Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries.

Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Gut Microbiota-Adversary or Ally? Its Role and Significance in Colorectal Cancer Pathogenesis, Progression, and Treatment.

In 2022, colorectal cancer (CRC) was the third most prevalent malignancy worldwide. The therapeutic approach for CRC typically involves a multimodal regimen. The human gut microbiota comprises over 35,000 bacterial species. The composition of the gut microbiota is influenced by dietary intake, which plays a crucial role in food absorption, nutrient extraction, and the development of low-grade inflammation. Dysbiosis in the gut microbiota is a key driver of inflammation and is strongly associated with CRC development. While the gut microbiome influences CRC initiation and progression, emerging evidence suggests a role for the gut microbiome in modulating the efficacy and toxicity of cancer treatments. Therapeutic strategies targeting the gut microbiome, such as probiotics, hold promise as effective interventions in the modern therapeutical approach to CRC. For example, Microbiota Implementation to Reduce Anastomotic Colorectal Leaks (MIRACLe) implementation has resulted in improvements in clinical outcomes, including reduced incidence of anastomotic leakage (AL), surgical site infections (SSIs), reoperation, as well as shorter recovery times and hospital stays compared with the control group. Therefore, this review aims to describe the current state of knowledge regarding the involvement of the gut microbiota in CRC pathogenesis and its potential therapeutic implications to treat CRC.

A microbiota-directed complementary food intervention in 12-18-month-old Bangladeshi children improves linear growth.

BACKGROUND: Globally, stunting affects ∼150 million children under five, while wasting affects nearly 50 million. Current interventions have had limited effectiveness in ameliorating long-term sequelae of undernutrition including stunting, cognitive deficits and immune dysfunction. Disrupted development of the gut microbiota has been linked to the pathogenesis of undernutrition, providing potentially new treatment approaches. METHODS: 124 Bangladeshi children with moderate acute malnutrition (MAM) enrolled (at 12-18 months) in a previously reported 3-month RCT of a microbiota-directed complementary food (MDCF-2) were followed for two years. Weight and length were monitored by anthropometry, the abundances of bacterial strains were assessed by quantifying metagenome-assembled genomes (MAGs) in serially collected fecal samples and levels of growth-associated proteins were measured in plasma. FINDINGS: Children who had received MDCF-2 were significantly less stunted during follow-up than those who received a standard ready-to-use supplementary food (RUSF) [linear mixed-effects model, ßtreatment group x study week (95% CI) = 0.002 (0.001, 0.003); P = 0.004]. They also had elevated fecal abundances of Agathobacter faecis, Blautia massiliensis, Lachnospira and Dialister, plus increased levels of a group of 37 plasma proteins (linear model; FDR-adjusted P < 0.1), including IGF-1, neurotrophin receptor NTRK2 and multiple proteins linked to musculoskeletal and CNS development, that persisted for 6-months post-intervention. INTERPRETATION: MDCF-2 treatment of Bangladeshi children with MAM, which produced significant improvements in wasting during intervention, also reduced stunting during follow-up. These results suggest that the effectiveness of supplementary foods for undernutrition may be improved by including ingredients that sponsor healthy microbiota-host co-development. FUNDING: This work was supported by the BMGF (Grants OPP1134649/INV-000247).

Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global healthcare, underscoring the importance of exploring the virus's effects on infected individuals beyond treatments and vaccines. Notably, recent findings suggest that SARS-CoV-2 can infect the gut, thereby altering the gut microbiota. This study aimed to analyze the gut microbiota composition differences between COVID-19 patients experiencing mild and severe symptoms. We conducted 16S rRNA metagenomic sequencing on fecal samples from 49 mild and 43 severe COVID-19 cases upon hospital admission. Our analysis identified a differential abundance of specific bacterial species associated with the severity of the disease. Severely affected patients showed an association with Enterococcus faecium, Akkermansia muciniphila, and others, while milder cases were linked to Faecalibacterium prausnitzii, Alistipes putredinis, Blautia faecis, and additional species. Furthermore, a network analysis using SPIEC-EASI indicated keystone taxa and highlighted structural differences in bacterial connectivity, with a notable disruption in the severe group. Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies. The findings from this study, based on retrospective data, establish a foundation for future prospective research to confirm the role of the gut microbiome as a predictive biomarker for the severity of COVID-19.

