Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing.

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

Microbial functional diversity across biogeochemical provinces in the central Pacific Ocean.

Enzymes catalyze key reactions within Earth's life-sustaining biogeochemical cycles. Here, we use metaproteomics to examine the enzymatic capabilities of the microbial community (0.2 to 3 µm) along a 5,000-km-long, 1-km-deep transect in the central Pacific Ocean. Eighty-five percent of total protein abundance was of bacterial origin, with Archaea contributing 1.6%. Over 2,000 functional KEGG Ontology (KO) groups were identified, yet only 25 KO groups contributed over half of the protein abundance, simultaneously indicating abundant key functions and a long tail of diverse functions. Vertical attenuation of individual proteins displayed stratification of nutrient transport, carbon utilization, and environmental stress. The microbial community also varied along horizontal scales, shaped by environmental features specific to the oligotrophic North Pacific Subtropical Gyre, the oxygen-depleted Eastern Tropical North Pacific, and nutrient-rich equatorial upwelling. Some of the most abundant proteins were associated with nitrification and C1 metabolisms, with observed interactions between these pathways. The oxidoreductases nitrite oxidoreductase (NxrAB), nitrite reductase (NirK), ammonia monooxygenase (AmoABC), manganese oxidase (MnxG), formate dehydrogenase (FdoGH and FDH), and carbon monoxide dehydrogenase (CoxLM) displayed distributions indicative of biogeochemical status such as oxidative or nutritional stress, with the potential to be more sensitive than chemical sensors. Enzymes that mediate transformations of atmospheric gases like CO, CO2, NO, methanethiol, and methylamines were most abundant in the upwelling region. We identified hot spots of biochemical transformation in the central Pacific Ocean, highlighted previously understudied metabolic pathways in the environment, and provided rich empirical data for biogeochemical models critical for forecasting ecosystem response to climate change.

Genus-Wide Transcriptional Landscapes Reveal Correlated Gene Networks Underlying Microevolutionary Divergence in Diatoms.

Marine microbes like diatoms make up the base of marine food webs and drive global nutrient cycles. Despite their key roles in ecology, biogeochemistry, and biotechnology, we have limited empirical data on how forces other than adaptation may drive diatom diversification, especially in the absence of environmental change. One key feature of diatom populations is frequent extreme reductions in population size, which can occur both in situ and ex situ as part of bloom-and-bust growth dynamics. This can drive divergence between closely related lineages, even in the absence of environmental differences. Here, we combine experimental evolution and transcriptome landscapes (t-scapes) to reveal repeated evolutionary divergence within several species of diatoms in a constant environment. We show that most of the transcriptional divergence can be captured on a reduced set of axes, and that repeatable evolution can occur along a single major axis of variation defined by core ortholog expression comprising common metabolic pathways. Previous work has associated specific transcriptional changes in gene networks with environmental factors. Here, we find that these same gene networks diverge in the absence of environmental change, suggesting these pathways may be central in generating phenotypic diversity as a result of both selective and random evolutionary forces. If this is the case, these genes and the functions they encode may represent universal axes of variation. Such axes that capture suites of interacting transcriptional changes during diversification improve our understanding of both global patterns in local adaptation and microdiversity, as well as evolutionary forces shaping algal cultivation.

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes.

BACKGROUND: With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth's natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes. RESULTS: In this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA's capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives. CONCLUSIONS: The VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

Mechanism-of-Action Classification of Antibiotics by Global Transcriptome Profiling.

Antimicrobial resistance (AMR) is an ever-growing public health problem worldwide. The low rate of antibiotic discovery coupled with the rapid spread of drug-resistant bacterial pathogens is causing a global health crisis. To facilitate the drug discovery processes, we present a large-scale study of reference antibiotic challenge bacterial transcriptome profiles, which included 37 antibiotics across 6 mechanisms of actions (MOAs) and provide an economical approach to aid in antimicrobial dereplication in the discovery process. We demonstrate that classical MOAs can be sorted based upon the magnitude of gene expression profiles despite some overlap in the secondary effects of antibiotic exposures across MOAs. Additionally, using gene subsets, we were able to subdivide broad MOA classes into subMOAs. Furthermore, we provide a biomarker gene set that can be used to classify most antimicrobial challenges according to their canonical MOA. We also demonstrate the ability of this rapid MOA diagnostic tool to predict and classify the expression profiles of pure compounds and crude extracts to their expression profile-associated MOA class.

