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Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin that accumulates in terrestrial and marine food webs, with potential impacts on human health. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet, British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicts that Marinimicrobia HgcAB proteins contain the highly conserved amino acid sites and folding structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognized.

Methane produced by methanogenic archaea has an important influence on Earth’s changing climate. Methanogenic archaea are phylogenetically diverse and widespread in anoxic environments. These microorganisms can be divided into two subgroups based on whether or not they use b-type cytochromes for energy conservation. Methanogens with b-type cytochromes have a wider substrate range and higher growth yields than those without them. To date, methanogens with b-type cytochromes were found exclusively in the phylum “Ca. Halobacteriota” (formerly part of the phylum Euryarchaeota). Here, we present the discovery of metagenome-assembled genomes harboring methyl-coenzyme M reductase genes reconstructed from mesophilic anoxic sediments, together with the previously reported thermophilic “Ca. Methylarchaeum tengchongensis”, representing a novel archaeal order, designated the “Ca. Methylarchaeales”, of the phylum Thermoproteota (formerly the TACK superphylum). These microorganisms contain genes required for methyl-reducing methanogenesis and the Wood-Ljundahl pathway. Importantly, the genus “Ca. Methanotowutia” of the “Ca. Methylarchaeales” encode a cytochrome b-containing heterodisulfide reductase (HdrDE) and methanophenazine-reducing hydrogenase complex that have similar gene arrangements to those found in methanogenic Methanosarcinales. Our results indicate that members of the “Ca. Methylarchaeales” are methanogens with cytochromes and can conserve energy via membrane-bound electron transport chains. Phylogenetic and amalgamated likelihood estimation analyses indicate that methanogens with cytochrome b-containing electron transfer complexes likely evolved before diversification of Thermoproteota or “Ca. Halobacteriota” in the early Archean Eon. Surveys of public sequence databases suggest that members of the lineage are globally distributed in anoxic sediments and may be important players in the methane cycle.

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

Polyethylene terephthalate (PET) is an important synthetic polymer accumulating in nature 2 and recent studies have identified microorganisms capable of degrading PET. While the majority of 3 known PET hydrolases originate from the Actinobacteria and Proteobacteria, here we describe the 4 first functional PET-active enzymes from the Bacteroidetes phylum. Using a PETase-specific 5 Hidden-Markov-Model (HMM)-based search algorithm we identified two promiscuous and cold6 active esterases derived from Aequorivita sp. (PET27) and Chryseobacterium jeonii (PET30) acting 7 on PET foil and powder. Notably, one of the enzymes (PET30) was able to hydrolyze PET at 8 temperatures between 4° - 30°C with a similar turnover rate compared to the well-known Ideonella 9 sakaiensis enzyme (IsPETase). 10 PET27 and PET30 homologues were detected in metagenomes encompassing a wide range 11 of different global climate zones. Additional transcript abundance mapping of marine samples imply 12 that these promiscuous enzymes and source organisms may play a role in the long-term 13 degradation of microplastic particles and fibers.

Marine sinking particles transport carbon from the surface and bury it in deep sea sediments where it can be sequestered on geologic time scales. The combination of the surface ocean food web that produces these particles and the particle-associated microbial community that degrades these particles, creates a complex set of variables that control organic matter cycling. We use targeted metabolomics to characterize a suite of small biomolecules, or metabolites, in sinking particles and compare their metabolite composition to that of the suspended particles in the euphotic zone from which they are likely derived. These samples were collected in the South Atlantic subtropical gyre, as well as in the equatorial Atlantic region and the Amazon River plume. The composition of targeted metabolites in the sinking particles was relatively similar throughout the transect, despite the distinct oceanic regions in which they were generated. Metabolites possibly derived from the degradation of nucleic acids and lipids, such as xanthine and glycine betaine, were an increased mole fraction of the targeted metabolites in the sinking particles relative to surface suspended particles, while algal-derived metabolites like the osmolyte dimethylsulfoniopropionate were a smaller fraction of the observed metabolites on the sinking particles. These compositional changes are shaped both by the removal of metabolites associated with detritus delivered from the surface ocean and by production of metabolites by the sinking particle-associated microbial communities. Further, they provide a basis for examining the types and quantities of metabolites that may be delivered to the deep sea by sinking particles.

