mSystems

Soil microbial communities (SMCs) play an important role in various ecological processes, including plant growth, carbon cycling, and greenhouse gas production and consumption. There have been many prior studies of soil microbiome function and structure. However, soil is a complex environment in which to conduct biological studies. Therefore, simplified SMC models, often adapted to liquid culture, have been employed in the laboratory to study specific microbial interactions and individual microbial functions. Specific advantages of these laboratory liquid SMC models include the ability to modulate community membership, control environmental conditions, and employ high-throughput assay techniques. The disadvantages of current laboratory liquid SMC models include long cycles for growing bacteria in vitro, the obligatory use of strains that are culturable in isolation, intricate media requirements, and complex community assembly protocols. To address some limitations of current liquid SMC models, we sought to create a streamlined process for extracting and maintaining a liquid culture of an existing SMC. Soil-Extracted Solubilized Organic Matter (SESOM) was made from four different soil types, including rich organic potting soils and environmental samples, and filtered to maintain the SMC. These SESOM liquid SMC models were cultured for 28 days, and SMC composition was measured by 16S rDNA sequencing. The SESOM SMCs maintain high alpha and beta diversity over time, including strains that are not culturable in isolation, with the greatest stability correlated with higher soil organic carbon. Further, the SESOM SMCs maintain unique signatures of their starting solid soils, suggesting that drift in SMC composition over extended time in liquid culture does not eliminate the defining microbial relationships of a given soil type. Network analysis of SESOM SMCs relative to solid soils suggests the functional roles of bacterial taxa were maintained in the liquid models over time. We further demonstrate that the platform can be applied to monitor the survival and persistence of a model engineered microbe - the common synthetic biology chassis Pseudomonas putida - within a native SMC. We conclude that the SESOM model is a valuable tool for facilitating the study of SMCs in the laboratory.

Antimicrobial use (AMU) in agricultural systems is frequently linked to antimicrobial resistance (AMR). Yet, the scale at which AMU reshapes host-associated resistomes remains unclear. This gap arises, in part, from the scarcity of farm-level AMU data from commercial production systems. Here, we combine detailed AMU records from commercial dairy farms with metagenomic analyses of bovine fecal resistomes from calves, lactating cows, sick cows, and cull cows. At a broad level, resistome profiles were similar regardless of farm AMU. Resistance associated with historically common antibiotics, such as tetracyclines, was frequent on low- and high-AMU farms, indicating that some resistance classes are ubiquitous in dairy systems regardless of current AMU. In contrast, resistance to other drug classes varied systematically with AMU. Higher AMU was associated with increased resistance to aminoglycosides, {beta}-lactams, and macrolides, drug classes that are critical for treating mastitis and bovine respiratory disease. Resistance gene richness and diversity were highest in calves, underscoring the importance of accounting for host traits alongside AMU when evaluating resistance patterns. Together, these findings underscore the need for detailed, farm-level AMU data to understand how management practices shape AMR and to inform strategies for sustaining the effectiveness of existing antimicrobials in agricultural and public-health contexts.

Gut bacteria shape the metabolism of their host and play an important role in human health. However, systems biology approaches to study their intracellular metabolic fluxes are largely underdeveloped. We present an experimental and computational workflow to quantify metabolic flux ratios in gut bacteria using 13C-labeled nutrient supplementation and a newly developed machine learning-based Flux Ratio Prediction Python PackagE (FRAPPPE). We apply FRAPPPE to investigate central carbon metabolism in two prevalent gut Bacteroidota, Bacteroides uniformis and Phocaeicola vulgatus, in comparison to Escherichia coli. FRAPPPE revealed altered tricarboxylic acid cycle bifurcation in Bacteroidota compared to E. coli under anaerobic conditions. Further, we used FRAPPPE to investigate co-metabolism of nucleosides and carbohydrates by B. uniformis and P. vulgatus. We found distinct species-specific patterns in how nucleosides affected growth and were utilized depending on the co-fed compound. We quantified co-metabolism and showed that the tested nucleosides were mainly contributing to anabolic metabolism closely related to the specific co-fed nucleoside. Together, these findings provide insights into central and nucleoside metabolism of two gut Bacteroidota, and showcase FRAPPPE as a generalizable workflow to investigate metabolic fluxes in gut bacteria.