Non-significant influence between aerobic and anaerobic sample transport materials on gut (fecal) microbiota in healthy and fat-metabolic disorder Thai adults.

Background: The appropriate sample handling for human fecal microbiota studies is essential to prevent changes in bacterial composition and quantities that could lead to misinterpretation of the data. Methods: This study firstly identified the potential effect of aerobic and anaerobic fecal sample collection and transport materials on microbiota and quantitative microbiota in healthy and fat-metabolic disorder Thai adults aged 23-43 years. We employed metagenomics followed by 16S rRNA gene sequencing and 16S rRNA gene qPCR, to analyze taxonomic composition, alpha diversity, beta diversity, bacterial quantification, Pearson's correlation with clinical factors for fat-metabolic disorder, and the microbial community and species potential metabolic functions. Results: Our study successfully obtained microbiota results in percent and quantitative compositions. Each sample exhibited quality sequences with a >99% Good's coverage index, and a relatively plateau rarefaction curve. Alpha diversity indices showed no statistical difference in percent and quantitative microbiota OTU richness and evenness, between aerobic and anaerobic sample transport materials. Obligate and facultative anaerobic species were analyzed and no statistical difference was observed. Supportively, the beta diversity analysis by non-metric multidimensional scale (NMDS) constructed using various beta diversity coefficients showed resembling microbiota community structures between aerobic and anaerobic sample transport groups (P = 0.86). On the other hand, the beta diversity could distinguish microbiota community structures between healthy and fat-metabolic disorder groups (P = 0.02), along with Pearson's correlated clinical parameters (i.e., age, liver stiffness, GGT, BMI, and TC), the significantly associated bacterial species and their microbial metabolic functions. For example, genera such as Ruminococcus and Bifidobacterium in healthy human gut provide functions in metabolisms of cofactors and vitamins, biosynthesis of secondary metabolites against gut pathogens, energy metabolisms, digestive system, and carbohydrate metabolism. These microbial functional characteristics were also predicted as healthy individual biomarkers by LEfSe scores. In conclusion, this study demonstrated that aerobic sample collection and transport (<48 h) did not statistically affect the microbiota and quantitative microbiota analyses in alpha and beta diversity measurements. The study also showed that the short-term aerobic sample collection and transport still allowed fecal microbiota differentiation between healthy and fat-metabolic disorder subjects, similar to anaerobic sample collection and transport. The core microbiota were analyzed, and the findings were consistent. Moreover, the microbiota-related metabolic potentials and bacterial species biomarkers in healthy and fat-metabolic disorder were suggested with statistical bioinformatics (i.e., Bacteroides plebeius).

Gut microbial interactions based on network construction and bacterial pairwise cultivation.

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.

Human gut microbiome, diet, and mental disorders.

Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.

Bioinformatic Analysis of Sulfotransferases from an Unexplored Gut Microbe, Sutterella wadsworthensis 3_1_45B: Possible Roles towards Detoxification via Sulfonation by Members of the Human Gut Microbiome.

Sulfonation, primarily facilitated by sulfotransferases, plays a crucial role in the detoxification pathways of endogenous substances and xenobiotics, promoting metabolism and elimination. Traditionally, this bioconversion has been attributed to a family of human cytosolic sulfotransferases (hSULTs) known for their high sequence similarity and dependence on 3'-phosphoadenosine 5'-phosphosulfate (PAPS) as a sulfo donor. However, recent studies have revealed the presence of PAPS-dependent sulfotransferases within gut commensals, indicating that the gut microbiome may harbor a diverse array of sulfotransferase enzymes and contribute to detoxification processes via sulfation. In this study, we investigated the prevalence of sulfotransferases in members of the human gut microbiome. Interestingly, we stumbled upon PAPS-independent sulfotransferases, known as aryl-sulfate sulfotransferases (ASSTs). Our bioinformatics analyses revealed that members of the gut microbial genus Sutterella harbor multiple asst genes, possibly encoding multiple ASST enzymes within its members. Fluctuations in the microbes of the genus Sutterella have been associated with various health conditions. For this reason, we characterized 17 different ASSTs from Sutterella wadsworthensis 3_1_45B. Our findings reveal that SwASSTs share similarities with E. coli ASST but also exhibit significant structural variations and sequence diversity. These differences might drive potential functional diversification and likely reflect an evolutionary divergence from their PAPS-dependent counterparts.

