Constraining the oxygen requirements for modern microbial eukaryote diversity.

Eukaryotes originated prior to the establishment of modern marine oxygen (O2) levels. According to the body fossil and lipid biomarker records, modern (crown) microbial eukaryote lineages began diversifying in the ocean no later than ~800 Ma. While it has long been predicted that increasing atmospheric O2 levels facilitated the early diversification of microbial eukaryotes, the O2 levels needed to permit this diversification remain unconstrained. Using time-resolved geochemical parameter and gene sequence information from a model marine oxygen minimum zone spanning a range of dissolved O2 levels and redox states, we show that microbial eukaryote taxonomic richness and phylogenetic diversity remain the same until O2 declines to around 2 to 3% of present atmospheric levels, below which these diversity metrics become significantly reduced. Our observations suggest that increasing O2 would have only directly promoted early crown-eukaryote diversity if atmospheric O2 was below 2 to 3% of modern levels when crown-eukaryotes originated and then later met or surpassed this range as crown-eukaryotes diversified. If atmospheric O2 was already consistently at or above 2 to 3% of modern levels by the time that crown-eukaryotes originated, then the subsequent diversification of modern microbial eukaryotes was not directly driven by atmospheric oxygenation.

Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea.

The anaerobic oxidation of methane coupled to sulfate reduction is a microbially mediated process requiring a syntrophic partnership between anaerobic methanotrophic (ANME) archaea and sulfate-reducing bacteria (SRB). Based on genome taxonomy, ANME lineages are polyphyletic within the phylum Halobacterota, none of which have been isolated in pure culture. Here, we reconstruct 28 ANME genomes from environmental metagenomes and flow sorted syntrophic consortia. Together with a reanalysis of previously published datasets, these genomes enable a comparative analysis of all marine ANME clades. We review the genomic features that separate ANME from their methanogenic relatives and identify what differentiates ANME clades. Large multiheme cytochromes and bioenergetic complexes predicted to be involved in novel electron bifurcation reactions are well distributed and conserved in the ANME archaea, while significant variations in the anabolic C1 pathways exists between clades. Our analysis raises the possibility that methylotrophic methanogenesis may have evolved from a methanotrophic ancestor.

The cyanobacterium Prochlorococcus has divergent light-harvesting antennae and may have evolved in a low-oxygen ocean.

Marine picocyanobacteria of the genus Prochlorococcus are the most abundant photosynthetic organisms in the modern ocean, where they exert a profound influence on elemental cycling and energy flow. The use of transmembrane chlorophyll complexes instead of phycobilisomes as light-harvesting antennae is considered a defining attribute of Prochlorococcus Its ecology and evolution are understood in terms of light, temperature, and nutrients. Here, we report single-cell genomic information on previously uncharacterized phylogenetic lineages of this genus from nutrient-rich anoxic waters of the eastern tropical North and South Pacific Ocean. The most basal lineages exhibit optical and genotypic properties of phycobilisome-containing cyanobacteria, indicating that the characteristic light-harvesting antenna of the group is not an ancestral attribute. Additionally, we found that all the indigenous lineages analyzed encode genes for pigment biosynthesis under oxygen-limited conditions, a trait shared with other freshwater and coastal marine cyanobacteria. Our findings thus suggest that Prochlorococcus diverged from other cyanobacteria under low-oxygen conditions before transitioning from phycobilisomes to transmembrane chlorophyll complexes and may have contributed to the oxidation of the ancient ocean.

Emerging enzyme surface display systems for waste resource recovery.