Microbes play a vital role in plant development, health, and resilience, yet relatively little is known about the specific metabolic mechanisms driving interactions in these host-associated communities. Systems biology models enable a computational approach to understanding metabolic interactions, which can be difficult to pinpoint experimentally; however, these methods cannot yet accommodate the large number of species in natural communities. Synthetic communities (SynComs) provide a more tractable alternative to explore targeted interactions. Here, we investigated metabolite exchange in a seven-member maize root-associated SynCom, specifically accounting for plant host context by designing a customized exudate medium. We constructed metabolic models for each bacterial species and curated them with in vitro phenotyping data to reflect experimentally based carbon uptake potential. Flux balance analysis of individual species demonstrated that integrating phenotype data and changing medium type had substantial impacts on predicted growth rates, which in turn shaped potential interspecies interactions. In silico community growth optimization of the seven-member community model showed that the exudate medium supported a more diverse community composition compared to minimal medium, with predictions of community member abundance closely aligned to literature-derived experimental results. Predicted metabolite exchange in the root exudate environment showed Enterobacter ludwigii as a community hub, and cross-feeding of indole suggested a potential effect of bacterial community interactions on the plant host. Our in silico findings indicate the host plays an important role in structuring microbial interactions and cross-feeding at the metabolic level, underscoring the importance of considering environmental context from both theoretical and experimental perspectives. IMPORTANCETrue understanding of a system is marked by the ability to predict its behavior. The complexity of natural host-microbe systems represents a frontier of knowledge that scientists are working to understand, and elucidating principles of interactions within multi-partite microbial communities remains a challenge in microbial ecology. Synthetic communities provide a tractable starting point for investigating interaction mechanisms, and computational approaches complement laboratory experiments by systematically evaluating multiple possibilities for metabolic pathway processing, thereby allowing us to comprehensively study the interconnected metabolic networks of host-associated microbiota. The model we developed for the seven-member maize root-associated bacterial community presents a step toward predicting plant-microbe behavior, providing hypotheses for future experimental testing and serving as a template for expanding model complexity to more members and other systems.

BackgroundBacteriophages are recognized modulators of microbiome composition and function, yet their role in the porcine upper respiratory tract, a primary gateway for pathogen colonization in the post-weaning period, remains unexplored. Unlike the porcine gut, no reference framework is available for respiratory sites. Furthermore, the low-biomass nature of nasopharyngeal specimens makes virome recovery highly sensitive to extraction strategy, but the extent to which workflow choice shapes ecological inference in this niche has not been evaluated. ResultsWe profiled the nasopharyngeal phageome of post-weaning piglets across ten commercial farms (30 pen-level pools) using paired DNA-microbiome (DNA-m) and virus-like particle-enriched (VLP-e) short-read metagenomics (n = 60 libraries). Protocol choice strongly reshaped viral recovery (PERMANOVA R{superscript 2} = 0.448, p < 0.0001), with a contig overlap between workflows of <1%. DNA-m favored assembly contiguity, while VLP-e maximized viral detection. By integrating both approaches, we constructed a curated catalogue of 2,501 non-redundant viral operational taxonomic units (vOTUs), with only 5.2% showing similarity to known phages, underscoring the extensive novelty of this niche. Ecologically, within the integrated community dataset (n = 4,357), predicted replication strategy emerged as a dominant organizing axis: lifestyle explained up to 40.6% of compositional variation at family level. Host prediction linked phages to dominant upper-airway colonizers, including Streptococcaceae, Moraxellaceae, Pasteurellaceae, with a marked lifestyle-host polarization: virulent phages were preferentially linked to Bacteroidota (particularly Prevotella), whereas temperate phages were enriched in Streptococcaceae and Moraxellaceae. Integration of viral taxonomy and host affiliation resolved a modular architecture in which a few recurrent phage-host couplings (e.g., Suoliviridae-Bacteroidota, Peduoviridae-Pasteurellaceae, Aliceevansviridae-Streptococcaceae) were conserved but differentially weighted between virulent and temperate fractions. ConclusionsThis study establishes the first phageome catalogue and ecological framework for a respiratory site in livestock. The nasopharyngeal phageome is organized into recurrent, host-linked taxonomic modules jointly constrained by viral lineage, host affiliation and replication strategy, with lifestyle-dependent connections to key colonizers implicated in the porcine respiratory disease complex. This catalogue and its modular architecture provide a foundation for investigating phage-mediated modulation of bacterial dynamics during the post-weaning transition and for the selection of lytic phage candidates targeting respiratory pathogens.

