Personalized Mapping of Drug Metabolism by the Human Gut Microbiome

Identification of Gut Bacterial Enzymes for Keto-Reductive Metabolism of Xenobiotics

Human gastrointestinal microbiota are known for the keto-reductive metabolism of small-molecule pharmaceuticals; however, the responsible enzymes remain poorly understood. Through in vitro biochemical assays, we report the identification of enzymes encoded in the genome of Clostridium bolteae that can reduce the ketone groups of nabumetone, hydrocortisone, and tacrolimus. The homologues to a newly identified enzyme (i.e., DesE) are potentially widely distributed in the gut microbiome. The selected enzymes display different levels of activities against additional chemicals such as two dietary compounds (i.e., raspberry ketone and zingerone), chemotherapeutic drug doxorubicin, and its aglycone metabolite doxorubicinone. Thus, our results expand the repertoire of enzymes that can reduce the ketone groups in small molecules and could serve as the basis for future personalized medicine approaches.

Gut Microbiota and Antidiabetic Drugs: Perspectives of Personalized Treatment in Type 2 Diabetes Mellitus

Alterations in the composition and function of the gut microbiota have been reported in patients with type 2 diabetes mellitus (T2DM). Emerging studies show that prescribed antidiabetic drugs distort the gut microbiota signature associated with T2DM. Even more importantly, accumulated evidence provides support for the notion that gut microbiota, in turn, mediates the efficacy and safety of antidiabetic drugs. In this review, we highlight the current state-of-the-art knowledge on the crosstalk and interactions between gut microbiota and antidiabetic drugs, including metformin, α-glucosidase inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter 2 inhibitors, traditional Chinese medicines and other antidiabetic drugs, as well as address corresponding microbial-based therapeutics, aiming to provide novel preventative strategies and personalized therapeutic targets in T2DM.

Real-Time Monitoring of Host-Gut Microbial Interspecies Interaction in Anticancer Drug Metabolism

Gut microbiome can affect drug metabolism considerably, leading to modified drug response. However, quantitative estimation of host vs. microbial contributions in a living host-gut microbiome system has been challenging. Using the interspecies system of Caenorhabditis elegans and gut bacteria, we developed a real-time approach for monitoring their metabolic interaction in vivo during anticancer drug 5-fluorouracil (5-FU) metabolism. The fluorine NMR-based approach yielded the quantitative contributions to the host 5-FU metabolism made by human gut-microbial species of variable genetic backgrounds. It also experimentally confirmed a bacterial gene-metabolism relationship. Differential 5-FU catabolism among bacterial substrains and the contributions to the host metabolism, unobservable by conventional 16S rRNA metagenomic sequencing, were also found. The metabolic contributions could be correlated with phenotypic developmental toxicity of 5-FU to the host fed with different substrains. Our convenient platform should help to reveal heterogeneity in host-gut microbiome interactions for many drugs in a living symbiotic system.

Reduced Enterohepatic Recirculation of Mycophenolate and Lower Blood Concentrations are Associated with the Stool Bacterial Microbiome After Hematopoietic Cell Transplantation

BACKGROUND

Mycophenolate mofetil (MMF) is an important immunosuppressant used after allogeneic hematopoietic cell transplant (HCT). MMF has a narrow therapeutic index and blood concentrations of mycophenolic acid (MPA), the active component of MMF, are highly variable. Low MPA concentrations are associated with risk of graft vs host disease (GvHD) while high concentrations are associated with toxicity. Reasons for variability are not well known and may be due, at least in part, to the presence of β-glucuronidase producing bacteria in the gastrointestinal tract which enhance MPA enterohepatic recirculation (EHR) by transforming MPA metabolites formed in the liver back to MPA.

OBJECTIVE

To determine if individuals with high MPA EHR have a greater abundance of β-glucuronidase producing bacteria in their stool and higher MPA concentrations relative to those with low EHR.

STUDY DESIGN

We conducted a pharmacomicrobiomics study in 20 adult HCT recipients receiving a myeloablative or reduced intensity preparative regimen. Participants received MMF 1g IV every 8 hours with tacrolimus. Intensive pharmacokinetic sampling of mycophenolate was conducted before hospital discharge. Total MPA, MPA glucuronide (MPAG) and acylMPAG were measured. EHR was defined as a ratio of MPA area under the concentration-versus-time curve (AUC)_4-8 to MPA AUC_0-8. Differences in stool microbiome diversity and composition, determined by shotgun metagenomic sequencing, were compared above and below the median EHR (22%, range 5-44%).

RESULTS

Median EHR was 12% and 29% in the low and high EHR groups, respectively. MPA troughs, MPA AUC_4-8 and acylMPAG AUC_4-8/AUC_0-8, were greater in the high EHR group vs low EHR group [1.53 vs 0.28 mcg/mL, p = 0.0001], [7.33 vs 1.79 hr*mcg/mL, p = 0.0003] and [0.33 vs 0.24 hr*mcg/mL, p = 0.0007], respectively. MPA AUC_0-8 was greater in the high EHR than the low EHR group and trended towards significance [22.8 vs. 15.3 hr*mcg/mL, p=0.06]. Bacteroides vulgatus, stercoris and thetaiotaomicron were 1.2-2.4 times more abundant (p=0.039, 0.024, 0.046, respectively) in the high EHR group. MPA EHR was positively correlated with B. vulgatus (⍴=0.58, p≤0.01) and B. thetaiotaomicron (⍴=0.46, p<0.05) and negatively correlated with Blautia hydrogenotrophica (⍴=-0.53, p<0.05). Therapeutic MPA troughs were achieved in 80% of patients in the high EHR group and 0% in the low EHR. There was a trend towards differences in MPA AUC_0-8 and MPA C_ss mcg/mL in high vs. low EHR groups (p=0.06).

