Substrate promiscuity of xenobiotic-transforming hydrolases from stream biofilms impacted by treated wastewater.

Organic contaminants enter aquatic ecosystems from various sources, including wastewater treatment plant effluent. Freshwater biofilms play a major role in the removal of organic contaminants from receiving water bodies, but knowledge of the molecular mechanisms driving contaminant biotransformations in complex stream biofilm (periphyton) communities remains limited. Previously, we demonstrated that biofilms in experimental flume systems grown at higher ratios of treated wastewater (WW) to stream water displayed an increased biotransformation potential for a number of organic contaminants. We identified a positive correlation between WW percentage and biofilm biotransformation rates for the widely-used insect repellent, N,N-diethyl-meta-toluamide (DEET) and a number of other wastewater-borne contaminants with hydrolyzable moieties. Here, we conducted deep shotgun sequencing of flume biofilms and identified a positive correlation between WW percentage and metagenomic read abundances of DEET hydrolase (DH) homologs. To test the causality of this association, we constructed a targeted metagenomic library of DH homologs from flume biofilms. We screened our complete metagenomic library for activity with four different substrates, including DEET, and a subset thereof with 183 WW-related organic compounds. The majority of active hydrolases in the metagenomic library preferred aliphatic and aromatic ester substrates while, remarkably, only a single reference enzyme was capable of DEET hydrolysis. Of the 626 total enzyme-substrate combinations tested, approximately 5% were active enzyme-substrate pairs. Metagenomic DH family homologs revealed a broad substrate promiscuity spanning 22 different compounds when summed across all enzymes tested. We biochemically characterized the most promiscuous and active enzymes identified based on metagenomic analysis from uncultivated Rhodospirillaceae and Planctomycetaceae. In addition to characterizing new DH family enzymes, we exemplified a framework for linking metagenome-guided hypothesis generation with experimental validation. Overall, this study expands the scope of known enzymatic contaminant biotransformations for metagenomic hydrolases from WW-receiving stream biofilm communities.

Microbial Biocatalysis within Us: The Underexplored Xenobiotic Biotransformation Potential of the Urinary Tract Microbiota.

Enzymatic biotransformation of xenobiotics by the human microbiota mediates diet-drug-microbe-host interactions and affects human health. Most research on xenobiotics has focused on the gut microbiota while neglecting other body sites, yet over two-thirds of pharmaceuticals are primarily excreted in urine. As a result, the urinary microbiota is exposed to many xenobiotics in much higher concentrations than in the gut. Microbial xenobiotic biocatalysis in the bladder has implications for urinary tract infections and the emergence of antibiotic resistance. However, we have limited knowledge of biotransformations catalyzed by the urinary microbiota. In this perspective, we investigated differences in physicochemical conditions and microbial community composition between the gut and urinary tract. We used a comparative enzyme class mining approach to profile the distribution of xenobiotic-transforming enzyme homologs in genomes of urinary bacteria. Our analysis revealed a discontinuous distribution of enzyme classes even among closely related organisms. We detected diverse amidase homologs involved in pharmaceutical and dietary additive biotransformation pathways, pinpointing microbial candidates to validate for their involvement in xenobiotic transformations in urine. Overall, we highlight the biocatalytic potential of urinary tract bacteria as a lens to study how the human microbiota may respond and adapt to xenobiotic inputs.

Microbial dynamics in soils of the Damma glacier forefield show succession in the functional genetic potential.

Glacier retreat is a visible consequence of climate change worldwide. Although taxonomic change of the soil microbiomes in glacier forefields have been widely documented, how microbial genetic potential changes along succession is little known. Here, we used shotgun metagenomics to analyse whether the soil microbial genetic potential differed between four stages of soil development (SSD) sampled along three transects in the Damma glacier forefield (Switzerland). The SSDs were characterized by an increasing vegetation cover, from barren soil, to biological soil crust, to sparsely vegetated soil and finally to vegetated soil. Results suggested that SSD significantly influenced microbial genetic potential, with the lowest functional diversity surprisingly occurring in the vegetated soils. Overall, carbohydrate metabolism and secondary metabolite biosynthesis genes overrepresented in vegetated soils, which could be partly attributed to plant-soil feedbacks. For C degradation, glycoside hydrolase genes enriched in vegetated soils, while auxiliary activity and carbohydrate esterases genes overrepresented in barren soils, suggested high labile C degradation potential in vegetated, and high recalcitrant C degradation potential in barren soils. For N-cycling, organic N degradation and synthesis genes dominated along succession, and gene families involved in nitrification were overrepresented in barren soils. Our study provides new insights into how the microbial genetic potential changes during soil formation along the Damma glacier forefield.