Access to efficient enzymes that can convert A and B type red blood cells to 'universal' donor O would greatly increase the supply of blood for transfusions. Here we report the functional metagenomic screening of the human gut microbiome for enzymes that can remove the cognate A and B type sugar antigens. Among the genes encoded in our library of 19,500 expressed fosmids bearing gut bacterial DNA, we identify an enzyme pair from the obligate anaerobe Flavonifractor plautii that work in concert to efficiently convert the A antigen to the H antigen of O type blood, via a galactosamine intermediate. The X-ray structure of the N-acetylgalactosamine deacetylase reveals the active site and mechanism of the founding member of an esterase family. The galactosaminidase expands activities within the CAZy family GH36. Their ability to completely convert A to O of the same rhesus type at very low enzyme concentrations in whole blood will simplify their incorporation into blood transfusion practice, broadening blood supply.

The construction of large, phylogenetically diverse libraries of synthetic genes from a sequence-based family allows rapid evaluation of the substrate specificity encoded within the gene products a...

Polyethylene terephthalate (PET) is an important synthetic polymer accumulating in nature and recent studies have identified microorganisms capable of degrading PET. While the majority of known PET hydrolases originate from the Actinobacteria and Proteobacteria, here we describe the first functional PET-active enzymes from the Bacteroidetes phylum. Using a PETase-specific Hidden-Markov-Model (HMM)-based search algorithm we identified two promiscuous and cold-active esterases derived from Aequorivita sp. (PET27) and Chryseobacterium jeonii (PET30) acting on PET foil and powder. Notably, one of the enzymes (PET30) was able to hydrolyze PET at temperatures between 4° - 30°C with a similar turnover rate compared to the well-known Ideonella sakaiensis enzyme (IsPETase). PET27 and PET30 homologues were detected in metagenomes encompassing a wide range of different global climate zones. Additional transcript abundance mapping of marine samples imply that these enzymes and source organisms play a significant role in the long-term degradation of microplastic particles and fibers.

Significance The marine unicellular cyanobacterium Prochlorococcus is the most abundant photosynthetic organism on Earth. Members of this genus are classically thought to be adapted to high-oxygen and nutrient-poor ocean conditions, with a principle divergence between high-light and low-light ecotypes. We show that the most basal Prochlorococcus lineages are adapted to the low-oxygen, low-light, and high-nutrient conditions found in the dimly illuminated waters of anoxic marine zones. The most basal lineages have retained phycobilisomes as light-harvesting antennae—a characteristic of most other cyanobacteria—whose loss was thought to define all Prochlorococcus. As oxygenic photosynthesis drove ocean oxidation in the ancient Earth, oxygen appears to have played as much a role as light and nutrients in driving Prochlorococcus evolution., 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.

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.

Metabolites, or the small organic molecules that are synthesized by cells during metabolism, comprise a complex and dynamic pool of carbon in the ocean. They are an essential form of information, linking genotype to phenotype at the individual, population and community levels of biological organization. Characterizing metabolite distributions inside microbial cells and dissolved in seawater is essential to understanding the controls on their production and fate, as well as their roles in shaping marine microbial food webs. Here, we apply a targeted metabolomics method to quantify particulate and dissolved distributions of a suite of biologically relevant metabolites including vitamins, amino acids, nucleic acids, osmolytes, and intermediates in biosynthetic pathways along a latitudinal transect in the western Atlantic Ocean. We find that, in the euphotic zone, most particulate or intracellular metabolites positively co-vary with the most abundant microbial taxa. In contrast, dissolved metabolites exhibited greater variability with differences in distribution between ocean regions. Although fewer particulate metabolites were detected below the euphotic zone, molecules identified in the deep ocean may be linked to preservation of organic matter or adaptive physiological strategies of deep-sea microbes. Based on the identified metabolite distributions, we propose relationships between certain metabolites and microbial populations, and find that dissolved metabolite distributions are not directly related to their particulate abundances.