CONCLUSION

MPA EHR was variable. Patients with high MPA EHR had greater abundance of Bacteroides species in stool and higher MPA exposure than patients with low MPA EHR. Bacteroides may therefore be protective from poor outcomes such as graft vs host disease but in others it may increase the risk of MPA adverse effects. These data need to be confirmed and studied after oral MMF.

Gut Microbiota Metabolism of Azathioprine: A New Hallmark for Personalized Drug-Targeted Therapy of Chronic Inflammatory Bowel Disease

Despite the growing number of new drugs approved for the treatment of inflammatory bowel disease (IBD), the long-term clinical use of thiopurine therapy and the well-known properties of conventional drugs including azathioprine have made their place in IBD therapy extremely valuable. Despite the fact that thiopurine S-methyltransferase (TPMT) polymorphism has been recognized as a major cause of the interindividual variability in the azathioprine response, recent evidence suggests that there might be some yet unknown causes which complicate dosing strategies causing either failure of therapy or toxicity. Increasing evidence suggests that gut microbiota, with its ability to release microbial enzymes, affects the pharmacokinetics of numerous drugs and subsequently drastically alters clinical effectiveness. Azathioprine, as an orally administered drug which has a complex metabolic pathway, is the prime illustrative candidate for such microbial metabolism of drugs. Comprehensive databases on microbial drug-metabolizing enzymes have not yet been generated. This study provides insights into the current evidence on microbiota-mediated metabolism of azathioprine and systematically accumulates findings of bacteria that possess enzymes required for the azathioprine biotransformation. Additionally, it proposes concepts for the identification of gut bacteria species responsible for the metabolism of azathioprine that could aid in the prediction of dose-response effects, complementing pharmacogenetic approaches already applied in the optimization of thiopurine therapy of IBD. It would be of great importance to elucidate to what extent microbiota-mediated metabolism of azathioprine contributes to the drug outcomes in IBD patients which could facilitate the clinical implementation of novel tools for personalized thiopurine treatment of IBD.

Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment

Colorectal cancer (CRC) is one of the most common tumors worldwide, with a higher rate of distant metastases than other malignancies and with regular occurrence of drug resistance. Therefore, scientists are forced to further develop novel and innovative therapeutic treatment strategies, whereby it has been discovered microorganisms, albeit linked to CRC pathogenesis, are able to act as highly selective CRC treatment agents. Consequently, researchers are increasingly focusing on bacteriotherapy as a novel therapeutic strategy with less or no side effects compared to standard cancer treatment methods. With multiple successful trials making use of various bacteria-associated mechanisms, bacteriotherapy in cancer treatment is on its way to become a promising tool in CRC targeting therapy. In this study, we describe the anti-cancer effects of bacterial therapy focusing on the treatment of CRC as well as diverse modulatory mechanisms and techniques that bacteriotherapy offers such as bacterial-related biotherapeutics including peptides, toxins, bacteriocins or the use of bacterial carriers and underlying molecular processes to target colorectal tumors.

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

Exploring Drug Metabolism by the Gut Microbiota: Modes of Metabolism and Experimental Approaches

Increasing evidence uncovers the involvement of gut microbiota in the metabolism of numerous pharmaceutical drugs. The human gut microbiome harbors 10-100 trillion symbiotic gut microbial bacteria that use drugs as substrates for enzymatic processes to alter host metabolism. Thus, microbiota-mediated drug metabolism can change the conventional drug action course and cause inter-individual differences in efficacy and toxicity, making it vital for drug discovery and development. This review focuses on drug biotransformation pathways and discusses different models for evaluating the role of gut microbiota in drug metabolism. SIGNIFICANCE STATEMENT: This review emphasizes the importance of gut microbiota and different modes of drug metabolism mediated by them. It provides information on in vivo, in vitro, ex vivo, in silico and multi-omics approaches for identifying the role of gut microbiota in metabolism. Further, it highlights the significance of gut microbiota-mediated metabolism in the process of new drug discovery and development as a rationale for safe and efficacious drug therapy.

Abundance and Metabolism Disruptions of Intratumoral Microbiota by Chemical and Physical Actions Unfreeze Tumor Treatment Resistance

Intratumoral or intestinal microbiota correlates with tumorigenesis and progression, and microbiota regulation for reinforcing various anti-tumor approaches is of significant importance, which, however, suffers from no precise regulation method and unclear underlying mechanism. Herein, a microbiome metabolism-engineered phototherapy strategy is established, wherein Nb2 C/Au nanocomposite and the corresponding phototherapy are harnessed to realize "chemical" and "physical" bacterial regulations. Flora analysis and mass spectrometry (MS) and metabonomics combined tests demonstrate that the synergistic microbiota regulations can alter the abundance, diversity of intratumoral microbiome, and disrupt metabolic pathways of microbiome and tumor microenvironment, wherein the differential singling pathways and biosynthetic necessities or metabolites that can affect tumor progression are identified. As well, anti-TNFα is introduced to unite with bacterial regulation to synergistically mitigate bacterial-induced inflammation, which, along with the metabolism disruptions of intratumoral microbiota and tumor microenvironment, unfreezes tumor resistance and harvests significantly-intensified phototherapy-based anti-tumor outcomes against 4T1 and CT26 tumors. The clear underlying principles of microbiome-regulated tumorigenesis and the established microbiome metabolism regulation method provide distinctive insights into tumor therapy, and can be also extended to other gut microbiome-associated lesions interference.