Regulation of nitrogenase, which converts nitrogen gas (N2) into ammonium (NH4+), typically involves a conserved set of regulatory proteins across diverse N2-fixing (diazotrophic) bacteria. However, the interactions and relative influence of these regulators can vary between species. Thus, one cannot make assumptions about nitrogenase regulation when working with uncharacterized diazotrophs like Vibrio natriegens, a {gamma}-proteobacterium of growing interest for synthetic biology. Little is known about V. natriegens nitrogenase regulation, which could be used to exploit inexpensive N2 for various applications, including NH4+ production. Here, we characterized the roles of several annotated V. natriegens nitrogenase regulatory proteins in response to NH4+ versus N2. Using functional genomics, targeted mutations, and reporter assays, we identified a typical regulatory hierarchy where the two-component system NtrBC governs a nitrogen-scavenging regulon that includes NifA, the transcriptional activator of nitrogenase genes. Unlike other diazotrophic {gamma}-proteobacteria, NifA was sufficient to activate nitrogenase gene expression, as a mutant lacking NtrBC grew normally with N2 after a lag phase. Thus, NtrBC was dispensable, but still important for timely nitrogenase expression. Furthermore, NtrBC was negatively regulated by the nitrogen-responsive PII proteins GlnB and GlnK; disruption of both PII proteins led to NtrBC-dependent nitrogenase overactivity, marked by NH4+ excretion. The redundant repression of NtrBC by GlnB and GlnK more closely resembles that of non-diazotrophic E. coli than other diazotrophic {gamma}-proteobacteria. Together, our findings provide a framework for V. natriegens nitrogenase regulation that can be leveraged for applications like NH4+ production. HIGHLIGHTSO_LIA genetic examination of Vibrio natriegens nitrogenase regulation is performed C_LIO_LINtrBC is important for early nitrogenase gene expression but is not essential C_LIO_LINifA autoactivation is sufficient for nitrogenase expression C_LIO_LIPII proteins GlnB and GlnK are redundant negative regulators of nitrogenase C_LIO_LIGenetic targets are identified that result in excretion of NH4+ C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/728972v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@aac4adorg.highwire.dtl.DTLVardef@1565c0corg.highwire.dtl.DTLVardef@b545a6org.highwire.dtl.DTLVardef@efb052_HPS_FORMAT_FIGEXP M_FIG C_FIG

Inferring bacterial growth rates is fundamental to understanding microbial interactions and community dynamics, but remains difficult in natural settings where timepoints are limited or organisms are unculturable. In these cases, a widely used method is the origin-to-terminus ratio, or peak-to-trough ratio (PTR), which estimates DNA replication activity by comparing the copy number of DNA at the replication origin and terminus. While PTR correlates well with cellular growth in uniform, idealized environments, it measures replication rather than net growth rate, and thus reflects growth only when there is no cell death. Despite this, PTR is widely applied across a range of laboratory and environmental contexts, where microbial populations frequently experience fluctuating stress, mortality, and subpopulation heterogeneity. Given its widespread use in such settings, we developed a stochastic, cell-based model that explicitly tracks DNA replication and cell death to quantify how different patterns and levels of mortality affect the relationship between PTR and net growth rate. We found that PTR and net growth rate are tightly correlated in idealized conditions; however, systematic deviations emerge when death rates vary over time or across subpopulations. We experimentally validated these predictions by exposing Escherichia coli to osmotic shock or antibiotics, and measuring net growth rate (by spot plating and observing the change in colony counts over time) and DNA replication activity (from qPCR with primers for the origin and terminus). Consistent with the predictions from our model, PTR correlated strongly with net growth rate in standard rich media, but not under stress. Together, these results provide a mechanistic and quantitative framework that clarifies the biological conditions under which PTR can be interpreted as a proxy for net growth rate.

