mSystems

Understanding pathogen metabolism is critical for identifying key functions for drug targeting, establishing effective in vitro experimental systems, etc., particularly for metabolically unique organisms such as Leptospira. Pathogenic Leptospira are thought to infect humans from environmental sources; however, direct isolation from environmental samples remains technically challenging and is not yet well established. Here, we report that a ubiquitous environmental bacterium, Massilia sp., produces metabolites that promote the growth of Leptospira interrogans, encountered through an incidental contamination event, and identified in this study. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis showed demonstrated that cultivating of Massilia sp. in R2A medium resulted in the accumulation of metabolites, including branched-chain amino acid (BCAA) intermediates, compared to fresh medium. By combining genome-scale metabolic modeling with experimental validation using cell-free culture supernatant supplementation assays, we demonstrate that BCAA intermediates, particularly 2-ketoisocaproic acid (4-methyl-2-oxopentanoate; 4MOP), a leucine biosynthetic intermediate produced by Massilia sp., enhance Leptospira growth. To investigate the metabolic role of 4MOP, we incorporated transcriptomic data into a genome-scale metabolic network model to generate condition-specific models. Resulted flux distributions indicated that Leptospira catabolized imported 4MOP to produce acetyl-CoA. Our results reveal a previously unrecognized metabolic interaction where metabolites produced by environmental bacteria support the growth of pathogenic Leptospira, offering mechanistic insight into its metabolic requirement. These findings have implications to understand the environmental persistence of Leptospira through its metabolic dependencies on coexisting microbes, and they also help develop better strategies for this pathogen. ImportancePathogenic Leptospira persist in environmental reservoirs, yet the mechanisms supporting their growth remain poorly defined. Here, we find that metabolites produced by common environmental bacteria, Massilia sp., can promote Leptospira growth, suggesting a previously unrecognized metabolic dependency on coexisting microbes. Importantly, this study indicates that combining genome-scale metabolic modeling with experimental validation provides a useful framework for identifying metabolic interactions that are otherwise difficult to resolve using conventional culture-based approaches. Current strategy may facilitate the systematic identification of growth-supporting metabolites and provide a basis for improving selective cultivation for uncultured or difficult to culture organisms. Determination of growth promoting metabolites advances our understanding of pathogen persistence in natural environments and offers a generalized framework to study metabolically dependent microorganisms.

Honeypot ants engage in a convergently-evolved phenotype called repletism, where specialized workers expand their crops and gasters to store vast amounts of food internally. They then store that food for months to support colonies during times of food scarcity. This fascinating phenotype is not well-understood and very little is known about the microbial interactions happening within the fructose-rich replete crop. Previous research using amplicon sequencing showed that Fructilactobacillus makes up nearly 100% of the crop microbiomes of Myrmecocystus mexicanus repletes. This striking result and successful isolation of those strains led to the present investigation into the phylogenetic diversity of these strains and any clues to the nature of the symbiotic relationship between them and the ant host. We find that the isolates from these repletes represented two evolutionary lineages, both most closely related to F. fructivorans. One of those lineages was also found to be phylogenetically and metabolically distinct from all other Fructilactobacillus reference genomes used in this study. This discovery in a genus of bacteria that are highly relevant for fermented human foods and will also lay the groundwork for future understanding of the convergent evolutionary mechanisms of repletism in ants. 3. Impact statementThese analyses add to the literature by identifying a new microbe within a genus that is relevant to food systems. In addition, the host phenotype is convergently evolved and likely microbe-mediated (or at least highly exposed). Understanding this system allows for the testing of ideas of coevolutionary hypotheses with natural replicates. We expect interest to come from food safety and probiotics researchers, evolutionary biologists that think about the impacts of microbes, microbial ecologists interested in novel systems, and those interested in bacteria that may display unique metabolic possibilities. This output allows for the clear future examination of this system with many clear hypotheses. Our analysis allows for the creation of a new and unique model system of host-microbe symbiosis. 4. Data summaryNew genomes assembled in this work can be found under BioProject ID PRJNA1449409. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1449409. Reference bacterial genomes were obtained from NCBI at the following accession numbers: SAMN20557570, SAMN28081009, SAMN28081010, SAMN02597458, SAMN43111088, SAMN28081011, SAMN28535231, SAMN04505734, SAMN28081013, SAMN33452149, SAMN37926504, SAMN02849426, SAMN02470196, SAMN02369432, SAMN02797779, SAMN02797782, SAMN02797768, SAMN09762388, SAMN12785275, SAMEA117660288. The honeypot ant genome was obtained from SAMN37666067. Raw proteomics files will be uploaded to ProteomeXchange with a unique identifier upon manuscript acceptance.