Structure-guided metagenome mining to tap microbial functional diversity.

Scientists now have access to millions of accurate three-dimensional (3D) models of protein structures. How do we leverage 3D structural models to learn about microbial functions encoded in metagenomes? Here, we review recent developments using protein structural features to mine metagenomes from diverse environments ranging from the human gut to soil and ocean viromes. We compare 3D protein structural methods to characterize antibiotic resistance phenotypes, nutrient cycling, and host-drug-microbe interactions. Broadly, we encourage the scientific community to look beyond global sequence and structure alignments by considering fine-grained descriptors such as distance to ligand, active site, and tertiary interactions between amino acid residues scaling to microbiomes. Finally, we highlight structure-inspired approaches to chart new areas of microbial protein-coding sequence space.

Biosynthetic potential of the global ocean microbiome.

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups1, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters ('Candidatus Eudoremicrobiaceae') that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.

Ribosomally derived lipopeptides containing distinct fatty acyl moieties.

Lipopeptides represent a large group of microbial natural products that include important antibacterial and antifungal drugs and some of the most-powerful known biosurfactants. The vast majority of lipopeptides comprise cyclic peptide backbones N-terminally equipped with various fatty acyl moieties. The known compounds of this type are biosynthesized by nonribosomal peptide synthetases, giant enzyme complexes that assemble their products in a non-gene-encoded manner. Here, we report the genome-guided discovery of ribosomally derived, fatty-acylated lipopeptides, termed selidamides. Heterologous reconstitution of three pathways, two from cyanobacteria and one from an arctic, ocean-derived alphaproteobacterium, allowed structural characterization of the probable natural products and suggest that selidamides are widespread over various bacterial phyla. The identified representatives feature cyclic peptide moieties and fatty acyl units attached to (hydroxy)ornithine or lysine side chains by maturases of the GCN5-related N-acetyltransferase superfamily. In contrast to nonribosomal lipopeptides that are usually produced as congener mixtures, the three selidamides are selectively fatty acylated with C10, C12, or C16 fatty acids, respectively. These results highlight the ability of ribosomal pathways to emulate products with diverse, nonribosomal-like features and add to the biocatalytic toolbox for peptide drug improvement and targeted discovery.

Artificial intelligence for microbial biotechnology: beyond the hype.

It has been a landmark year for artificial intelligence (AI) and biotechnology. Perhaps the most noteworthy of these advances was Google DeepMind's AlphaFold2 algorithm which smashed records in protein structure prediction (Jumper et al., 2021, Nature, 596, 583) complemented by progress made by other research groups around the globe (Baek et al., 2021, Science, 373, 871; Zheng et al., 2021, Proteins). For the first time in history, AI achieved protein structure models rivalling the accuracy of experimentally determined structures. The power of accurate protein structure prediction at our fingertips has countless implications for drug discovery, de novo protein design and fundamental research in chemical biology. While acknowledging the significance of these breakthroughs, this perspective aims to cut through the hype and examine some key limitations using AlphaFold2 as a lens to consider the broader implications of AI for microbial biotechnology for the next 15 years and beyond.

Microbial storage and its implications for soil ecology.

Organisms throughout the tree of life accumulate chemical resources, in particular forms or compartments, to secure their availability for future use. Here we review microbial storage and its ecological significance by assembling several rich but disconnected lines of research in microbiology, biogeochemistry, and the ecology of macroscopic organisms. Evidence is drawn from various systems, but we pay particular attention to soils, where microorganisms play crucial roles in global element cycles. An assembly of genus-level data demonstrates the likely prevalence of storage traits in soil. We provide a theoretical basis for microbial storage ecology by distinguishing a spectrum of storage strategies ranging from surplus storage (storage of abundant resources that are not immediately required) to reserve storage (storage of limited resources at the cost of other metabolic functions). This distinction highlights that microorganisms can invest in storage at times of surplus and under conditions of scarcity. We then align storage with trait-based microbial life-history strategies, leading to the hypothesis that ruderal species, which are adapted to disturbance, rely less on storage than microorganisms adapted to stress or high competition. We explore the implications of storage for soil biogeochemistry, microbial biomass, and element transformations and present a process-based model of intracellular carbon storage. Our model indicates that storage can mitigate against stoichiometric imbalances, thereby enhancing biomass growth and resource-use efficiency in the face of unbalanced resources. Given the central roles of microbes in biogeochemical cycles, we propose that microbial storage may be influential on macroscopic scales, from carbon cycling to ecosystem stability.

