Consumption of atmospheric hydrogen (H2) enables diverse aerobic microorganisms to grow and persist in resource-deprived environments. In the aerobic saprophyte Mycobacterium smegmatis, hydrogen oxidation is catalyzed by two differentially expressed, high-affinity, oxygen-insensitive uptake hydrogenases, Huc and Hhy. Huc enables mixotrophic growth and facilitates the transition from growth to dormancy. Although the huc operon is known to be upregulated in response to organic carbon deprivation, the specific signals and regulators modulating its expression remain unresolved. Here, we show that GylR, a glycerol-3-phosphate-sensing regulator of glycerol metabolism, plays a role in the repression of huc expression in response to the availability of glycerol but not other carbon sources. Based on proteomic analyses and activity assays, mutation or knockdown of gylR leads to enhanced Huc production and activity. GylR and other key catabolite repressor proteins (Crp1 and Crp2) do not directly bind to the huc operon, indicating that repression is mediated by unidentified transcription factors, with GylR acting as an upstream sensor. Here, we present data that suggest atmospheric H2 oxidation is regulated in response to organic carbon source availability through the process of catabolite repression. By identifying a key signal that prompts atmospheric H2 oxidation, these findings advance understanding of how aerobic bacteria adapt to changing environmental conditions and suggest that organic carbon levels are a key factor regulating the main sink of atmospheric H2 in soils globally.IMPORTANCESoil microorganisms collectively consume 70 million tonnes of atmospheric hydrogen (H2) a year, regulating atmospheric composition and climate change. In turn, consuming this dependable trace gas enables these microorganisms to survive even when their preferred organic energy sources are exhausted. Despite the importance of H2 consumption for soil biodiversity and atmospheric regulation, the signals and sensors that regulate this process remain to be understood. Here, we demonstrate that a model soil bacterium turns on the machinery required for atmospheric H2 consumption in direct response to being limited by organic carbon availability, through the process of catabolite repression. Specifically, in the absence of a sensor of the organic carbon source glycerol, a H2-consuming hydrogenase is highly expressed and active. These findings suggest that organic carbon levels have a major role in regulating trace gas oxidation, with implications for predicting how trace gas consumption and soil biodiversity respond to environmental change.
Antimicrobial resistance (AMR) poses a major global public health threat, and ongoing surveillance of antimicrobial resistance genes (ARGs) is critical to mitigate current and future risks. Sewage-based ARG surveillance is gaining traction, but insight into how it compares to surveillance by clinical bacterial isolates is limited, especially when it comes to ARG mutational variants. We compared ARGs identified in clinical bacterial isolates (n = 2,989) with those detected in sewage metagenomes (n = 468) across 33 countries. ARG variant detection data from clinical isolates and sewage metagenomes shared some regional patterns in detection, but many ARG variants were detected exclusively in either sewage metagenomes or clinical isolates. We found that across all samples, only 69% of ARG clusters detected in clinical isolates were also detected via read mapping in sewage. Some ARGs highly prevalent in clinical isolates were not detected in sewage. Among clinically widespread ARGs, prevalence varied across bacterial species and clinical isolate types depending on whether the ARGs were also detected in sewage. This could indicate that sewage surveillance is better suited for detection of clinically relevant ARGs prevalent in certain bacterial species and infection sites than others. Spearman correlation between ARG abundance in sewage and the proportion of clinical isolates from the same country with detection was 0.28 overall, with stronger correlations for certain ARGs. The results demonstrate that sewage ARG profiles correlate, to some extent, to the clinical AMR landscape, but do not capture the full spectrum of clinically relevant ARGs at currently realistic sequencing depths.IMPORTANCEAntimicrobial resistance (AMR) is a major public health threat. Surveillance of AMR is important and can be conducted via the detection of antimicrobial resistance genes (ARGs). Sewage can be used as a medium for surveillance as an alternative to analyzing individual bacterial isolates from health clinics. We compared detection in large global data collections of sewage metagenomes and clinical isolates. We found that while there were significant positive correlations between findings in sewage and clinical isolates, some widespread clinical ARGs were not detectable in sewage. This should be considered if establishing sewage surveillance systems.