Metaproteomics enables the functional characterization of microbiomes and host-microbe interactions by detecting and quantifying thousands of proteins. In data-dependent acquisition metaproteomics, protein quantification is commonly performed using either MS1-based area under the curve (AUC) or MS2-based peptide spectral counts (SpC). In AUC quantification, match between runs (MBR) is frequently employed to minimize data sparsity, yet its impact on metaproteomic data remains unclear. Understanding MBRs impact on metaproteomics data is especially important due to the high peak density in the MS1 mass spectra and the potential presence of not only proteins, but even entire organisms, in one sample and their absence in the other, which would complicate accurate feature mapping and transfer. While accurate quantification is essential for deriving meaningful biological inferences from metaproteomic analyses, systematic evaluations of AUC and SpC quantification in metaproteomics remain scarce. In this study, we used defined complex metaproteomic samples to perform a ground truth-based evaluation of AUC and SpC quantification and to determine the impact of MBR on AUC quantification. We found that MBR led to a substantial number of falsely identified proteins in complex samples. Protein identifications from an organism not present in the sample were wrongly transferred from other samples when MBR was used. We found that MBR-free AUC data had a wider dynamic range, higher quantitative accuracy, and more sensitive detection of abundance differences. Significance of the StudyAlthough metaproteomics is increasingly used to advance microbiome research, quantification strategies in metaproteomics are mostly selected based on convention rather than evidence, due to a lack of ground truth-based evaluation of quantification strategies in metaproteomics. Accurate protein quantification is key to deriving meaningful biological inferences from metaproteomic samples, yet it remains challenging due to their high complexity and uneven protein abundances. Here, we used defined metaproteomic samples to evaluate widely used quantification strategies in metaproteomics and to determine the effects of match between runs (MBR) on quantitative accuracy. Based on our findings, MBR adds falsely identified proteins to metaproteomic data. While MBR-free AUC offers a broader dynamic range and higher quantitative accuracy, SpC offers better proteome coverage. With this study, we provide an evidence-based framework for the informed selection of quantification strategies in metaproteomics, and highlight the strengths and limitations of these approaches with respect to proteome coverage, dynamic range, quantitative accuracy, and error propagation. Our findings also have important implications for the biological interpretation of data derived from these strategies and lay the groundwork for future studies validating quantitative approaches in data-independent acquisition workflows.

Mycobacterium abscessus (Mab) is an opportunistic pathogen that can cause chronic, debilitating lung disease. Mab is intrinsically resistant to most antibiotics, making Mab infections challenging to manage and frequently incurable. During infection, Mab adapts to survive various stresses, including hypoxia and nutrient starvation. In vitro, these conditions drive Mab into a drug-tolerant, non-replicating state. Changes in the Mab proteome that result from entering a non-replicating state have been minimally described despite the clinical importance of this physiological state. Using Mab reference strain ATCC 19977, we collected proteomic data comparing replicating to non-replicating states using a carbon starvation (CS) model of persistence. We identified 2251 proteins overall (46% proteome coverage), and 17% of these proteins were found in only one of the two conditions. A third of identified proteins were significantly changed in abundance, indicating an extensive proteomic response to CS. The response regulator DosR and many DosRS responsive proteins were significantly more abundant under CS, suggesting that the DosRS stress response regulator plays a key role in CS-induced Mab persistence. Many aspects of cell wall biosynthesis were changed, including changes in glycolipid abundance under CS. Proteins involved in other key cellular processes such as secretion, oxidative phosphorylation, and nutrient metabolism were altered under CS. The proteomic analysis presented provides new insights and clarity into how the Mab proteome is regulated during non-replicating persistence, a key consideration for understanding Mab pathophysiology.