A procedure for removal of cyanuric acid in swimming pools using a cell-free thermostable cyanuric acid hydrolase.

Cyanuric acid (CYA) is used commercially for maintaining active chlorine to inactivate microbial and viral pathogens in swimming pools and hot tubs. Repeated CYA addition can cause a lack of available chlorine and adequate disinfection. Acceptable CYA levels can potentially be restored via cyanuric acid hydrolases (CAH), enzymes that hydrolyze CYA to biuret under mild conditions. Here we describe a previously unknown CAH enzyme from Pseudolabrys sp. Root1462 (CAH-PR), mined from public databases by bioinformatic analysis of potential CAH genes, which we show to be suitable in a cell-free form for industrial applications based upon favorable enzymatic and physical properties, combined with high-yield expression in aerobic cell culture. The kinetic parameters and modeled structure were similar to known CAH enzymes, but the new enzyme displayed a surprising thermal and storage stability. The new CAH enzyme was applied, following addition of inexpensive sodium sulfite, to hydrolyze CYA to biuret. At the desired endpoint, hypochlorite addition inactivated remaining enzyme and oxidized biuret to primarily dinitrogen and carbon dioxide gases. The mechanism of biuret oxidation with hypochlorite under conditions relevant to recreational pools is described.

A roadmap for metagenomic enzyme discovery.

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

Dissecting Disease-Suppressive Rhizosphere Microbiomes by Functional Amplicon Sequencing and 10× Metagenomics.

Disease-suppressive soils protect plants against soilborne fungal pathogens that would otherwise cause root infections. Soil suppressiveness is, in most cases, mediated by the antagonistic activity of the microbial community associated with the plant roots. Considering the enormous taxonomic and functional diversity of the root-associated microbiome, identification of the microbial genera and mechanisms underlying this phenotype is challenging. One approach to unravel the underlying mechanisms is to identify metabolic pathways enriched in the disease-suppressive microbial community, in particular, pathways that harbor natural products with antifungal properties. An important class of these natural products includes peptides produced by nonribosomal peptide synthetases (NRPSs). Here, we applied functional amplicon sequencing of NRPS-associated adenylation domains (A domains) to a collection of eight soils that are suppressive or nonsuppressive (i.e., conducive) to Fusarium culmorum, a fungal root pathogen of wheat. To identify functional elements in the root-associated bacterial community, we developed an open-source pipeline, referred to as dom2BGC, for amplicon annotation and putative gene cluster reconstruction through analyzing A domain co-occurrence across samples. We applied this pipeline to rhizosphere communities from four disease-suppressive and four conducive soils and found significant similarities in NRPS repertoires between suppressive soils. Specifically, several siderophore biosynthetic gene clusters were consistently associated with suppressive soils, hinting at competition for iron as a potential mechanism of suppression. Finally, to validate dom2BGC and to allow more unbiased functional metagenomics, we performed 10× metagenomic sequencing of one suppressive soil, leading to the identification of multiple gene clusters potentially associated with the disease-suppressive phenotype. IMPORTANCE Soil-borne plant-pathogenic fungi continue to be a major threat to agriculture and horticulture. The genus Fusarium in particular is one of the most devastating groups of soilborne fungal pathogens for a wide range of crops. Our approach to develop novel sustainable strategies to control this fungal root pathogen is to explore and exploit an effective, yet poorly understood naturally occurring protection, i.e., disease-suppressive soils. After screening 28 agricultural soils, we recently identified four soils that were suppressive to root disease of wheat caused by Fusarium culmorum. We also confirmed, via sterilization and transplantation, that the microbiomes of these soils play a significant role in the suppressive phenotype. By adopting nonribosomal peptide synthetase (NRPS) functional amplicon screening of suppressive and conducive soils, we here show how computationally driven comparative analysis of combined functional amplicon and metagenomic data can unravel putative mechanisms underlying microbiome-associated plant phenotypes.

Development of the Organonitrogen Biodegradation Database: Teaching Bioinformatics and Collaborative Skills to Undergraduates during a Pandemic.

