Virus ecology and 7-year temporal dynamics across a permafrost thaw gradient.

Soil microorganisms are pivotal in the global carbon cycle, but the viruses that affect them and their impact on ecosystems are less understood. In this study, we explored the diversity, dynamics, and ecology of soil viruses through 379 metagenomes collected annually from 2010 to 2017. These samples spanned the seasonally thawed active layer of a permafrost thaw gradient, which included palsa, bog, and fen habitats. We identified 5051 virus operational taxonomic units (vOTUs), doubling the known viruses for this site. These vOTUs were largely ephemeral within habitats, suggesting a turnover at the vOTU level from year to year. While the diversity varied by thaw stage and depth-related patterns were specific to each habitat, the virus communities did not significantly change over time. The abundance ratios of virus to host at the phylum level did not show consistent trends across the thaw gradient, depth, or time. To assess potential ecosystem impacts, we predicted hosts in silico and found viruses linked to microbial lineages involved in the carbon cycle, such as methanotrophy and methanogenesis. This included the identification of viruses of Candidatus Methanoflorens, a significant global methane contributor. We also detected a variety of potential auxiliary metabolic genes, including 24 carbon-degrading glycoside hydrolases, six of which are uniquely terrestrial. In conclusion, these long-term observations enhance our understanding of soil viruses in the context of climate-relevant processes and provide opportunities to explore their role in terrestrial carbon cycling.

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations. AVAILABILITY AND IMPLEMENTATION: For kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

The diversity and evolution of microbial dissimilatory phosphite oxidation.

Phosphite is the most energetically favorable chemotrophic electron donor known, with a half-cell potential (Eo') of -650 mV for the PO43-/PO33- couple. Since the discovery of microbial dissimilatory phosphite oxidation (DPO) in 2000, the environmental distribution, evolution, and diversity of DPO microorganisms (DPOMs) have remained enigmatic, as only two species have been identified. Here, metagenomic sequencing of phosphite-enriched microbial communities enabled the genome reconstruction and metabolic characterization of 21 additional DPOMs. These DPOMs spanned six classes of bacteria, including the Negativicutes, Desulfotomaculia, Synergistia, Syntrophia, Desulfobacteria, and Desulfomonilia_A Comparing the DPO genes from the genomes of enriched organisms with over 17,000 publicly available metagenomes revealed the global existence of this metabolism in diverse anoxic environments, including wastewaters, sediments, and subsurface aquifers. Despite their newfound environmental and taxonomic diversity, metagenomic analyses suggested that the typical DPOM is a chemolithoautotroph that occupies low-oxygen environments and specializes in phosphite oxidation coupled to CO2 reduction. Phylogenetic analyses indicated that the DPO genes form a highly conserved cluster that likely has ancient origins predating the split of monoderm and diderm bacteria. By coupling microbial cultivation strategies with metagenomics, these studies highlighted the unsampled metabolic versatility latent in microbial communities. We have uncovered the unexpected prevalence, diversity, biochemical specialization, and ancient origins of a unique metabolism central to the redox cycling of phosphorus, a primary nutrient on Earth.

ORT: a workflow linking genome-scale metabolic models with reactive transport codes.

MOTIVATION: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As 'omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. RESULTS: We propose a workflow ('Omics to Reactive Transport-ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. AVAILABILITY AND IMPLEMENTATION: Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Methane-Oxidizing Activity Enhances Sulfamethoxazole Biotransformation in a Benthic Constructed Wetland Biomat.

Ammonia monooxygenase and analogous oxygenase enzymes contribute to pharmaceutical biotransformation in activated sludge. In this study, we hypothesized that methane monooxygenase can enhance pharmaceutical biotransformation within the benthic, diffuse periphytic sediments (i.e., "biomat") of a shallow, open-water constructed wetland. To test this hypothesis, we combined field-scale metatranscriptomics, porewater geochemistry, and methane gas fluxes to inform microcosms targeting methane monooxygenase activity and its potential role in pharmaceutical biotransformation. In the field, sulfamethoxazole concentrations decreased within surficial biomat layers where genes encoding for the particulate methane monooxygenase (pMMO) were transcribed by a novel methanotroph classified as Methylotetracoccus. Inhibition microcosms provided independent confirmation that methane oxidation was mediated by the pMMO. In these same incubations, sulfamethoxazole biotransformation was stimulated proportional to aerobic methane-oxidizing activity and exhibited negligible removal in the absence of methane, in the presence of methane and pMMO inhibitors, and under anoxia. Nitrate reduction was similarly enhanced under aerobic methane-oxidizing conditions with rates several times faster than for canonical denitrification. Collectively, our results provide convergent in situ and laboratory evidence that methane-oxidizing activity can enhance sulfamethoxazole biotransformation, with possible implications for the combined removal of nitrogen and trace organic contaminants in wetland sediments.

Sugar-Phosphate Toxicities Attenuate Salmonella Fitness in the Gut.