BackgroundThe female lower reproductive tract harbors a complex microbiome that plays a critical role in reproductive health. A vaginal microbiome dominated by Lactobacillus crispatus (LC; Community State Type (CST) I) supports vaginal health, whereas a microbiome enriched with anaerobic species, such as Gardnerella vaginalis (GV) and Mobiluncus mulieris (MM) (CST IV) is linked to bacterial vaginosis (BV) and adverse outcomes, including sexually transmitted infections, infertility, and preterm birth. Although antibiotics such as metronidazole and clindamycin are commonly prescribed to treat BV, recurrence rates remain high, and the impact of these treatments on bacterial extracellular vesicles (bEVs), critical mediators of host-microbe interactions, is poorly understood. ResultWe investigated how antibiotic treatment at a dose below minimum inhibitory concentration alters the production and immunomodulatory function of bEVs derived from GV, MM, and LC. Using nanoparticle tracking analysis, cytokine profiling, and TLR pathway analyses, we found that antibiotic treatment significantly enhanced the inflammatory properties of bEVs in a species- and antibiotic-specific manner. Notably, bEVs from antibiotic-exposed GV and MM cultures induced elevated cytokine responses in epithelial and immune cells, primarily through TLR2 activation for GV bEVs, and through both TLR2 and TLR5 activation for MM bEVs. While LC bEVs are typically non-inflammatory, exposure to metronidazole, even at a lower dose than what is used clinically, rendered them immunostimulatory, suggesting a potential unintended proinflammatory consequence of treatment on beneficial microbes. We also detected bEVs in human vaginal swabs, including vaginolysin-positive bEVs, even in CST I microbiomes, indicating that low-abundance microbes, including pathogens, remain transcriptionally active. ConclusionsThese findings suggest that antibiotics not only reduce microbial load but also reshape bacterial communication via bEVs, potentially contributing to inflammation, epithelial barrier disruption, persistent dysbiosis, and recurrent infections. This work underscores the need for precision antimicrobial strategies that eliminate pathogens while preserving beneficial bacteria and their functional bEVs. Future therapies may benefit from considering the ecosystem-wide effects of antibiotics on the vaginal microbiome and its bEV-mediated signaling network.

Microbial communities are foundational to marine ecosystem function, yet their diversity is often obscured by broad taxonomic groupings and relative-abundance surveys that mask the dynamics of individual populations. This limitation is especially important across estuarine-coastal gradients, where microbial standing stocks, environmental conditions, and community composition vary sharply over space and time. Here, we used quantitative, genome-resolved metagenomics to examine microbial population dynamics across a one-year estuary-to-ocean transect spanning the Neuse River Estuary, Pamlico Sound, and adjacent North Atlantic shelf waters. Internal standard normalization enabled absolute abundance estimates for single-copy genes and metagenome-assembled genomes (MAGs), allowing individual populations to be tracked as genome equivalents per liter. Bacterial standing stocks were higher in estuarine waters, and communities varied primarily with salinity and season. We recovered 415 MAGs, including 386 bacterial genomes that represented, on average, 52% of bacterial genome equivalents, along with archaeal and eukaryotic representatives. Many abundant MAGs lacked close reference genomes, demonstrating that numerically important coastal populations remain poorly characterized. Genome-resolved abundances revealed pronounced niche partitioning among closely related taxa, including seasonal and spatial turnover of Synechococcus, Cyanobium, and Vulcanococcus populations associated with distinct pigment-defined cytometric groups. Rhodobacteraceae MAGs also showed population-specific correlations with picoeukaryotic MAGs, including a winter offshore Planktomarina population that reached 18% of total bacterial genome equivalents during a Micromonas-associated bloom. By providing absolute population abundances, this study transformed coastal microbiome surveys into numerical frameworks for resolving microbial population structure, ecological interactions, and biogeochemical relevance across dynamic environmental gradients. IMPORTANCEEstuarine and coastal waters contain diverse microbial communities that help regulate food webs and the cycling of carbon and nutrients, but many of the individual microbial populations responsible for these processes remain poorly understood. In this study, we examined bacteria, archaea, and small algae across the Neuse River Estuary, Pamlico Sound, and nearby coastal ocean waters over one year. By measuring the absolute abundance of individual microbial genomes, rather than only their relative proportions, we showed that closely related populations can have very different seasonal and spatial patterns. This was especially clear for cyanobacteria related to Synechococcus and heterotrophic bacteria in the family Rhodobacteraceae, which showed distinct population dynamics and associations with small algae. These results demonstrate how quantitative genome-resolved measurements can reveal hidden microbial population structure and improve our understanding of how microorganisms shape coastal ecosystem function.