The microbiome is increasingly recognized as a key factor in health. Intestinal microbiota modulates gut homeostasis via a range of diverse metabolites. In particular, molecules such as short-chain fatty acids (SCFAs), the microbial fermentation products of dietary fiber, have been established to be reflective of microbiome and/or dietary shifts, and SCFAs alterations have been linked to multiple gastrointestinal disorders, from cancer to colitis. Despite their potential as biomarkers, technical challenges in stool collection have limited clinical translation. Here, we present Stool Wipe (S'Wipe), an ultra-low-cost fecal collection method using lint-free, mass spectrometry (MS)-compatible cellulose wipes as toilet paper. Specimens are preserved in ethanol without refrigeration and can be shipped via regular mail. Mass spectrometry analysis demonstrated that S'Wipe captures both volatile and non-volatile metabolites with reproducibility and stability validated for diagnostically relevant molecules. We show that S'Wipe performs equivalently to direct stool collection, enabling interchangeable use and comparison with existing studies. This methodology is ideally suited for large-scale population studies, longitudinal tracking, and personalized medicine applications.
Importance: Gut microbiome and intestinal metabolome present invaluable diagnostic and therapeutic targets. However, conventional stool testing has several barriers, limiting bioassessment from populations. Routine, high-temporal-resolution monitoring of stool metabolome, including extensively validated and broadly informative biomarkers such as short chain fatty acids (SCFAs), is not implemented due to relatively high cost and inconvenience of sampling, possible need for clinical setting for sample collection, difficulty in collecting samples reproducibly-especially due to potential for user errors-requirement for freezer storage and maintenance of the cold chain during shipment. We present a sampling strategy specifically designed to overcome these obstacles. We demonstrate how this method can enable capturing accurate molecular snapshots at massive scales, at ultra-low cost. The approach collapses complex medical-grade collection into easy self-administration. Individuals can thereby self-monitor therapeutic responses through routine metabolome tracking, including the volatilome, otherwise hindered by infrastructure restrictions. Ultimately, this sampling approach is intended to enable participatory wellness transformation through practical high-frequency self-sampling.
This study aimed to characterize salivary microbiome compositions that can classify periodontal health and various stages of periodontitis. We collected saliva samples from 250 study subjects, including 100 periodontally healthy controls and 150 periodontitis patients in stages I/II/III. We performed 16S ribosomal RNA gene sequencing to characterize their salivary microbiomes. Alpha diversities show significant differences between healthy and periodontitis. Differentially abundant taxa were identified by ANCOM. Random forest machine learning models were used to classify each periodontitis stage based on the centered log-ratio of differentially abundant taxa. We identified 20 differentially abundant taxa among the groups in the salivary microbiomes of all groups. Among these differentially abundant taxa, Porphyromonas gingivalis and Actinomyces spp. are the most important taxa on the random forest model to classify the periodontitis statuses. Our random forest model classified multiple periodontitis statuses with an area-under-curve of 0.829 ± 0.124, sensitivity 0.884 ± 0.022, and specificity 0.652 ± 0.065. Moreover, because it can be difficult to diagnose in dentistry practice, we performed our classifier model to distinguish healthy or stage I, providing an area-under-curve of 0.736 ± 0.168, sensitivity 0.789 ± 0.102, and specificity 0.622 ± 0.196. Furthermore, our random forest model detected periodontitis patients from healthy individuals with an area-under-curve of 0.924 ± 0.088, sensitivity of 0.862 ± 0.175, and specificity of 0.921 ± 0.061. Finally, we evaluated our classification model with external data sets from Spanish and Portuguese subjects. Some evaluations showed a slight decrease, but it might be due to different salivary microbiome compositions from ethnicity. Significant differences were identified in the differentially abundant taxa among healthy controls and the various stages of periodontitis.IMPORTANCEPeriodontitis is a common but complex oral disease that can lead to tooth loss and contribute to systemic health issues. Early and accurate diagnosis is essential for effective intervention, yet traditional diagnostic methods often rely on invasive clinical assessments that may miss early signs. This study demonstrates that salivary microbiome profiles can be used to classify both periodontal health and multiple periodontitis stages using a machine learning approach. By identifying the 20 key microbial taxa, including Actinomyces spp., we developed a non-invasive predictive model with high diagnostic accuracy. Importantly, the model was also able to detect early-stage disease and performed well across external data sets, highlighting its potential for broader clinical application. These findings suggest that a salivary microbiome-based diagnostic tool may support more precise, accessible, and early diagnosis of periodontitis in dental disease management.