With growing global use of methanol as a fuel additive and extensive use in other industrial processes, there is the potential for unintended release and spills into soils and aquifers. In these subsurface systems it is likely that methanol will be readily biodegraded; however, degradation may lead to the production of by-products, most importantly methane possibly resulting in explosion hazards and volatile fatty acids (VFAs) causing aesthetic issues for groundwater. In this study, the formation of these potentially harmful by-products due to methanol biodegradation was investigated in natural sand and silt sediments using microcosms inoculated with neat methanol (100%) ranging in concentration from 100 to 100,000 ppm. To assess the rate of degradation and by-product formation, water and headspace samples were collected and analyzed for methanol, volatile fatty acids (VFAs, including acetic, butyric, and propionic acid), cation (metal) concentrations (Al, Ca, Fe, K, Mg, Mn and Na), microbial community structure and activity, headspace pressure, gas composition (CH

Metabolic pathways are composed of reaction sequences catalyzed by enzymes. The set of reactions within and between cells comprises a reactome. Pathways and reactomes can be predicted from organismal or multi-organismal genomes using rule-based or machine learning methods. While machine learning methods overcome issues of probability and scale associated with rule-based methods, several complications remain that can degrade performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of pathways within a dataset. Here, we present leADS (multi-label learning based on active dataset subsampling), a machine learning method, that uses subsampling to reduce the negative impact of training loss due to class imbalance. We demonstrate leADs performance using organismal and multi-organismal datasets in relation to other machine learning pathway prediction methods.Availability and implementationleADS is available under the GNU license at github.com/hallamlab/leADS. A wiki, including a tutorial, is available at github.com//hallamlab/leADS/wikiContactshallam@mail.ubc.ca

Monitoring population dynamics in co-culture is necessary in engineering microbial consortia involved in distributed metabolic processes or biosensing applications. However, it remains difficult to measure strain-specific growth dynamics high-throughput formats. This is especially vexing in plate-based functional screens leveraging whole-cell biosensors to detect specific metabolic signals. Here we develop an experimental high-throughput co-culture system to measure and model the relationship between fluorescence and cell abundance, combining chassis-independent recombinase-assisted genome engineering (CRAGE) and whole-cell biosensing with a PemrR-green fluorescent protein (GFP) monoaromatic reporter used in plate-based functional screening. CRAGE was used to construct E. coli EPI300 strains constitutively expressing red fluorescent protein (RFP) and the relationship between RFP expression and optical density (OD600) was determined throughout the EPI300 growth cycle. A linear equation describing the increase of normalized RFP fluorescence during deceleration phase was derived and used to predict biosensor strain dynamics in co-culture. Measured and predicted values were compared using flow cytometric detection methods. Induction of the biosensor lead to increased GFP fluorescence normalized to biosensor cell abundance, as expected, but a significant decrease in relative abundance of the biosensor strain in co-culture and a decrease in bulk GFP fluorescence. Taken together, these results highlight sensitivity of population dynamics to variations in metabolic activity in co-culture and the potential effect of these dynamics on the performance of functional screens in plate-based formats. The engineered strains and model used to evaluate these dynamics provide a framework for optimizing growth of synthetic co-cultures used in screening, testing and pathway engineering applications

Microbial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate (dsrA, dsrC/tusE, soxC, soxD and soxYZ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides fundamental insights on the distribution, diversity, and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations., Some bacteriophage encode auxiliary metabolic genes (AMGs) that impact host metabolism and biogeochemical cycling during infection. Here the authors identify hundreds of AMGs in environmental phage encoding sulfur oxidation genes and use their global distribution to infer phage-mediated biogeochemical impacts.

