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Gut microbiome changes have been associated with several human diseases, but the molecular and functional details underlying these associations remain largely unknown. Here, we performed a meta-analysis of small molecule biosynthetic gene clusters (BGCs) in metagenomic samples of the gut microbiome from inflammatory bowel disease (IBD) patients and matched healthy subjects and identified two Clostridia-derived BGCs that are significantly associated with Crohn's disease (CD), a main IBD type. Using synthetic biology, we discovered and solved the structures of six fatty acid amides as the products of the CD-enriched BGCs, which we subsequently detected in fecal samples from IBD patients. Finally, we show that the discovered molecules disrupt gut permeability and exacerbate disease in chemically or genetically susceptible mouse models of colitis. These findings suggest that microbiome-derived small molecules may play a role in the etiology of IBD and represent a generalizable approach for discovering molecular mediators of disease-relevant microbiome-host interactions. [Display omitted] • Biosynthetic gene clusters (BGCs) were discovered from the human gut microbiome • A meta-analysis approach identified BGCs that are enriched in Crohn's disease • Two disease-enriched BGCs produce a unique set of fatty acid amides (ebf - ecf -FAAs) • ebf - ecf -FAAs exist in patient fecal samples and exacerbate colitis in mouse models Elmassry et al. employed computational and experimental approaches to identify gut microbiome-encoded biosynthetic gene clusters that are enriched in Crohn's disease patients and the small molecules they produce, which exacerbated disease when introduced to mouse models of colitis. [ABSTRACT FROM AUTHOR]

The interactions between macronutrients, the human gut microbiome, and their metabolites (short-chain fatty acids) were comprehensively investigated via an in vitro digestion and fermentation model subjected to eight pulse species. 16S rRNA sequencing and taxonomic analysis of pulse digesta fermented for up to 24 h revealed an increase in the relative abundance of gut health-detrimental genera represented by Escherichia-Shigella in kidney bean, soybean, cowpea, chickpea, and black bean samples. In contrast, the relative abundance of health-positive genera, including Bacteroides , Eubacterium , and Akkermansia , was elevated in red bean, mung bean, and Heunguseul. At the same time, the proportion of the pathogenic Escherichia-Shigella decreased. Concurrently, these three species exhibited an increase in microbial diversity as evidenced by the calculation of α -diversity (Shannon index) and β -diversity (Bray-Curtis distance). Despite the lower nutrient contents in the three pulses, represented by carbohydrates, amino acids, and fatty acids, network analysis revealed that the nutrient contents in the pulse digesta possess complex positive or negative correlations with a variety of bacteria, as well as their metabolites. These correlations were more pronounced in red bean, mung bean, and Heunguseul than in the other pulses. It was postulated that the overall potential to nourish gut environments in these species was due to the balance of their nutritional components. The linear regression analysis demonstrated that there was a negative association between carbohydrate and amino acid contents and the increase in Shannon indices. Furthermore, the ratio of carbohydrates to fatty acids and amino acids to fatty acids displayed negative correlations with the diversity increase. The ratio of carbohydrates to amino acids showed a weak positive correlation. It is noteworthy that a diet comprising foods with a balanced nutritional profile supports the growth of beneficial gut microbes, thereby promoting microbial eubiosis. Consistent work on different ingredients is essential for precise insight into the interplay between food and the human microbiome in complex dietary patterns. [ABSTRACT FROM AUTHOR]

Background: The treatment effects are heterogenous across patients due to the differences in their microbiomes, which in turn implies that we can enhance the treatment effect by manipulating the patient's microbiome profile. Then, the coadministration of microbiome-based dietary supplements/therapeutics along with the primary treatment has been the subject of intensive investigation. However, for this, we first need to comprehend which microbes help (or prevent) the treatment to cure the patient's disease. Results: In this paper, we introduce a cloud platform, named microbiome causal machine learning (MiCML), for the analysis of treatment effects using microbiome profiles on user-friendly web environments. MiCML is in particular unique with the up-to-date features of (i) batch effect correction to mitigate systematic variation in collective large-scale microbiome data due to the differences in their underlying batches, and (ii) causal machine learning to estimate treatment effects with consistency and then discern microbial taxa that enhance (or lower) the efficacy of the primary treatment. We also stress that MiCML can handle the data from either randomized controlled trials or observational studies. Conclusion: We describe MiCML as a useful analytic tool for microbiome-based personalized medicine. MiCML is freely available on our web server (http://micml.micloud.kr). MiCML can also be implemented locally on the user's computer through our GitHub repository (https://github.com/hk1785/micml). [ABSTRACT FROM AUTHOR]