The female reproductive tract harbors complex microbial communities that may influence reproductive success. In previous work using 16S rRNA gene sequencing, we identified bacterial taxa in the vagina and uterus of beef cattle associated with pregnancy outcomes, but taxonomic resolution and functional inference was limited. Here we used shotgun metagenomic sequencing to characterize the taxonomic composition, functional potential, and antimicrobial resistome of vaginal and uterine microbiomes at the time of artificial insemination (AI) in cows that subsequently became pregnant or remained open. Vaginal (pregnant n = 54; open n = 7) and uterine (pregnant, n = 41; open, n = 9) samples were collected prior to AI. Microbial community structure did not differ between pregnancy outcome groups in either anatomical site (PERMANOVA; P > 0.05). However, cows that remained open showed significantly greater species-level richness and diversity in the vaginal microbiome (P < 0.05). No diversity differences were observed in the uterine microbiome. In contrast, significant differences were detected between anatomical sites, with distinct dominant taxa and functional profiles. Vaginal microbiomes were enriched in pathways related to genetic information processing, whereas uterine microbiomes exhibited greater representation of metabolic pathways. A total of 105 ARGs spanning 11 antimicrobial classes were identified, with tetracycline resistance genes [tet(Q), tet(W), and tet(M)] predominating, and blaTEM-116 more abundant in the uterine microbiome. Overall, while vaginal and uterine microbiomes were compositionally and functionally distinct, no robust pregnancy-associated taxonomic or functional signatures were detected, likely reflecting limited statistical power and challenges inherent to low-biomass metagenomic datasets. IMPORTANCEUnderstanding the role of the reproductive tract microbiome in fertility could improve reproductive efficiency in cattle. We used shotgun metagenomic sequencing to characterize the taxonomic composition, functional potential, and antimicrobial resistome of vaginal and uterine microbiomes at the time of artificial insemination in cows that subsequently became pregnant or remained open. Using paired samples from the same animals, we directly compared microbial communities between the upper and lower reproductive tract to identify shared and site-specific features. Although no distinct microbial signatures associated with pregnancy outcomes were detected, this may reflect limited statistical power and low microbial biomass inherent to these samples. Despite these challenges, our study provides high-resolution insights into the composition, functional potential, and resistome of bovine reproductive microbiomes and highlights important technical considerations for studying low-biomass microbial ecosystems.

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.

Diverse bacterial pathogens have evolved complex regulatory mechanisms to adapt to various environmental stresses during infection. The uncertainty in mRNA-protein levels in response to environmental stressors complicates our understanding of bacterial physiology and their adaptation to stressful environments. To examine this issue, we have integrated transcriptomics and proteomics data on three human bacterial pathogens Salmonella enterica Typhimurium, Yersinia pseudotuberculosis, and Staphylococcus aureus under ten infection-relevant stress conditions. We observed positive correlations between mRNA and protein levels, which were decreased under different stress conditions. Essential genes exhibited higher expression levels with lower variation across the conditions and stronger mRNA-protein correlations compared to non-essential genes, highlighting their critical role in bacterial adaptability and survival. Moreover, we identified a substantial number of genes with stress-induced non-correlating mRNA-protein levels, particularly under conditions triggering strong stress responses. Particularly this level was dramatically lowered for osmotic stress specific genes affected by impaired translational activity under osmotic stress. Our findings highlight the prevalence of non-correlating mRNA-protein levels and the potential role of post-translational modifications in modulating protein levels in response to environmental stressors during infection. This study provides a comprehensive framework for integrating transcriptomics and proteomics data and identifies potential gene products that might significantly impact the ability of diverse bacterial pathogens to adapt to hostile infection environments.

Short-chain fatty acids (SCFAs) like butyrate and propionate are abundant microbiota-derived metabolites that influence bacterial physiology in host-associated niches such as the gastrointestinal tract. However, their effects on Staphylococcus aureus under varying nutritional conditions remain incompletely understood. Here we investigated how SCFAs interact with glucose or galactose to regulate anaerobic growth, biofilm formation, and global transcription in S. aureus. Both SCFAs inhibit growth in a dose-dependent manner. Biofilm formation was differentially affected, with butyrate promoting and propionate suppressing biofilm formation. Glucose and galactose alleviated SCFA-mediated growth inhibition, with glucose exerting the strongest effect. Notably, glucose enhanced butyrate-associated growth and biofilm formation beyond glucose alone, whereas galactose produced more modest effects. Enzymatic and genetic analyses indicated that SCFA-sugar biofilms contain proteins and extracellular DNA and involve VraSR-dependent regulation. Transcriptomic profiling revealed broad metabolic reprogramming, including induction of urease genes, amino acid biosynthesis, and stress response pathways. Synergistic effects between butyrate and glucose were partially dependent on anaplerotic metabolism via pyruvate carboxylase, linking the TCA cycle to SCFA adaptation. Together these findings demonstrate that the nutritional environment dictates whether SCFAs impair S. aureus growth or reprogram its physiology, promoting metabolic adaptation and biofilm formation under sugar-replete conditions.