The current century marks an inflection point for human progress, as the developed world increasingly comes to recognize that the ecological and socioeconomic impacts of resource extraction must be balanced with more sustainable modes of growth that are less reliant on non-renewable sources of energy and materials. This has opened a window of opportunity for cross-sector development of biotechnologies that harness the metabolic problem-solving power of microbial communities. In this context, recovery has emerged as an organizing principal to create value from industrial and municipal waste streams, and the search is on for new enzymes and platforms that can be used for waste resource recovery at scale. Enzyme surface display on cells or functionalized materials has emerged as a promising platform for waste valorization. Typically, surface display involves the use of substrate binding or catalytic domains of interest translationally fused with extracellular membrane proteins in a microbial chassis. Novel display systems with improved performance features include S-layer display with increased protein density, spore display with increased resistance to harsh conditions, and intracellular inclusions including DNA-free cells or nanoparticles with improved social licence for in situ applications. Combining these display systems with advances in bioprinting, electrospinning and high-throughput functional screening have potential to transform outmoded extractive paradigms into 'trans-metabolic" processes for remediation and waste resource recovery within an emerging circular bioeconomy.

Leveraging heterogeneous network embedding for metabolic pathway prediction.

MOTIVATION: Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. RESULTS: Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes. AVAILABILITY AND IMPLEMENTATION: The software package and installation instructions are published on http://github.com/pathway2vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hive Panel Explorer: an interactive network visualization tool.

MOTIVATION: Networks are used to relate topological structure to system dynamics and function, particularly in ecology systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive one of many network visualizations, distinguish themselves as providing a general, consistent and coherent rule-based representation to motivate hypothesis development and testing. RESULTS: Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous of multiple network views. We demonstrate HyPE's features by exploring a microbial co-occurrence network constructed from forest soil microbiomes. AVAILABILITY AND IMPLEMENTATION: HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler.

MOTIVATION: Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. RESULTS: We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations. AVAILABILITY AND IMPLEMENTATION: TreeSAPP was developed in Python 3 as an open-source Python package and is available on GitHub at https://github.com/hallamlab/TreeSAPP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic pathway inference using multi-label classification with rich pathway features.

Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabolic relationships within and between cells. Here, we present mlLGPR, multi-label based on logistic regression for pathway prediction, a software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks in organismal and multi-organismal datasets. We evaluated mlLGPR performance using a corpora of 12 experimental datasets manifesting diverse multi-label properties, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previous reports for organismal genomes and identify specific challenges associated with features engineering and training data for community-level metabolic inference.

Prospecting for microbial α-N-acetylgalactosaminidases yields a new class of GH31 O-glycanase.

α-Linked GalNAc (α-GalNAc) is most notably found at the nonreducing terminus of the blood type-determining A-antigen and as the initial point of attachment to the peptide backbone in mucin-type O-glycans. However, despite their ubiquity in saccharolytic microbe-rich environments such as the human gut, relatively few α-N-acetylgalactosaminidases are known. Here, to discover and characterize novel microbial enzymes that hydrolyze α-GalNAc, we screened small-insert libraries containing metagenomic DNA from the human gut microbiome. Using a simple fluorogenic glycoside substrate, we identified and characterized a glycoside hydrolase 109 (GH109) that is active on blood type A-antigen, along with a new subfamily of glycoside hydrolase 31 (GH31) that specifically cleaves the initial α-GalNAc from mucin-type O-glycans. This represents a new activity in this GH family and a potentially useful new enzyme class for analysis or modification of O-glycans on protein or cell surfaces.

Structural and mechanistic analysis of a ß-glycoside phosphorylase identified by screening a metagenomic library.