Metabolic pathway inference from genomic sequence information is an integral scientific problem with wide ranging applications in the life sciences. As sequencing throughput increases, scalable and performative methods for pathway prediction at different levels of genome complexity and completion become compulsory. In this paper, we present reMap (relabeling metabolic pathway data with groups) a simple, and yet, generic framework, that performs relabeling examples to a different set of labels, characterized as groups. A pathway group is comprised of a subset of statistically correlated pathways that can be further distributed between multiple pathway groups. This has important implications for pathway prediction, where a learning algorithm can revisit a pathway multiple times across groups to improve sensitivity. The relabeling process in reMap is achieved through an alternating feedback process. In the first feed-forward phase, a minimal subset of pathway groups is picked to label each example. In the second feed-backward phase, reMap’s internal parameters are updated to increase the accuracy of mapping examples to pathway groups. The resulting pathway group dataset is then be used to train a multi-label learning algorithm. reMap’s effectiveness was evaluated on metabolic pathway prediction where resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved predictive performance.

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, post-secondary 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 potential to improve research and learning outcomes and 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 co-curricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants, 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.Availability and implementationThe EDUCE teaching and learning framework is accessible at educe-ubc.github.io

Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin in terrestrial and marine food webs. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet (SI), British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last by far the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicted that Marinimicrobia HgcAB proteins contain the highly conserved structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several SI putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognised.

Viruses play an important role in the ecology and biogeochemistry of marine ecosystems. Beyond mortality and gene transfer, viruses can reprogram microbial metabolism during infection by expressing auxiliary metabolic genes (AMGs) involved in photosynthesis, central carbon metabolism, and nutrient cycling. While previous studies have focused on AMG diversity in the sunlit and dark ocean, less is known about the role of viruses in shaping metabolic networks along redox gradients associated with marine oxygen minimum zones (OMZs). Here, we analyzed relatively quantitative viral metagenomic datasets that profiled the oxygen gradient across Eastern Tropical South Pacific (ETSP) OMZ waters, assessing whether OMZ viruses might impact nitrogen (N) cycling via AMGs. Identified viral genomes encoded six N-cycle AMGs associated with denitrification, nitrification, assimilatory nitrate reduction, and nitrite transport. The majority of these AMGs (80%) were identified in T4-like Myoviridae phages, predicted to infect Cyanobacteria and Proteobacteria, or in unclassified archaeal viruses predicted to infect Thaumarchaeota. Four AMGs were exclusive to anoxic waters and had distributions that paralleled homologous microbial genes. Together, these findings suggest viruses modulate N-cycling processes within the ETSP OMZ and may contribute to nitrogen loss throughout the global oceans thus providing a baseline for their inclusion in the ecosystem and geochemical models.

The Pacific coastal temperate rainforest (PCTR) is a global hot-spot for carbon cycling and export. Yet the influence of microorganisms on carbon cycling processes in PCTR soil is poorly characterized. We developed and tested a conceptual model of seasonal microbial carbon cycling in PCTR soil through integration of geochemistry, micro-meteorology, and eukaryotic and prokaryotic ribosomal amplicon (rRNA) sequencing from 216 soil DNA and RNA libraries. Soil moisture and pH increased during the wet season, with significant correlation to net CO2 flux in peat bog and net CH4 flux in bog forest soil. Fungal succession in these sites was characterized by the apparent turnover of Archaeorhizomycetes phylotypes accounting for 41% of ITS libraries. Anaerobic prokaryotes, including Syntrophobacteraceae and Methanomicrobia increased in rRNA libraries during the wet season. Putatively active populations of these phylotypes and their biogeochemical marker genes for sulfate and CH4 cycling, respectively, were positively correlated following rRNA and metatranscriptomic network analysis. The latter phylotype was positively correlated to CH4 fluxes (r = 0.46, p

We developed an efficient, scalable and inexpensive method for recovering cellular biomass from complex fluid matrices that cannot be processed using conventional filtration methods. The method uses chemical flocculation with iron oxyhydroxides, is capable of recovering greater than 90% of cellular biomass from fluids with more than 103 cells ml-1 , and was validated using both mock communities and field samples. High quality DNA can be readily extracted from iron flocs using standard soil extraction kits. We applied chemical flocculation to fracing fluids from British Columbia, Canada and recovered a diversity of microbial taxa including abundant members of the Epsilon- and Deltaproteobacteria previously recovered from shale gas operations in the United States. Application of chemical flocculation presents new opportunities for scalable time-series monitoring and experimentation on complex fluid matrices including microbial community profiling and shotgun metagenomics over gas production well completion cycles.

