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Abstract Background Prokaryotic microbes have impacted marine biogeochemical cycles for billions of years. Viruses also impact these cycles, through lysis, horizontal gene transfer, and encoding and expressing genes that contribute to metabolic reprogramming of prokaryotic cells. While this impact is difficult to quantify in nature, we hypothesized that it can be examined by surveying virus-encoded auxiliary metabolic genes (AMGs) and assessing their ecological context. Results We systematically developed a global ocean AMG catalog by integrating previously described and newly identified AMGs and then placed this catalog into ecological and metabolic contexts relevant to ocean biogeochemistry. From 7.6 terabases of Tara Oceans paired prokaryote- and virus-enriched metagenomic sequence data, we increased known ocean virus populations to 579,904 (up 16%). From these virus populations, we then conservatively identified 86,913 AMGs that grouped into 22,779 sequence-based gene clusters, 7248 (~ 32%) of which were not previously reported. Using our catalog and modeled data from mock communities, we estimate that ~ 19% of ocean virus populations carry at least one AMG. To understand AMGs in their metabolic context, we identified 340 metabolic pathways encoded by ocean microbes and showed that AMGs map to 128 of them. Furthermore, we identified metabolic “hot spots” targeted by virus AMGs, including nine pathways where most steps (≥ 0.75) were AMG-targeted (involved in carbohydrate, amino acid, fatty acid, and nucleotide metabolism), as well as other pathways where virus-encoded AMGs outnumbered cellular homologs (involved in lipid A phosphates, phosphatidylethanolamine, creatine biosynthesis, phosphoribosylamine-glycine ligase, and carbamoyl-phosphate synthase pathways). Conclusions Together, this systematically curated, global ocean AMG catalog and analyses provide a valuable resource and foundational observations to understand the role of viruses in modulating global ocean metabolisms and their biogeochemical implications. Video Abstract

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

Abstract Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus, as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway.

The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.

Abstract Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.

Traving et al. use small subunit ribosomal RNA gene sequencing to examine spatial and temporal trends in bacterial and archaeal community structure during a large marine warm water surface anomaly, the Blob. Their findings suggest that community structure shifted during the Blob, with taxa considered free-living and chemoautotrophic prevailing under these unusual conditions.

Scientific and technological advances within the life sciences have enabled the generation of very large datasets that must be processed, stored, and managed computationally. Researchers increasingly require data science skills to work with these datasets at scale in order to convert information into actionable insights, and undergraduate educators have started to adapt pedagogies to fulfill this need. Course-based undergraduate research experiences (CUREs) have emerged as a leading model for providing large numbers of students with authentic research experiences including data science. Originally designed around wet-lab research experiences, CURE models have proliferated and diversified globally to accommodate a broad range of academic disciplines. Within microbiology, diversity metrics derived from microbiome sequence information have become standard data products in research. In some cases, researchers have deposited data in publicly accessible repositories, providing opportunities for reproducibility and comparative analysis. In 2020, with the onset of the COVID-19 pandemic and concomitant shift to remote learning, the University of British Columbia set out to develop an online data science CURE in microbiology. A team of faculty with collective domain expertise in microbiome research and CUREs developed and implemented a data science CURE in which teams of students learn to work with large publicly available datasets, develop and execute a novel scientific research project, and disseminate their findings in the online Undergraduate Journal of Experimental Microbiology and Immunology. Analysis of the resulting student-authored research articles, including comments from peer reviews conducted by subject matter experts, demonstrate high levels of learning effectiveness. Here, we describe core insights from course development and implementation based on a reverse course design model. Our approach to course design may be applicable to the development of other data science CUREs.

