Fitness-Conditional Genes for Soil Adaptation in the Bioaugmentation Agent Pseudomonas veronii 1YdBTEX2.

Strain inoculation (bioaugmentation) is a potentially useful technology to provide microbiomes with new functionalities. However, there is limited understanding of the genetic factors contributing to successful establishment of inoculants. This work aimed to characterize the genes implicated in proliferation of the monoaromatic compound-degrading Pseudomonas veronii 1YdBTEX2 in nonsterile polluted soils. We generated two independent mutant libraries by random minitransposon-delivered marker insertion followed by deep sequencing (Tn-seq) with a total of 5.0 × 105 unique insertions. Libraries were grown in multiple successive cycles for up to 50 generations either in batch liquid medium or in two types of soil microcosms with different resident microbial content (sand or silt) in the presence of toluene. Analysis of gene insertion abundances at different time points (passed generations of metapopulation growth), in comparison to proportions at start and to in silico generated randomized insertion distributions, allowed to define ~800 essential genes common to both libraries and ~2,700 genes with conditional fitness effects in either liquid or soil (195 of which resulted in fitness gain). Conditional fitness genes largely overlapped among all growth conditions but affected approximately twice as many functions in liquid than in soil. This indicates soil to be a more promiscuous environment for mutant growth, probably because of additional nutrient availability. Commonly depleted genes covered a wide range of biological functions and metabolic pathways, such as inorganic ion transport, fatty acid metabolism, amino acid biosynthesis, or nucleotide and cofactor metabolism. Only sparse gene sets were uncovered whose insertion caused fitness decrease exclusive for soils, which were different between silt and sand. Despite detectable higher resident bacteria and potential protist predatory counts in silt, we were, therefore, unable to detect any immediately obvious candidate genes affecting P. veronii biological competitiveness. In contrast to liquid growth conditions, mutants inactivating flagella biosynthesis and motility consistently gained strong fitness advantage in soils and displayed higher growth rates than wild type. In conclusion, although many gene functions were found to be important for growth in soils, most of these are not specific as they affect growth in liquid minimal medium more in general. This indicates that P. veronii does not need major metabolic reprogramming for proliferation in soil with accessible carbon and generally favorable growth conditions. IMPORTANCE Restoring damaged microbiomes is still a formidable challenge. Classical widely adopted approaches consist of augmenting communities with pure or mixed cultures in the hope that these display their intended selected properties under in situ conditions. Ecological theory, however, dictates that introduction of a nonresident microbe is unlikely to lead to its successful proliferation in a foreign system such as a soil microbiome. In an effort to study this systematically, we used random transposon insertion scanning to identify genes and possibly, metabolic subsystems, that are crucial for growth and survival of a bacterial inoculant (Pseudomonas veronii) for targeted degradation of monoaromatic compounds in contaminated nonsterile soils. Our results indicate that although many gene functions are important for proliferation in soil, they are general factors for growth and not exclusive for soil. In other words, P. veronii is a generalist that is not a priori hindered by the soil for its proliferation and would make a good bioaugmentation candidate.

Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data.

Dynamic analysis of microbial composition is crucial for understanding community functioning and detecting dysbiosis. Compositional information is mostly obtained through sequencing of taxonomic markers or whole meta-genomes, which may be productively complemented by real-time quantitative community multiparametric flow cytometry data (FCM). Patterns and clusters in FCM community data can be distinguished and compared by unsupervised machine learning. Alternatively, FCM data from preselected individual strain phenotypes can be used for supervised machine-training in order to differentiate similar cell types within communities. Both types of machine learning can quantitatively deconvolute community FCM data sets and rapidly analyse global changes in response to treatment. Procedures may further be optimized for recurrent microbiome samples to simultaneously quantify physiological and compositional states.

Subcellular Localization Defects Characterize Ribose-Binding Mutant Proteins with New Ligand Properties in Escherichia coli.