Metabolites profiling reveals gut microbiome-mediated biotransformation of green tea polyphenols in the presence of N-nitrosamine as pro-oxidant

The gut microbiome contributes to host physiology and nutrition metabolism. The interaction between nutrition components and the gut microbiota results in thousands of metabolites that can contribute to various health and disease outcomes. In parallel, the interactions between foods and their toxicants have captured increasing interest due to their impact on human health.  Taken together, investigating dietary interactions with endogenous and exogenous factors and detecting interaction biomarkers in a specific and sensitive manner is an important task. The present study sought  to identify for the first time the metabolites produced during the interaction of diet-derived toxicants e.g., N-nitrosamines with green tea polyphenols, using liquid chromatography electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS). In addition, the metabolic products resulting from the incubation of green tea with a complex gut microbiome in the presence of N-nitrosamine were assessed in the same manner. The quinone products of (epi)catechin, quercetin, and kaempferol were identified when green tea was incubated with N-nitrosamine only; whereas, incubation of green tea with N-nitrosamine and a complex gut microbiome prevented the formation of these metabolites. This study provides a new perspective on the role of gut microbiome in protecting against potential negative interactions between food-derived toxicants and dietary polyphenols.

Evaluating live microbiota biobanking using an ex vivo microbiome assay and metaproteomics

Biobanking of live microbiota is becoming indispensable for mechanistic and clinical investigations of drug-microbiome interactions and fecal microbiota transplantation. However, there is a lack of methods to rapidly and systematically evaluate whether the biobanked microbiota maintains their cultivability and functional activity. In this study, we use a rapid ex vivo microbiome assay and metaproteomics to evaluate the cultivability and the functional responses of biobanked microbiota to treatment with a prebiotic (fructo-oligosaccharide, FOS). Our results indicate that the microbiota cultivability and their functional responses to FOS treatment were well maintained by freezing in a deoxygenated glycerol buffer at -80°C for 12 months. We also demonstrate that the fecal microbiota is functionally stable for 48 hours on ice in a deoxygenated glycerol buffer, allowing off-site fecal sample collection and shipping to laboratory for live microbiota biobanking. This study provides a method for rapid evaluation of the cultivability of biobanked live microbiota. Our results show minimal detrimental influences of long-term freezing in deoxygenated glycerol buffer on the cultivability of fecal microbiota.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

Visual identification of gut bacteria and determination of natural inhibitors using a fluorescent probe selective for PGP-1

PGP-1 is a bacterial hydrolase that can hydrolyze the amide bond of the l-pyroglutamate (L-pGlu) residue at the amino terminus of proteins and peptides. Guided by the biological function of PGP-1, an off-on NIR fluorescent probe DDPA was developed for the visual sensing of PGP-1 by conjugating pyroglutamic acid (recognition group) and DDAN (fluorophore). Using intestinal bacteria cultivation, eight bacteria strains with active PGP-1 were identified and cultivated efficiently using DDPA. In addition, three natural inhibitors against PGP-1 were isolated from the medical herb Psoralea corylifolia, which could be used to interfere with bacterial metabolism in the gut. As such, the fluorescent probe DDPA provides an efficient method and potential tool for the investigation of intestinal microbiota.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

The promise of the gut microbiome as part of individualized treatment strategies

Variability in disease presentation, progression and treatment response has been a central challenge in medicine. Although variability in host factors and genetics are important, it has become evident that the gut microbiome, with its vast genetic and metabolic diversity, must be considered in moving towards individualized treatment. In this Review, we discuss six broad disease groups: infectious disease, cancer, metabolic disease, cardiovascular disease, autoimmune or inflammatory disease, and allergic and atopic diseases. We highlight current knowledge on the gut microbiome in disease pathogenesis and prognosis, efficacy, and treatment-related adverse events and its promise for stratifying existing treatments and as a source of novel therapies. The Review is not meant to be comprehensive for each disease state but rather highlights the potential implications of the microbiome as a tool to individualize treatment strategies in clinical practice. Although early, the outlook is optimistic but challenges need to be overcome before clinical implementation, including improved understanding of underlying mechanisms, longitudinal studies with multiple data layers reflecting gut microbiome and host response, standardized approaches to testing and reporting, and validation in larger cohorts. Given progress in the microbiome field with concurrent basic and clinical studies, the microbiome will likely become an integral part of clinical care within the next decade.

Integrative Analysis of the Gut Microbiota and Metabolome for In Vitro Human Gut Fermentation Modeling

This study aimed to find the best in vitro fermentation method by integrative analysis of the gut microbiota and metabolome. We selected five different media: brain heart infusion broth, Luria-Bertani broth, Mueller-Hinton broth, anaerobe basal broth, and anaerobic medium base (AMB). After in vitro fermentation, the gut microbiota and metabolites were analyzed at different culture times. The results showed that different culture media have different effects on the bacterial community structure and metabolites. The integrative analysis of gut microbiota and metabolism also proved that AMB medium is effective in keeping a stable bacterial community structure and producing less metabolites and short-chain fatty acids by simulating the nutrient-poor microenvironment in the human gut during in vitro fermentation. Thus, culturing with AMB medium for 48 h is the most suitable in vitro model for human gut microbiota fermentation, which provides an alternative approach for diet and health research.

Bioaccumulation as a mechanism of microbiome/drug interactions

Microbes of the human gut can change drug disposition in the human body and gut ecology via 'bioaccumulation', a process in which bacteria take up compounds intracellularly without chemically modifying them or experiencing changes in growth, as described in Klünemann et al. This work expands current understanding of the types of drug-bacterial interactions in the gut environment.