Despite their recognized potential, current applications of cyanobacteria as microbial cell factories remain in early stages of development. This is partly due to the fact that engineered strains are often difficult to grow at scale. This technical challenge contrasts with the dense and highly productive cyanobacteria populations thriving in many natural environments. It has been proposed that the selection of strains pre-adapted for growth in industrial photobioreactors could enable more productive cultivation outcomes. Here, we described the initial morphological, physiological, and genomic characterization of Phormidium yuhuli AB48 isolated from an industrial photobioreactor environment. P. yuhuli AB48 is a filamentous phototactic cyanobacterium with a growth rate comparable to Synechocystis sp. PCC 6803. The isolate forms dense biofilms under high salinity and alkaline conditions and manifests a similar nutrient profile to Arthrospira platensis (Spirulina). We sequenced, assembled, and analyzed the P. yuhuli AB48 genome, the first closed circular isolate reference genome for a member of the Phormidium genus. We then used cultivation experiments in combination with proteomics and metabolomics to investigate growth characteristics and phenotypes related to industrial scale cultivation, including nitrogen and carbon utilization, salinity, and pH acclimation, as well as antibiotic resistance. These analyses provide insight into the biological mechanisms behind the desirable growth properties manifested by P. yuhuli AB48 and position it as a promising microbial cell factory for industrial-scale bioproduction[221, 1631].

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.

Here, the authors report a large-scale comparative analysis of

Microbial communities play integral roles in driving nutrient and energy transformations in the ocean, collectively contributing to fundamental biogeochemical cycles. Although it is well known that these communities are stratified within the water column, there remains limited knowledge of how metabolic pathways are distributed and expressed. Here, we investigate pathway distribution and expression patterns from surface (5 m) to deep dark ocean (4000 m) at three stations along a 2765 km transect in the western South Atlantic Ocean. This study is based on new data, consisting of 43 samples for 16S rRNA gene sequencing, 20 samples for metagenomics and 19 samples for metatranscriptomics. Consistent with previous observations, we observed vertical zonation of microbial community structure largely partitioned between light and dark ocean waters. The metabolic pathways inferred from genomic sequence information and gene expression stratified with depth. For example, expression of photosynthetic pathways increased in sunlit waters. Conversely, expression of pathways related to carbon conversion processes, particularly those involving recalcitrant and organic carbon degradation pathways (i.e., oxidation of formaldehyde) increased in dark ocean waters. We also observed correlations between indicator taxa for specific depths with the selective expression of metabolic pathways. For example, SAR202, prevalent in deep waters, was strongly correlated with expression of the methanol oxidation pathway. From a biogeographic perspective, microbial communities along the transect encoded similar metabolic potential with some latitudinal stratification in gene expression. For example, at a station influenced by input from the Amazon River, expression of pathways related to oxidative stress was increased. Finally, when pairing distinct correlations between specific particulate metabolites (e.g., DMSP, AMP and MTA) and both the taxonomic microbial community and metatranscriptomic pathways across depth and space, we were able to observe how changes in the marine metabolite pool may be influenced by microbial function and vice versa. Taken together, these results indicate that marine microbial communities encode a core repertoire of widely distributed metabolic pathways that are differentially regulated along nutrient and energy gradients. Such pathway distribution patterns are consistent with robustness in microbial food webs and indicate a high degree of functional redundancy.

Extracellular enzyme activity is a well-established parameter for evaluating microbial biogeochemical roles in marine ecosystems. The presence and activity of extracellular enzymes in seawater provide insights into the quality and quantity of organic matter being processed by the present microorganisms. A key challenge in our understanding of these processes is to decode the extracellular enzyme repertoire and activities of natural communities at the single-cell level. Current measurements are carried out on bulk or size-fractionated samples capturing activities of mixed populations. This approach – even with size-fractionation – cannot be used to trace enzymes back to their producers, nor distinguish the active microbial members, leading to a disconnect between measured activities and the producer cells. By targeting extracellular enzymes and resolving their activities at the single-cell level, we can investigate underlying phenotypic heterogeneity among clonal or closely related organisms, characterize enzyme kinetics under varying environmental conditions, and resolve spatio-temporal distribution of individual enzyme producers within natural communities. In this perspective piece, we discuss state-of-the-art technologies in the fields of microfluidic droplets and functional screening of prokaryotic cells for measuring enzyme activity in marine seawater samples, one cell at a time. We further elaborate on how this single-cell approach can be used to address research questions that cannot be answered with current methods, as pertinent to the enzymatic degradation of organic matter by marine microorganisms.