The seasonally stratified water column of Saanich Inlet, Vancouver, B.C., Canada provides a natural laboratory for examining how ocean deoxygenation shapes microbial community population dynamics and interactions. Microbial eukaryotes within these communities play significant roles as phototrophs, mixotrophs, symbionts, predators, parasites, and saprotrophs, with population abundances and interactions changing seasonally, and along water column redox gradients. Here, we use time series observations of ribosomal RNA and DNA diversity in combination with co-occurrence correlation and indicator species analyses to observe microbial eukaryote community dynamics over a 12-month period, uncovering significant correlations between parasitic dinoflagellates within Syndiniales and other eukaryotic taxa during months of peak water column stratification. Syndiniales operational taxonomic units (OTUs) were the most abundant indicator OTUs during stratification. Syndiniales OTUs exhibited significant correlations with a broad range of taxa, including members of Apicomplexa, Ciliophora, Dinoflagellata, Chlorophyta, Metazoa and a variety of Stramenopiles including MAST groups. Correlation analyses suggest Syndiniales may influence phytoplankton bloom dynamics in Saanich Inlet as well as abundances of protist and metazoan grazers that are indicator species for peak stratification periods. These observations shed light on the potential role of parasitic protists on organic matter release and bloom termination events during stratification and renewal periods in Saanich Inlet with potential implications for coastal marine foodwebs and fisheries productivity.

The North American beaver (Castor canadensis) has long been considered an engineering marvel, transforming landscapes and shaping biological diversity through its dam building behavior. While the beaver possesses conspicuous morphological features uniquely adapted for the use of woody plants as construction materials and dietary staples, relatively little is known about the specialized microorganisms inhabiting the beaver gastrointestinal tract and their functional roles in determining host nutrition. Here we use a combination of shotgun metagenomics, functional screening and carbohydrate biochemistry to chart the community structure and metabolic power of the beaver fecal microbiome. We relate this information to the metabolic capacity of other wood feeding and hindgut fermenting organisms and profile the functional repertoire of glycoside hydrolase (GH) families distributed among and between population genome bins. Metagenomic screening revealed novel mechanisms of xylan oligomer degradation involving GH43 enzymes from uncharacterized subfamilies and divergent polysaccharide utilization loci, indicating the potential for synergistic biomass deconstruction. Together, these results open a functional metagenomic window on less conspicuous adaptations enabling the beaver microbiome to efficiently convert woody plants into host nutrition and point toward rational design of enhanced enzyme mixtures for biorefining process streams.

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 β-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 β-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 β-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.

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 which 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.

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We present an 8-yr time-series of monthly water column N2O measurements from Saanich Inlet, a seasonally anoxic fjord in southern British Columbia. We document seasonal and inter-annual variability in N2O concentrations driven by physical and biological forcing, and examine the relationship between N2O and O2 concentrations across the redoxcline in this system. Near-surface N2O concentrations were typically supersaturated, and increased with depth to a maximum near the oxic-anoxic transition. Genes associated with both the nitrification and denitrification pathways were widely distributed throughout the water column, suggesting the potential for simultaneous N2O production from nitrification and incomplete denitrification. Maximum N2O concentrations in Saanich Inlet were similar to other anoxic basins, but lower than those previously documented in coastal upwelling and open ocean oxygen minimum zones. N2O and O2 were inversely correlated throughout the water column, except below 10 μmol O2 L−1 where net N2O consumption appears to occur. In surface waters, maximum N2O concentrations and sea–air fluxes occurred during late summer/early fall, when O2 levels in the oxycline were low. Annual deep basin renewals often persisted over multiple months, supplying O2, NO3−, and N2O to the deep basin, with N2O subsequently declining to undetectable levels (< 0.4 nmol N2O L−1) by late spring. An unusually weak renewal was observed during 2009, possibly linked to reduced upwelling off the coast of British Columbia during El Nino. Our results can be used as a baseline to identify the effect of longer-term changes in Saanich Inlet, such as deoxygenation and its effect on N2O concentrations.