Glycoside phosphorylases have considerable potential as catalysts for the assembly of useful glycans for products ranging from functional foods and prebiotics to novel materials. However, the substrate diversity of currently identified phosphorylases is relatively small, limiting their practical applications. To address this limitation, we developed a high-throughput screening approach using the activated substrate 2,4-dinitrophenyl ß-d-glucoside (DNPGlc) and inorganic phosphate for identifying glycoside phosphorylase activity and used it to screen a large insert metagenomic library. The initial screen, based on release of 2,4-dinitrophenyl from DNPGlc in the presence of phosphate, identified the gene bglP, encoding a retaining ß-glycoside phosphorylase from the CAZy GH3 family. Kinetic and mechanistic analysis of the gene product, BglP, confirmed a double displacement ping-pong mechanism involving a covalent glycosyl-enzyme intermediate. X-ray crystallographic analysis provided insights into the phosphate-binding mode and identified a key glutamine residue in the active site important for substrate recognition. Substituting this glutamine for a serine swapped the substrate specificity from glucoside to N-acetylglucosaminide. In summary, we present a high-throughput screening approach for identifying ß-glycoside phosphorylases, which was robust, simple to implement, and useful in identifying active clones within a metagenomics library. Implementation of this screen enabled discovery of a new glycoside phosphorylase class and has paved the way to devising simple ways in which enzyme specificity can be encoded and swapped, which has implications for biotechnological applications.

Insights into the phylogeny and coding potential of microbial dark matter.

Genome sequencing enhances our understanding of the biological world by providing blueprints for the evolutionary and functional diversity that shapes the biosphere. However, microbial genomes that are currently available are of limited phylogenetic breadth, owing to our historical inability to cultivate most microorganisms in the laboratory. We apply single-cell genomics to target and sequence 201 uncultivated archaeal and bacterial cells from nine diverse habitats belonging to 29 major mostly uncharted branches of the tree of life, so-called 'microbial dark matter'. With this additional genomic information, we are able to resolve many intra- and inter-phylum-level relationships and to propose two new superphyla. We uncover unexpected metabolic features that extend our understanding of biology and challenge established boundaries between the three domains of life. These include a novel amino acid use for the opal stop codon, an archaeal-type purine synthesis in Bacteria and complete sigma factors in Archaea similar to those in Bacteria. The single-cell genomes also served to phylogenetically anchor up to 20% of metagenomic reads in some habitats, facilitating organism-level interpretation of ecosystem function. This study greatly expands the genomic representation of the tree of life and provides a systematic step towards a better understanding of biological evolution on our planet.

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone.

Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet-a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite "leakage" during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.

Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters.

A major percentage of fixed nitrogen (N) loss in the oceans occurs within nitrite-rich oxygen minimum zones (OMZs) via denitrification and anammox. It remains unclear to what extent ammonium and nitrite oxidation co-occur, either supplying or competing for substrates involved in nitrogen loss in the OMZ core. Assessment of the oxygen (O2) sensitivity of these processes down to the O2 concentrations present in the OMZ core (<10 nmol⋅L(-1)) is therefore essential for understanding and modeling nitrogen loss in OMZs. We determined rates of ammonium and nitrite oxidation in the seasonal OMZ off Concepcion, Chile at manipulated O2 levels between 5 nmol⋅L(-1) and 20 µmol⋅L(-1) Rates of both processes were detectable in the low nanomolar range (5-33 nmol⋅L(-1) O2), but demonstrated a strong dependence on O2 concentrations with apparent half-saturation constants (Kms) of 333 ± 130 nmol⋅L(-1) O2 for ammonium oxidation and 778 ± 168 nmol⋅L(-1) O2 for nitrite oxidation assuming one-component Michaelis-Menten kinetics. Nitrite oxidation rates, however, were better described with a two-component Michaelis-Menten model, indicating a high-affinity component with a Km of just a few nanomolar. As the communities of ammonium and nitrite oxidizers were similar to other OMZs, these kinetics should apply across OMZ systems. The high O2 affinities imply that ammonium and nitrite oxidation can occur within the OMZ core whenever O2 is supplied, for example, by episodic intrusions. These processes therefore compete with anammox and denitrification for ammonium and nitrite, thereby exerting an important control over nitrogen loss.

LCA*: an entropy-based measure for taxonomic assignment within assembled metagenomes.