Decoding gut microbiota by imaging analysis of fecal samples

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.

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A deep convolution network classifies gut microbiota based on fecal sample images

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Image-based quantitative prediction of gut microbiota composition is demonstrated

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This result provides a simple and inexpensive method of gut microbiota analysis

Validation of self-reported medication use applying untargeted mass spectrometry-based metabolomics techniques in the Rhineland study

AIMS

To assess the validity of self-reported continuous medication use with drug metabolites measured in plasma by using untargeted mass spectrometric techniques.

METHODS

In a population-based cohort in Bonn, Germany, we compared interview-based, self-reported medication intake with drug-specific metabolites measured in plasma (based on participants who completed their study visits between March 2016 and February 2020). Analyses were done stratified by sex and age (<65 years vs ≥65 years). Cohen's kappa (κ) statistics with 95% confidence intervals (CI) were calculated.

RESULTS

A total of 13 drugs used to treat hypertension, gout, diabetes, epilepsy and depression were analysed in a sample of 4386 individuals (mean age 55 years, 56.1% women). Eleven drugs showed almost perfect agreement (κ > 0.8), whereas sitagliptin and hydrochlorothiazide showed substantial (κ = 0.8, 95% CI 0.71-0.90) and moderate agreement (κ = 0.61, 95% CI 0.56-0.66), respectively. Frequency of use allowed sex- and age-stratified analyses for eight and nine drugs, respectively. For five drugs, concordance tended to be higher for women than for men. For most drugs, concordance was higher among individuals aged ≥65 years than among individuals aged <65 years, but these age-related differences were not statistically significant.

CONCLUSION

High concordance rates between self-reported drug use and metabolites measured in plasma suggest that self-reported drug use is reliable and accurate for assessing drug use.

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug-microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug-microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

Roles of the Cell Surface Architecture of Bacteroides and Bifidobacterium in the Gut Colonization

There are numerous bacteria reside within the mammalian gastrointestinal tract. Among the intestinal bacteria, Akkermansia, Bacteroides, Bifidobacterium, and Ruminococcus closely interact with the intestinal mucus layer and are, therefore, known as mucosal bacteria. Mucosal bacteria use host or dietary glycans for colonization via adhesion, allowing access to the carbon source that the host’s nutrients provide. Cell wall or membrane proteins, polysaccharides, and extracellular vesicles facilitate these mucosal bacteria-host interactions. Recent studies revealed that the physiological properties of Bacteroides and Bifidobacterium significantly change in the presence of co-existing symbiotic bacteria or markedly differ with the spatial distribution in the mucosal niche. These recently discovered strategic colonization processes are important for understanding the survival of bacteria in the gut. In this review, first, we introduce the experimental models used to study host-bacteria interactions, and then, we highlight the latest discoveries on the colonization properties of mucosal bacteria, focusing on the roles of the cell surface architecture regarding Bacteroides and Bifidobacterium.

Microbiota and Colorectal Cancer: From Gut to Bedside

Colorectal cancer (CRC) is a complex condition with heterogeneous aetiology, caused by a combination of various environmental, genetic, and epigenetic factors. The presence of a homeostatic gut microbiota is critical to maintaining host homeostasis and determines the delicate boundary between health and disease. The gut microbiota has been identified as a key environmental player in the pathogenesis of CRC. Perturbations of the gut microbiota structure (loss of equilibrium and homeostasis) are associated with several intestinal diseases including cancer. Such dysbiosis encompasses the loss of beneficial microorganisms, outgrowth of pathogens and pathobionts and a general loss of local microbiota diversity and richness. Notably, several mechanisms have recently been identified how bacteria induce cellular transformation and promote tumour progression. In particular, the formation of biofilms, the production of toxic metabolites or the secretion of genotoxins that lead to DNA damage in intestinal epithelial cells are newly discovered processes by which the microbiota can initiate tumour formation. The gut microbiota has also been implicated in the metabolism of therapeutic drugs (conventional chemotherapy) as well as in the modulation of radiotherapy responses and targeted immunotherapy. These new findings suggest that the efficacy of a given therapy depends on the composition of the host’s gut microbiota and may therefore vary from patient to patient. In this review we discuss the role of host-microbiota interactions in cancer with a focus on CRC pathogenesis. Additionally, we show how gut bacteria can be exploited in current therapies and how mechanisms directed by microbiota, such as immune cell boost, probiotics and oncolytic bacteria, can be applied in the development of novel therapies.

Microbiota-Gut-Brain Axis and Epilepsy: A Review on Mechanisms and Potential Therapeutics

The gut-brain axis refers to the bidirectional communication between the gut and brain, and regulates intestinal homeostasis and the central nervous system via neural networks and neuroendocrine, immune, and inflammatory pathways. The development of sequencing technology has evidenced the key regulatory role of the gut microbiota in several neurological disorders, including Parkinson's disease, Alzheimer's disease, and multiple sclerosis. Epilepsy is a complex disease with multiple risk factors that affect more than 50 million people worldwide; nearly 30% of patients with epilepsy cannot be controlled with drugs. Interestingly, patients with inflammatory bowel disease are more susceptible to epilepsy, and a ketogenic diet is an effective treatment for patients with intractable epilepsy. Based on these clinical facts, the role of the microbiome and the gut-brain axis in epilepsy cannot be ignored. In this review, we discuss the relationship between the gut microbiota and epilepsy, summarize the possible pathogenic mechanisms of epilepsy from the perspective of the microbiota gut-brain axis, and discuss novel therapies targeting the gut microbiota. A better understanding of the role of the microbiota in the gut-brain axis, especially the intestinal one, would help investigate the mechanism, diagnosis, prognosis evaluation, and treatment of intractable epilepsy.