Drugs represent our first, and sometimes last, line of defense for many diseases, yet despite decades of research we still do not fully understand why a given drug works in one patient and fails in the next. The human gut microbiome is one of the missing puzzle pieces, due to its ability to parallel and extend host pathways for drug metabolism, along with more complex host–microbiome interactions. Herein, we focus on the well-established links between the gut microbiome and drugs for heart disease and cancer, plus emerging data on neurological disease. We highlight the interdisciplinary methods that are available and how they can be used to address major remaining knowledge gaps, including the consequences of microbial drug metabolism for treatment outcomes. Continued progress in this area promises fundamental biological insights into humans and their associated microbial communities and strategies for leveraging the microbiome to improve the practice of medicine. [ABSTRACT FROM AUTHOR]

ABSTRACT Considering that the human microbiota plays a critical role in health and disease, an accurate and high-resolution taxonomic classification is thus essential for meaningful microbiome analysis. In this study, we developed an automatic system, named MultiTax pipeline, for generating de novo taxonomy from full-length 16S rRNA sequences using the Genome Taxonomy Database and other existing reference databases. We first constructed the MultiTax-human database, a high-resolution resource specifically designed for human microbiome research and clinical applications. The database includes 842,649 high-quality full-length 16S rRNA sequences, extracted from multiple public repositories and human-related studies, offering a comprehensive and accurate portrayal of the human microbiome. To validate the MultiTax-human database, we profiled the human microbiome across various body sites, identified core microbial taxa, and tested its performance using an independent data set. Additionally, the database is equipped with a user-friendly web interface for easy querying and data exploration. The MultiTax-human database is poised to serve as a valuable tool for researchers, enhancing the precision of human microbiome studies and advancing our understanding of its impact on human health and diseases.IMPORTANCEUnderstanding the human microbiome, the collection of microorganisms in and on our bodies, is essential for advancing health research. Current methods often lack precision and consistency, hindering our ability to study these microorganisms effectively. Our study presents the MultiTax-human database, a high-resolution reference tool specifically designed for human microbiome research. By integrating data from multiple sources and employing advanced classification techniques, this database offers an accurate and detailed map of the human microbiome. This resource enhances the ability of researchers and clinicians to explore the roles of microorganisms in health and disease, potentially leading to improved diagnostics, treatments, and insights into various medical conditions.

ABSTRACT The concept of genome–microbiome interactions, in which the microenvironment determined by host genetic polymorphisms regulates the local microbiota, is important in the pathogenesis of human disease. In otolaryngology, the resident bacterial microbiota is reportedly altered in non-infectious ear diseases, such as otitis media pearls and exudative otitis media. We hypothesized that a single-nucleotide polymorphism in the ATP-binding cassette sub-family C member 11 (ABCC11) gene, which determines earwax properties, regulates the ear canal microbiota. We analyzed ABCC11 gene polymorphisms and ear canal microbiota in healthy individuals to understand the relationship between genome–microbiome interactions in the ear canal. The study included 21 subjects who were divided into two groups: 538GA (9) and 538AA (12). Staphylococcus auricularis and Corynebacterium spp. were observed in the 538GA group, whereas Methylocella spp. was observed in the 538AA group. PICRUSt analysis revealed significant enrichment of certain pathways, such as superpathway of N-acetylglucosamine, N-acetylmannosamine and N-acetylneuraminate degradation, chlorosalicylate degradation, mycothiol biosynthesis, and enterobactin biosynthesis in the GA group, whereas allantoin degradation IV (anaerobic), nitrifier denitrification, starch degradation III, L-valine degradation I, and nicotinate degradation I were significantly enriched in the AA group. The ABCC11 gene polymorphism regulates the composition of the ear canal microbiota and its metabolic pathways. This study revealed a genome–microbiome interaction within the resident microbiota of the external auditory canal that may help to elucidate the pathogenesis of ear diseases and develop novel therapies.IMPORTANCEThe ABCC11 gene polymorphism, which determines earwax characteristics, regulates the composition of the ear canal microbiota and its metabolic pathways. We determined the presence of genome–microbiome interactions in the resident microbiota of the ear canal. Future studies should focus on ABCC11 gene polymorphisms to elucidate the pathogenesis of ear diseases and develop therapeutic methods.