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.

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.

Changes in the sequence of an organism’s genome, i.e. mutations, are the raw material of evolution1. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as Diversity-generating retroelements (DGRs)2–4. DGRs introduce mutations in specific target genes, and were characterized from several cultivated bacteria and bacteriophages2. Whilst a larger diversity of DGR loci has been identified in genomic data from environmental samples, i.e. metagenomes, the ecological role of these DGRs and their associated evolutionary drivers remain poorly understood5–7. Here we built and analyzed an extensive dataset of >30,000 metagenome-derived DGRs, and determine that DGRs have a single evolutionary origin and a universal bias towards adenine mutations. We further identified six major lineages of DGRs, each associated with a specific ecological niche defined as a genome type, i.e. whether the DGR is encoded on a viral or cellular genome, a limited set of taxa and environments, and a distinct type of target. Finally, we leverage read mapping and metagenomic time series to demonstrate that DGRs are consistently and broadly active, and responsible for >10% of all amino acid changes in some organisms at a conservative estimate. Overall, these results highlight the strong constraints under which DGRs diversify and expand, and elucidate several distinct roles these elements play in natural communities and in shaping microbial community structure and function in our environment.

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.

The generation of new biocatalysts for plant biomass degradation and glycan synthesis has typically relied on the characterization and investigation of one or a few enzymes at a time. By coupling functional metagenomic screening and high-throughput functional characterization, we can progress beyond the current scale of catalyst discovery and provide rapid annotation of catalyst function. By functionally screening environmental DNA from many diverse sources, we have generated a suite of active glycoside hydrolase-containing clones and demonstrated their reaction parameters. We then demonstrated the utility of this collection through the generation of a new catalyst for the formation of azido-modified glycans. Further interrogation of this collection of clones will expand our biocatalytic toolbox, with potential application to biomass deconstruction and synthesis of glycans., Functional metagenomics is a powerful tool for both the discovery and development of biocatalysts. This study presents the high-throughput functional screening of 22 large-insert fosmid libraries containing over 300,000 clones sourced from natural and engineered ecosystems, characterization of active clones, and a demonstration of the utility of recovered genes or gene cassettes in the development of novel biocatalysts. Screening was performed in a 384-well-plate format with the fluorogenic substrate 4-methylumbelliferyl cellobioside, which releases a fluorescent molecule when cleaved by β-glucosidases or cellulases. The resulting set of 164 active clones was subsequently interrogated for substrate preference, reaction mechanism, thermal stability, and optimal pH. The environmental DNA harbored within each active clone was sequenced, and functional annotation revealed a cornucopia of carbohydrate-degrading enzymes. Evaluation of genomic-context information revealed both synteny and polymer-targeting loci within a number of sequenced clones. The utility of these fosmids was then demonstrated by identifying clones encoding activity on an unnatural glycoside (4-methylumbelliferyl 6-azido-6-deoxy-β-d-galactoside) and transforming one of the identified enzymes into a glycosynthase capable of forming taggable disaccharides. IMPORTANCE The generation of new biocatalysts for plant biomass degradation and glycan synthesis has typically relied on the characterization and investigation of one or a few enzymes at a time. By coupling functional metagenomic screening and high-throughput functional characterization, we can progress beyond the current scale of catalyst discovery and provide rapid annotation of catalyst function. By functionally screening environmental DNA from many diverse sources, we have generated a suite of active glycoside hydrolase-containing clones and demonstrated their reaction parameters. We then demonstrated the utility of this collection through the generation of a new catalyst for the formation of azido-modified glycans. Further interrogation of this collection of clones will expand our biocatalytic toolbox, with potential application to biomass deconstruction and synthesis of glycans.

