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.

Understanding microbial interactions within the gut microbiome is challenging due to the sheer number of microorganisms present. Each additional microbe introduces new layers of complexity, especially when studying the conditions and dynamics of infectious progression. To address this challenge, we have developed Enteroscape: an in silico agent-based model, grounded in previous research, laboratory experiments, and in vivo assays. Enteroscape enables simulation of diverse microbial interactions within a nematode host environment. In this study, we used Enteroscape to model the progression of pathogenic yeast infection and evaluate the effects of probiotic yeast treatment in the Caenorhabditis elegans intestine. Drawing upon empirical and literature-based behavioral rules for individual microbes, Enteroscape simulations produced emergent behaviors of the microbial population that mirror experimentally observed infection dynamics, including visual patterns of infectious progression, as well as the effect of probiotic treatment on the host lifespan. Enteroscape demonstrates how computational models can generate testable predictions that deepen our insight into microbial community interactions and their impact on host biology. Author SummaryThe human microbiome is a complicated ecosystem, consisting of trillions of microbes of hundreds of different species that live in the human intestine. Understanding how different species interact to maintain a healthy balanced microbiological ecosystem that keeps pathogenic microbes at bay is critically important to human health. We have developed a simple experimental system that allows us to examine the interactions of just a few thousand microbes at a time in a living host, the microscopic nematode worm, C. elegans. In prior laboratory work, we have demonstrated how ingestion by the worm of the human pathogenic microbe C. albicans shortens the worms lifespan, while co-ingestion of various probiotic microbial species restores a longer lifespan. In this paper, we report the creation of a computational model of this system, which we named Enteroscape. We show that Enteroscape mimics the results of the experimental system, including the visual progression of infection by a pathogenic species, the effects on worm lifespan, and the amelioration of the pathogens effects by a probiotic species. Enteroscape will allow us to develop and test hypotheses about the mechanisms underlying remediation by probiotic microbes, with potential applications to better probiotic treatment of human infections.

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.

The cattle gastrointestinal tract harbors a diverse community of microorganisms, including pathogenic and commensal strains of Escherichia coli. Antimicrobial use in cattle can disrupt the gut microbiome leading to shifts in bacterial diversity and abundance. Here, we combined shotgun metagenomics and single-cell sequencing to assess how ceftiofur antibiotic treatment impacted microbial diversity and structure. At the start of the experiment, ceftiofur was administered intramuscularly in parallel with the inoculation of a cocktail of extended-beta-lactamase-producing E. coli strains, to simulate environmental exposure and acquisition of resistant strains while animals are under antibiotic treatment. Fecal samples were collected from both the antibiotic-treated (ceftiofur and inoculation) and control (inoculation only) calves over the course of 35 days. Read mapping to genome and gene databases showed substantial differences in microbial richness and beta diversity between treatment groups. Treatment group-enriched taxa included Bacteroidaceae and Fibrobacter, which were more abundant in samples that did not receive ceftiofur, and Akkermansia in ceftiofur-treated calves. In ceftiofur-exposed animals, we observed a gradual loss of virulence factors alongside increased abundances of beta-lactam resistance genes, including cfxA5 and cfxA6 likely encoded by CAG-485 (Muribaculaceae). We further profiled individual cells using single-cell sequencing, which revealed a high number of Clostridium carrying macrolide resistance genes lnu(P) and mph(N) in both ceftiofur-treated and control samples. Overall, our complementary approaches reveal distinct remodeling of the calf microbiome following antibiotic and E. coli administration, tied to key functional genes that can be assigned to specific genera or recurrently detected across diverse taxa. IMPORTANCECattle serve as natural reservoirs of zoonotic strains of E. coli, which can cause severe gastrointestinal infections in humans. Antibiotic usage on cattle farms can drive the emergence of antimicrobial resistant bacterial strains and alter the underlying cattle gastrointestinal microbiome. Consequently, there is a need to understand how antibiotic administration impacts population dynamics of cattle rumen and intestinal microbes. In this study, we combined both shotgun metagenomics and single-cell genomics on feces from ruminating calves to determine microbiome changes following administration of both ceftiofur and E. coli cocktails. We observed considerable variation in prevalence and abundance of virulence factors, antimicrobial resistance-related genes, and taxa with key roles in animal nutrition and health between the microbiomes of antibiotic-treated and antibiotic-free calves, with potential implications for their subsequent development and overall well-being.

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.