ABSTRACT Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software’s rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.IMPORTANCEMicrobiome studies routinely gather complementary data to capture different aspects of a microbiome’s response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. Multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

The interactions between macronutrients, the human gut microbiome, and their metabolites (short-chain fatty acids) were comprehensively investigated via an in vitro digestion and fermentation model subjected to eight pulse species. 16S rRNA sequencing and taxonomic analysis of pulse digesta fermented for up to 24 h revealed an increase in the relative abundance of gut health-detrimental genera represented by Escherichia-Shigella in kidney bean, soybean, cowpea, chickpea, and black bean samples. In contrast, the relative abundance of health-positive genera, including Bacteroides, Eubacterium, and Akkermansia, was elevated in red bean, mung bean, and Heunguseul. At the same time, the proportion of the pathogenic Escherichia-Shigella decreased. Concurrently, these three species exhibited an increase in microbial diversity as evidenced by the calculation of α-diversity (Shannon index) and β-diversity (Bray-Curtis distance). Despite the lower nutrient contents in the three pulses, represented by carbohydrates, amino acids, and fatty acids, network analysis revealed that the nutrient contents in the pulse digesta possess complex positive or negative correlations with a variety of bacteria, as well as their metabolites. These correlations were more pronounced in red bean, mung bean, and Heunguseul than in the other pulses. It was postulated that the overall potential to nourish gut environments in these species was due to the balance of their nutritional components. The linear regression analysis demonstrated that there was a negative association between carbohydrate and amino acid contents and the increase in Shannon indices. Furthermore, the ratio of carbohydrates to fatty acids and amino acids to fatty acids displayed negative correlations with the diversity increase. The ratio of carbohydrates to amino acids showed a weak positive correlation. It is noteworthy that a diet comprising foods with a balanced nutritional profile supports the growth of beneficial gut microbes, thereby promoting microbial eubiosis. Consistent work on different ingredients is essential for precise insight into the interplay between food and the human microbiome in complex dietary patterns.

ABSTRACT The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographic regions. In this study, 220 samples, consisting of sterile swabs from palmar skin and oral and nasal cavities were collected from Chinese Han individuals living in Shanghai, Chifeng, Kunming, and Urumqi, representing the geographic regions of east, northeast, southwest, and northwest China. The full-length 16S rRNA gene of the microbiota in each sample was sequenced using the PacBio single-molecule real-time sequencing platform, followed by clustering the sequences into operational taxonomic units (OTUs). The analysis revealed significant differences in microbial communities among the four regions. Cutibacterium was the most abundant bacterium in palmar samples from Shanghai and Kunming, Psychrobacter in Chifeng samples, and Psychrobacillus in Urumqi samples. Additionally, Streptococcus and Staphylococcus were the dominant bacteria in the oral and nasal cavities. Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. The prediction accuracy using hypervariable regions (V3-V4 and V4-V5) was comparable with that of using the entire 16S rRNA. Overall, our study highlights the distinctiveness of the human microbiome in individuals living in these four regions. Furthermore, the microbiome can serve as a biomarker for geographic origin inference, which has immense application value in forensic science.IMPORTANCEMicrobial communities in human hosts play a significant role in health and disease, varying in species, quantity, and composition due to factors such as gender, ethnicity, health status, lifestyle, and living environment. The characteristics of microbial composition at various body sites of individuals from different regions remain largely unexplored. This study utilized single-molecule real-time sequencing technology to detect the entire 16S rRNA gene of bacteria residing in the palmar skin, oral, and nasal cavities of Han individuals from four regions in China. The composition and structure of the bacteria at these three body sites were well characterized and found to differ regionally. The results elucidate the differences in bacterial communities colonizing these body sites across different regions and reveal the influence of geographical factors on human bacteria. These findings not only contribute to a deeper understanding of the diversity and geographical distribution of human bacteria but also enrich the microbiome data of the Asian population for further studies.

[Display omitted] Differences in microbial communities at different body habitats define the microbiome composition of the human body. The gut, oral, skin vaginal fluid and tissue microbiome, are pivotal for human development and immune response and cross talk between these microbiomes is evident. Population studies reveal that various factors, such as host genetics, diet, lifestyle, aging, and geographical location are strongly associated with population-specific microbiome differences. The present review discusses the factors that shape microbiome diversity in humans, and microbiome differences in African, Asian and Caucasian populations. Gut microbiome studies show that microbial species Bacteroides is commonly found in individuals living in Western countries (Caucasian populations), while Prevotella is prevalent in non-Western countries (African and Asian populations). This association is mainly due to the high carbohydrate, high fat diet in western countries in contrast to high fibre, low fat diets in African/ Asian regions. Majority of the microbiome studies focus on the bacteriome component; however, interesting findings reveal that increased bacteriophage richness, which makes up the virome component, correlates with decreased bacterial diversity, and causes microbiome dysbiosis. An increase of Caudovirales (bacteriophages) is associated with a decrease in enteric bacteria in inflammatory bowel diseases. Future microbiome studies should evaluate the interrelation between bacteriome and virome to fully understand their significance in the pathogenesis and progression of human diseases. With ethnic health disparities becoming increasingly apparent, studies need to emphasize on the association of population-specific microbiome differences and human diseases, to develop microbiome-based therapeutics. Additionally, targeted phage therapy is emerging as an attractive alternative to antibiotics for bacterial infections. With rapid rise in microbiome research, focus should be on standardizing protocols, advanced bioinformatics tools, and reducing sequencing platform related biases. Ultimately, integration of multi-omics data (genomics, transcriptomics, proteomics and metabolomics) will lead to precision models for personalized microbiome therapeutics advancement. [ABSTRACT FROM AUTHOR]