Small Molecule Metabolites at the Host-Microbiota Interface

The trillions of bacteria that constitutively colonize the human gut collectively generate thousands of unique small molecules. These microbial metabolites can accumulate both locally and systemically and potentially influence nearly all aspects of mammalian biology, including immunity, metabolism, and even mood and behavior. In this review, we briefly summarize recent work identifying bioactive microbiota metabolites, the means through which they are synthesized, and their effects on host physiology. Rather than offering an exhaustive list of all known bioactive microbial small molecules, we select a few examples from each key class of metabolites to illustrate the diverse impacts of microbiota-derived compounds on the host. In addition, we attempt to address the microbial logic behind specific biotransformations. Finally, we outline current and emerging strategies for identifying previously undiscovered bioactive microbiota metabolites that may shape human health and disease.

Gut microbiome in liver pathophysiology and cholestatic liver disease

An increasing amount of evidence has shown critical roles of gut microbiome in host pathophysiology. The gut and the liver are anatomically and physiologically connected. Given the critical role of gut-liver axis in the homeostasis of the liver, gut microbiome interplays with a diverse spectrum of hepatic changes, including steatosis, inflammation, fibrosis, cholestasis, and tumorigenesis. In clinic, cholestasis manifests with fatigue, pruritus, and jaundice, caused by the impairment in bile formation or flow. Studies have shown that the gut microbiome is altered in cholestatic liver disease. In this review, we will explore the interaction between the gut microbiome and the liver with a focus on the alteration and the role of gut microbiome in cholestatic liver disease. We will also discuss the prospect of exploiting the gut microbiome in the development of novel therapies for cholestatic liver disease.

Acute oral colchicine caused gastric mucosal injury and disturbance of associated microbiota in mice

Colchicine (COL), an ancient and well-known drug, has been used in clinical practice for centuries. On the other hand, COL has also attracted extensive concerns for its potent toxic effects, especially gastrointestinal adverse reactions (nausea, vomiting, and diarrhea) before clinical symptoms relief. In this study, we used a rodent model to study the effects of COL on gastric mucosa and associated microbiota. The mice were exposed to various concentrations of COL (0.1, 0.5, and 2.5 mg kg^-1 body weight per day) for 7 days, and the results showed that COL treatment caused severe gastric mucosal damage, accompanied by a significant decrease in gastric mucosal proinflammatory cytokines (IL-1β, IL-6, and TNF-α). The 16S rRNA gene sequencing revealed that COL significantly perturbed the gastric microbiota composition and reduced the gastric microbiota diversity in mice. Also, we identified bacterial biomarkers associated with diarrhea, including phylum Firmicutes, class Bacilli, order Lactobacillales, family Lactobacillaceae, genu Lactobacillus, and genu Blautia, suggesting that COL-triggered adverse reactions are closely related to gastric microbial perturbations. Our findings open new paths for understanding the mechanism of COL-related adverse gastrointestinal reactions, broadening the scientific view on the interaction between drugs and host gastrointestinal microbiota.

Simple Energy Balance or Microbiome for Childhood Obesity Prevention?

Obesity prevention interventions generally have either not worked or had effects inadequate to mitigate the problem. They have been predicated on the simple energy balance model, which has been severely questioned by biological scientists. Numerous other etiological mechanisms have been proposed, including the intestinal microbiome, which has been related to childhood obesity in numerous ways. Public health research is needed in regard to diet and the microbiome, which hopefully will lead to effective child obesity prevention.

Strength of Cu-efflux response in E. coli coordinates metal resistance in C. elegans and contributes to the severity of environmental toxicity

Without effective homeostatic systems in place, excess copper (Cu) is universally toxic to organisms. While increased utilization of anthropogenic Cu in the environment has driven the diversification of Cu-resistance systems within enterobacteria, little research has focused on how this change in bacterial architecture impacts host organisms that need to maintain their own Cu homeostasis. Therefore, we utilized a simplified host–microbe system to determine whether the efficiency of one bacterial Cu-resistance system, increasing Cu-efflux capacity via the ubiquitous CusRS two-component system, contributes to the availability and subsequent toxicity of Cu in host Caenorhabditis elegans nematode. We found that a fully functional Cu-efflux system in bacteria increased the severity of Cu toxicity in host nematodes without increasing the C. elegans Cu-body burden. Instead, increased Cu toxicity in the host was associated with reduced expression of a protective metal stress-response gene, numr-1, in the posterior pharynx of nematodes where pharyngeal grinding breaks apart ingested bacteria before passing into the digestive tract. The spatial localization of numr-1 transgene activation and loss of bacterially dependent Cu-resistance in nematodes without an effective numr-1 response support the hypothesis that numr-1 is responsive to the bacterial Cu-efflux capacity. We propose that the bacterial Cu-efflux capacity acts as a robust spatial determinant for a host’s response to chronic Cu stress.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Qualitatively and quantitatively investigating the metabolism of 20(S)-protopanaxadiol-type ginsenosides by gut microbiota of different species