Microorganisms play essential roles in mediating biogeochemical cycling of carbon across Earths ecosystems. Understanding the processes and underlying mechanisms for microbially mediated carbon cycling is therefore critical for advancing global ecology and climate change research. To comprehensively depict these complex biogeochemical processes, we developed CCycDB, a knowledge-based functional gene database, to accurately fingerprint microbially-mediated carbon cycling pathways and gene families, particularly from shotgun metagenomes. The CCycDB database comprises 4,676 gene families classified into six major functional categories, further structured into 45 level-1 and 188 level-2 sub-categories, encompassing a total of 10,991,724 high-quality reference sequences. Validation using both synthetic and real-world datasets demonstrated that CCycDB outperforms existing orthology databases in terms of accuracy, coverage and specificity. By directly targeting carbon-cycling functional gene families, CCycDB provided promising routines to reconstruct both functional gene and taxonomic profiles associated with microbially mediated carbon cycling. Application of CCycDB to shotgun metagenomes from diverse and complex ecosystems revealed pronounced habitat-specific differences in carbon cycling processes and their associated microbial taxa. Collectively, CCycDB provides a powerful and reliable tool for profiling carbon cycling processes from both functional and taxonomic perspectives in complex ecosystems. CCycDB is accessible at https://ccycdb.github.io/. Impact StatementThe microbially mediated carbon cycling processes are the most complex biogeochemical processes in the Earths biosphere, playing profound regulatory roles on global climate changes. A key bottleneck in linking microbial communities to global change is the lack of integrated tools for comprehensive carbon cycle profiling. Here, we present CCycDB, a tool that serves a dual purpose--first being a reference database that obtains functional gene and taxonomic profiles and functioning as a customized routine for efficiently aligning sequences and querying associated functional information. CCycDB enables researchers to accurately link microbial community dynamics to carbon cycling and transforming pathways, thereby advancing integrated global change studies with microbes and ecological research via complex metagenomic datasets.

Microbial ecological dynamics in temporally varying environments are often mediated by the physiological responses of community members. Linking physiological responses to ecological dynamics remains challenging and ultimately limits our ability to understand the response of microbial communities to environmental change. Here, we evaluated the physiological response of microorganisms to changing conditions by applying a macroecological approach to a multi-year timeseries of paired ribosomal RNA and DNA measurements from a freshwater microbial community. We found that the dynamics of both microbial RNA and DNA displayed strong seasonal oscillations, with phylogenetically distant species oscillating on similar timescales with varying amplitudes. Despite this variation, several fundamental macroecological patterns displayed the same regularities observed in other biomes, while others clearly deviated due to the sustained oscillations. These deviations motivated the development of a minimal ecological model that accounts for oscillations, with seasonal dynamics captured by a time-dependent carrying capacity. Based on previous studies, we interpreted the ratio of RNA and DNA (RNA:DNA) as a proxy of ribosome concentration and evaluated two physiological hypotheses. First, we tested whether RNA:DNA explained changes in DNA over time within a given community member, finding that the commonly-used ratio had a limited predictive capacity. However, RNA:DNA across community members was predictive of proxies of growth, a result consistent with the interpretation that RNA:DNA reflects growth. By examining environmental variables with similar seasonality, we found that temperature provided a reasonable explanation for dynamics of both RNA and DNA, though not RNA:DNA. The results of this work provide a macroecological understanding of ribosomal RNA barcoding and identify the limitations of RNA:DNA as a measure of microbial physiology.

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.

Staphylococcus aureus is a prevalent human pathogen responsible for an array of invasive infections, such as osteomyelitis and bacteremia, which may be life threatening, recurrent, and cause permanent tissue damage. S. aureus infections are exacerbated in patients with co-morbidities including obesity and type 2 diabetes (obesity/T2D) due to impaired immune function, which leads to chronic inflammation and poor healing. Immune dysfunction can be attributed to gut microbiome dysbiosis, characterized by altered community composition and abundance, with aberrant production of gut-immune axis metabolites. Alongside the heightened infection susceptibility exhibited by obese/T2D hosts, S. aureus adapts to the hyperglycemic and hyperfibrinogenemic host environment for robust colonization. S. aureus can persist in host tissues by forming staphylococcal abscess communities (SACs) encapsulated by a fibrin pseudocapsule that protect the bacteria from antimicrobials and immune cell killing. Our current work aims to investigate how S. aureus adapts to the hyperglycemic and hyperfibrinogenemic obese/T2D environment. Our data show that two S. aureus clinical isolates, USA300 FPR3757 and JAR06.01.31, utilize fibrinogen differently in obese/T2D-like conditions to form unique pseudocapsule structures. Furthermore, RNAseq data show that in obese/T2D-like conditions, S. aureus upregulates virulence and tissue invasion gene expression. Additionally, our data suggest that antibiotic susceptibility in obese/T2D-like conditions is affected by antibiotic size, charge and metabolic activity of S. aureus. Collectively, these investigations will elucidate the impact of hyperglycemia and hyperfibrinogenemia on S. aureus abscess formation in two clinically relevant strains and may inform future therapies for obese/T2D patients. ImportanceType 2 diabetes associated with obesity creates a unique host environment that promotes the severity and persistence of Staphylococcus aureus infections. Elevated blood glucose and fibrinogen disrupt the normal immune response and create conditions that favor bacterial persistence and dissemination. S. aureus is an opportunistic pathogen capable of causing a broad spectrum of diseases ranging from skin infection to life threatening blood and bone infection. A critical step in its pathogenesis is the formation of abscesses, which shield the bacteria from immune clearance and antibiotic treatment. In this study, we demonstrate that the altered metabolic and inflammatory state of the obese diabetic host reshapes the way Staphylococcus aureus constructs these protective abscesses. We show that S. aureus modifies its use of host fibrin and adjusts its gene expression in response to high blood glucose and fibrinogen, thereby enhancing its ability to persist in host tissues.