Development and validation of LC-MS/MS for quantifying omadacycline from stool for gut microbiome studies.

This study developed and validated a new liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify omadacycline and its epimerization in stool to facilitate microbiome studies. Omadacycline was extracted in a methanol-water-ethylenediaminetetraacetic acid (ETDA) solvent containing deuterated omadacycline as internal standard, followed by dilution. In an optimal gradient elution mode, omadacycline and its C4 epimer were separated within 5 min on reversed-phase C18 column. The method showed a broad working range of 0.1-200 ng/ml with a limitation of detection (LOD) of 0.03 ng/ml, little fecal matrix effect, good intra-day and inter-day accuracy (90-101 %), precision (2-15 %), and recovery rate (99-105 %). The method was sufficiently sensitive to quantify omadacycline in human fecal samples (n = 82) collected during a 10-day therapy course and at follow-up (day 13 and day 30) that ranged from 1 to 4785 µg/g. Further analysis revealed that ∼9 % of omadacycline was epimerized in fecal matrix control while, on average, 37.4 % was epimerized in human fecal samples. This study developed and validated a novel, simple, sensitive, and accurate method utilizing LC-MS/MS to quantify omadacycline its epimerization in the human gut. This has important implications for future studies of omadacycline and other tetracycline-class antibiotics as part of gut microbiome studies.

Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation.

The human gut microbiota is a dynamic community of microorganisms that undergo variable changes over the entire life span. To thoroughly investigate the possible fluctuations of the microbiota throughout human life, we performed a pooled analysis of healthy fecal samples across different age groups covering the entire human life span. Our study integrated data from 79 publicly available studies and new stool samples from an Italian cohort, i.e., the Parma Microbiota project, resulting in 6,653 samples processed through the shotgun metagenomic approach. This approach has allowed species-level taxonomic reconstruction of the gut microbiota and investigation of its metabolic potential across the human life span. From a taxonomic point of view, our findings confirmed and detailed at species-level accuracy that the microbial richness of the gut microbiota gradually increases in the first stage of life, becoming relatively stable during adolescence. Moreover, the analysis identified the potential core microbiota representative of distinct age groups, revealing age-related bacterial patterns and the continuous rearrangement of the microbiota in terms of relative abundances across the life span rather than the acquisition and loss of taxa. Furthermore, the shotgun approach provided insights into the functional contribution of the human gut microbiome. The metagenomic analysis revealed functional age-related differences, particularly in carbohydrate and fiber metabolism, suggesting a co-evolution of the microbiome assembly with diet. Additionally, we identified correlations between vitamin synthesis, such as thiamine and niacin, and early life, suggesting a potential role of the microbiome in human physiology, in particular in the functions of the host's nervous and immune systems. IMPORTANCE: In this study, we provided comprehensive insights into the dynamic nature of the human gut microbiota across the human life span. In detail, we analyzed a large data set based on a shotgun metagenomic approach, combining public data sets and new samples from the Parma Microbiota project and obtaining a detailed overview of the possible relationship between gut microbiota development and aging. Our findings confirmed the main stages in microbial richness development and revealed specific core microbiota associated with different age stages. Moreover, the shotgun metagenomic approach allowed the disentangling of the functional changes in the microbiome across the human life span, particularly in diet-related metabolism, which is probably correlated to bacterial co-evolution with dietary habits. Notably, our study also uncovered positive correlations with vitamin synthesis in early life, suggesting a possible impact of the microbiota on human physiology.

A cryptic plasmid is among the most numerous genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Dinickel enzyme evolved to metabolize the pharmaceutical metformin and its implications for wastewater and human microbiomes.