Certain members of the Actinobacteria and Proteobacteria are known to degrade polyethylene terephthalate (PET). 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 several PETase candidates from Flavobacteriaceae and Porphyromonadaceae. Among them, two promiscuous and cold-active esterases derived from Aequorivita sp. (PET27) and Kaistella jeonii (PET30) showed depolymerizing activity on polycaprolactone (PCL), amorphous PET foil and on the polyester polyurethane Impranil® DLN. PET27 is a 37.8 kDa enzyme that released an average of 174.4 nmol terephthalic acid (TPA) after 120 h at 30°C from a 7 mg PET foil platelet in a 200 μl reaction volume, 38-times more than PET30 (37.4 kDa) released under the same conditions. The crystal structure of PET30 without its C-terminal Por-domain (PET30ΔPorC) was solved at 2.1 Å and displays high structural similarity to the IsPETase. PET30 shows a Phe-Met-Tyr substrate binding motif, which seems to be a unique feature, as IsPETase, LCC and PET2 all contain Tyr-Met-Trp binding residues, while PET27 possesses a Phe-Met-Trp motif that is identical to Cut190. Microscopic analyses showed that K. jeonii cells are indeed able to bind on and colonize PET surfaces after a few days of incubation. Homologs of PET27 and PET30 were detected in metagenomes, predominantly aquatic habitats, encompassing a wide range of different global climate zones and suggesting a hitherto unknown influence of this bacterial phylum on man-made polymer degradation.

Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabolic relationships within and between cells. Here, we present mlLGPR, multi-label based on logistic regression for pathway prediction, a software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks in organismal and multi-organismal datasets. We evaluated mlLGPR performance using a corpora of 12 experimental datasets manifesting diverse multi-label properties, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previous reports for organismal genomes and identify specific challenges associated with features engineering and training data for community-level metabolic inference. Author summary Predicting the complex series of metabolic interactions e.g. pathways, within and between cells from genomic sequence information is an integral problem in biology linking genotype to phenotype. This is a prerequisite to both understanding fundamental life processes and ultimately engineering these processes for specific biotechnological applications. A pathway prediction problem exists because we have limited knowledge of the reactions and pathways operating in cells even in model organisms like Esherichia coli where the majority of protein functions are determined. To improve pathway prediction outcomes for genomes at different levels of complexity and completion we have developed mlLGPR, multi-label based on logistic regression for pathway prediction, a scalable open source software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks. We benchmark mlLGPR performance against other inference methods providing a code base and metrics for continued application of machine learning methods to the pathway prediction problem.

Little is known about Marinimicrobia, a group of bacteria that are prevalent in the oceans. Here, the authors study global populations of Marinimicrobia using single-cell genomics, metagenomics and metatranscriptomics, showing potential co-metabolic interactions and participation in the sulfur and nitrogen cycles.

ABSTRACT 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.