Ginsenosides Rb1, Rb2, Rb3 and Rc, four major protopanaxadiol (PPD)-type ginsenosides, can be metabolized by gut microbiota. The composition of gut microbiota varies in different species. Existing publications have reported the metabolite fates of ginsenosides by gut microbiota from single species. However, their microbiota-related metabolic species differences have not been evaluated yet. In current study, in vitro anaerobic incubations of PPD-type ginsenosides with gut microbiota from humans, rabbits and rats were conducted. The metabolites of each ginsenoside were then identified by LC-MS. A total of 15 metabolites from the four ginsenosides were identified. The major metabolic pathways were stepwise removals of the C-20 and C-3 sugar moieties to obtain aglycone PPD. The results showed that the hydrolysis rate of C-20 terminal β-D-glucopyranosyl was significantly higher than those of α-L-arabinopyranosyl, β-D-xylopyranosyl and α-L-arabinofuranosyl in different species. The activity of β-glucosidase, the metabolic rates of parent compounds and the formation rates of their metabolites were significantly higher in gut microbiota from rabbits than from humans and rats. Our research draws researchers' attention to the species differences of microbiota-related drug metabolism.

Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Involvement of Gut Microbiota in Schizophrenia and Treatment Resistance to Antipsychotics

The gut microbiota is constituted by more than 40,000 bacterial species involved in key processes including high order brain functions. Altered composition of gut microbiota has been implicated in psychiatric disorders and in modulating the efficacy and safety of psychotropic medications. In this work we characterized the composition of the gut microbiota in 38 patients with schizophrenia (SCZ) and 20 healthy controls (HC), and tested if SCZ patients with different response to antipsychotics (18 patients with treatment resistant schizophrenia (TRS), and 20 responders (R)) had specific patterns of gut microbiota composition associated with different response to antipsychotics. Moreover, we also tested if patients treated with typical antipsychotics (n = 20) presented significant differences when compared to patients treated with atypical antipsychotics (n = 31). Our findings showed the presence of distinct composition of gut microbiota in SCZ versus HC, with several bacteria at the different taxonomic levels only present in either one group or the other. Similar findings were observed also depending on treatment response and exposure to diverse classes of antipsychotics. Our results suggest that composition of gut microbiota could constitute a biosignatures of SCZ and TRS.

Discovery and mining of enzymes from the human gut microbiome

Advances in technological and bioinformatics approaches have led to the generation of a plethora of human gut metagenomic datasets. Metabolomics has also provided substantial data regarding the small metabolites produced and modified by the microbiota. Comparatively, the microbial enzymes mediating the transformation of metabolites have not been intensively investigated. Here, we discuss the recent efforts and technologies used for discovering and mining enzymes from the human gut microbiota. The wealth of knowledge on metabolites, reactions, genome sequences, and structures of proteins, may drive the development of strategies for enzyme mining. Ongoing efforts to annotate gut microbiota enzymes will explain catalytic mechanisms that may guide the clinical applications of the gut microbiome for diagnostic and therapeutic purposes.

From gut microbiota to host appetite: gut microbiota-derived metabolites as key regulators

Feelings of hunger and satiety are the key determinants for maintaining the life of humans and animals. Disturbed appetite control may disrupt the metabolic health of the host and cause various metabolic disorders. A variety of factors have been implicated in appetite control, including gut microbiota, which develop the intricate interactions to manipulate the metabolic requirements and hedonic feelings. Gut microbial metabolites and components act as appetite-related signaling molecules to regulate appetite-related hormone secretion and the immune system, or act directly on hypothalamic neurons. Herein, we summarize the effects of gut microbiota on host appetite and consider the potential molecular mechanisms. Furthermore, we propose that the manipulation of gut microbiota represents a clinical therapeutic potential for lessening the development and consequence of appetite-related disorders. Video abstract.

Are Probiotics and Prebiotics Safe for Use during Pregnancy and Lactation? A Systematic Review and Meta-Analysis

Probiotic and prebiotic products have shown potential health benefits, including for the prevention of adverse pregnancy outcomes. The incidence of adverse effects in pregnant people and their infants associated with probiotic/prebiotic/synbiotic intake, however, remains unclear. The objectives of this study were to evaluate the evidence on adverse effects of maternal probiotic, prebiotic, and/or synbiotic supplementation during pregnancy and lactation and interpret the findings to help inform clinical decision-making and care of this population. A systematic review was conducted following PRISMA guidelines. Scientific databases were searched using pre-determined terms, and risk of bias assessments were conducted to determine study quality. Inclusion criteria were English language studies, human studies, access to full-text, and probiotic/prebiotic/synbiotic supplementation to the mother and not the infant. In total, 11/100 eligible studies reported adverse effects and were eligible for inclusion in quantitative analysis, and data were visualised in a GOfER diagram. Probiotic and prebiotic products are safe for use during pregnancy and lactation. One study reported increased risk of vaginal discharge and changes in stool consistency (relative risk [95% CI]: 3.67 [1.04, 13.0]) when administering Lactobacillus rhamnosus and L. reuteri. Adverse effects associated with probiotic and prebiotic use do not pose any serious health concerns to mother or infant. Our findings and knowledge translation visualisations provide healthcare professionals and consumers with information to make evidence-informed decisions about the use of pre- and probiotics.

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.

The Gut Microbiome as a Reservoir for Antimicrobial Resistance

This review will consider the gut as a reservoir for antimicrobial resistance, colonization resistance, and how disruption of the microbiome can lead to colonization by pathogenic organisms. There is a focus on the gut as a reservoir for β-lactam and plasmid-mediated quinolone resistance. Finally, the role of functional metagenomics and long-read sequencing technologies to detect and understand antimicrobial resistance genes within the gut microbiome is discussed, along with the potential for future microbiome-directed methods to detect and prevent infection.