The human gastrointestinal tract hosts a diverse population of microorganisms that have a significant impact on host health. Among this population, Enterococcus faecalis (Ef) represents a common member of intestinal microbiota, colonizing humans early in life but also capable of opportunistically infecting its host. Despite its importance in human health, investigations of its physiological adaptation to the mucosal environment remain limited. Building on recent advances in tissue engineering, we here leverage human colonic organoids (colonoids) to investigate the Ef's mechanisms of mucosal surface colonization across space and time. Using high-resolution microscopy, we visualized Ef growth within the natively formed colonic mucus layer in colonoids. Leveraging a custom perfusion chamber, we tracked Ef growth within the mucus of live colonoids over time under flow, which revealed specific colonization strategies, including biofilm-like microcolony formation. To identify Ef fitness determinants in this niche, we implemented transposon insertion sequencing (Tn-seq) in the natively formed mucus of live colonoids. This approach revealed a large fitness rearrangement compared to typical liquid culture, mainly involving metabolic activity and regulatory response during mucosal colonization, as well as factors that may contribute to colony formation at the mucosal surface. Altogether, our results show important physiological and biophysical adaptation of Ef to the mucosal surface that are not captured by in vitro conditions and that cannot be revealed in vivo at high resolution.
Importance: Gut microbiota interactions with mucus during early intestinal colonization are critical for establishing stable communities and influencing host health. Using human colonic organoids combined with Tn-seq and live imaging, this study reveals how Enterococcus faecalis adapts to the mucosal surface by forming microcolonies and reprogramming its metabolism. This integrative approach provides a powerful platform to study other microbiota members in the native-like environment of the large intestine and evaluate potential therapeutic interventions.
The earliest responses of pathogenic bacteria to antibiotics can affect the outcome of an infection. While long-term adaptations have been extensively studied, the immediate transcriptional changes that unfold immediately following antibiotic exposure remain poorly understood. Here, we applied iModulon analysis to time-resolved transcriptomic data from Escherichia coli exposed to subinhibitory concentrations of two antibiotics (ampicillin and ciprofloxacin), capturing transcriptional regulatory changes occurring within the first 30 min of exposure. This analysis proposes an integrated, three-phase response model: an immediate and sustained primary response that broadly activates stress programs, a transient secondary response that restores redox balance, and a tertiary response that supports long-term survival through metabolic remodeling and antibiotic-specific defenses. These results highlight a coordinated and dynamic regulatory strategy describing how metabolic, redox, and stress responses are integrated to manage the physiological challenges of antibiotic stress. By disentangling these overlapping transcriptional regulatory programs, this work offers a genome-scale understanding of how early regulatory programs are engaged immediately after antibiotic exposure. Together, these findings provide a structured framework for characterizing complex transcriptomic responses and generating testable hypotheses about the regulatory logic that shapes the understudied early phase of antibiotic exposure.IMPORTANCEInitial bacterial responses to antibiotics are important for survival and can influence the development of tolerance and resistance. However, this period remains poorly understood, in part, because the transcriptional responses that unfold within minutes of antibiotic exposure are complex and difficult to interpret. In this study, we applied novel data generation and data analytics approaches to resolve the regulatory structure of the initial response of Escherichia coli to two antibiotics. We identify a three-phase process that explains how E. coli coordinates stress responses, maintains redox homeostasis, and initiates downstream protective programs. The novel transcriptomic analytics elucidate independently regulated sets of genes that constitute cellular processes. By identifying the regulatory modules that change over this initial timescale, we can deconvolute the response based on first principles of cellular physiology.
Emerging evidence suggests that reduced exposure to biodiversity, including rich environmental microbiota, is associated with negative outcomes in the health and well-being of children. Biodiversity loss not only impacts individual health but also poses significant threats to planetary health. It destabilizes systems that regulate climate, purify air and water, maintain soil fertility, and support plant and microbial life essential for environmental health. Here, we review the scientific evidence on microbiome-supportive strategies in eco-centric, child-friendly playground environments. Investigating how environmental features influence soil microbiomes and exposure pathways could provide insights into how playgrounds function as living interfaces. These are places where environmental microbes shape children's microbial colonization patterns, immune and endocrine regulatory systems, while also contributing to ecosystem services such as biodiversity support and pollutant mitigation-particularly relevant given that many pollutants are known to disrupt immune and endocrine functions in children. These dynamics have far-reaching implications for child well-being, preventive health strategies, physical activity, environmental literacy, and broader sustainability. A multi-omic systems approach offers a critical pathway to uncover the ecological and health-related impacts of nature-associated microbial exposure and characterize host-microbiome interactions underlying immune and endocrine regulation, brain development, cognition, and stress-related disorders. Our review highlights a lack of such integrative studies, underscoring the need to advance this line of research to inform evidence-based, sustainable, and health-promoting urban design.