Non-typeable Haemophilus influenzae (NTHi) is a prominent opportunistic pathogen relevant to chronic respiratory diseases. NTHis metabolic diversity enables its survival in a wide range of environmental conditions within the host. As such, deeper research into the metabolic pathways of NTHi may open an avenue for novel therapies aimed at combating NTHi-associated respiratory diseases. Draft genome sequences from nine NTHi clinical strains from three isolation sites - ear (ear sample, ES), pharynx (pharynx sample, PS), and lower respiratory tract (Lungs) - were analyzed and annotated using RAST, PROKKA, KEGG KAAS, and antiSMASH. Pathway module coverage per-strain was computed and summarized by per-group for significant annotated metabolites. Metabolites were analyzed by LC/HRMS, identified by Metaboscape, and statistically compared using MetaboAnalyst and R software. Gene content across the tested NTHi strains was largely conserved, with limited core-genome SNP variation. Gene annotation for metabolite-related pathways revealed that all nine strains possessed largely similar sets of metabolic pathway genes, despite minor nucleotide-level differences, indicating broadly comparable metabolic capacities. In contrast, metabolomics data revealed differential metabolic profiles among the body-site groups. In a principal component analysis (PCA), the ES group was significantly separated from both the PS and Lung groups, which overlapped considerably. Detailed metabolite analyses showed that inosine, hypoxanthine, and uracil were highly significant in the ES group compared to the PS and Lung groups. For the first time, our study sheds light on the extent of metabolic differences associated with NTHi inhabiting diverse host niches. The observed metabolic differences suggest that NTHi may modulate its metabolism in a site-specific manner that is affected by environmental factors. These findings add to our understanding of how NTHi metabolism contributes to site-specific colonization. Author summaryHaemophilus influenzae is widely recognized as a causative agent of meningitis and pneumonia. In particular, H. influenzae strains with a polysaccharide capsule--known as H. influenzae type b (Hib)--were historically a major cause of invasive disease. However, Hib has been largely eradicated following implementation of the Hib vaccine. Nonetheless, there are H. influenzae strains that lack this capsule and are therefore not targeted by the vaccine. These are known as non-typeable H. influenzae (NTHi). Following the decline of Hib, NTHi has rapidly occupied the ecological niche in the lower respiratory tract, becoming the most prominent pathogen in patients with chronic respiratory infections--particularly in those with chronic obstructive pulmonary disease (COPD), where it frequently triggers exacerbations. Importantly, NTHi is also a common component of the normal microbiome in healthy individuals, typically residing in the upper respiratory tract without causing disease. In our study, we investigated the metabolic characteristics of NTHi isolates obtained from different body sites in patients to better understand what distinguishes strains capable of colonizing specific anatomical niches. We successfully identified several distinct metabolic features associated with NTHi strains from the ear, pharynx, and lung. These findings may serve as a foundation for future research into patient-tailored biomarkers and targeted therapies, ultimately aiming to eradicate NTHi in chronic lung infections.

Viruses of the phylum Nucleocytoviricota are paradigm-shifting entities due to their exceptionally large genomes and complex gene repertoires, which blur the lines between viral and cellular life. Previous research has leveraged computational approaches to map their extensive diversity, while experimental work has started to elucidate the intricate networks they form with hosts, bacterial and other symbionts, co-infecting virophages and other mobile genetic elements. Here, we analyzed deeply sequenced metagenomes sampled from wastewater treatment plants in Denmark, an environment with rapid abiotic changes and known to be a hotbed of dense microbial communities. We discovered 61 novel nucleocytoviruses, 15 virophages and 14 polinton-like viruses. By integrating them with microbial contigs into a multilayered interaction network, we explore the role of these entities on a mesocosm scale. We demonstrate the centrality of nucleocytoviruses, positioning them as important players shaping microbial community structure and evolution in wastewater treatment plants.