International audience; Methanogenesis is an ancient metabolism of key ecological relevance, with direct impact on the evolution of Earth's climate. Recent results suggest that the diversity of methane metabolisms and their derivations have probably been vastly underestimated. Here, by probing thousands of publicly available metagenomes for homologues of methyl-coenzyme M reductase complex (MCR), we have obtained ten metagenome-assembled genomes (MAGs) belonging to potential methanogenic, anaerobic methanotrophic and short-chain alkane-oxidizing archaea. Five of these MAGs represent under-sampled (Verstraetearchaeota, Methanonatronarchaeia, ANME-1 and GoM-Arc1) or previously genomically undescribed (ANME-2c) archaeal lineages. The remaining five MAGs correspond to lineages that are only distantly related to previously known methanogens and span the entire archaeal phylogeny. Comprehensive comparative annotation substantially expands the metabolic diversity and energy conservation systems of MCR-bearing archaea. It also suggests the potential existence of a yet uncharacterized type of methanogenesis linked to short-chain alkane/fatty acid oxidation in a previously undescribed class of archaea ('Candidatus Methanoliparia'). We redefine a common core of marker genes specific to methanogenic, anaerobic methanotrophic and short-chain alkane-oxidizing archaea, and propose a possible scenario for the evolutionary and functional transitions that led to the emergence of such metabolic diversity.

Screening of large enzyme libraries such as those derived from metagenomic sources requires sensitive substrates. Fluorogenic glycosides typically offer the best sensitivity but typically must be used in a stopped format to generate good signal. Use of fluorescent phenols of pKa < 7, such as halogenated coumarins, allows direct screening at neutral pH. The synthesis and characterisation of a set of nine different glycosides of 6-chloro-4-methylumbelliferone are described. The use of these substrates in a pooled format for screening of expressed metagenomic libraries yielded a "hit rate" of 1 in 60. Hits were then readily deconvoluted with the individual substrates in a single plate to identify specific activities within each clone. The use of such a collection of substrates greatly accelerates the screening process.

© 2016 International Society for Microbial Ecology All rights reserved. Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.

Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.

Extensive and expanding oxygen minimum zones (OMZs) exist at variable depths in coastal and open ocean waters. As oxygen levels decline, nutrients and energy are increasingly diverted away from higher trophic levels into microbial community metabolism, resulting in fixed nitrogen loss and production of climate active trace gases including nitrous oxide and methane. While ocean deoxygenation has been reported on a global scale, our understanding of OMZ biology and geochemistry is limited by a lack of time-resolved data sets. Here, we present a historical dataset of oxygen concentrations spanning fifty years and nine years of monthly geochemical time series observations in Saanich Inlet, a seasonally anoxic fjord on the coast of Vancouver Island, British Columbia, Canada that undergoes recurring changes in water column oxygenation status. This compendium provides a unique geochemical framework for evaluating long-term trends in biogeochemical cycling in OMZ waters.

Marine sinking particles transport carbon from the surface and bury it in deep sea sediments where it can be sequestered on geologic time scales. The combination of the surface ocean food web that produces these particles and the particle-associated microbial community that degrades these particles, creates a complex set of variables that control organic matter cycling. We use targeted metabolomics to characterize a suite of small biomolecules, or metabolites, in sinking particles and compare their metabolite composition to that of the suspended particles in the euphotic zone from which they are likely derived. These samples were collected in the South Atlantic subtropical gyre, as well as in the equatorial Atlantic region and the Amazon River plume. The composition of targeted metabolites in the sinking particles was relatively similar throughout the transect, despite the distinct oceanic regions in which they were generated. Metabolites possibly derived from the degradation of nucleic acids and lipids, such as xanthine and glycine betaine, were an increased mole fraction of the targeted metabolites in the sinking particles relative to surface suspended particles, while algal-derived metabolites like the osmolyte dimethylsulfoniopropionate were a smaller fraction of the observed metabolites on the sinking particles. These compositional changes are shaped both by the removal of metabolites associated with detritus delivered from the surface ocean and by production of metabolites by the sinking particle-associated microbial communities. Further, they provide a basis for examining the types and quantities of metabolites that may be delivered to the deep sea by sinking particles.