Applications of weighted association networks applied to compositional data in biology.

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Correction: Transcriptional Orchestration of the Global Cellular Response of a Model Pennate Diatom to Diel Light Cycling under Iron Limitation.

[This corrects the article DOI: 10.1371/journal.pgen.1006490.].

Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum.

Photoacclimation consists of short- and long-term strategies used by photosynthetic organisms to adapt to dynamic light environments. Observable photophysiology changes resulting from these strategies have been used in coarse-grained models to predict light-dependent growth and photosynthetic rates. However, the contribution of the broader metabolic network, relevant to species-specific strategies and fitness, is not accounted for in these simple models. We incorporated photophysiology experimental data with genome-scale modeling to characterize organism-level, light-dependent metabolic changes in the model diatom Phaeodactylum tricornutum. Oxygen evolution and photon absorption rates were combined with condition-specific biomass compositions to predict metabolic pathway usage for cells acclimated to four different light intensities. Photorespiration, an ornithine-glutamine shunt, and branched-chain amino acid metabolism were hypothesized as the primary intercompartment reductant shuttles for mediating excess light energy dissipation. Additionally, simulations suggested that carbon shunted through photorespiration is recycled back to the chloroplast as pyruvate, a mechanism distinct from known strategies in photosynthetic organisms. Our results suggest a flexible metabolic network in P. tricornutum that tunes intercompartment metabolism to optimize energy transport between the organelles, consuming excess energy as needed. Characterization of these intercompartment reductant shuttles broadens our understanding of energy partitioning strategies in this clade of ecologically important primary producers.

Transcriptional Orchestration of the Global Cellular Response of a Model Pennate Diatom to Diel Light Cycling under Iron Limitation.

Environmental fluctuations affect distribution, growth and abundance of diatoms in nature, with iron (Fe) availability playing a central role. Studies on the response of diatoms to low Fe have either utilized continuous (24 hr) illumination or sampled a single time of day, missing any temporal dynamics. We profiled the physiology, metabolite composition, and global transcripts of the pennate diatom Phaeodactylum tricornutum during steady-state growth at low, intermediate, and high levels of dissolved Fe over light:dark cycles, to better understand fundamental aspects of genetic control of physiological acclimation to growth under Fe-limitation. We greatly expand the catalog of genes involved in the low Fe response, highlighting the importance of intracellular trafficking in Fe-limited diatoms. P. tricornutum exhibited transcriptomic hallmarks of slowed growth leading to prolonged periods of cell division/silica deposition, which could impact biogeochemical carbon sequestration in Fe-limited regions. Light harvesting and ribosome biogenesis transcripts were generally reduced under low Fe while transcript levels for genes putatively involved in the acquisition and recycling of Fe were increased. We also noted shifts in expression towards increased synthesis and catabolism of branched chain amino acids in P. tricornutum grown at low Fe whereas expression of genes involved in central core metabolism were relatively unaffected, indicating that essential cellular function is protected. Beyond the response of P. tricornutum to low Fe, we observed major coordinated shifts in transcript control of primary and intermediate metabolism over light:dark cycles which contribute to a new view of the significance of distinctive diatom pathways, such as mitochondrial glycolysis and the ornithine-urea cycle. This study provides new insight into transcriptional modulation of diatom physiology and metabolism across light:dark cycles in response to Fe availability, providing mechanistic understanding for the ability of diatoms to remain metabolically poised to respond quickly to Fe input and revealing strategies underlying their ecological success.

Meta-omic analyses of Baltic Sea cyanobacteria: diversity, community structure and salt acclimation.

Cyanobacteria are important phytoplankton in the Baltic Sea, an estuarine-like environment with pronounced north to south gradients in salinity and nutrient concentrations. Here, we present a metagenomic and -transcriptomic survey, with subsequent analyses targeting the genetic identity, phylogenetic diversity, and spatial distribution of Baltic Sea cyanobacteria. The cyanobacterial community constituted close to 12% of the microbial population sampled during a pre-bloom period (June-July 2009). The community was dominated by unicellular picocyanobacteria, specifically a few highly abundant taxa (Synechococcus and Cyanobium) with a long tail of low abundance representatives, and local peaks of bloom-forming heterocystous taxa. Cyanobacteria in the Baltic Sea differed genetically from those in adjacent limnic and marine waters as well as from cultivated and sequenced picocyanobacterial strains. Diversity peaked at brackish salinities 3.5-16 psu, with low N:P ratios. A shift in community composition from brackish to marine strains was accompanied by a change in the repertoire and expression of genes involved in salt acclimation. Overall, the pre-bloom cyanobacterial population was more genetically diverse, widespread and abundant than previously documented, with unicellular picocyanobacteria being the most abundant clade along the entire Baltic Sea salinity gradient.