Alzheimers disease (AD) is a neurodegenerative disease and the leading cause of dementia among elderly. Gut microbiome alterations precede pathogenesis and may affect disease outcomes. We evaluated the role of Bacteroides fragilis in triple transgenic mice modeling AD pathologies (3xTg-AD) and wild-type controls (WT). Subsets of 3xTg-AD and WT mice were longitudinally treated with B. fragilis or sterile vehicle control for five consecutive days at 8 weeks of age and then monthly up to 52-56 weeks. Fecal samples were collected fortnightly from 8 through 52-56 weeks of age. Mice were sacrificed at 8 (baseline), 24 (amyloid-{beta} plaques modeled), and 52-56 (amyloid-{beta} plaques and neurofibrillary tangles modeled) weeks of age. Expression of genes involved in neuroinflammation and neurotransmission were quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Fecal bacterial microbiota were assessed by sequencing the V4 region of the 16S rRNA gene, and microbiome sequence data were analyzed using QIIME 2. Frontal cortex and fecal metabolomes were evaluated using LC-MS/MS. We observed that B. fragilis colonized the gut microbiota of 3xTg-AD mice earlier and more consistently than WT mice. 3xTg-AD mice treated with B. fragilis demonstrate lower gene expression of GFAP, SLC1A3, and FOXO3 in the frontal cortex. Consistent with this finding, treatment with B. fragilis restores levels of amino acid derivatives and neurotransmitters in 3xTg-AD mice to resemble levels in WT mice. These results highlight the role of the gut microbiome in AD-associated neuroinflammation and neurotransmission and the need for future studies to elucidate the mechanisms underlying these changes. ImportancePrevious studies in animals modeling Alzheimers disease pathologies and humans living with Alzheimers disease demonstrate shifts in the gut microbial community composition prior to and concomitant with pathological onset. The bacterial genus, Bacteroides, is commonly found differentially abundant in these studies but its effects on disease outcomes are poorly understood. In this study, we explore the effects of chronic exposure to Bacteroides fragilis in triple transgenic mice modeling Alzheimers disease pathologies and healthy, wild-type controls. We observed changes in microbial community composition in mice modeling Alzheimers disease when treated with B. fragilis, and associated changes in neuroinflammation, biomarkers of neurotransmission, and the brain metabolome. Taken together, these results suggest that Bacteroides fragilis exerts neuromodulatory effects that may be beneficial in Alzheimers disease.

Scientists use microscopy images to visualize and describe organisms and their interactions. Microscale visual inspections of individual cells and colonies, paired with genetic approaches, have revealed key developmental steps within microbial communities including, for example, swarms, biofilms, and fruiting bodies. However, there is a lack of formal, quantitative descriptions of these structures. This gap limits understanding of the structure and relevance of cell clusters, especially regarding development and interactions. Here, we develop biologically informed mathematical methods to formally assess cell clusters that assemble and dissolve over time in bacterial colonies. Our approaches to this analysis of the developmental stages of swarming, a type of collective migration, focus on cell length (as a proxy for developmental stage), cell-to-cell contacts, and groupings. We apply these to data from Proteus mirabilis strains with genetic disruptions in different aspects of its communication mechanisms to explore how identity signaling and cell-to-cell contact affect population structure at different scales. We found that kin recognition and contact-dependent, cell-cell communication govern population architecture during collective migration, such as swarming. This integration of microbiology with applied mathematics and computer science is a frontier for analyzing experimental data, leading to testable biological insights. SignificanceCollective behaviors are often exhibited by social organisms. Open questions regarding these behaviors include: how do communication and familial identity influence local interactions, and how do local interactions combine to create collective behaviors? Qualitative descriptions from visual examination of microbes can be useful in approaching these questions, but formal quantification of these structures will be essential if we are to understand the mechanisms that underlie organismal behavior and organization. This manuscript bridges microbiology and applied mathematics, interleaving the mathematics and the experiments in an iterative fashion. We offer critical advances to better understand, analyze, and characterize the multiscale structures that form and dissolve as a colony engages in collective migration. Our findings suggest that cell-cell communication plays an important role in population architecture, specifically that communication may "prime" cells for collective migration and prevent stagnation during life stage transitions.

BackgroundThe exponential growth of public metagenomic datasets offers an unprecedented opportunity to explore microbial diversity. However, analyzing this vast amount of data presents significant computational challenges. While shotgun metagenomics provides deep functional and taxonomic resolution, its high cost still limits its application. On the other hand, 16S rRNA gene sequencing remains a cost-effective and widely used alternative, but tools are needed to maximize its discovery potential. Traditional clustering is not scalable, obstructing the creation of a comprehensive and continuously updated catalog of microbial life from 16S data. MethodsWe developed a reproducible and scalable Snakemake pipeline for the incremental clustering of 16S rRNA amplicons. The workflow begins by constructing a reference database from bacterial and archaeal genomes. It then processes 16S rRNA samples sequentially. For each new sample, sequences are first mapped against the existing cluster centroids. Sequences that match known centroids are assigned accordingly, while unmapped sequences are clustered independently to form novel operational taxonomic units (OTUs). These new centroids are then merged with the existing database, allowing it to grow dynamically without the need for computationally prohibitive all-at-once re-clustering. ResultsOur pipeline enables the efficient and continuous expansion of a 16S rRNA cluster database. By processing a large corpus of public 16S rRNA samples, we generated a comprehensive atlas of tens of thousands of OTUs. A significant fraction of these clusters, particularly at the genus and family levels, were classified as unknown. ConclusionsThis work provides a powerful, open-source tool for large-scale analysis of 16S rRNA samples. The incremental clustering strategy overcomes the scalability limitations of traditional methods, allowing researchers to leverage public data and discover novel microbes in their own microbiome samples.