Physical distancing and inaccessibility to laboratory facilities created an opportunity to transition undergraduate research experiences to remote, digital platforms, adding another level of pedagogy to their training. Basic bioinformatics skills together with critical analysis of scientific literature are essential for addressing research questions in modern biology. The work presented here describes a fully online, collaborative research experience created to allow undergraduate students to learn those skills. The research experience was focused on the development and implementation of the Organonitrogen Biodegradation Database (ONDB, z.umn.edu/ondb). The ONDB was developed to catalog information about the cost, chemical properties, and biodegradation potential of commonly used organonitrogen compounds. A cross-institutional team of undergraduate researchers worked in collaboration with two faculty members and a postdoctoral fellow to develop the database. Students carried out extensive online literature searches and used a biodegradation prediction website to research and represent the microbial catabolism of different organonitrogen compounds. Participants employed computational tools such as R, Shiny, and flexdashboard to construct the database pages and interactive web interface for the ONDB. Worksheets and forms were created to encourage other students and researchers to gather information about organonitrogen compounds and expand the database. Student progress was evaluated through biweekly project meetings, presentations, and a final reflection. The ONDB undergraduate research experience provided a platform for students to learn bioinformatics skills while simultaneously developing a teaching and research tool for others.

Analysis and modeling of Blastomyces dermatitidis environmental prevalence in Minnesota using soil collected to compare basal and outbreak levels.

Outbreaks of blastomycosis, caused by the fungus Blastomyces dermatitidis, occur in endemic areas of the United States and Canada but the geographic range of blastomycosis is expanding. Previous studies inferred the location of B. dermatitidis through epidemiologic data associated with outbreaks because culture of B. dermatitidis from the environment is often unsuccessful. In this study, we used a culture-independent, PCR-based method to identify B. dermatitidis DNA in environmental samples using the BAD1 promoter region. We tested 250 environmental samples collected in Minnesota, either associated with blastomycosis outbreaks or environmental samples collected from high- and low-endemic regions to determine basal prevalence of B. dermatitidis in the environment. We identified a fifth BAD1 promoter haplotype of B. dermatitidis prevalent in Minnesota. Ecological niche analysis identified latitude, longitude, elevation, and site classification as environmental parameters associated with the presence of B. dermatitidis Using this analysis, a Random Forest model predicted B. dermatitidis presence in basal environmental samples with 75% accuracy. These data support use of culture-independent, PCR-based environmental sampling to track spread into new regions and to characterize the unknown B. dermatitidis environmental niche.Importance Upon inhalation of spores from the fungus Blastomyces dermatitidis from the environment, humans and animals can develop the disease blastomycosis. Based on disease epidemiology, B. dermatitidis is known to be endemic in the United States and Canada around the Great Lakes and in the Ohio and Mississippi River Valleys but is starting to emerge in other areas. B. dermatitidis is extremely difficult to culture from the environment so little is known about the environmental reservoirs for this pathogen. We used a culture-independent PCR-based assay to identify the presence of B. dermatitidis DNA in soil samples from Minnesota. By combining molecular data with ecological niche modeling, we were able to predict the presence of B. dermatitidis in environmental samples with 75% accuracy and to define characteristics of the B. dermatitidis environmental niche. Importantly, we showed the effectiveness of using a PCR-based assay to identify B. dermatitidis in environmental samples.

Global analysis of adenylate-forming enzymes reveals ß-lactone biosynthesis pathway in pathogenic Nocardia.

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and ß-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including ß-lactone synthetases. Our classifier predicted ß-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate ß-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the ß-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

The ever-expanding limits of enzyme catalysis and biodegradation: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds.

Biodegradation is simply the metabolism of anthropogenic, or otherwise unwanted, chemicals in our environment, typically by microorganisms. The metabolism of compounds commonly found in living things is limited to several thousand metabolites whereas ∼100 million chemical substances have been devised by chemical synthesis, and ∼100 000 are used commercially. Since most of those compounds are not natively found in living things, and some are toxic or carcinogenic, the question arises as to whether there is some organism somewhere with the enzymes that can biodegrade them. Repeatedly, anthropogenic chemicals have been denoted 'non-biodegradable,' only to find they are reactive with one or more enzyme(s). Enzyme reactivity has been organized into categories of functional group transformations. The discovery of new functional group transformations has continually expanded our knowledge of enzymes and biodegradation. This expansion of new-chemical biodegradation is driven by the evolution and spread of newly evolved enzymes. This review describes the biodegradation of widespread commercial chemicals with a focus on four classes: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds. Polyaromatic hydrocarbons include some of the most carcinogenic compounds known. Polychlorinated compounds include polychlorinated biphenyls (PCBs) and many pesticides of the twentieth century. Polyfluorinated compounds are a major focus of bioremediation efforts today. Polymers are clogging landfills, killing aquatic species in the oceans and increasingly found in our bodies. All of these classes of compounds, each thought at one time to be non-biodegradable, have been shown to react with natural enzymes. The known limits of enzyme catalysis, and hence biodegradation, are continuing to expand.

