microbial community influences Drosophila olfactory and egg-laying behaviors differently than individual members. Drosophila prefers a Saccharomyces-Acetobacter co-culture to the same microorganisms grown individually and then mixed, a response mainly due to the conserved olfactory receptor, Or42b. Acetobacter metabolism of Saccharomyces-derived ethanol was necessary, and acetate and its metabolic derivatives were sufficient, for co-culture preference. Preference correlated with three emergent co-culture properties: ethanol catabolism, a distinct volatile profile, and yeast population decline. Egg-laying preference provided a context-dependent fitness benefit to larvae. We describe a molecular mechanism by which a microbial community affects animal behavior. Our results support a model whereby emergent metabolites signal a beneficial multispecies microbiome.

DOI:http://dx.doi.org/10.7554/eLife.18855.001

Animals associate with communities of microorganisms, also known as their microbiome, that live in or on their bodies. Within these communities, microbes – such as yeast and bacteria – interact by producing chemical compounds called metabolites that can influence the activity of other members of the community. These metabolites can also affect the host, helping with nutrition or causing disease.

The behavior of an animal may help it to acquire its microbiome, although this has not been properly explored experimentally. For example, the fruit fly Drosophila melanogaster acquires members of its microbiome from the microbes found on the fermented fruit that it eats. It is possible that the flies – and other animals – respond to microbial metabolites, which act as signals or cues that cause the animal to avoid or seek the microbial community.

The fruit fly microbiome is commonly studied in the laboratory because it has a much simpler composition than mammalian microbiomes. Previous studies have explored how the flies respond to odors produced by individual types of microbes, but none have explored how the behavior of the flies changes in response to the odors produced by a mixed microbial community.

Fischer et al. now show that fruit flies are preferentially attracted to microbiome members that are interacting with each other. The flies detected members of the microbiome by responding to chemicals that are only produced when community members grew together. For example, one member of the microbial community produces ethanol that is then converted to acetate by another community member. Neither ethanol nor acetate alone attracted flies as strongly.

Fischer et al. also discovered that both adult fruit flies and their larvae benefit from acquiring a mixture of different microbes at the same time. Adult flies benefit by avoiding harmful concentrations of either ethanol or acetic acid, and larvae benefit from developing in an environment that reduces how quickly disease-causing microbes can grow.

Overall, the results presented by Fischer et al. detail how flies select a beneficial, interactive microbiome from an external reservoir of microorganisms. Flies also have internal mechanisms, like their immune system, that help them to select their microbiome. Therefore a future challenge will be to integrate the behavioral and internal selection mechanisms into a single model of microbiome acquisition.

DOI:http://dx.doi.org/10.7554/eLife.18855.002

Coupling Spatiotemporal Community Assembly Processes to Changes in Microbial Metabolism

Community assembly processes generate shifts in species abundances that influence ecosystem cycling of carbon and nutrients, yet our understanding of assembly remains largely separate from ecosystem-level functioning. Here, we investigate relationships between assembly and changes in microbial metabolism across space and time in hyporheic microbial communities. We pair sampling of two habitat types (i.e., attached and planktonic) through seasonal and sub-hourly hydrologic fluctuation with null modeling and temporally explicit multivariate statistics. We demonstrate that multiple selective pressures-imposed by sediment and porewater physicochemistry-integrate to generate changes in microbial community composition at distinct timescales among habitat types. These changes in composition are reflective of contrasting associations of Betaproteobacteria and Thaumarchaeota with ecological selection and with seasonal changes in microbial metabolism. We present a conceptual model based on our results in which metabolism increases when oscillating selective pressures oppose temporally stable selective pressures. Our conceptual model is pertinent to both macrobial and microbial systems experiencing multiple selective pressures and presents an avenue for assimilating community assembly processes into predictions of ecosystem-level functioning.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

A two-part mixed-effects model for analyzing longitudinal microbiome compositional data

MOTIVATION

The human microbial communities are associated with many human diseases such as obesity, diabetes and inflammatory bowel disease. High-throughput sequencing technology has been widely used to quantify the microbial composition in order to understand its impacts on human health. Longitudinal measurements of microbial communities are commonly obtained in many microbiome studies. A key question in such microbiome studies is to identify the microbes that are associated with clinical outcomes or environmental factors. However, microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures in longitudinal studies are correlated. A method that takes into account these features is needed for association analysis in longitudinal microbiome data.

RESULTS

In this paper, we propose a two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. The model includes a logistic regression component to model presence/absence of a microbe in the samples and a Beta regression component to model non-zero microbial abundance, where each component includes a random effect to account for the correlations among the repeated measurements on the same subject. Both simulation studies and the application to real microbiome data have shown that ZIBR model outperformed the previously used methods. The method provides a useful tool for identifying the relevant taxa based on longitudinal or repeated measures in microbiome research.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chvlyl/ZIBR CONTACT: hongzhe@upenn.edu.