The emergence of antibiotic resistance among pathogenic bacteria is a significant global health challenge with multidrug resistance becoming increasingly common. Moreover, since antibiotic resistance genes (ARGs) can be transferred horizontally more bacteria are rapidly evolving resistance. In addition, emerging bacterial pathogens continue to arise from a combination of urbanization, animal agriculture, global movements of people, and inadequate sewage infrastructure. Researchers have begun applying deep sequencing and shotgun metagenomics to detect known and unknown pathogenic organisms and ARGs directly from environmental samples. Here, we describe a bioinformatics workflow that uses a co-assembly approach to assemble contigs across metagenomes and bin them into high coverage metagenomic assembled genomes (MAGs), while segregating out unbinned contigs that includes mobile elements (e.g., plasmids). The workflow includes annotation of coding sequences and differential determination of ARGs and virulence factors (VF) within the sets of both MAG genome bins and unbinned contigs and allows quantification of MAG, ARG and VF abundances for ecological (alpha and beta diversity) and network analyses. Workflow analysis of metagenomic samples collected from the heavily polluted Tijuana River identified hundreds of MAGs, including many high-quality bins and many novel potential pathogens, and found the vast majority of ARG sequence matches in the unbinned contigs. A combined network analysis found strong correlations (r > 0.90) between ARGs and specific MAGs, indicating which bacterial species is likely to contain the ARG. This workflow provides a powerful approach for public health metagenomics studies of emerging pathogens and ARGs.

Glyphosate, the worlds most widely used herbicide, targets the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which is conserved across plants and many bacteria. While its environmental effects are increasingly recognized, its role on antimicrobial resistance (AMR) remains incompletely understood. In particular, the link between intrinsic glyphosate sensitivity and AMR gene content or evolutionary dynamics has not been systematically explored. We examined the relationship between bacterial sensitivity to glyphosate, AMR profiles, and the evolution of AMR genes. We analyzed genome datasets from the human gut microbiota and the Alignable Tight Genomic Clusters (ATGC). EPSPS sequences were identified via BLAST and annotations and classified based on the intrinsic sensitivity to glyphosate using the EPSPSClass webserver. AMR genes, including associated drug classes and resistance mechanisms, were annotated using the Comprehensive Antibiotic Resistance Database (CARD). Across datasets, glyphosate-sensitive bacteria carried a greater diversity of AMR genes and mechanisms. In contrast, probabilistic modeling revealed that glyphosate-resistant bacteria accumulate AMR genes at significantly higher rates. Phylogenetic birth-and-death analyses and stochastic mapping further revealed elevated AMR gene gain, loss, expansion, and reduction in resistant strains. These results indicate a decoupling between AMR gene diversity and evolutionary dynamics: sensitive bacteria maintain more resistance genes, whereas resistant bacteria display accelerated AMR gene turnover. This suggests that glyphosate resistance is linked to increased genome dynamics, potentially enhancing bacterias adaptability under combined herbicide and antimicrobial pressures. Given glyphosates extensive agricultural use and potential human exposure, these findings highlight an underappreciated link between herbicide resistance and the evolution of AMR in bacterial populations.

Understanding the kinetic processes that govern bacterial population dynamics within hosts is critical in developing effective strategies to control microbiota. However, inferring population dynamics is challenging due to large host-to-host bacterial population variability stemming from stochastic colonization events, as well as the inability to continuously monitor the bacterial population without disturbing the host. Using C. elegans fed E. coli under different diets, we show that early colonization acts as a stochastic bottleneck that drives substantial divergence in host-level bacterial loads, and that the spreading of bacteria from colonized worms to sterile ones regulates this variability by altering effective colonization pressure. These conclusions are drawn using a simulation-based inference framework that quantifies stochastic within-host population dynamics from discrete snapshot data, enabling inference of effective colonization and growth rates across heterogeneous hosts with variable carrying capacities. Applying this framework, we further demonstrate that the bacterial predator B. bacteriovorus reduces average gut bacterial loads by two orders of magnitude, primarily by suppressing environmental recolonization and subsequent host-to-host transmission rather than eliminating established intra-host populations. Together, these results reveal that host-associated microbial population dynamics are strongly impacted by environmental colonization processes that modulate stochastic entry events.