Trace Metal Acquisition by Marine Heterotrophic Bacterioplankton with Contrasting Trophic Strategies.

Heterotrophic bacteria in the SAR11 and Roseobacter lineages shape the marine carbon, nitrogen, phosphorous, and sulfur cycles, yet they do so having adopted divergent ecological strategies. Currently, it is unknown whether these globally significant groups partition into specific niches with respect to micronutrients (e.g., trace metals) and how that may affect marine trace metal cycling. Here, we used comparative genomics to identify diverse iron, cobalt, nickel, copper, and zinc uptake capabilities in SAR11 and Roseobacter genomes and uncover surprising unevenness within and between lineages. The strongest predictors for the extent of the metal uptake gene content are the total number of transporters per genome, genome size, total metal transporters, and GC content, but numerous exceptions exist in both groups. Taken together, our results suggest that SAR11 have strongly minimized their trace metal uptake versatility, with high-affinity zinc uptake being a unique exception. The larger Roseobacter genomes have greater trace metal uptake versatility on average, but they also appear to have greater plasticity, resulting in phylogenetically similar genomes having largely different capabilities. Ultimately, phylogeny is predictive of the diversity and extent of 20 to 33% of all metal uptake systems, suggesting that specialization in metal utilization mostly occurred independently from overall lineage diversification in both SAR11 and Roseobacter. We interpret these results as reflecting relatively recent trace metal niche partitioning in both lineages, suggesting that concentrations and chemical forms of metals in the marine environment are important factors shaping the gene content of marine heterotrophic Alphaproteobacteria of the SAR11 and Roseobacter lineages.

High Bacterial Diversity and Phylogenetic Novelty in Dark Euxinic Freshwaters Analyzed by 16S Tag Community Profiling.

Microbial communities growing under extreme low redox conditions are present in anoxic and sulfide-rich (euxinic) environments such as karstic lakes and experience limitation of electron acceptors. The fine natural chemical gradients and the large diversity of organic and inorganic compounds accumulated in bottom waters are impossible to mimic under laboratory conditions, and only a few groups have been cultured. We investigated the bacterial composition in the oxic-anoxic interface and in the deep waters of three sulfurous lakes from the Lake Banyoles karstic area (NE Spain) through 16S rRNA gene tag sequencing and identified the closest GenBank counterpart. High diversity indices were found in most of the samples with >15 phyla/classes and >45 bacterial orders. A higher proportion of operational taxonomic units (OTUs) of the "highest novelty" was found in the hypolimnia (38 % of total sequences) than in the metalimnia (17 %), whereas the percentage of OTUs closer to cultured counterparts (i.e., 97 % identity in the 16S rRNA gene) was 6 to 21 %, respectively. Elusimicrobia, Chloroflexi, Fibrobacteres, and Spirochaetes were the taxa with the highest proportion of novel sequences. Interestingly, tag sequencing results comparison with metagenomics data available from the same dataset, showed a systematic underestimation of sulfur-oxidizing Epsilonproteobacteria with the currently available 907R "universal" primer. Overall, despite the limitation of electron acceptors, a highly diverse and novel assemblage was present in dark and euxinic hypolimnetic freshwaters, unveiling a hotspot of microbial diversity with a remarkable gap with cultured counterparts.

Closing the gaps on the viral photosystem-I†psaDCAB gene organization.