Periplasmic binding proteins have been previously proclaimed as a general scaffold to design sensor proteins with new recognition specificities for nonnatural compounds. Such proteins can be integrated in bacterial bioreporter chassis with hybrid chemoreceptors to produce a concentration-dependent signal after ligand binding to the sensor cell. However, computationally designed new ligand-binding properties ignore the more general properties of periplasmic binding proteins, such as their periplasmic translocation, dynamic transition of open and closed forms, and interactions with membrane receptors. In order to better understand the roles of such general properties in periplasmic signaling behavior, we studied the subcellular localization of ribose-binding protein (RbsB) in Escherichia coli in comparison to a recently evolved set of mutants designed to bind 1,3-cyclohexanediol. As proxies for localization, we calibrated and deployed C-terminal end mCherry fluorescent protein fusions. Whereas RbsB-mCherry coherently localized to the periplasmic space and accumulated in (periplasmic) polar regions depending on chemoreceptor availability, mutant RbsB-mCherry expression resulted in high fluorescence cell-to-cell variability. This resulted in higher proportions of cells devoid of clear polar foci and of cells with multiple fluorescent foci elsewhere, suggesting poorer translocation, periplasmic autoaggregation, and mislocalization. Analysis of RbsB mutants and mutant libraries at different stages of directed evolution suggested overall improvement to more RbsB-wild-type-like characteristics, which was corroborated by structure predictions. Our results show that defects in periplasmic localization of mutant RbsB proteins partly explain their poor sensing performance. Future efforts should be directed to predicting or selecting secondary mutations outside computationally designed binding pockets, taking folding, translocation, and receptor interactions into account. IMPORTANCE Biosensor engineering relies on transcription factors or signaling proteins to provide the actual sensory functions for the target chemicals. Since for many compounds there are no natural sensory proteins, there is a general interest in methods that could unlock routes to obtaining new ligand-binding properties. Bacterial periplasmic binding proteins (PBPs) form an interesting family of proteins to explore for this purpose, because there is a large natural variety suggesting evolutionary trajectories to bind new ligands. PBPs are conserved and amenable to accurate computational binding pocket predictions. However, studying ribose-binding protein in Escherichia coli, we discovered that designed variants have defects in their proper localization in the cell, which can impair appropriate sensor signaling. This indicates that functional sensing capacity of PBPs cannot be obtained solely through computational design of the ligand-binding pocket but must take other properties of the protein into account, which are currently very difficult to predict.

Environmental connectivity controls diversity in soil microbial communities.

Interspecific interactions are thought to govern the stability and functioning of microbial communities, but the influence of the spatial environment and its structural connectivity on the potential of such interactions to unfold remain largely unknown. Here we studied the effects on community growth and microbial diversity as a function of environmental connectivity, where we define environmental connectivity as the degree of habitat fragmentation preventing microbial cells from living together. We quantitatively compared growth of a naturally-derived high microbial diversity community from soil in a completely mixed liquid suspension (high connectivity) to growth in a massively fragmented and poorly connected environment (low connectivity). The low connectivity environment consisted of homogenously-sized miniature agarose beads containing random single or paired founder cells. We found that overall community growth was the same in both environments, but the low connectivity environment dramatically reduced global community-level diversity compared to the high connectivity environment. Experimental observations were supported by community growth modeling. The model predicts a loss of diversity in the low connectivity environment as a result of negative interspecific interactions becoming more dominant at small founder species numbers. Counterintuitively for the low connectivity environment, growth of isolated single genotypes was less productive than that of random founder genotype cell pairs, suggesting that the community as a whole profited from emerging positive interspecific interactions. Our work demonstrates the importance of environmental connectivity for growth of natural soil microbial communities, which aids future efforts to intervene in or restore community composition to achieve engineering and biotechnological objectives.

Miniaturized electrochemical biosensor based on whole-cell for heavy metal ions detection in water.