Pathogens are becoming resistant to antimicrobials at an increasing rate, and novel therapeutic strategies are needed. Using Salmonella as a model, we have investigated the induction of sugar-phosphate toxicity as a potential therapeutic modality. The approach entails providing a nutrient while blocking the catabolism of that nutrient, resulting in the accumulation of a toxic intermediate. We hypothesize that this build-up will decrease the fitness of the organism during infection given nutrient availability. We tested this hypothesis using mutants lacking one of seven genes whose mutation is expected to cause the accumulation of a toxic metabolic intermediate. The araD, galE, rhaD, glpD, mtlD, manA, and galT mutants were then provided the appropriate sugars, either in vitro or during gastrointestinal infection of mice. All but the glpD mutant had nutrient-dependent growth defects in vitro, suggestive of sugar-phosphate toxicity. During gastrointestinal infection of mice, five mutants had decreased fitness. Providing the appropriate nutrient in the animal's drinking water was required to cause fitness defects with the rhaD and manA mutants and to enhance the fitness defect of the araD mutant. The galE and mtlD mutants were severely attenuated regardless of the nutrient being provided in the drinking water. Homologs of galE are widespread among bacteria and in humans, rendering the specific targeting of bacterial pathogens difficult. However, the araD, mtlD, and rhaD genes are not present in humans, appear to be rare in most phyla of bacteria, and are common in several genera of Enterobacteriaceae, making the encoded enzymes potential narrow-spectrum therapeutic targets. IMPORTANCE Bacterial pathogens are becoming increasingly resistant to antibiotics. There is an urgent need to identify novel drug targets and therapeutic strategies. In this work we have assembled and characterized a collection of mutations in our model pathogen, Salmonella enterica, that block a variety of sugar utilization pathways in such a way as to cause the accumulation of a toxic sugar-phosphate. Mutations in three genes, rhaD, araD, and mtlD, dramatically decrease the fitness of Salmonella in a mouse model of gastroenteritis, suggesting that RhaD, AraD, and MtlD may be good narrow-spectrum drug targets. The induction of sugar-phosphate toxicities may be a therapeutic strategy that is broadly relevant to other bacterial and fungal pathogens.

Variation in Root Exudate Composition Influences Soil Microbiome Membership and Function.

Root exudation is one of the primary processes that mediate interactions between plant roots, microorganisms, and the soil matrix, yet the mechanisms by which exudation alters microbial metabolism in soils have been challenging to unravel. Here, utilizing distinct sorghum genotypes, we characterized the chemical heterogeneity between root exudates and the effects of that variability on soil microbial membership and metabolism. Distinct exudate chemical profiles were quantified and used to formulate synthetic root exudate treatments: a high-organic-acid treatment (HOT) and a high-sugar treatment (HST). To parse the response of the soil microbiome to different exudate regimens, laboratory soil reactors were amended with these root exudate treatments as well as a nonexudate control. Amplicon sequencing of the 16S rRNA gene illustrated distinct microbial diversity patterns and membership in response to HST, HOT, or control amendments. Exometabolite changes reflected these microbial community changes, and we observed enrichment of organic and amino acids, as well as possible phytohormones in the HST relative to the HOT and control. Linking the metabolic capacity of metagenome-assembled genomes in the HST to the exometabolite patterns, we identified microorganisms that could produce these phytohormones. Our findings emphasize the tractability of high-resolution multiomics tools to investigate soil microbiomes, opening the possibility of manipulating native microbial communities to improve specific soil microbial functions and enhance crop production. IMPORTANCE Decrypting the chemical interactions between plant roots and the soil microbiome is a gateway for future manipulation and management of the rhizosphere, a soil compartment critical to promoting plant fitness and yields. Our experimental results demonstrate how soil microbial community and genomic diversity is influenced by root exudates of differing chemical compositions and how changes in this microbiome result in altered production of plant-relevant metabolites. Together, these findings demonstrate the tractability of high-resolution multiomics tools to investigate soil microbiomes and provide new information on plant-soil environments useful for the development of efficient and precise microbiota management strategies in agricultural systems.

Pharmaceutical Biotransformation is Influenced by Photosynthesis and Microbial Nitrogen Cycling in a Benthic Wetland Biomat.

In shallow, open-water engineered wetlands, design parameters select for a photosynthetic microbial biomat capable of robust pharmaceutical biotransformation, yet the contributions of specific microbial processes remain unclear. Here, we combined genome-resolved metatranscriptomics and oxygen profiling of a field-scale biomat to inform laboratory inhibition microcosms amended with a suite of pharmaceuticals. Our analyses revealed a dynamic surficial layer harboring oxic-anoxic cycling and simultaneous photosynthetic, nitrifying, and denitrifying microbial transcription spanning nine bacterial phyla, with unbinned eukaryotic scaffolds suggesting a dominance of diatoms. In the laboratory, photosynthesis, nitrification, and denitrification were broadly decoupled by incubating oxic and anoxic microcosms in the presence and absence of light and nitrogen cycling enzyme inhibitors. Through combining microcosm inhibition data with field-scale metagenomics, we inferred microbial clades responsible for biotransformation associated with membrane-bound nitrate reductase activity (emtricitabine, trimethoprim, and atenolol), nitrous oxide reduction (trimethoprim), ammonium oxidation (trimethoprim and emtricitabine), and photosynthesis (metoprolol). Monitoring of transformation products of atenolol and emtricitabine confirmed that inhibition was specific to biotransformation and highlighted the value of oscillating redox environments for the further transformation of atenolol acid. Our findings shed light on microbial processes contributing to pharmaceutical biotransformation in open-water wetlands with implications for similar nature-based treatment systems.

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

DRAM for distilling microbial metabolism to automate the curation of microbiome function.

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.