We developed an efficient, scalable and inexpensive method for recovering cellular biomass from complex fluid matrices that cannot be processed using conventional filtration methods. The method uses chemical flocculation with iron oxyhydroxides, is capable of recovering greater than 90% of cellular biomass from fluids with more than 10

The profitability of next-generation biorefineries is acutely contingent on the discovery and utilization of biocatalysts that can valorize lignin. To this end, the metabolic catalogues of diverse microbiota have been mined previously using functional metagenomics in order to identify biocatalysts that can selectively degrade lignin into monoaromatic compounds. Herein, we have further improved the valorization factor of biorefining by deploying functional metagenomics toward the identification of a novel transaminase that can selectively functionalize lignin-derived monoaromatics to produce value-added feedstocks for pharmaceutical synthesis. We implemented a high-throughput colorimetric assay using o-xylylenediamine as the amino donor and successfully identified a transaminase that utilizes the canonical cofactor, pyridoxal 5′-phosphate, to aminate as many as 14 monoaromatic aldehydes and ketones. We subsequently identified the optimal conditions for enzyme activity towards the most favoured amino acceptor, benzaldehyde, including temperature, pH and choice of co-solvent. We also evaluated the specificity of the enzyme towards a variety of amino donors, as well as the optimal concentration of the most favoured amino donor. Significantly, the novel enzyme is markedly smaller than typical transaminases, and it is stably expressed in E. coli without any modifications to its amino acid sequence. Finally, we developed and implemented a computational methodology to assess the activity of the novel transaminase. The methodology is generalizable for assessing any transaminase and facilitates in silico screening of enzyme–substrate combinations in order to develop efficient biocatalytic routes to value-added amines. The computational pipeline is an ideal complement to metagenomics and opens new possibilities for biocatalyst discovery.

Humanized mice represent a valuable model system to study the development and functionality of the human immune system. In the RAG-hu mouse model highly immunodeficient Rag2−/−γc−/− mice are transplanted with human CD34+ hematopoietic stem cells, resulting in human hematopoiesis and a predominant production of B and T lymphocytes. Human adaptive immune responses have been detected towards a variety of antigens in humanized mice but both cellular and humoral immune responses tend to be weak and sporadically detected. The underlying mechanisms for inconsistent responses are poorly understood. Here, we analyzed the kinetics of human B cell development and antibody production in RAG-hu mice to better understand the lack of effective antibody responses. We found that T cell levels in blood did not significantly change from 8 to 28 weeks post-engraftment, while B cells reached a peak at 14 weeks. Concentrations of 3 antibody classes (IgM, IgG, IgA) were found to be at levels about 0.1% or less of normal human levels, but human antibodies were still detected up to 32 weeks after engraftment. Human IgM was detected in 92.5% of animals while IgG and IgA were detected in about half of animals. We performed flow cytometric analysis of human B cells in bone marrow, spleen, and blood to examine the presence of precursor B cells, immature B cells, naive B cells, and plasma B cells. We detected high levels of surface IgM+ B cells (immature and naive B cells) and low levels of plasma B cells in these organs, suggesting that B cells do not mature properly in this model. Low levels of human T cells in the spleen were observed, and we suggest that the lack of T cell help may explain poor B cell development and antibody responses. We conclude that human B cells that develop in humanized mice do not receive the signals necessary to undergo class-switching or to secrete antibody effectively, and we discuss strategies to potentially overcome these barriers.