The heavy metals pollution represents one of the important issues in the environmental field since it is involved in many pathologies from cancer, neurodegenerative, and metabolic diseases. We propose an innovative portable biosensor for the determination of traces of trivalent arsenic (As(III)) and bivalent mercury (Hg(II)) in water. The system implements a strategy combining two advanced sensing modules consisting in (a) a whole cell based on engineered Escherichia coli as selective sensing element towards the metals and (b) an electrochemical miniaturised silicon device with three microelectrodes and a portable reading system. The sensing mechanism relies on the selective recognition from the bacterium of given metals producing the 4-aminophenol redox active mediator detected through a cyclic voltammetry analysis. The miniaturized biosensor is able to operate a portable, robust, and high-sensitivity detection of As(III) with a sensitivity of 0.122 µA ppb-1 , LoD of 1.5 ppb, and a LoQ of 5 ppb. The LoD value is one order of magnitude below of the value indicated to WHO to be dangerous (10 µg/L). The system was proved to be fully versatile being effective in the detection of Hg(II) as well. A first study on Hg(II) showed sensitivity value of 2.11 µA/ppb a LOD value of 0.1 ppb and LoQ value of 0.34 ppb. Also in this case, the detected LOD was 10 times lower than that indicated by WHO (1 ppb). These results pave the way for advanced sensing strategies suitable for the environmental monitoring and the public safety.

Greater topoclimatic control of above- versus below-ground communities.

Assessing the degree to which climate explains the spatial distributions of different taxonomic and functional groups is essential for anticipating the effects of climate change on ecosystems. Most effort so far has focused on above-ground organisms, which offer only a partial view on the response of biodiversity to environmental gradients. Here including both above- and below-ground organisms, we quantified the degree of topoclimatic control on the occurrence patterns of >1,500 taxa and phylotypes along a c. 3,000 m elevation gradient, by fitting species distribution models. Higher model performances for animals and plants than for soil microbes (fungi, bacteria and protists) suggest that the direct influence of topoclimate is stronger on above-ground species than on below-ground microorganisms. Accordingly, direct climate change effects are predicted to be stronger for above-ground than for below-ground taxa, whereas factors expressing local soil microclimate and geochemistry are likely more important to explain and forecast the occurrence patterns of soil microbiota. Detailed mapping and future scenarios of soil microclimate and microhabitats, together with comparative studies of interacting and ecologically dependent above- and below-ground biota, are thus needed to understand and realistically forecast the future distribution of ecosystems.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

Author Correction: Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5.

Bacteria that engage in long-standing associations with particular hosts are expected to evolve host-specific adaptations that limit their capacity to thrive in other environments. Consistent with this, many gut symbionts seem to have a limited host range, based on community profiling and phylogenomics. However, few studies have experimentally investigated host specialization of gut symbionts and the underlying mechanisms have largely remained elusive. Here, we studied host specialization of a dominant gut symbiont of social bees, Lactobacillus Firm5. We show that Firm5 strains isolated from honey bees and bumble bees separate into deep-branching host-specific phylogenetic lineages. Despite their divergent evolution, colonization experiments show that bumble bee strains are capable of colonizing the honey bee gut. However, they were less successful than honey bee strains, and competition with honey bee strains completely abolished their colonization. In contrast, honey bee strains of divergent phylogenetic lineages were able to coexist within individual bees. This suggests that both host selection and interbacterial competition play important roles in host specialization. Using comparative genomics of 27 Firm5 isolates, we found that the genomes of honey bee strains harbour more carbohydrate-related functions than bumble bee strains, possibly providing a competitive advantage in the honey bee gut. Remarkably, most of the genes encoding carbohydrate-related functions were not conserved among the honey bee strains, which suggests that honey bees can support a metabolically more diverse community of Firm5 strains than bumble bees. These findings advance our understanding of the genomic changes underlying host specialization.

Host diet mediates a negative relationship between abundance and diversity of Drosophila gut microbiota.

Nutrient supply to ecosystems has major effects on ecological diversity, but it is unclear to what degree the shape of this relationship is general versus dependent on the specific environment or community. Although the diet composition in terms of the source or proportions of different nutrient types is known to affect gut microbiota composition, the relationship between the quantity of nutrients supplied and the abundance and diversity of the intestinal microbial community remains to be elucidated. Here, we address this relationship using replicate populations of Drosophila melanogaster maintained over multiple generations on three diets differing in the concentration of yeast (the only source of most nutrients). While a 6.5-fold increase in yeast concentration led to a 100-fold increase in the total abundance of gut microbes, it caused a major decrease in their alpha diversity (by 45-60% depending on the diversity measure). This was accompanied by only minor shifts in the taxonomic affiliation of the most common operational taxonomic units (OTUs). Thus, nutrient concentration in host diet mediates a strong negative relationship between the nutrient abundance and microbial diversity in the Drosophila gut ecosystem.

