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Abstract Archaea are vital components of the human microbiome, yet their study within the gastrointestinal tract (GIT) is limited by the scarcity of cultured representatives. Our study presents a method for the targeted enrichment and isolation of methanogenic archaea from human fecal samples. The procedure combines methane breath testing, in silico metabolic modeling, media optimization, FACS, dilution series, and genomic sequencing through Nanopore technology. Additional analyzes include the co-cultured bacteriome, comparative genomics of archaeal genomes, functional comparisons, and structure-based protein function prediction of unknown differential traits. Successful establishment of stable archaeal cultures from 14 out of 16 fecal samples yielded nine previously uncultivated strains, eight of which are absent from a recent archaeome genome catalog. Comparative genomic and functional assessments of Methanobrevibacter smithii and Candidatus Methanobrevibacter intestini strains from individual donors revealed features potentially associated with gastrointestinal diseases. Our work broadens available archaeal representatives for GIT studies, and offers insights into Candidatus Methanobrevibacter intestini genomes’ adaptability in critical microbiome contexts.

ABSTRACT The human gut microbiome is crucial in health and disease. Longitudinal studies are becoming increasingly important compared to traditional cross-sectional approaches, as precision medicine and individualized interventions are coming to the forefront. Investigating the temporal dynamics of the microbiome is essential for comprehending its function and impact on health. This knowledge has implications for targeted therapeutic strategies, such as personalized diets or probiotic therapy. In this study, we focused on developing and implementing methods specifically designed for analyzing gut microbiome time series. Our statistical framework provides researchers with tools to examine the temporal behavior of the gut microbiome. Key features of our framework include statistical tests for time series properties, predictive modeling, classification of bacterial species based on stability and noise, and clustering analyses to identify groups of bacteria with similar temporal patterns. We analyzed dense amplicon sequencing time series from four generally healthy subjects. Using our developed statistical framework, we analyzed both the overall community dynamics and the behavior of individual bacterial species. We showed six longitudinal regimes within the gut microbiome and discussed their features. Additionally, we explored whether specific bacterial clusters undergo similar fluctuations, suggesting potential functional relationships and interactions within the microbiome. Our development of specialized methods for analyzing human gut microbiome time series significantly enhances the understanding of its dynamic nature and implications for human health. The guidelines and tools provided by our framework support scientists in studying the complex dynamics of the gut microbiome, fostering further research and advancements in microbiome analysis. The gut microbiome is integral to human health, influencing various diseases. Longitudinal studies offer deeper insights into its temporal dynamics compared to cross-sectional approaches. In this study, we developed a statistical framework for analyzing the time series of the human gut microbiome. This framework provides robust tools for examining microbial community dynamics over time. It includes statistical tests for time series properties, predictive modeling, classification of bacterial species based on stability and noise, and clustering analyses. Our approach significantly enhances the methodologies available to researchers, promoting further exploration and innovation in microbiome analysis.IMPORTANCEThis project developed innovative methods to analyze gut microbiome time series data, offering fresh insights into its dynamic nature. Unlike many studies that focus on static snapshots, we found that the healthy gut microbiome is predictably stable over time, with only a small subset of bacteria showing significant changes. By identifying groups of bacteria with diverse temporal behaviors and clusters that change together, we pave the way for more effective probiotic therapies and dietary interventions, addressing the overlooked dynamic aspects of gut microbiome changes.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

The influence of human gut microbiota on health and disease is now commonly appreciated. Therefore, it is not surprising that microbiome research has found interest in the sports community, hoping to improve health and optimize performance. Comparative studies found new species or pathways that were more enriched in elites than sedentary controls. In addition, sport-specific and performance-level-specific microbiome features have been identified. However, the results remain inconclusive and indicate the need for further assessment. In this case-control study, we tested two athletic populations (i.e. strength athletes, endurance athletes) and a non-athletic, but physically active, control group across two acute exercise bouts, separated by a 2-week period, that measured explosive and high intensity fitness level (repeated 30-s all-out Wingate test (WT)) and cardiorespiratory fitness level (Bruce Treadmill Test). While we did not identify any group differences in alpha and beta diversity or significant differential abundance of microbiome components at baseline, one-third of the species identified were unique to each group. Longitudinal sample (pre- and post-exercise) analysis revealed an abundance of Alistipes communis in the strength group during the WT and 88 species with notable between-group differences during the Bruce Test. SparCC recognized Bifidobacterium longum and Bifidobacterium adolescentis, short-chain fatty acid producers with probiotic properties, species strongly associated with VO2max. Ultimately, we identified several taxa with different baseline abundances and longitudinal changes when comparing individuals based on their VO2max, average power, and maximal power parameters. Our results confirmed that the health status of individuals are consistent with assumptions about microbiome health. Furthermore, our findings indicate that microbiome features are associated with better performance previously identified in elite athletes.

Intestinal alkaline phosphatase (IAP) is an enzyme that plays a protective role in the gut. This study investigated the effect of IAP treatment on experimental colitis in mice subjected to forced exercise on a high-fat diet. C57BL/6 mice with TNBS colitis were fed a high-fat diet and subjected to forced treadmill exercise with or without IAP treatment. Disease activity, oxidative stress, inflammatory cytokines, and gut microbiota were assessed. Forced exercise exacerbated colitis in obese mice, as evidenced by increased disease activity index (DAI), oxidative stress markers, and proinflammatory adipokines and cytokines. IAP treatment significantly reduced these effects and promoted the expression of barrier proteins in the colonic mucosa. Additionally, IAP treatment altered the gut microbiota composition, favoring beneficial Verrucomicrobiota and reducing pathogenic Clostridia and Odoribacter. IAP treatment ameliorates the worsening effect of forced exercise on murine colitis by attenuating oxidative stress, downregulating proinflammatory biomarkers, and modulating the gut microbiota. IAP warrants further investigation as a potential therapeutic strategy for ulcerative colitis.