In Vivo Assay Reveals Microbial OleA Thiolases Initiating Hydrocarbon and ß-Lactone Biosynthesis.

OleA, a member of the thiolase superfamily, is known to catalyze the Claisen condensation of long-chain acyl coenzyme A (acyl-CoA) substrates, initiating metabolic pathways in bacteria for the production of membrane lipids and ß-lactone natural products. OleA homologs are found in diverse bacterial phyla, but to date, only one homodimeric OleA has been successfully purified to homogeneity and characterized in vitro A major impediment for the identification of new OleA enzymes has been protein instability and time-consuming in vitro assays. Here, we developed a bioinformatic pipeline to identify OleA homologs and a new rapid assay to screen OleA enzyme activity in vivo and map their taxonomic diversity. The screen is based on the discovery that OleA displayed surprisingly high rates of p-nitrophenyl ester hydrolysis, an activity not shared by other thiolases, including FabH. The high rates allowed activity to be determined in vitro and with heterologously expressed OleA in vivo via the release of the yellow p-nitrophenol product. Seventy-four putative oleA genes identified in the genomes of diverse bacteria were heterologously expressed in Escherichia coli, and 25 showed activity with p-nitrophenyl esters. The OleA proteins tested were encoded in variable genomic contexts from seven different phyla and are predicted to function in distinct membrane lipid and ß-lactone natural product metabolic pathways. This study highlights the diversity of unstudied OleA proteins and presents a rapid method for their identification and characterization.IMPORTANCE Microbially produced ß-lactones are found in antibiotic, antitumor, and antiobesity drugs. Long-chain olefinic membrane hydrocarbons have potential utility as fuels and specialty chemicals. The metabolic pathway to both end products share bacterial enzymes denoted as OleA, OleC, and OleD that transform acyl-CoA cellular intermediates into ß-lactones. Bacteria producing membrane hydrocarbons via the Ole pathway additionally express a ß-lactone decarboxylase, OleB. Both ß-lactone and olefin biosynthesis pathways are initiated by OleA enzymes that define the overall structure of the final product. There is currently very limited information on OleA enzymes apart from the single representative from Xanthomonas campestris In this study, bioinformatic analysis identified hundreds of new, putative OleA proteins, 74 proteins were screened via a rapid whole-cell method, leading to the identification of 25 stably expressed OleA proteins representing seven bacteria phyla.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function.

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

Cyanuric Acid Biodegradation via Biuret: Physiology, Taxonomy, and Geospatial Distribution.

Cyanuric acid is an industrial chemical produced during the biodegradation of s-triazine pesticides. The biodegradation of cyanuric acid has been elucidated using a single model system, Pseudomonas sp. strain ADP, in which cyanuric acid hydrolase (AtzD) opens the s-triazine ring and AtzEG deaminates the ring-opened product. A significant question remains as to whether the metabolic pathway found in Pseudomonas sp. ADP is the exception or the rule in bacterial genomes globally. Here, we show that most bacteria utilize a different pathway, metabolizing cyanuric acid via biuret. The new pathway was determined by reconstituting the pathway in vitro with purified enzymes and by mining more than 250,000 genomes and metagenomes. We isolated soil bacteria that grow on cyanuric acid as a sole nitrogen source and showed that the genome from a Herbaspirillum strain had a canonical cyanuric acid hydrolase gene but different flanking genes. The flanking gene trtB encoded an enzyme that we show catalyzed the decarboxylation of the cyanuric acid hydrolase product, carboxybiuret. The reaction generated biuret, a pathway intermediate further transformed by biuret hydrolase (BiuH). The prevalence of the newly defined pathway was determined by cooccurrence analysis of cyanuric acid hydrolase genes and flanking genes. Here, we show the biuret pathway was more than 1 order of magnitude more prevalent than the original Pseudomonas sp. ADP pathway. Mining a database of over 40,000 bacterial isolates with precise geospatial metadata showed that bacteria with concurrent cyanuric acid and biuret hydrolase genes were distributed throughout the United States.IMPORTANCE Cyanuric acid is produced naturally as a contaminant in urea fertilizer, and it is used as a chlorine stabilizer in swimming pools. Cyanuric acid-degrading bacteria are used commercially in removing cyanuric acid from pool water when it exceeds desired levels. The total volume of cyanuric acid produced annually exceeds 200 million kilograms, most of which enters the natural environment. In this context, it is important to have a global understanding of cyanuric acid biodegradation by microbial communities in natural and engineered systems. Current knowledge of cyanuric acid metabolism largely derives from studies on the enzymes from a single model organism, Pseudomonas sp. ADP. In this study, we obtained and studied new microbes and discovered a previously unknown cyanuric acid degradation pathway. The new pathway identified here was found to be much more prevalent than the pathway previously established for Pseudomonas sp. ADP. In addition, the types of environment, taxonomic prevalences, and geospatial distributions of the different cyanuric acid degradation pathways are described here.