Bacterial bioreporter detection of arsenic associated with iron oxides.

Bacterial bioreporters are engineered microorganisms that have found recent application as a low-cost method of detecting arsenic (As) in environmental systems. However, no assessment exists of bioreporter detection of particle-bound As. We applied an Escherichia coli-based bioreporter to assess the bioavailability of As(v) adsorbed by goethite (α-FeOOH), 2-line ferrihydrite and As(v) co-precipitated with Fe(iii). We found that As(v) bound to the surface of crystalline goethite was not detected by the bioreporters, which contrasted sharply the 50% detection of As(v) adsorbed by ferrihydrite. In addition, the presence of Ca2+ caused a systematic decrease in the bioreporter-detected As(v) fraction in the ferrihydrite samples. For co-precipitated As(v)-Fe(iii) samples, we found a similar bioreporter-detected As(v) fraction (<0.2) regardless of crystallite size (0.7-2.5 nm) or As Fe-1 surface loading (10-60 mol%). Our results reveal that the bioreporter response depends largely on aggregated particle size, which is expected to physically isolate As(v) from bioreporters by encapsulating surface-bound As(v) in coagulated flocs. Our results show that while bioreporters do not perform optimally in water that contains Fe particles, this method could be developed for sludge testing and for monitoring As levels in the product water of decentralized Fe-based As treatment systems.

Prospects for harnessing biocide resistance for bioremediation and detoxification.

Prokaryotes in natural environments respond rapidly to high concentrations of chemicals and physical stresses. Exposure to anthropogenic toxic substances-such as oil, chlorinated solvents, or antibiotics-favors the evolution of resistant phenotypes, some of which can use contaminants as an exclusive carbon source or as electron donors and acceptors. Microorganisms similarly adapt to extreme pH, metal, or osmotic stress. The metabolic plasticity of prokaryotes can thus be harnessed for bioremediation and can be exploited in a variety of ways, ranging from stimulated natural attenuation to bioaugmentation and from wastewater treatment to habitat restoration.

Adaptation to Chronic Nutritional Stress Leads to Reduced Dependence on Microbiota in Drosophila melanogaster.

Numerous studies have shown that animal nutrition is tightly linked to gut microbiota, especially under nutritional stress. In Drosophila melanogaster, microbiota are known to promote juvenile growth, development, and survival on poor diets, mainly through enhanced digestion leading to changes in hormonal signaling. Here, we show that this reliance on microbiota is greatly reduced in replicated Drosophila populations that became genetically adapted to a poor larval diet in the course of over 170 generations of experimental evolution. Protein and polysaccharide digestion in these poor-diet-adapted populations became much less dependent on colonization with microbiota. This was accompanied by changes in expression levels of dFOXO transcription factor, a key regulator of cell growth and survival, and many of its targets. These evolutionary changes in the expression of dFOXO targets to a large degree mimic the response of the same genes to microbiota, suggesting that the evolutionary adaptation to poor diet acted on mechanisms that normally mediate the response to microbiota. Our study suggests that some metazoans have retained the evolutionary potential to adapt their physiology such that association with microbiota may become optional rather than essential.IMPORTANCE Animals depend on gut microbiota for various metabolic tasks, particularly under conditions of nutritional stress, a relationship usually regarded as an inherent aspect of animal physiology. Here, we use experimental evolution in fly populations to show that the degree of host dependence on microbiota can substantially and rapidly change as the host population evolves in response to poor diet. Our results suggest that, although microbiota may initially greatly facilitate coping with suboptimal diets, chronic nutritional stress experienced over multiple generations leads to evolutionary adaptation in physiology and gut digestive properties that reduces dependence on the microbiota for growth and survival. Thus, despite its ancient evolutionary history, the reliance of animal hosts on their microbial partners can be surprisingly flexible and may be relaxed by short-term evolution.

Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation.