Diet influences the composition, diversity, and functional capacity of the cattle gut microbiome. However, the extent to which feeding practices affect the microbial community and resistome under real-world conditions remains poorly understood, particularly in low- and middle-income settings. Here, we applied metagenomics to fecal samples from Bangladeshi cattle fed either a natural or a mixed diet to examine differences in microbial composition, functional potential, and resistome associated with feed type. Natural-fed cattle harbored higher microbial diversity and distinct bacterial phyla, including Bacteroidota, Campylobacteriota, and Mycoplasmatota. Acinetobacter, Aliarcobacter, Comamonas, Dysosmobacter, and Sharpea were enriched in natural-fed cattle, whereas Anaerotignum, Aristaeella, Oscillibacter, and Clostridium were more abundant in the mixed-fed group. Notably, the emerging zoonotic genus Aliarcobacter was detected in the natural-fed cohort. Alpha diversity analysis showed higher richness and evenness in natural-fed cattle, and a clear separation between dietary groups in beta diversity analysis (PERMANOVA, p = 0.01). Differential analysis identified Oscillibacter ruminantium as a biomarker of natural feeding, while Succinivibrio faecicola and Anaerovibrio slackiae for mixed feeding. Resistome profiles demonstrated clear differences. Mixed-fed cattle showed a consistent enrichment of tetracycline resistance genes, whereas the natural-fed group displayed a more variable resistome. Functional analysis suggested diet-associated differences in metabolic potential, with glutathione metabolism enriched in natural-fed cattle (p<0.05) and bile secretion and fatty acid metabolism moderately enriched in the mixed-fed group. These findings indicate that feeding practices are associated with differences in rumen microbial communities and resistome profiles in Bangladeshi cattle, providing baseline insights into microbiome-resistome relationships under field conditions.

AO_SCPLOWBSTRACTC_SCPLOWMicrobial populations frequently experience periodic lethal stresses from natural and anthropogenic sources, including routine disinfection in clinical, industrial, and domestic environments. However, periodic disinfection can rapidly select for tolerant strains with increased survival but reduced growth during permissive conditions, creating a trade-off that shapes competitive outcomes in microbial communities. Here, we develop a mathematical framework to quantify and predict selection between microbial strains competing for growth-limiting resources under periodic disinfection. The framework is validated through a competition experiment simulating periodic disinfection with benzalkonium chloride between a wild-type Escherichia coli strain and a tolerant mutant exhibiting increased survival but decreased growth. A minimal model incorporating growth rate during permissive conditions and disinfection survival quantitatively captures selection across different experimental scenarios, including uncertainty estimates propagated from parameter variance. We further provide analytical expressions and a web-based interface to determine selection outcomes and quantify the contributions of survival and growth rate to selection. Our framework establishes a quantitative basis for predicting when periodic disinfection shifts population composition towards tolerant strains and is generalizable to other lethal stresses, including antibiotic chemotherapy and physical inactivation, thereby contributing to our understanding of the impact of periodic selective pressures on microbial competition.

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.

Enterococci are Gram-positive opportunistic pathogens responsible for a wide range of nosocomial infections. One enterococcocal species, Enterococcus faecium, is steadily increasing in prevalence and has been listed among major multidrug-resistant ESKAPE pathogens. To gain systems-level insights into its metabolism and support discovery of potential therapeutic targets, we constructed iDR479, a comprehensive manually curated genome-scale metabolic model (GEM) to serve as a digital twin for E. faecium TX0016 (strain DO). The reconstruction was curated through extensive homology searches and literature evidence, and further refined and gap-filled through experimental validation. Phenotypic profiling using Biolog microarrays enabled assessment of carbon source utilization, while amino acid leave-out growth assays allowed the evaluation of auxotrophies. The final refined model is 100% accurate in predicting amino acid auxotrophy and 85% accurate in predicting growth on sole carbon sources. Discrepancies between model predictions and experimental phenotypes identified specific knowledge gaps across metabolic pathways, including unresolved carbon source utilization phenotypes, e.g., psicose, sorbitol, and palatinose utilization. Those gaps will guided future experimental characterization. Additionally, gene essentiality analysis was conducted to evaluate the predictive capacity of iDR479 model. Since no experimental gene essentiality data are currently available for E. faecium, model predictions were compared against Tn-seq experimental results from E. faecalis MMH594. Under simulated rich medium conditions, iDR479 achieved 86.7% concordance with the experimental essentiality results of E. faecalis MMH594. iDR479 thus provides a framework for studying E. faecium, offers insights into its metabolic network, and serves as a source for guiding future research and identification of therapeutic targets.