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.

Understanding the environmental dissemination of antimicrobial resistance (AMR) is crucial for mitigating its spread and for reducing the clinical burden of resistant infections. Within a One Health framework, animal husbandry systems, and particularly animal gut microbiomes, are widely used as models for agricultural environments, because their high densities and recurrent antibiotic exposure create strong selective pressures that can impact both environmental and human resistomes. In a previous study, we demonstrated that copiotrophic enrichment in medium that stimulates growth of gut-associated bacteria, can reveal clinically associated bacterial populations and associated antibiotic resistance genes (ARGs) that are not detected by direct environmental sampling. In the present study, we applied this enrichment approach to poultry manure to examine how {beta}-lactamase-resistant antibiotics (cefotaxime and meropenem) shape copiotrophic gut-like microbial community composition and resistance profiles relative to an earlier {beta}-lactam (ampicillin) and to antibiotic-free controls. Our results show that {beta}-lactamase-resistant antibiotics induced more pronounced shifts in both microbial diversity and ARG profiles than the early {beta}-lactam. When focusing on specific taxa, differential responses were observed correlating to antimicrobial range of action. Together, these findings underline how the diversity of pathogen and AMR indicators in gut microbiomes may be shaped by different selective pressures imposed by {beta}-lactamase-resistant antibiotics.

The history of cheesemaking is deeply intertwined with the evolution of microbial communities, from spontaneous fermentation to modern, standardized practices. Despite centuries of refinement, the most profound shifts in cheese production occurred in the last century, driven by advances in microbiology and production technologies. These changes have shaped the bacterial and viral communities within cheese, yet the specific impacts remain underexplored. Using shotgun metagenomics and 16S rRNA gene amplicon sequencing approaches, we examined microbial community changes in Raclette du Valais, a traditional Swiss cheese, using preserved cheese wheels from 1875 to 2017 from the same alpine dairy in Switzerland. Our results reveal that significant shifts in microbial community composition coincide with changes in production practices. Notably, the oldest cheese harbored a distinct bacterial community, dominated by Lactiplantibacillus paraplantarum, Streptococcus thermophilus, Pseudolactococcus laudensis, and taxa commonly associated with the gut environment, indicative of spontaneous fermentation and the use of calf stomach for milk coagulation. Functionally, we can also track the rise and fall of antibiotic resistance genes mirroring their use. Furthermore, we found that domestication of lactic acid bacteria predates the studied period, and that bacteriophage genera detected in 1875 are representatives of those commonly found in modern cheesemaking. These findings highlight how microbial communities have adapted to changing production methods and how human intervention, through practices like antibiotic use in animal husbandry, has influenced these ecosystems in remote alpine cheesemaking. Significance StatementCheesemaking relies on complex microbial ecosystems shaped by long-standing human practices, yet how these communities responded to the modernization of food production has remained largely unknown. By analyzing DNA preserved in historical cheese wheels from a single alpine dairy, we examine microbial community changes across a key technological transition. We show that modernization impacted bacterial composition and functional potential, that cheese microbiomes record the rise of agricultural antibiotic use, and that major cheese-associated phages and domesticated starter bacteria were already established over a century ago. These findings demonstrate that historical cheeses preserve long-term microbial records and offer a glimpse how changes in food production practices shape fermented-food microbiomes.

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.

