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/.

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 CH4from the atmosphere, but so far, bacteria that can grow on atmospheric CH4have 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, namedMethylocapsa gorgona, is globally distributed in soils and closely related to uncultured members of the upland soil cluster α. CH4oxidation experiments and13C-single cell isotope analyses demonstrated that it oxidizes atmospheric CH4aerobically and assimilates carbon from both CH4and CO2. Its estimated specific affinity for CH4(a0s) is the highest for any cultivated methanotroph. However, growth on ambient air was also confirmed forMethylocapsa acidiphilaandMethylocapsa aurea, close relatives with a lower specific affinity for CH4, suggesting that the ability to utilize atmospheric CH4for growth is more widespread than previously believed. The closed genome ofM. gorgonaMG08 encodes a single particulate methane monooxygenase, the serine cycle for assimilation of carbon from CH4and CO2, and CO2fixation 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 CH4oxidizers 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.).

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target while remaining amenable to variable degrees of protein characterization, in the context of available information about the chemical/protein interaction and the molecular target itself. To accommodate this flexibility in the analysis, 3 levels of evaluation were developed. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of orthologs); the second level evaluates sequence similarity within selected functional domains (eg, ligand-binding domain); and the third level of analysis compares individual amino acid residue positions of importance for protein conformation and/or interaction with the chemical upon binding. Each level of the SeqAPASS analysis provides additional evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further evaluation, selection of appropriate species for testing, extrapolation of empirical toxicity data, and/or assessment of the cross-species relevance of adverse outcome pathways. Three case studies are described herein to demonstrate application of the SeqAPASS tool: the first 2 focused on predictions of pollinator susceptibility to molt-accelerating compounds and neonicotinoid insecticides, and the third on evaluation of cross-species susceptibility to strobilurin fungicides. These analyses illustrate challenges in species extrapolation and demonstrate the broad utility of SeqAPASS for risk-based decision making and research.

Antibiotic exposure in children has been associated with the risk of Inflammatory Bowel Disease (IBD). Since antibiotic use in children or in their pregnant mother can affect how the intestinal microbiome develops, we asked whether the transfer of an antibiotic-perturbed microbiota from mothers to their children could affect their risk of developing IBD. Here we demonstrate that germ-free adult pregnant mice inoculated with a gut microbial community shaped by antibiotic exposure transmitted their perturbed microbiota to their offspring with high fidelity. Without any direct or continued exposure to antibiotics, this dysbiotic microbiota in the offspring remained distinct from controls for at least 21 weeks. By using both IL-10-deficient and wild type mothers, we showed that both inoculum and genotype shape the microbiota populations in the offspring. Since IL10−/− mice are genetically susceptible to colitis, we could assess the risk due to maternal transmission of an antibiotic-perturbed microbiota. We found that the IL10−/− offspring that had received the perturbed gut microbiota developed markedly increased colitis. Taken together, our findings indicate that antibiotic exposure shaping the maternal gut microbiota has effects that extend to their offspring with both ecological and long-term disease consequences.

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.

This review focuses on biosynthesis of β-lactone rings in natural products. Biosynthetic routes to β-lactones and β-lactams are compared in the context of their chemical diversity and production by divergent organisms around the tree of life.

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.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.