The Scientific Committee on Oceanographic Research (SCOR) Working Group 144 Microbial Community Responses to Ocean Deoxygenation workshop held in Vancouver, B.C on July 2014 had the primary objective of initiating a process to standardize operating procedures for compatible process rate and multi-omic (DNA, RNA, protein, and metabolite) data collection in marine oxygen minimum zones and other oxygen depleted waters. Workshop attendees participated in practical sampling and experimental activities in Saanich Inlet, British Columbia, a seasonally anoxic fjord. Experiments were designed to compare and cross-calibrate in situ versus bottle sampling methods to determine effects on microbial community structure and potential activity when using different filter combinations, filtration methods, and sample volumes. Resulting biomass was preserved for small subunit ribosomal RNA (SSU or 16S rRNA) and SSU rRNA gene (rDNA) amplicon sequencing followed by downstream statistical and visual analyses. Results from these analyses showed that significant community shifts occurred between in situ versus on ship processed samples. For example, Bacteroidetes, Alphaproteobacteria, and Opisthokonta associated with on-ship filtration onto 0.4 μm filters increased fivefold compared to on-ship in-line 0.22 μm filters or 0.4 μm filters processed and preserved in situ. In contrast, Planctomycetes associated with 0.4 μm in situ filters increased fivefold compared to on-ship filtration onto 0.4 μm filters and on-ship in-line 0.22 μm filters. In addition, candidate divisions and Chloroflexi were primarily recovered when filtered onto 0.4 μm filters in situ. Results based on rRNA:rDNA ratios for microbial indicator groups revealed previously unrecognized roles of candidate divisions, Desulfarculales, and Desulfuromandales in sulfur cycling, carbon fixation and fermentation within anoxic basin waters. Taken together, filter size and in situ versus on-ship filtration had the largest impact on recovery of microbial groups with the potential to influence downstream metabolic reconstruction and process rate measurements. These observations highlight the need for establishing standardized and reproducible techniques that facilitate cross-scale comparisons and more accurately assess in situ activities of microbial communities.

Marine oxygen minimum zones (OMZs) support 30–50% of global fixed-nitrogen (N) loss but comprise only 7% of total ocean volume. This N-loss is driven by canonical denitrification and anaerobic ammonium oxidation (anammox), and the distribution and activity of these two processes vary greatly in space and time. Factors that regulate N-loss processes are complex, including organic matter availability, oxygen concentrations, and NO2− and NH4+ concentrations. While both denitrification and anammox produce N2, the overall geochemical outcome of these processes are different, as incomplete denitrification, for example, produces N2O, which is a potent greenhouse gas. Information on rates of anammox and denitrification and more detailed ecophysiological knowledge of the microorganisms catalyzing these processes are needed to develop more robust models of N-loss in OMZs. To this end, we conducted monthly incubations with 15N-labeled N during under anoxic conditions and during a deep water renewal cycle in Saanich Inlet, British Columbia, a persistently anoxic fjord. Both denitrification and anammox operated throughout the low oxygen water column with depth integrated rates of anammox and denitrification ranging from 0.15 ± 0.03 to 3.4 ± 0.3 and 0.02 ± 0.006 to 14 ± 2 mmol N2 m−2 d−1, respectively. Most N2 production in Saanich Inlet was driven by denitrification, with high rates developing in response to enhanced substrate supply from deep water renewal. Dynamics in rates of denitrification were linked to shifts in microbial community composition. Notably, periods of intense denitrification were accompanied by blooms in an Arcobacter population against a background community dominated by SUP05 and Marinimicrobia. Rates of N2 production through denitrification and anammox, and their dynamics, were then explored through flux-balance modeling with higher rates of denitrification linked to the physiology of substrate uptake. Overall, both denitrification and anammox operated throughout the year, contributing to an annual N-loss of 2 × 10−3 Tg N2 yr−1, 37% of which we attribute to anammox and 63% to complete denitrification. Extrapolating these rates from Saanich Inlet to all similar coastal inlets in BC (2478 km2), we estimate that these inlets contribute 0.1% to global pelagic N-loss.

The family Arenaviridae is divided into three genera: Mammarenavirus, Reptarenavirus, and Hartmanivirus. The Mammarenaviruses contain viruses responsible for causing human hemorrhagic fever diseases including New World viruses Junin, Machupo, Guanarito, Sabia, and Chapare virus and Old World viruses Lassa, and Lujo virus. These two groups of arenaviruses share the same genome organization composed of two ambisense RNA segments. These segments contain four open reading frames that encode for four proteins: the nucleoprotein, glycoprotein precursor, L protein, and Z. Despite their genome similarities, these groups exhibit marked differences in their replication life cycles. This includes differences in attachment, entry, and immune evasion. By understanding the intricacy of replication in each of these viral species we can work to develop counter measures against human diseases. This includes the development of vaccines and antivirals for these emerging viral threats. Currently only the vaccine against Junin virus, Candid#1, is in use as well as Ribavirin for treatment of Lassa Fever. In addition, small molecule inhibitors can be developed to target various aspects of the virus life cycle. In these ways an understanding of the arenavirus replication cycle can be used to alleviate the mortality and morbidity of these infections worldwide.