Bioreporters are living cells that generate an easily measurable signal in the presence of a chemical compound. They acquire their functionality from synthetic gene circuits, the configuration of which defines the response signal and signal-to-noise ratio. Bioreporters based on the Escherichia coli ArsR system have raised significant interest for quantifying arsenic pollution, but they need to be carefully optimized to accurately work in the required low concentration range (1-10 µg arsenite L-1). To better understand the general functioning of ArsR-based genetic circuits, we developed a comprehensive mechanistic model that was empirically tested and validated in E. coli carrying different circuit configurations. The model accounts for the different elements in the circuits (proteins, DNA, chemical species), and their detailed affinities and interactions, and predicts the (fluorescent) output from the bioreporter cell as a function of arsenite concentration. The model was parametrized using existing ArsR biochemical data, and then complemented by parameter estimations from the accompanying experimental data using a scatter search algorithm. Model predictions and experimental data were largely coherent for feedback and uncoupled circuit configurations, different ArsR alleles, promoter strengths, and presence or absence of arsenic efflux in the bioreporters. Interestingly, the model predicted a particular useful circuit variant having steeper response at low arsenite concentrations, which was experimentally confirmed and may be useful as arsenic bioreporter in the field. From the extensive validation we expect the mechanistic model to further be a useful framework for detailed modeling of other synthetic circuits.

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Comparison of differential gene expression to water stress among bacteria with relevant pollutant-degradation properties.

Resistance to semi-dry environments has been considered a crucial trait for superior growth and survival of strains used for bioaugmentation in contaminated soils. In order to compare water stress programmes, we analyse differential gene expression among three phylogenetically different strains capable of aromatic compound degradation: Arthrobacter chlorophenolicus A6, Sphingomonas wittichii RW1 and Pseudomonas veronii  1YdBTEX2. Standardized laboratory-induced water stress was imposed by shock exposure of liquid cultures to water potential decrease, induced either by addition of solutes (NaCl, solute stress) or by addition of polyethylene glycol (matric stress), both at absolute similar stress magnitudes and at those causing approximately similar decrease of growth rates. Genome-wide differential gene expression was recorded by micro-array hybridizations. Growth of P. veronii 1YdBTEX2 was the most sensitive to water potential decrease, followed by S. wittichii RW1 and A. chlorophenolicus A6. The number of genes differentially expressed under decreasing water potential was lowest for A. chlorophenolicus A6, increasing with increasing magnitude of the stress, followed by S. wittichii RW1 and P. veronii 1YdBTEX2. Gene inspection and gene ontology analysis under stress conditions causing similar growth rate reduction indicated that common reactions among the three strains included diminished expression of flagellar motility and increased expression of compatible solutes (which were strain-specific). Furthermore, a set of common genes with ill-defined function was found between all strains, including ABC transporters and aldehyde dehydrogenases, which may constitute a core conserved response to water stress. The data further suggest that stronger reduction of growth rate of P. veronii 1YdBTEX2 under water stress may be an indirect result of the response demanding heavy NADPH investment, rather than the presence or absence of a suitable stress defence mechanism per se.

Sphingomonas wittichii†RW1 gene reporters interrogating the dibenzofuran metabolic network highlight conditions for early successful development in contaminated microcosms.

In order to better understand the fate and activity of bacteria introduced into contaminated material for the purpose of enhancing biodegradation rates, we constructed Sphingomonas wittichii RW1 variants with gene reporters interrogating dibenzofuran metabolic activity. Three potential promoters from the dibenzofuran metabolic network were selected and fused to the gene for enhanced green fluorescent protein (EGFP). The stability of the resulting genetic constructions in RW1 was examined, with plasmids based on the broad-host range vector pME6012 being the most reliable. One of the selected promoters, upstream of the gene Swit_4925 for a putative 2-hydroxy-2,4-pentadienoate hydratase, was inducible by growth on dibenzofuran. Sphingomonas wittichii RW1 equipped with the Swit_4925 promoter egfp fusion grew in a variety of non-sterile sandy microcosms contaminated with dibenzofuran and material from a former gasification site. The strain also grew in microcosms without added dibenzofuran but to a very limited extent, and EGFP expression indicated the formation of consistent small subpopulations of cells with an active inferred dibenzofuran metabolic network. Evidence was obtained for competition for dibenzofuran metabolites scavenged by resident bacteria in the gasification site material, which resulted in a more rapid decline of the RW1 population. Our results show the importance of low inoculation densities in order to observe the population development of the introduced bacteria and further illustrate that the limited availability of unique carbon substrate may be the most important factor impinging growth.