MOTIVATION: A perennial problem in the analysis of environmental sequence information is the assignment of reads or assembled sequences, e.g. contigs or scaffolds, to discrete taxonomic bins. In the absence of reference genomes for most environmental microorganisms, the use of intrinsic nucleotide patterns and phylogenetic anchors can improve assembly-dependent binning needed for more accurate taxonomic and functional annotation in communities of microorganisms, and assist in identifying mobile genetic elements or lateral gene transfer events. RESULTS: Here, we present a statistic called LCA* inspired by Information and Voting theories that uses the NCBI Taxonomic Database hierarchy to assign taxonomy to contigs assembled from environmental sequence information. The LCA* algorithm identifies a sufficiently strong majority on the hierarchy while minimizing entropy changes to the observed taxonomic distribution resulting in improved statistical properties. Moreover, we apply results from the order-statistic literature to formulate a likelihood-ratio hypothesis test and P-value for testing the supremacy of the assigned LCA* taxonomy. Using simulated and real-world datasets, we empirically demonstrate that voting-based methods, majority vote and LCA*, in the presence of known reference annotations, are consistently more accurate in identifying contig taxonomy than the lowest common ancestor algorithm popularized by MEGAN, and that LCA* taxonomy strikes a balance between specificity and confidence to provide an estimate appropriate to the available information in the data. AVAILABILITY AND IMPLEMENTATION: The LCA* has been implemented as a stand-alone Python library compatible with the MetaPathways pipeline; both of which are available on GitHub with installation instructions and use-cases (http://www.github.com/hallamlab/LCAStar/). CONTACT: shallam@mail.ubc.caSupplementary information: Supplementary data are available at Bioinformatics online.

Metaproteomics reveals differential modes of metabolic coupling among ubiquitous oxygen minimum zone microbes.

Marine oxygen minimum zones (OMZs) are intrinsic water column features arising from respiratory oxygen demand during organic matter degradation in stratified waters. Currently OMZs are expanding due to global climate change with resulting feedback on marine ecosystem function. Here we use metaproteomics to chart spatial and temporal patterns of gene expression along defined redox gradients in a seasonally stratified fjord to better understand microbial community responses to OMZ expansion. The expression of metabolic pathway components for nitrification, anaerobic ammonium oxidation (anammox), denitrification, and inorganic carbon fixation were differentially expressed across the redoxcline and covaried with distribution patterns of ubiquitous OMZ microbes including Thaumarchaeota, Nitrospina, Nitrospira, Planctomycetes, and SUP05/ARCTIC96BD-19 Gammaproteobacteria. Nitrification and inorganic carbon fixation pathways affiliated with Thaumarchaeota dominated dysoxic waters, and denitrification, sulfur oxidation, and inorganic carbon fixation pathways affiliated with the SUP05 group of nitrate-reducing sulfur oxidizers dominated suboxic and anoxic waters. Nitrifier nitrite oxidation and anammox pathways affiliated with Nirospina, Nitrospira, and Planctomycetes, respectively, also exhibited redox partitioning between dysoxic and suboxic waters. The numerical abundance of SUP05 proteins mediating inorganic carbon fixation under anoxic conditions suggests that SUP05 will become increasingly important in global ocean carbon and nutrient cycling as OMZs expand.

Metagenomic scaffolds enable combinatorial lignin transformation.

Engineering the microbial transformation of lignocellulosic biomass is essential to developing modern biorefining processes that alleviate reliance on petroleum-derived energy and chemicals. Many current bioprocess streams depend on the genetic tractability of Escherichia coli with a primary emphasis on engineering cellulose/hemicellulose catabolism, small molecule production, and resistance to product inhibition. Conversely, bioprocess streams for lignin transformation remain embryonic, with relatively few environmental strains or enzymes implicated. Here we develop a biosensor responsive to monoaromatic lignin transformation products compatible with functional screening in E. coli. We use this biosensor to retrieve metagenomic scaffolds sourced from coal bed bacterial communities conferring an array of lignin transformation phenotypes that synergize in combination. Transposon mutagenesis and comparative sequence analysis of active clones identified genes encoding six functional classes mediating lignin transformation phenotypes that appear to be rearrayed in nature via horizontal gene transfer. Lignin transformation activity was then demonstrated for one of the predicted gene products encoding a multicopper oxidase to validate the screen. These results illuminate cellular and community-wide networks acting on aromatic polymers and expand the toolkit for engineering recombinant lignin transformation based on ecological design principles.