Residential indoor surfaces are recognized as diverse microbial ecosystems, while their genome-based organization and functional repertoires remain understudied. We recovered 2304 metagenome-assembled genomes (MAGs) from shotgun metagenomic sequencing of 10 houses in New Delhi, India. Genome-resolved analysis revealed a highly structured microbial community and substantial unexplored diversity, with 60% Species-level Genome Bins (SGBs) (629/1014) unclassified at the species level. Metabolism reveals a conserved metabolic core, along with spatial functional enrichment: the living area was significantly different from the bathroom and kitchen areas. The prevalent MAG species of the house microbiome, Paracoccus marcusii, Ottowia sp. 018060485, and Kocuria palustris, showed strain-level diversity with no stratification by house, but a subtle location-wise grouping. Potential pathogens, along with a wide range of antimicrobial resistance genes (ARGs), were identified across the MAGs, with 64 ARGs associated with mobile elements. Phylogenomic analysis of Escherichia coli MAGs indicated a split between commensal-like fecal lineages and pathotype-associated clusters, like Intestinal Pathogenic E. coli (InPEC). These results suggest that residential house microbiomes harbor microbial communities with both diverse metabolic capacity and clinical relevance. Together, these findings establish a reference for future indoor microbiome research and provide a foundation for antimicrobial resistance surveillance and the development of bio-informed building-infrastructures.

MMseqs2 clustering was used to examine the uniformity of proteomes from 20 bacterial species. Clusters with proteins from [&ge;]50% of proteomes typically contain proteins from 95% of the proteomes and capture more than 80% of the proteins in an organism. Protein clusters are highly uniform in length; across the 20 bacteria, the median cluster has more than 99% of the proteins at the mode length. In contrast to this uniformity, some clusters contain dozens to hundreds of proteins that are considerably shorter (<75%) than the mode-length, and a few clusters include proteins that are >133% the mode length. Most "outlier" proteins are found in fewer than 10% of clusters, and "high-outlier" clusters are over-represented in a small fraction of proteomes, that often have poor Proteome BUSCO fragment scores. Short-outlier proteins are artifacts; at least 80% of short-outlier genomes contain mode-length copies of the protein, which were missed because of frame-shifts, termination codons, or initiation codon choice. As with "short-outlier" proteins, the [~]5% of proteomes missing from the core (50% participation) cluster set encode the missing protein more than 98% of the time. MMseqs2 clustering with 50% participation provides robust sets of core bacterial proteins.

PurposeBacterial vaginosis (BV) is associated with disruption of the vaginal microbiome and extracellular matrix (ECM) remodeling, yet the contribution of host proteases to this process remains unclear. This study investigated whether expression and activity of cathepsins K, L, S, and V differ by BV diagnosis and community state type (CST). We hypothesized that BV and BV associated CSTs would exhibit increased expression and activity of collagen and elastin-degrading cathepsins. MethodsVaginal fluid samples were collected and classified by BV diagnosis and CST. Cathepsin expression was evaluated by Western blotting to distinguish inactive and active enzyme forms. Proteolytic activity was assessed using multiplex cathepsin zymography. Statistical analyses compared cathepsin expression and activity across diagnoses and CSTs. Principal component analysis and linear regression were performed to assess associations between cathepsin activity, microbial diversity, and CST. ResultsProcathepsin K expression was significantly increased in BV-positive and CST IV samples, while total cathepsin L expression was significantly elevated in samples with Nugent-intermediate scores. Cathepsins S and V showed variation in inactive and active forms in Nugent-intermediate and CST III samples. In contrast, total cathepsin activity, including cathepsins K and V, did not significantly differ across BV diagnoses or CSTs. Overall, cathepsin activity varied between individuals rather than by clinical classification. ConclusionsCathepsin expression and maturation state differ by microbiome composition, suggesting that the vaginal microbiome may regulate post-translational processing of cathepsins. As a result, cathepsin activity appears to be regulated at the individual level rather than strictly by BV diagnosis or CST. These findings link vaginal microbiome composition to ECM remodeling and potential adverse reproductive outcomes.