Recovery of human gut microbiota genomes with third-generation sequencing

Human gut microbiota modulates normal physiological functions, such as maintenance of barrier homeostasis and modulation of metabolism, as well as various chronic diseases including type 2 diabetes and gastrointestinal cancer. Despite decades of research, the composition of the gut microbiota remains poorly understood. Here, we established an effective extraction method to obtain high quality gut microbiota genomes, and analyzed them with third-generation sequencing technology. We acquired a large quantity of data from each sample and assembled large numbers of reliable contigs. With this approach, we constructed tens of completed bacterial genomes in which there were several new bacteria species. We also identified a new conditional pathogen, Enterococcus tongjius, which is a member of Enterococci. This work provided a novel and reliable approach to recover gut microbiota genomes, facilitating the discovery of new bacteria species and furthering our understanding of the microbiome that underlies human health and diseases.

Influences of sex, rhythm and generation on the obesogenic potential of erythromycin to Drosophila melanogaster

Antibiotics are gaining attention due to their roles as emerging pollutants and environmental obesogens, yet several aspects between their environmental exposure and obesogenic influence on organisms remain poorly explored. Here, Drosophila melanogaster were exposed to erythromycin (ERY, 0.1 μg/L) for three consecutive generations (F1 to F3). Body weight, circadian rhythm (represented by eclosion timing) and lipid metabolism were measured. ERY increased the size of lipid droplets in larvae of all three generations. It modestly inhibited body weight in adults that abnormally eclosed in the morning (AM adults) in the F1 and F2 generations, and the inhibition was less in adults that eclosed in the afternoon (PM adults). In contrast, it stimulated body weight in F3 adults. Notably, ERY promoted morning eclosion of females. Combining the effects from F1 to F3, acyl-CoA oxidase (ACO) was commonly increased in AM female and male adults and also in PM female ones, while it was commonly decreased in PM male adults. Glucokinase (GCK) was commonly increased in both sexes of AM adults but decreased in PM male adults across generations. The IIS pathway showed a common up-regulation in the AM adults despite some differences between sexes, but it did not show any shared changes in the PM adults with dysrhythmia. The AMPK pathway was involved across generations without particular shared changes. Collectively, the effects of ERY on the key metabolites and enzymes in glucolipid metabolism and the genetic regulations depended on sex, rhythm and exposure generation.

Evidences for a Role of Gut Microbiota in Pathogenesis and Management of Epilepsy

Epilepsy as a chronic neurological disorder is characterized by recurrent, unprovoked epileptic seizures. In about half of the people who suffer from epilepsy, the root cause of the disorder is unknown. In the other cases, different factors can cause the onset of epilepsy. In recent years, the role of gut microbiota has been recognized in many neurological disorders, including epilepsy. These data are based on studies of the gut microbiota–brain axis, a relationship starting by a dysbiosis followed by an alteration of brain functions. Interestingly, epileptic patients may show signs of dysbiosis, therefore the normalization of the gut microbiota may lead to improvement of epilepsy and to greater efficacy of anticonvulsant drugs. In this descriptive review, we analyze the evidences for the role of gut microbiota in epilepsy and hypothesize a mechanism of action of these microorganisms in the pathogenesis and treatment of the disease. Human studies revealed an increased prevalence of Firmicutes in patients with refractory epilepsy. Exposure to various compounds can change microbiota composition, decreasing or exacerbating epileptic seizures. These include antibiotics, epileptic drugs, probiotics and ketogenic diet. Finally, we hypothesize that physical activity may play a role in epilepsy through the modulation of the gut microbiota.

5-Aminolevulinic Acid as a Novel Therapeutic for Inflammatory Bowel Disease

5-Aminolevulinic acid (5-ALA) is a naturally occurring nonprotein amino acid licensed as an optical imaging agent for the treatment of gliomas. In recent years, 5-ALA has been shown to possess anti-inflammatory and immunoregulatory properties through upregulation of heme oxygenase-1 via enhancement of porphyrin, indicating that it may be beneficial for the treatment of inflammatory conditions. This study systematically examines 5-ALA for use in inflammatory bowel disease (IBD). Firstly, the ex vivo colonic stability and permeability of 5-ALA was assessed using human and mouse fluid and tissue. Secondly, the in vivo efficacy of 5-ALA, in the presence of sodium ferrous citrate, was investigated via the oral and intracolonic route in an acute DSS colitis mouse model of IBD. Results showed that 5-ALA was stable in mouse and human colon fluid, as well as in colon tissue. 5-ALA showed more tissue restricted pharmacokinetics when exposed to human colonic tissue. In vivo dosing demonstrated significantly improved colonic inflammation, increased local heme oxygenase-1 levels, and decreased concentrations of proinflammatory cytokines TNF-α, IL-6, and IL-1β in both plasma and colonic tissue. These effects were superior to that measured concurrently with established anti-inflammatory treatments, ciclosporin and 5-aminosalicylic acid (mesalazine). As such, 5-ALA represents a promising addition to the IBD armamentarium, with potential for targeted colonic delivery.

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

C. elegans: A biosensor for host-microbe interactions

Microbes are an integral part of life on this planet. Microbes and their hosts influence each other in an endless dance that shapes how the meta-organism interacts with its environment. Although great advances have been made in microbiome research over the past 20 years, the mechanisms by which both hosts and their microbes interact with each other and the environment are still not well understood. The nematode Caenorhabditis elegans has been widely used as a model organism to study a remarkable number of human-like processes. Recent evidence shows that the worm is a powerful tool to investigate in fine detail the complexity that exists in microbe-host interactions. By combining the large array of genetic tools available for both organisms together with deep phenotyping approaches, it has been possible to uncover key effectors in the complex relationship between microbes and their hosts. In this perspective, we survey the literature for insightful discoveries in the microbiome field using the worm as a model. We discuss the latest conceptual and technological advances in the field and highlight the strengths that make C. elegans a valuable biosensor tool for the study of microbe-host interactions.