Marine photosynthesis is largely driven by cyanobacteria, namely Synechococcus and Prochlorococcus. Genes encoding for photosystem (PS) I and II reaction centre proteins are found in cyanophages and are believed to increase their fitness. Two viral PSI gene arrangements are known, psaJF→C→A→B→K→E→D and psaD→C→A→B. The shared genes between these gene cassettes and their encoded proteins are distinguished by %G + C and protein sequence respectively. The data on the psaD→C→A→B gene organization were reported from only two partial gene cassettes coming from Global Ocean Sampling stations in the Pacific and Indian oceans. Now we have extended our search to 370 marine stations from six metagenomic projects. Genes corresponding to both PSI gene arrangements were detected in the Pacific, Indian and Atlantic oceans, confined to a strip along the equator (30°N and 30°S). In addition, we found that the predicted structure of the viral PsaA protein from the psaD→C→A→B organization contains a lumenal loop conserved in PsaA proteins from Synechococcus, but is completely absent in viral PsaA proteins from the psaJF→C→A→B→K→E→D gene organization and most Prochlorococcus strains. This may indicate a co-evolutionary scenario where cyanophages containing either of these gene organizations infect cyanobacterial ecotypes biogeographically restricted to the 30°N and 30°S equatorial strip.

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing.

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.

Efficient formation of single-copy human artificial chromosomes.

Large DNA assembly methodologies underlie milestone achievements in synthetic prokaryotic and budding yeast chromosomes. While budding yeast control chromosome inheritance through ~125-base pair DNA sequence-defined centromeres, mammals and many other eukaryotes use large, epigenetic centromeres. Harnessing centromere epigenetics permits human artificial chromosome (HAC) formation but is not sufficient to avoid rampant multimerization of the initial DNA molecule upon introduction to cells. We describe an approach that efficiently forms single-copy HACs. It employs a ~750-kilobase construct that is sufficiently large to house the distinct chromatin types present at the inner and outer centromere, obviating the need to multimerize. Delivery to mammalian cells is streamlined by employing yeast spheroplast fusion. These developments permit faithful chromosome engineering in the context of metazoan cells.

Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton.

Among eukaryotes, four major phytoplankton lineages are responsible for marine photosynthesis; prymnesiophytes, alveolates, stramenopiles, and prasinophytes. Contributions by individual taxa, however, are not well known, and genomes have been analyzed from only the latter two lineages. Tiny "picoplanktonic" members of the prymnesiophyte lineage have long been inferred to be ecologically important but remain poorly characterized. Here, we examine pico-prymnesiophyte evolutionary history and ecology using cultivation-independent methods. 18S rRNA gene analysis showed pico-prymnesiophytes belonged to broadly distributed uncultivated taxa. Therefore, we used targeted metagenomics to analyze uncultured pico-prymnesiophytes sorted by flow cytometry from subtropical North Atlantic waters. The data reveal a composite nuclear-encoded gene repertoire with strong green-lineage affiliations, which contrasts with the evolutionary history indicated by the plastid genome. Measured pico-prymnesiophyte growth rates were rapid in this region, resulting in primary production contributions similar to the cyanobacterium Prochlorococcus. On average, pico-prymnesiophytes formed 25% of global picophytoplankton biomass, with differing contributions in five biogeographical provinces spanning tropical to subpolar systems. Elements likely contributing to success include high gene density and genes potentially involved in defense and nutrient uptake. Our findings have implications reaching beyond pico-prymnesiophytes, to the prasinophytes and stramenopiles. For example, prevalence of putative Ni-containing superoxide dismutases (SODs), instead of Fe-containing SODs, seems to be a common adaptation among eukaryotic phytoplankton for reducing Fe quotas in low-Fe modern oceans. Moreover, highly mosaic gene repertoires, although compositionally distinct for each major eukaryotic lineage, now seem to be an underlying facet of successful marine phytoplankton.

Microbial Catalysis for CO2 Sequestration: A Geobiological Approach.

One of the greatest threats facing the planet is the continued increase in excess greenhouse gasses, with CO2 being the primary driver due to its rapid increase in only a century. Excess CO2 is exacerbating known climate tipping points that will have cascading local and global effects including loss of biodiversity, global warming, and climate migration. However, global reduction of CO2 emissions is not enough. Carbon dioxide removal (CDR) will also be needed to avoid the catastrophic effects of global warming. Although the drawdown and storage of CO2 occur naturally via the coupling of the silicate and carbonate cycles, they operate over geological timescales (thousands of years). Here, we suggest that microbes can be used to accelerate this process, perhaps by orders of magnitude, while simultaneously producing potentially valuable by-products. This could provide both a sustainable pathway for global drawdown of CO2 and an environmentally benign biosynthesis of materials. We discuss several different approaches, all of which involve enhancing the rate of silicate weathering. We use the silicate mineral olivine as a case study because of its favorable weathering properties, global abundance, and growing interest in CDR applications. Extensive research is needed to determine both the upper limit of the rate of silicate dissolution and its potential to economically scale to draw down significant amounts (Mt/Gt) of CO2 Other industrial processes have successfully cultivated microbial consortia to provide valuable services at scale (e.g., wastewater treatment, anaerobic digestion, fermentation), and we argue that similar economies of scale could be achieved from this research.