Metformin is the first-line treatment for type II diabetes patients and a pervasive pollutant with more than 180 million kg ingested globally and entering wastewater. The drug's direct mode of action is currently unknown but is linked to effects on gut microbiomes and may involve specific gut microbial reactions to the drug. In wastewater treatment plants, metformin is known to be transformed by microbes to guanylurea, although genes encoding this metabolism had not been elucidated. In the present study, we revealed the function of two genes responsible for metformin decomposition (mfmA and mfmB) found in isolated bacteria from activated sludge. MfmA and MfmB form an active heterocomplex (MfmAB) and are members of the ureohydrolase protein superfamily with binuclear metal-dependent activity. MfmAB is nickel-dependent and catalyzes the hydrolysis of metformin to dimethylamine and guanylurea with a catalytic efficiency (kcat/KM) of 9.6 × 103 M-1s-1 and KM for metformin of 0.82 mM. MfmAB shows preferential activity for metformin, being able to discriminate other close substrates by several orders of magnitude. Crystal structures of MfmAB show coordination of binuclear nickel bound in the active site of the MfmA subunit but not MfmB subunits, indicating that MfmA is the active site for the MfmAB complex. Mutagenesis of residues conserved in the MfmA active site revealed those critical to metformin hydrolase activity and its small substrate binding pocket allowed for modeling of bound metformin. This study characterizes the products of the mfmAB genes identified in wastewater treatment plants on three continents, suggesting that metformin hydrolase is widespread globally in wastewater.

Horizontal gene transfer after faecal microbiota transplantation in adolescents with obesity.

BACKGROUND: Horizontal gene transfer (HGT) describes the transmission of DNA outside of direct ancestral lineages. The process is best characterised within the bacterial kingdom and can enable the acquisition of genetic traits that support bacterial adaptation to novel niches. The adaptation of bacteria to novel niches has particular relevance for faecal microbiota transplantation (FMT), a therapeutic procedure which aims to resolve gut-related health conditions of individuals, through transplanted gut microbiota from healthy donors. RESULTS: Three hundred eighty-one stool metagenomic samples from a placebo-controlled FMT trial for obese adolescents (the Gut Bugs Trial) were analysed for HGT, using two complementary methodologies. First, all putative HGT events, including historical HGT signatures, were quantified using the bioinformatics application WAAFLE. Second, metagenomic assembly and gene clustering were used to assess and quantify donor-specific genes transferred to recipients following the intervention. Both methodologies found no difference between the level of putative HGT events in the gut microbiomes of FMT and placebo recipients, post-intervention. HGT events facilitated by engrafted donor species in the FMT recipient gut at 6 weeks post-intervention were identified and characterised. Bacterial strains contributing to this subset of HGT events predominantly belonged to the phylum Bacteroidetes. Engraftment-dependent horizontally transferred genes were retained within recipient microbiomes at 12 and 26 weeks post-intervention. CONCLUSION: Our study suggests that novel microorganisms introduced into the recipient gut following FMT have no impact on the basal rate of HGT within the human gut microbiome. Analyses of further FMT studies are required to assess the generalisability of this conclusion across different FMT study designs and for the treatment of different gut-related conditions. Video Abstract.

Functional antagonism and insights into the biosynthetic potential of human gut-derived microbes.

The specialised small molecules encoded by commensal microbes mediate distinct functional interactions. However, there is a landscape of antagonistic interactions mediated by specialised strains and their small molecules. Herein, the antagonistic landscape within a collection of 330 human gut-derived commensal microbial strains was elucidated to evaluate antimicrobial interactions as a defensive contributor, and gain new insights into structure-related functions. The potential antagonistic gut-derived strains displayed strain-specific selective inhibition. This is in contrast to common antimicrobial drugs, which typically wipe out a broad range of species and are usually found in environmental microbes. Genome sequencing of representative gut strains revealed the presence of significant biosynthetic gene clusters (BGCs) encoding compound families that contribute to antagonistic activities, and are important in host defence and maintaining gut homeostasis. Subsets of these BGCs were abundant in metagenomic sequencing data from healthy individuals. Furthermore, the cell culture secretome of these strains revealed potential biomarkers linked to hallmark pathways. These microorganisms have biosynthetic novelty and are a source of biologically significant natural products. Such natural products are essential in the development of new antimicrobial agents to reduce the usage of broad-spectrum antibiotics and combat antimicrobial resistance.

Characterisation of Indian gut microbiome for B-vitamin production and its comparison with Chinese cohort.