Adaptive laboratory evolution is used to improve the phenotypes of microorganisms and to characterise the mechanisms underlying resistance against complex growth inhibition. Here we focused on lactic acid bacteria (LAB) as starter cultures for food fermentations. Production of LAB starter cultures is challenging due to growth inhibition by organic acids, mainly lactate, produced during fermentation. By utilising stressostat cultivation we generated Lactococcus lactis isolates with enhanced lactate resistance. Using a combination of (meta)genomics, proteomics and pH-controlled batch fermentations, we deciphered the lactate resistance mechanisms of these L. lactis isolates. Proteome responses of L. lactis, combined with similar growth inhibition at high salt, suggest that high lactate mainly causes osmotic stress. We identified RNA polymerase (RNAP) mutations in subunits {beta} (rpoB) and {beta} (rpoC) as key mutations, causing pleiotropic effects in the proteome. These proteome adaptations are linked to enhanced lactate resistance, particularly the resistance to hyperosmotic stress without glycine-betaine supplementation, likely by altering cross-linking of the peptidoglycan in the cell envelope via downregulation of MurE. The proteome changes indicate that lactate might (indirectly) cause oxidative stress. Combined, our study shows that RNAP mutations enhanced lactate resistance through pleotropic effects in the proteome that changed L. lactis responses against multiple stresses. HighlightsO_LILactate resistant variants were isolated from stressostat cultivations C_LIO_LIMetagenomics revealed evolutionary trajectory during stressostat cultivation C_LIO_LIRNAP mutations were identified as key drivers for improved lactate resistance C_LIO_LIRNAP mutations altered the proteome, enhancing the osmotic stress resistance C_LI

Leptospirosis, caused by pathogenic Leptospira spp. such as L. interrogans, is a bacterial zoonosis of increasing prevalence with no consistently effective treatments in severe cases. We sought to characterize metabolic mechanisms that support L. interrogans infection in the host setting, with the ultimate goal of revealing unexplored therapeutic opportunities. We first established and validated a culture medium, which we refer to as supplemented Human Plasma-Like Medium (sHPLM). sHPLM more closely resembles the physiological environment of the human host than standard culture media, such as the EMJH (Ellinghausen-McCullough-Johnson-Harris) medium typically used for Leptospira culture. To better understand bacterial metabolism, we pioneered metabolomics in sHPLM-cultured Leptospira. Specifically, we developed a liquid chromatography mass spectrometry (LC/MS) metabolomics-based workflow for both medium analysis and stable isotope tracing with L. interrogans cultures. The application of these innovations revealed that the amino acid glutamine is a major nitrogen source for L. interrogans. A small-molecule inhibitor blocking glutamine utilization, JHU-083, effectively impaired the proliferation of sHPLM cultures. Further, adding glutamine to non-physiological EMJH medium rapidly induced a short-term proliferative boost in L. interrogans and increased biofilm formation. RNA-sequencing after glutamine exposure revealed transcriptional trends for increases in biosynthesis to support these phenotypes. Although ammonium has long been thought to be the sole nitrogen source for L. interrogans, our results demonstrate that glutamine provides a second source of nitrogen for biosynthesis and may act as a metabolite signal to alter L. interrogans physiology in ways that could influence infection. This work highlights that studying L. interrogans under physiological conditions is key to understanding mechanisms supporting infection and points to nitrogen assimilation as a potential target for therapies. Author SummaryLeptospirosis is a potentially fatal disease transmitted through water and soil contaminated with pathogenic Leptospira bacteria. Much research is currently focused on the idea that an improved understanding of how Leptospira infects hosts and causes disease may inspire the development of improved therapeutics, which are urgently needed. Focusing on Leptospira interrogans, a clinically important pathogenic species, we determined that conventional growth media are inadequate for understanding how the bacterium behaves when inside hosts. Instead, we designed an optimized formulation to mimic human blood, and we applied an underutilized technique for measuring the biochemical reactions that enable pathogen survival. These two innovations revealed that L. interrogans uses glutamine, an abundant nutrient in host blood and tissues, as a source of nitrogen for the production of biomolecules that are required for replication and infection. This discovery is notable as nitrogen demands were previously thought to be met using ammonium. Treating L. interrogans with inhibitors of both glutamine and ammonium metabolism blocked bacterial replication. We also discovered that L. interrogans increases its growth rate, upregulates its expression of biosynthetic pathways when exposed to glutamine, and increases its formation of biofilm. Our results reveal the importance of glutamine in supporting the lifecycle of leptospirosis-causing 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.