Identification of genes potentially involved in solute stress response in Sphingomonas wittichii RW1 by transposon mutant recovery.

The term water stress refers to the effects of low water availability on microbial growth and physiology. Water availability has been proposed as a major constraint for the use of microorganisms in contaminated sites with the purpose of bioremediation. Sphingomonas wittichii RW1 is a bacterium capable of degrading the xenobiotic compounds dibenzofuran and dibenzo-p-dioxin, and has potential to be used for targeted bioremediation. The aim of the current work was to identify genes implicated in water stress in RW1 by means of transposon mutagenesis and mutant growth experiments. Conditions of low water potential were mimicked by adding NaCl to the growth media. Three different mutant selection or separation method were tested which, however recovered different mutants. Recovered transposon mutants with poorer growth under salt-induced water stress carried insertions in genes involved in proline and glutamate biosynthesis, and further in a gene putatively involved in aromatic compound catabolism. Transposon mutants growing poorer on medium with lowered water potential also included ones that had insertions in genes involved in more general functions such as transcriptional regulation, elongation factor, cell division protein, RNA polymerase ß or an aconitase.

First day of an oil spill on the open sea: early mass transfers of hydrocarbons to air and water.

During the first hours after release of petroleum at sea, crude oil hydrocarbons partition rapidly into air and water. However, limited information is available about very early evaporation and dissolution processes. We report on the composition of the oil slick during the first day after a permitted, unrestrained 4.3 m(3) oil release conducted on the North Sea. Rapid mass transfers of volatile and soluble hydrocarbons were observed, with >50% of ≤C17 hydrocarbons disappearing within 25 h from this oil slick of <10 km(2) area and <10 µm thickness. For oil sheen, >50% losses of ≤C16 hydrocarbons were observed after 1 h. We developed a mass transfer model to describe the evolution of oil slick chemical composition and water column hydrocarbon concentrations. The model was parametrized based on environmental conditions and hydrocarbon partitioning properties estimated from comprehensive two-dimensional gas chromatography (GC×GC) retention data. The model correctly predicted the observed fractionation of petroleum hydrocarbons in the oil slick resulting from evaporation and dissolution. This is the first report on the broad-spectrum compositional changes in oil during the first day of a spill at the sea surface. Expected outcomes under other environmental conditions are discussed, as well as comparisons to other models.

Community-wide plasmid gene mobilization and selection.

Plasmids have long been recognized as an important driver of DNA exchange and genetic innovation in prokaryotes. The success of plasmids has been attributed to their independent replication from the host's chromosome and their frequent self-transfer. It is thought that plasmids accumulate, rearrange and distribute nonessential genes, which may provide an advantage for host proliferation under selective conditions. In order to test this hypothesis independently of biases from culture selection, we study the plasmid metagenome from microbial communities in two activated sludge systems, one of which receives mostly household and the other chemical industry wastewater. We find that plasmids from activated sludge microbial communities carry among the largest proportion of unknown gene pools so far detected in metagenomic DNA, confirming their presumed role of DNA innovators. At a system level both plasmid metagenomes were dominated by functions associated with replication and transposition, and contained a wide variety of antibiotic and heavy metal resistances. Plasmid families were very different in the two metagenomes and grouped in deep-branching new families compared with known plasmid replicons. A number of abundant plasmid replicons could be completely assembled directly from the metagenome, providing insight in plasmid composition without culturing bias. Functionally, the two metagenomes strongly differed in several ways, including a greater abundance of genes for carbohydrate metabolism in the industrial and of general defense factors in the household activated sludge plasmid metagenome. This suggests that plasmids not only contribute to the adaptation of single individual prokaryotic species, but of the prokaryotic community as a whole under local selective conditions.