The human gut microbiota can biosynthesize essential micronutrients such as B-vitamins and is also known for its metabolic cooperative behaviour. The present study characterises such B-vitamin biosynthesizers, their biosynthetic pathways, explores their prevalence and abundance, examines how lifestyle or diet affects them in multiple Indian cohorts and compares it with the Chinese cohort. To achieve this, publicly available faecal metagenome data of healthy individuals from multiple Indian (two urban and three tribal populations) and a Chinese cohort were analysed. The distribution of prevalence and abundance of B-vitamin biosynthesizers showed similar profiles to that of the entire gut community of the Indian cohort, and there were 28 B-vitamin biosynthesizers that had modest or higher prevalence and abundance. The omnivorous diet affected only the prevalence of a few B-vitamin biosynthesizers; however, lifestyle and/or location affected both prevalence and abundance. A comparison with the Chinese cohort showed that fourteen B-vitamin biosynthesizers were significantly more prevalent and abundant in Chinese as compared with Indian samples (False Discovery Rate (FDR) <= 0·05). The metabolic potential of the entire gut community for B-vitamin production showed that within India, the tribal cohort has a higher abundance of B-vitamin biosynthesis pathways as compared with two urban cohorts namely, Bhopal and Kasargod, and comparison with the Chinese cohort revealed a higher abundance in the latter group. Potential metabolic cooperative behaviour of the Indian gut microbiome for biosynthesis of the B-vitamins showed multiple pairs of species showed theoretical complementarity for complete biosynthetic pathways genes of thiamine, riboflavin, niacin and pantothenate.

Exploring Bifidobacterium species community and functional variations with human gut microbiome structure and health beyond infancy.

Aim: The human gut Bifidobacterium community has been studied in detail in infants and following dietary interventions in adults. However, the variability of the distribution of Bifidobacterium species and intra-species functions have been little studied, particularly beyond infancy. Here, we explore the ecology of Bifidobacterium communities in a large public dataset of human gut metagenomes, mostly corresponding to adults. Methods: We selected 9.515 unique gut metagenomes from curatedMetagenomicData. Samples were partitioned by applying Dirichlet's multinomial mixture to Bifidobacterium species. A functional analysis was performed on > 2.000 human-associated Bifidobacterium metagenome-assembled genomes (MAGs) paired with participant gut microbiome and health features. Results: We identified several Bifidobacterium-based partitions in the human gut microbiome differing in terms of the presence and abundance of Bifidobacterium species. The partitions enriched in both B. longum and B. adolescentis were associated with gut microbiome diversity and a higher abundance of butyrate producers and were more prevalent in healthy individuals. B. bifidum MAGs harboring a set of genes potentially related to phages were more prevalent in partitions associated with a lower gut microbiome diversity and were genetically more closely related. Conclusion: This study expands our knowledge of the ecology and variability of the Bifidobacterium community, particularly in adults, and its specific association with the gut microbiota and health. Its findings may guide the rational selection of Bifidobacterium strains for gut microbiome complementation according to the individual's endogenous Bifidobacterium community. Our results also suggest that gut microbiome stratification for particular genera may be relevant for studies of variations of species and associations with the gut microbiome and health.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features.

BACKGROUND: The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. RESULTS: The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. CONCLUSION: Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.

Simvastatin induces human gut bacterial cell surface genes.

Drugs intended to target mammalian cells can have broad off-target effects on the human gut microbiota with potential downstream consequences for drug efficacy and side effect profiles. Yet, despite a rich literature on antibiotic resistance, we still know very little about the mechanisms through which commensal bacteria evade non-antibiotic drugs. Here, we focus on statins, one of the most prescribed drug types in the world and an essential tool in the prevention and treatment of high circulating cholesterol levels. Prior work in humans, mice, and cell culture support an off-target effect of statins on human gut bacteria; however, the genetic determinants of statin sensitivity remain unknown. We confirmed that simvastatin inhibits the growth of diverse human gut bacterial strains grown in communities and in pure cultures. Drug sensitivity varied between phyla and was dose-dependent. We selected two representative simvastatin-sensitive species for more in-depth analysis: Eggerthella lenta (phylum: Actinobacteriota) and Bacteroides thetaiotaomicron (phylum: Bacteroidota). Transcriptomics revealed that both bacterial species upregulate genes in response to simvastatin that alter the cell membrane, including fatty acid biogenesis (E. lenta) and drug efflux systems (B. thetaiotaomicron). Transposon mutagenesis identified a key efflux system in B. thetaiotaomicron that enables growth in the presence of statins. Taken together, these results emphasize the importance of the bacterial cell membrane in countering the off-target effects of host-targeted drugs. Continued mechanistic dissection of the various mechanisms through which the human gut microbiota evades drugs will be essential to understand and predict the effects of drug administration in human cohorts and the potential downstream consequences for health and disease.