Widespread soil bacterium that oxidizes atmospheric methane.

The global atmospheric level of methane (CH4), the second most important greenhouse gas, is currently increasing by ∼10 million tons per year. Microbial oxidation in unsaturated soils is the only known biological process that removes CH4 from the atmosphere, but so far, bacteria that can grow on atmospheric CH4 have eluded all cultivation efforts. In this study, we have isolated a pure culture of a bacterium, strain MG08 that grows on air at atmospheric concentrations of CH4 [1.86 parts per million volume (p.p.m.v.)]. This organism, named Methylocapsa gorgona, is globally distributed in soils and closely related to uncultured members of the upland soil cluster α. CH4 oxidation experiments and 13C-single cell isotope analyses demonstrated that it oxidizes atmospheric CH4 aerobically and assimilates carbon from both CH4 and CO2 Its estimated specific affinity for CH4 (a0s) is the highest for any cultivated methanotroph. However, growth on ambient air was also confirmed for Methylocapsa acidiphila and Methylocapsa aurea, close relatives with a lower specific affinity for CH4, suggesting that the ability to utilize atmospheric CH4 for growth is more widespread than previously believed. The closed genome of M. gorgona MG08 encodes a single particulate methane monooxygenase, the serine cycle for assimilation of carbon from CH4 and CO2, and CO2 fixation via the recently postulated reductive glycine pathway. It also fixes dinitrogen and expresses the genes for a high-affinity hydrogenase and carbon monoxide dehydrogenase, suggesting that atmospheric CH4 oxidizers harvest additional energy from oxidation of the atmospheric trace gases carbon monoxide (0.2 p.p.m.v.) and hydrogen (0.5 p.p.m.v.).

Mechanism of a Standalone ß-Lactone Synthetase: New Continuous Assay for a Widespread ANL Superfamily Enzyme.

Enzyme-catalyzed ß-lactone formation from ß-hydroxy acids is a crucial step in bacterial biosynthesis of ß-lactone natural products and membrane hydrocarbons. We developed a novel, continuous assay for ß-lactone synthetase activity using synthetic ß-hydroxy acid substrates with alkene or alkyne moieties. ß-Lactone formation is followed by rapid decarboxylation to form a conjugated triene chromophore for real-time evaluation by UV/Vis spectroscopy. The assay was used to determine steady-state kinetics of a long-chain ß-lactone synthetase, OleC, from the plant pathogen Xanthomonas campestris. Site-directed mutagenesis was used to test the involvement of conserved active site residues in Mg2+ and ATP binding. A previous report suggested OleC adenylated the substrate hydroxy group. Here we present several lines of evidence, including hydroxylamine trapping of the AMP intermediate, to demonstrate the substrate carboxyl group is adenylated prior to making the ß-lactone final product. A panel of nine substrate analogues were used to investigate the substrate specificity of X. campestris OleC by HPLC and GC-MS. Stereoisomers of 2-hexyl-3hydroxyoctanoic acid were synthesized and OleC preferred the (2R,3S) diastereomer consistent with the stereo-preference of upstream and downstream pathway enzymes. This biochemical knowledge was used to guide phylogenetic analysis of the ß-lactone synthetases to map their functional diversity within the acyl-CoA synthetase, NRPS adenylation domain, and luciferase superfamily.