Bridging the divide: a model-data approach to Polar and Alpine microbiology

Advances in microbial ecology in the cryosphere continue to be driven by empirical approaches including field sampling and laboratory-based analyses. Although mathematical models are commonly used to investigate the physical dynamics of Polar and Alpine regions, they are rarely applied in microbial studies. Yet integrating modelling approaches with ongoing observational and laboratory-based work is ideally suited to Polar and Alpine microbial ecosystems given their harsh environmental and biogeochemical characteristics, simple trophic structures, distinct seasonality, often difficult accessibility, geographical expansiveness and susceptibility to accelerated climate changes. In this opinion paper, we explain how mathematical modelling ideally complements field and laboratory-based analyses. We thus argue that mathematical modelling is a powerful tool for the investigation of these extreme environments and that fully integrated, interdisciplinary model-data approaches could help the Polar and Alpine microbiology community address some of the great research challenges of the 21st century (e.g. assessing global significance and response to climate change). However, a better integration of field and laboratory work with model design and calibration/validation, as well as a stronger focus on quantitative information is required to advance models that can be used to make predictions and upscale processes and fluxes beyond what can be captured by observations alone.

Long-term soil transplant simulating climate change with latitude significantly alters microbial temporal turnover

To understand soil microbial community stability and temporal turnover in response to climate change, a long-term soil transplant experiment was conducted in three agricultural experiment stations over large transects from a warm temperate zone (Fengqiu station in central China) to a subtropical zone (Yingtan station in southern China) and a cold temperate zone (Hailun station in northern China). Annual soil samples were collected from these three stations from 2005 to 2011, and microbial communities were analyzed by sequencing microbial 16S ribosomal RNA gene amplicons using Illumina MiSeq technology. Our results revealed a distinctly differential pattern of microbial communities in both northward and southward transplantations, along with an increase in microbial richness with climate cooling and a corresponding decrease with climate warming. The microbial succession rate was estimated by the slope (w value) of linear regression of a log-transformed microbial community similarity with time (time-decay relationship). Compared with the low turnover rate of microbial communities in situ (w=0.046, P<0.001), the succession rate at the community level was significantly higher in the northward transplant (w=0.058, P<0.001) and highest in the southward transplant (w=0.094, P<0.001). Climate warming lead to a faster succession rate of microbial communities as well as lower species richness and compositional changes compared with in situ and climate cooling, which may be related to the high metabolic rates and intense competition under higher temperature. This study provides new insights into the impacts of climate change on the fundamental temporal scaling of soil microbial communities and microbial phylogenetic biodiversity.

Microbial community dynamics in the forefield of glaciers

Retreating ice fronts (as a result of a warming climate) expose large expanses of deglaciated forefield, which become colonized by microbes and plants. There has been increasing interest in characterizing the biogeochemical development of these ecosystems using a chronosequence approach. Prior to the establishment of plants, microbes use autochthonously produced and allochthonously delivered nutrients for growth. The microbial community composition is largely made up of heterotrophic microbes (both bacteria and fungi), autotrophic microbes and nitrogen-fixing diazotrophs. Microbial activity is thought to be responsible for the initial build-up of labile nutrient pools, facilitating the growth of higher order plant life in developed soils. However, it is unclear to what extent these ecosystems rely on external sources of nutrients such as ancient carbon pools and periodic nitrogen deposition. Furthermore, the seasonal variation of chronosequence dynamics and the effect of winter are largely unexplored. Modelling this ecosystem will provide a quantitative evaluation of the key processes and could guide the focus of future research. Year-round datasets combined with novel metagenomic techniques will help answer some of the pressing questions in this relatively new but rapidly expanding field, which is of growing interest in the context of future large-scale ice retreat.

Flea-Associated Bacterial Communities across an Environmental Transect in a Plague-Endemic Region of Uganda

The vast majority of human plague cases currently occur in sub-Saharan Africa. The primary route of transmission of Yersinia pestis, the causative agent of plague, is via flea bites. Non-pathogenic flea-associated bacteria may interact with Y. pestis within fleas and it is important to understand what factors govern flea-associated bacterial assemblages. Six species of fleas were collected from nine rodent species from ten Ugandan villages between October 2010 and March 2011. A total of 660,345 16S rRNA gene DNA sequences were used to characterize bacterial communities of 332 individual fleas. The DNA sequences were binned into 421 Operational Taxonomic Units (OTUs) based on 97% sequence similarity. We used beta diversity metrics to assess the effects of flea species, flea sex, rodent host species, site (i.e. village), collection date, elevation, mean annual precipitation, average monthly precipitation, and average monthly temperature on bacterial community structure. Flea species had the greatest effect on bacterial community structure with each flea species harboring unique bacterial lineages. The site (i.e. village), rodent host, flea sex, elevation, precipitation, and temperature also significantly affected bacterial community composition. Some bacterial lineages were widespread among flea species (e.g. Bartonella spp. and Wolbachia spp.), but each flea species also harbored unique bacterial lineages. Some of these lineages are not closely related to known bacterial diversity and likely represent newly discovered lineages of insect symbionts. Our finding that flea species has the greatest effect on bacterial community composition may help future investigations between Yersinia pestis and non-pathogenic flea-associated bacteria. Characterizing bacterial communities of fleas during a plague epizootic event in the future would be helpful.