Mycobacteria form rough and smooth colonies. The Mycobacterium marinum strain 1218S is a smooth colony forming variant isolated from the 1218R strain, which forms rough colonies and is more virulent than 1218S in infecting fish. Genes for the type VII secretion ESX-1 system, which includes mycobacterial virulence genes, have been partially duplicated in M. marinum and is refered to as ESX-6. We recently reported that several ESX-1 genes are missing in the 1218S strain. On the basis of the complete genomes of these two and three other M. marinum strains we provide insight into strain differences and similarities focusing on 1218R and 1218S, and ESX genes, selected virulence genes, and LOS genes, which are involved in lipooligosaccharide synthesis and smooth colony formation. We provide RNA-Seq data for 1218R and 1218S and two other well-characterized M. marinum strains suggesting that loss of ESX-1 genes in 1218S results in increased transcript levels of ESX-6 genes. Furthermore, while there is no difference in gene synteny and sequence of LOS genes comparing 1218R and 1218S, with the exception of duplication of lsr2, a regulator of LOS genes, in 1218S. Our RNA-Seq data show increased transcript levels of LOS genes in stationary 1218S cells relative to 1218R indicating that transcription and/or RNA degradation of LOS genes influence smooth and rough colony formation. We finally provide data suggesting that Ms1 RNA affect the transcription of LOS genes (and ESX-1 genes), and that loss of ESX-1 genes influence biofilm formation.

The supernatant of Bacillus toyonensis biovar thuringiensis Bto_UNVM-42 exhibits nematicidal activity, although its molecular basis remains unclear. While pesticidal proteins in Bacillus thuringiensis and related species are classically considered to be intracellular and associated with parasporal crystals, their potential presence in the extracellular fraction has been largely unexplored. Here, LC-MS/MS analysis of the secretome from LB-grown cultures revealed the extracellular presence of pesticidal protein homologs related to Cry32-, Cyt1-, and Mpp3-like protein families, together with degradative enzymes including collagenase, chitinase, proteases, and cytolysins. Signal peptide prediction supported classical secretion for several proteins, while others were consistent with non-classical secretion pathways. The consistent detection of these proteins in cell-free supernatants provides strong proteomic evidence for their extracellular localization. These findings challenge the prevailing crystal-centric paradigm of Bt-like pesticidal proteins and support an expanded model in which soluble extracellular components contribute to pathogenicity. This work highlights the value of secretome analysis for the characterization of Bt-like strains and provides new insights into the molecular basis of nematicidal activity in B. toyonensis. O_LIFirst report of Cry32-, Cyt-, and Mpp-like homologs in the Bto_UNVM-42 secretome. C_LIO_LIExtracellular detection challenges classical intracellular Bt-like toxin paradigm. C_LIO_LILC-MS/MS reveals toxin homologs in culture supernatant. C_LIO_LIEvidence supports secretion beyond crystal-associated proteins. C_LIO_LISecretome suggests expanded functional repertoire in Bt-related strains. C_LI

BackgroundPelagic Sargassum has undergone significant range expansion and dramatic blooms in the Atlantic over the past 15 years. This algaes microbiome provides symbiotic functions that are believed to contribute to its ecological success. Recent research shows that Sargassum-associated bacteria are enriched in integrated prophages compared to the surrounding seawater and that these prophages are inducible by chemical and ultraviolet treatment. ResultsHere, we investigated a Sargassum-derived in vitro multispecies biofilm encompassing the dominant heterotrophic microbial members associated with Sargassum to probe the impacts of prophage induction on the composition of Sargassum microbiomes. Induction was quantified by coverage-based virus-to-host ratios in chemically induced treatments with Mitomycin C and non-induced controls, and the community composition and metabolic profiles were analyzed after a period of recovery post-induction. Chemical induction led to a significant increase in abundance and virus-to-host ratio of viral genomes linked to Vibrio metagenome-assembled genomes. This was accompanied by altered biofilm community composition, with a reduction in Vibrio bacterial abundance that opened niche space for other biofilm members in the genera Pseudoalteromonas, Alteromonas, and Cobetia. The induced Vibrio-associated phages encoded genes involved in quorum sensing, biofilm formation, virulence, and host metabolism. Induction led to a relative loss of 17 metabolic modules, including functions related to energy metabolism and nitrogen utilization. ConclusionDue to the high frequency of lysogeny in the Sargassum microbiome and the susceptibility of prophages to chemical and ultraviolet light induction, these results suggest that prophage integration and induction are mechanisms that significantly contribute to structuring the Sargassum microbiome and its functional profiles, potentially aiding in microbiome flexibility in changing environmental contexts.