Xylella fastidiosa is a xylem-limited phytopathogen bacterium responsible for severe diseases in numerous plant species of major agricultural importance. Despite its economic impact, its metabolism remains poorly characterized due to the bacteriums fastidious growth and the limited availability of defined culture media. In this work, we reconstructed the first pangenome-based genome-scale metabolic model for X. fastidiosa, integrating the conserved metabolic capabilities of 18 strains representing five described subspecies. The resulting core metabolic model, Xfcore, was manually curated and used to investigate the metabolic potential of the species. Model simulations predict minimal nutritional requirements that guide the formulation of defined media capable of supporting biofilm formation in vitro. Analysis of the metabolic network also suggests an undescribed metabolic pathway that enables growth on acetate as a sole carbon source. Furthermore, the model predicts that X. fastidiosa could overproduce polyamines, compounds previously associated with virulence in other phytopathogens. Experimental analyses confirm the production and secretion of polyamines in several X. fastidiosa strains, providing the first in vitro evidence of polyamine production in this pathogen. These findings suggest that polyamine biosynthesis may represent an uncharacterized virulence factor in X. fastidiosa, potentially contributing to bacterial protection against host-induced oxidative stress. Overall, the Xfcore model provides a systems-level framework to explore the metabolism of X. fastidiosa, generate testable hypotheses about its physiology and virulence, and establish a basis for future strain-specific reconstructions and host-pathogen metabolic interaction studies.

Urbanization can result in shifts in abiotic and biotic factors, including temperature, pollution, habitat type, pathogens, and diet, among others. These shifts can, in turn, shape the ecological and evolutionary trajectory of urban wildlife. The gut microbiota has the potential to mediate host-environment interactions, especially in the context of diet and disease, and thus may be a useful lens for understanding the impacts of urbanization. House mice (Mus musculus domesticus) are a cosmopolitan human commensal with a wealth of genomic and metagenomic resources. Here, we investigate patterns of variation in diet and gut microbial diversity, community composition, and function using a paired urban-rural sampling design in house mice from three metro regions in the eastern United States. First, using stable isotope analysis, we found that habitat--urban versus rural--was a major driver of variation in {delta}15N, suggesting a diet richer in animal proteins in cities. Next, using short-read sequencing of the 16S rRNA gene, we found that urban mice have lower gut microbial taxonomic diversity than their rural counterparts. We also found that community composition varied among urban and rural habitats, with differences largely reflecting shifts among closely related taxa. In particular, Prevotellaceae, a family known to be responsive to dietary quality, was differentially abundant, with lower abundance in urban habitats. Finally, we found differentiation in a few predicted microbial functions across habitat, primarily related to metabolism. Together, data across three independent sampling regions provide strong evidence that urbanization has the potential to shape the diet and the microbiome of house mice.

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.

The human microbiome plays a critical role in health and disease, and its dynamic nature has made longitudinal sampling a key strategy for elucidating microbiome-disease relationships. Although the gut microbiome generally stabilizes over time, a subset of samples frequently shows marked deviations from an individuals baseline profile. We refer to these as abnormal samples. To analyze these abnormal samples, we developed a three-stage workflow to identify and classify these abnormal samples to figure out the underlying causes of these abnormal samples. Moreover, we systematically investigated abnormal samples across 16 publicly available metagenomic datasets, comprising a total of 5,171 metagenomes. Our analysis revealed that abnormal samples are often the result of mislabeling during sample collection, processing, or sequencing. Of which, fecal samples from family are more likely mislabeled. We found evidence of mislabeling in 75% of longitudinal datasets, involving up to dozens of samples per study, and in 25% of randomly selected cross-sectional datasets. Additional factors such as disease status (e.g., inflammatory bowel disease), sampling intervals, and sampling density may also contribute to sample abnormalities owing to true biological variations. These findings highlight that mislabeling is a common yet underrecognized issue in microbiome research. Our work underscores the importance of identifying and correcting abnormal samples to ensure data integrity in microbiome studies and provides a practical solution for quality control in large-scale metagenomic datasets.