ABSTRACT Steroids are abundant growth substrates for bacteria in natural, engineered, and host-associated environments. This study analyzed the distribution of the aerobic 9,10-seco steroid degradation pathway in 346 publically available metagenomes from diverse environments. Our results show that steroid-degrading bacteria are globally distributed and prevalent in particular environments, such as wastewater treatment plants, soil, plant rhizospheres, and the marine environment, including marine sponges. Genomic signature-based sequence binning recovered 45 metagenome-assembled genomes containing a majority of 9,10-seco pathway genes. Only Actinobacteria and Proteobacteria were identified as steroid degraders, but we identified several alpha- and gammaproteobacterial lineages not previously known to degrade steroids. Actino- and proteobacterial steroid degraders coexisted in wastewater, while soil and rhizosphere samples contained mostly actinobacterial ones. Actinobacterial steroid degraders were found in deep ocean samples, while mostly alpha- and gammaproteobacterial ones were found in other marine samples, including sponges. Isolation of steroid-degrading bacteria from sponges confirmed their presence. Phylogenetic analysis of key steroid degradation proteins suggested their biochemical novelty in genomes from sponges and other environments. This study shows that the ecological significance as well as taxonomic and biochemical diversity of bacterial steroid degradation has so far been largely underestimated, especially in the marine environment. IMPORTANCE Microbial steroid degradation is a critical process for biomass decomposition in natural environments, for removal of important pollutants during wastewater treatment, and for pathogenesis of bacteria associated with tuberculosis and other bacteria. To date, microbial steroid degradation was mainly studied in a few model organisms, while the ecological significance of steroid degradation remained largely unexplored. This study provides the first analysis of aerobic steroid degradation in diverse natural, engineered, and host-associated environments via bioinformatic analysis of an extensive metagenome data set. We found that steroid-degrading bacteria are globally distributed and prevalent in wastewater treatment plants, soil, plant rhizospheres, and the marine environment, especially in marine sponges. We show that the ecological significance as well as the taxonomic and biochemical diversity of bacterial steroid degradation has been largely underestimated. This study greatly expands our ecological and evolutionary understanding of microbial steroid degradation.

Anoxygenic photosynthesis evolved prior to oxygenic photosynthesis and harnessed energy from sunlight to support biomass production on the early Earth. Models that consider the availability of electron donors predict that anoxygenic photosynthesis using Fe(II), known as photoferrotrophy, would have supported most global primary production before the proliferation of oxygenic phototrophs at approximately 2.3 billion years ago. These photoferrotrophs have also been implicated in the deposition of banded iron formations, the world’s largest sedimentary iron ore deposits that formed mostly in late Archean and early Proterozoic Eons. In this work we present new data and analyses that illuminate the metabolic capacity of photoferrotrophy in the phylum Chlorobi. Our laboratory growth experiments and biochemical analyses demonstrate that photoferrotrophic Chlorobi are capable of assimilatory sulfate reduction and nitrogen fixation under sulfate and nitrogen limiting conditions, respectively. Furthermore, the evolutionary histories of key enzymes in both sulfur (CysH and CysD) and nitrogen fixation (NifDKH) pathways are convoluted; protein phylogenies, however, suggest that early Chlorobi could have had the capacity to assimilate sulfur and fix nitrogen. We argue, then, that the capacity for photoferrotrophic Chlorobi to acquire these key nutrients enabled them to support primary production and underpin global biogeochemical cycles in the Precambrian.