The gastrointestinal (GI) tract is a site of replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and GI symptoms are often reported by patients. SARS-CoV-2 cell entry depends upon heparan sulfate (HS) proteoglycans, which commensal bacteria that bathe the human mucosa are known to modify. To explore human gut HS-modifying bacterial abundances and how their presence may impact SARS-CoV-2 infection, we developed a task-based analysis of proteoglycan degradation on large-scale shotgun metagenomic data. We observed that gut bacteria with high predicted catabolic capacity for HS differ by age and sex, factors associated with coronavirus disease 2019 (COVID-19) severity, and directly by disease severity during/after infection, but do not vary between subjects with COVID-19 comorbidities or by diet. Gut commensal bacterial HS-modifying enzymes reduce spike protein binding and infection of authentic SARS-CoV-2, suggesting that bacterial grooming of the GI mucosa may impact viral susceptibility.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019, can infect the gastrointestinal (GI) tract, and individuals who exhibit GI symptoms often have more severe disease. The GI tract's glycocalyx, a component of the mucosa covering the large intestine, plays a key role in viral entry by binding SARS-CoV-2's spike protein via heparan sulfate (HS). Here, using metabolic task analysis of multiple large microbiome sequencing data sets of the human gut microbiome, we identify a key commensal human intestinal bacteria capable of grooming glycocalyx HS and modulating SARS-CoV-2 infectivity in vitro . Moreover, we engineered the common probiotic Escherichia coli Nissle 1917 (EcN) to effectively block SARS-CoV-2 binding and infection of human cell cultures. Understanding these microbial interactions could lead to better risk assessments and novel therapies targeting viral entry mechanisms.

The role of the gut archaeal microbiome (archaeome) in health and disease remains poorly understood. Methanogenic archaea have been linked to multiple sclerosis (MS), but prior studies were limited by small cohorts and inconsistent methodologies. To address this, we re-evaluated the association between methanogenic archaea and MS using metagenomic data from the International Multiple Sclerosis Microbiome Study. We analyzed gut microbiome profiles from 115 MS patients and 115 healthy household controls across Buenos Aires (27.8%), Edinburgh (33.9%), New York (10.4%), and San Francisco (27.8%). Metagenomic sequences were taxonomically classified using kraken2/bracken and a curated profiling database to detect archaea, specifically Methanobrevibacter species. Most MS patients were female (80/115), aged 25-72 years (median: 44.5), and 70% were undergoing treatment, including dimethyl fumarate ( n = 21), fingolimod ( n = 20), glatiramer acetate ( n = 14), interferon ( n = 18), natalizumab ( n = 6), or ocrelizumab/rituximab ( n = 1). We found no significant differences in overall archaeome profiles between MS patients and controls. However, treated MS patients exhibited higher abundances of Methanobrevibacter smithii and M. sp900766745 compared to untreated patients. Notably, M. sp900766745 abundance correlated with lower disease severity scores in treated patients. Our results suggest that gut methanogens are not directly associated with MS onset or progression but may reflect microbiome health during treatment. These findings highlight potential roles for M. smithii and M. sp900766745 in modulating treatment outcomes, warranting further investigation into their relevance to gut microbiome function and MS management.IMPORTANCEMultiple sclerosis (MS) is a chronic neuroinflammatory disease affecting the central nervous system, with approximately 2.8 million people diagnosed worldwide, mainly young adults aged 20-30 years. While recent studies have focused on bacterial changes in the MS microbiome, the role of gut archaea has been less explored. Previous research suggested a potential link between methanogenic archaea and MS disease status, but these findings remained inconclusive. Our study addresses this gap by investigating the gut archaeal composition in MS patients and examining how it changes in response to treatment. By focusing on methanogens, we aim to uncover novel insights into their role in MS, potentially revealing new biomarkers or therapeutic targets. This research is crucial for enhancing our understanding of the gut microbiome's impact on MS and improving patient management.