Differential network analysis of oral microbiome metatranscriptomes identifies community scale metabolic restructuring in dental caries.

Dental caries is a microbial disease and the most common chronic health condition, affecting nearly 3.5 billion people worldwide. In this study, we used a multiomics approach to characterize the supragingival plaque microbiome of 91 Australian children, generating 658 bacterial and 189 viral metagenome-assembled genomes with transcriptional profiling and gene-expression network analysis. We developed a reproducible pipeline for clustering sample-specific genomes to integrate metagenomics and metatranscriptomics analyses regardless of biosample overlap. We introduce novel feature engineering and compositionally-aware ensemble network frameworks while demonstrating their utility for investigating regime shifts associated with caries dysbiosis. These methods can be applied when differential abundance modeling does not capture statistical enrichments or the results from such analysis are not adequate for providing deeper insight into disease. We identified which organisms and metabolic pathways were central in a coexpression network as well as how these networks were rewired between caries and caries-free phenotypes. Our findings provide evidence of a core bacterial microbiome that was transcriptionally active in the supragingival plaque of all participants regardless of phenotype, but also show highly diagnostic changes in the ways that organisms interact. Specifically, many organisms exhibit high connectedness with central carbon metabolism to Cardiobacterium and this shift serves a bridge between phenotypes. Our evidence supports the hypothesis that caries is a multifactorial ecological disease.

A Silent Operon of Photorhabdus luminescens Encodes a Prodrug Mimic of GTP.

With the overmining of actinomycetes for compounds acting against Gram-negative pathogens, recent efforts to discover novel antibiotics have been focused on other groups of bacteria. Teixobactin, the first antibiotic without detectable resistance that binds lipid II, comes from an uncultured Eleftheria terra, a betaproteobacterium; odilorhabdins, from Xenorhabdus, are broad-spectrum inhibitors of protein synthesis, and darobactins from Photorhabdus target BamA, the essential chaperone of the outer membrane of Gram-negative bacteria. Xenorhabdus and Photorhabdus are symbionts of the nematode gut microbiome and attractive producers of secondary metabolites. Only small portions of their biosynthetic gene clusters (BGC) are expressed in vitro. To access their silent operons, we first separated extracts from a small library of isolates into fractions, resulting in 200-fold concentrated material, and then screened them for antimicrobial activity. This resulted in a hit with selective activity against Escherichia coli, which we identified as a novel natural product antibiotic, 3'-amino 3'-deoxyguanosine (ADG). Mutants resistant to ADG mapped to gsk and gmk, kinases of guanosine. Biochemical analysis shows that ADG is a prodrug that is converted into an active ADG triphosphate (ADG-TP), a mimic of GTP. ADG incorporates into a growing RNA chain, interrupting transcription, and inhibits cell division, apparently by interfering with the GTPase activity of FtsZ. Gsk of the purine salvage pathway, which is the first kinase in the sequential phosphorylation of ADG, is restricted to E. coli and closely related species, explaining the selectivity of the compound. There are probably numerous targets of ADG-TP among GTP-dependent proteins. The discovery of ADG expands our knowledge of prodrugs, which are rare among natural compounds. IMPORTANCE Drug-resistant Gram-negative bacteria have become the major problem driving the antimicrobial resistance crisis. Searching outside the overmined actinomycetes, we focused on Photorhabdus, gut symbionts of enthomopathogenic nematodes that carry up to 40 biosynthetic gene clusters coding for secondary metabolites. Most of these are silent and do not express in vitro. To gain access to silent operons, we first fractionated supernatant from Photorhabdus and then tested 200-fold concentrated material for activity. This resulted in the isolation of a novel antimicrobial, 3'-amino 3'-deoxyguanosine (ADG), active against E. coli. ADG is an analog of guanosine and is converted into an active ADG-TP in the cell. ADG-TP inhibits transcription and probably numerous other GTP-dependent targets, such as FtsZ. Natural product prodrugs have been uncommon; discovery of ADG broadens our knowledge of this type of antibiotic.