The gut virome is an emerging but underexplored component of the human microbiota, especially in pediatric Crohn's disease (CD). This study aimed to characterize the fecal virome in children with CD and evaluate its association with clinical response to infliximab (IFX) therapy. A total of 85 participants, including 60 pediatric CD patients and 25 healthy controls (HC), were recruited. Among the CD patients, 53 received ≥3 IFX infusions, 41 achieved remission (IFX-R), and 12 did not (IFX-NR). Viral-like particles in fecal samples were enriched and profiled by metagenomic sequencing, while bacterial communities were assessed via 16S rRNA gene sequencing. Pediatric CD patients exhibited significantly reduced viral richness and altered viral community compared to HCs. Functional analyses revealed that CD patients exhibit a shift in fecal virome function from DNA repair to viral replication and assembly. Trans-kingdom correlations were disrupted in CD, particularly between Torque teno viruses and beneficial bacteria, such as Blautia. An integrated machine learning model combining viral and bacterial markers achieved a certain level of diagnostic accuracy for pediatric CD (area under the curve [AUC] = 89.3%). IFX treatment influences the gut virome, with remission associated with higher abundances of Microviridae and Siphoviridae, while Anelloviridae, Myoviridae, and Podoviridae were enriched in IFX-NR at baseline. These findings suggest the virome as a potential biomarker for predicting clinical outcome in pediatric CD, offering a novel avenue for disease diagnosis and personalized treatment strategies.
Importance: Crohn's disease (CD) in children poses a growing clinical challenge, with increasing incidence and variable response to biologic therapies such as infliximab (IFX). While gut bacterial dysbiosis has been extensively studied, the role of the gut virome in pediatric CD remains largely unexplored. This study provides the first longitudinal characterization of the fecal virome in children with CD undergoing IFX therapy. We reveal distinct viral community patterns, functional alterations, and virus-bacteria interactions in pediatric CD patients. Notably, integration of virome and bacteriome profiles enhances diagnostic accuracy, offering a promising avenue for predictive biomarker development. Furthermore, virome changes may be associated with the IFX treatment outcomes in children with CD. These findings highlight the gut virome as a critical but overlooked dimension of host-microbiome interactions in pediatric CD, with potential implications for personalized therapy and mechanistic understanding of treatment resistance.
Proton pump inhibitors (PPIs) are standard therapy for gastroesophageal reflux disease (GERD), but long-term use causes dysbiosis, gastrointestinal side effects, and symptom relapse after discontinuation. Probiotics may offer adjunctive benefits by modulating the gut ecosystem. The study aimed to evaluate the efficacy of a multi-strain probiotic (Lihuo) with rabeprazole in GERD and its impact on gut microbiota and metabolome. A randomized, double-blind, placebo-controlled trial was conducted in 120 GERD patients assigned to receive rabeprazole with either Lihuo (n = 64) or placebo (n = 56) for 8 weeks, followed by 4 weeks of probiotic or placebo alone. The primary outcome was change in the Reflux Disease Questionnaire (RDQ) score. Secondary outcomes included Gastrointestinal Symptom Rating Scale, endoscopic healing, and multi-omics profiling (shotgun metagenomics, phageome, and untargeted/targeted metabolomics). Compared with the placebo group, the probiotic group exhibited a pronounced 36.51% reduction in RDQ scores after 12 weeks of intervention (P = 0.017), alongside a higher numerical endoscopic healing rate (36.84% vs 12.50%; P = 0.365). Metagenomics revealed enrichment of Bifidobacterium animalis, Lactiplantibacillus plantarum, and Clostridium sp900540255, with reductions in Bacteroides uniformis and Clostridium Q fessum. Metabolomics showed increased γ-aminobutyric acid, succinate, citrulline, and short-chain fatty acids levels, with interesting microbe-metabolite correlations such as Bifidobacterium animalis-γ-aminobutyric acid and Bacteroides fragilis-succinate (r ≥ 0.30, P < 0.01). Our findings support that adjunctive probiotic therapy sustains post-PPI symptom relief, associated with targeted modulation of gut microbiota and bioactive metabolites.IMPORTANCELong-term proton pump inhibitor use in gastroesophageal reflux disease (GERD) may disrupt gut microbiota and cause symptom relapse after discontinuation. We found that adjunctive probiotic therapy sustained reflux reduction post-proton pump inhibitor. Probiotic use enriched beneficial taxa (Bifidobacterium and Lactiplantibacillus plantarum) and increased γ-aminobutyric acid, succinate, citrulline, and short-chain fatty acids. Strong correlations linked microbial shifts to metabolic and clinical improvements. This study demonstrates that adjunctive probiotic therapy enhances symptom control and supports microbial-metabolic homeostasis in GERD.CLINICAL TRIALSThis study is registered with the Chinese Clinial Trial Registry as ChiCTR2000038409.
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