Gut Microbiome in a Russian Cohort of Pre- and Post-Cholecystectomy Female Patients

The last decade saw extensive studies of the human gut microbiome and its relationship to specific diseases, including gallstone disease (GSD). The information about the gut microbiome in GSD-afflicted Russian patients is scarce, despite the increasing GSD incidence worldwide. Although the gut microbiota was described in some GSD cohorts, little is known regarding the gut microbiome before and after cholecystectomy (CCE). By using Illumina MiSeq sequencing of 16S rRNA gene amplicons, we inventoried the fecal bacteriobiome composition and structure in GSD-afflicted females, seeking to reveal associations with age, BMI and some blood biochemistry. Overall, 11 bacterial phyla were identified, containing 916 operational taxonomic units (OTUs). The fecal bacteriobiome was dominated by Firmicutes (66% relative abundance), followed by Bacteroidetes (19%), Actinobacteria (8%) and Proteobacteria (4%) phyla. Most (97%) of the OTUs were minor or rare species with ≤1% relative abundance. Prevotella and Enterocossus were linked to blood bilirubin. Some taxa had differential pre- and post-CCE abundance, despite the very short time (1–3 days) elapsed after CCE. The detailed description of the bacteriobiome in pre-CCE female patients suggests bacterial foci for further research to elucidate the gut microbiota and GSD relationship and has potentially important biological and medical implications regarding gut bacteria involvement in the increased GSD incidence rate in females.

Gut feelings about bacterial steroid-17,20-desmolase

Advances in technology are only beginning to reveal the complex interactions between hosts and their resident microbiota that have co-evolved over centuries. In this review, we present compelling evidence that implicates the host-associated microbiome in the generation of 11β-hydroxyandrostenedione, leading to the formation of potent 11-oxy-androgens. Microbial steroid-17,20-desmolase cleaves the side-chain of glucocorticoids (GC), including cortisol (and its derivatives of cortisone, 5α-dihydrocortisol, and also (allo)- 3α, 5α-tetrahydrocortisol, but not 3α-5β-tetrahydrocortisol) and drugs (prednisone and dexamethasone). In addition to side-chain cleavage, we discuss the gut microbiome's robust potential to transform a myriad of steroids, mirroring much of the host's metabolism. We also explore the overlooked role of intestinal steroidogenesis and efflux pumps as a potential route for GC transport into the gut. Lastly, we propose several health implications from microbial steroid-17,20-desmolase function, including aberrant mineralocorticoid, GC, and androgen receptor signaling in colonocytes, immune cells, and prostate cells, which may exacerbate disease states.

Impact of gastrointestinal tract variability on oral drug absorption and pharmacokinetics: An UNGAP review

The absorption of oral drugs is frequently plagued by significant variability with potentially serious therapeutic consequences. The source of variability can be traced back to interindividual variability in physiology, differences in special populations (age- and disease-dependent), drug and formulation properties, or food-drug interactions. Clinical evidence for the impact of some of these factors on drug pharmacokinetic variability is mounting: e.g. gastric pH and emptying time, small intestinal fluid properties, differences in pediatrics and the elderly, and surgical changes in gastrointestinal anatomy. However, the link of colonic factors variability (transit time, fluid composition, microbiome), sex differences (male vs. female) and gut-related diseases (chronic constipation, anorexia and cachexia) to drug absorption variability has not been firmly established yet. At the same time, a way to decrease oral drug pharmacokinetic variability is provided by the pharmaceutical industry: clinical evidence suggests that formulation approaches employed during drug development can decrease the variability in oral exposure. This review outlines the main drivers of oral drug exposure variability and potential approaches to overcome them, while highlighting existing knowledge gaps and guiding future studies in this area.

Recent advances in the ontogeny of drug disposition

Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.

The Kobe University Human Intestinal Microbiota Model for gut intervention studies

The human gut harbors a complex microbial community that performs a range of metabolic, physiological, and immunological functions. The host and its inhabiting microorganisms are often referred to as a "superorganism." Dysbiosis of gut microflora has been associated with the pathogenesis of intestinal disorders including inflammatory bowel disease, colorectal cancer, and extra-intestinal disorders such as cardiovascular disease. Therefore, gut microbiome interventions are important for the prevention and treatment of diseases. However, ethical, economic, scientific, and time constraints limit the outcome of human intervention or animal studies targeting gut microbiota. We recently developed an in vitro batch fermentation model (the Kobe University Human Intestinal Microbiota Model, KUHIMM) that is capable of hosting a majority of gut microbial species in humans and also detects the metabolites produced by microorganisms in real time. In this mini review, we elucidated the characteristics of the KUHIMM and its applicability in analyzing the effect of diet, drugs, probiotics, and prebiotics on intestinal bacteria. In addition, we introduce as examples its application to disease models, such as ulcerative colitis, in which intestinal bacteria are intricately involved in the process of pathogenesis. We also discuss the potential of the KUHIMM in precision medicine. KEY POINTS: • In vitro gut fermentation model to simulate human colonic microbiota • Screening of potential prebiotics and probiotic candidates in healthy model • Construction of disease models of ulcerative colitis and coronary artery disease.