MetaPathways v2.5: quantitative functional, taxonomic and usability improvements.

UNLABELLED: Next-generation sequencing is producing vast amounts of sequence information from natural and engineered ecosystems. Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements. Here, we present improvements to MetaPathways, an annotation and analysis pipeline for environmental sequence information that expedites this transformation. We specifically address pathway prediction hazards through integration of a weighted taxonomic distance and enable quantitative comparison of assembled annotations through a normalized read-mapping measure. Additionally, we improve LAST homology searches through BLAST-equivalent E-values and output formats that are natively compatible with prevailing software applications. Finally, an updated graphical user interface allows for keyword annotation query and projection onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme database. AVAILABILITY AND IMPLEMENTATION: MetaPathways v2.5 is available on GitHub: http://github.com/hallamlab/metapathways2. CONTACT: shallam@mail.ubc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean.

Planktonic bacteria dominate surface ocean biomass and influence global biogeochemical processes, but remain poorly characterized owing to difficulties in cultivation. Using large-scale single cell genomics, we obtained insight into the genome content and biogeography of many bacterial lineages inhabiting the surface ocean. We found that, compared with existing cultures, natural bacterioplankton have smaller genomes, fewer gene duplications, and are depleted in guanine and cytosine, noncoding nucleotides, and genes encoding transcription, signal transduction, and noncytoplasmic proteins. These findings provide strong evidence that genome streamlining and oligotrophy are prevalent features among diverse, free-living bacterioplankton, whereas existing laboratory cultures consist primarily of copiotrophs. The apparent ubiquity of metabolic specialization and mixotrophy, as predicted from single cell genomes, also may contribute to the difficulty in bacterioplankton cultivation. Using metagenome fragment recruitment against single cell genomes, we show that the global distribution of surface ocean bacterioplankton correlates with temperature and latitude and is not limited by dispersal at the time scales required for nucleotide substitution to exceed the current operational definition of bacterial species. Single cell genomes with highly similar small subunit rRNA gene sequences exhibited significant genomic and biogeographic variability, highlighting challenges in the interpretation of individual gene surveys and metagenome assemblies in environmental microbiology. Our study demonstrates the utility of single cell genomics for gaining an improved understanding of the composition and dynamics of natural microbial assemblages.

Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems.

Enhanced biological phosphorus removal (EBPR) relies on diverse but specialized microbial communities to mediate the cycling and ultimate removal of phosphorus from municipal wastewaters. However, little is known about microbial activity and dynamics in relation to process fluctuations in EBPR ecosystems. Here, we monitored temporal changes in microbial community structure and potential activity across each bioreactor zone in a pilot-scale EBPR treatment plant by examining the ratio of small subunit ribosomal RNA (SSU rRNA) to SSU rRNA gene (rDNA) over a 120 day study period. Although the majority of operational taxonomic units (OTUs) in the EBPR ecosystem were rare, many maintained high potential activities based on SSU rRNA : rDNA ratios, suggesting that rare OTUs contribute substantially to protein synthesis potential in EBPR ecosystems. Few significant differences in OTU abundance and activity were observed between bioreactor redox zones, although differences in temporal activity were observed among phylogenetically cohesive OTUs. Moreover, observed temporal activity patterns could not be explained by measured process parameters, suggesting that other ecological drivers, such as grazing or viral lysis, modulated community interactions. Taken together, these results point towards complex interactions selected for within the EBPR ecosystem and highlight a previously unrecognized functional potential among low abundance microorganisms in engineered ecosystems.