Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Delivery mode is a larger determinant of infant gut microbiome composition at 6 weeks than exposure to peripartum antibiotics

Background. Previous research has shown that delivery mode can shape infant gut microbiome composition. However, mothers delivering by caesarean section routinely receive prophylactic antibiotics prior to delivery, resulting in antibiotic exposure to the infant via the placenta. Previously, only a small number of studies have examined the effect of delivery mode versus antibiotic exposure on the infant gut microbiome with mixed findings.Objective. We aimed to determine the effect of delivery mode compared to antibiotic use during labour and delivery on the infant and maternal gut microbiome at 6 weeks post-partum.Methodology. Twenty-five mother-infant dyads were selected from the longitudinal Queensland Family Cohort Study. The selected dyads comprised nine vaginally delivered infants without antibiotics, seven vaginally delivered infants exposed to antibiotics and nine infants born by caesarean section with routine maternal prophylactic antibiotics. Shotgun-metagenomic sequencing of DNA from stool samples collected at 6 weeks post-partum from mother and infant was used to assess microbiome composition.Results. Caesarean section infants exhibited decreases in Bacteroidetes (ANCOM-BC q<0.0001, MaAsLin 2 q=0.041), changes to several functional pathways and altered beta diversity (R 2=0.056, P=0.029), while minimal differences due to antibiotic exposure were detected. For mothers, caesarean delivery (P=0.0007) and antibiotic use (P=0.016) decreased the evenness of the gut microbiome at 6 weeks post-partum without changing beta diversity. Several taxa in the maternal microbiome were altered in association with antibiotic use, with few differentially abundant taxa associated with delivery mode.Conclusion. For infants, delivery mode appears to have a larger effect on gut microbiome composition at 6 weeks post-partum than intrapartum antibiotic exposure. For mothers, both delivery mode and intrapartum antibiotic use have a small effect on gut microbiome composition at 6 weeks post-partum.

A graph neural network approach for predicting drug susceptibility in the human microbiome

Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations.

Discovery of a botanical compound as a broad-spectrum inhibitor against gut microbial β-glucuronidases from the Tibetan medicine Rhodiola crenulata

Gut microbial β-glucuronidases (gmβ-GUS) played crucial roles in regulating a variety of endogenous substances and xenobiotics on the circulating level, thus had been recognized as key modulators of drug toxicity and human diseases. Inhibition or inactivation of gmβ-GUS enzymes has become a promising therapeutic strategy to alleviate drug-induced intestinal toxicity. Herein, the Rhodiola crenulata extract (RCE) was found with potent and broad-spectrum inhibition on multiple gmβ-GUS enzymes. Subsequently, the anti-gmβ-GUS activities of the major constituents in RCE were tested and the results showed that 1,2,3,4,6-penta-O-galloyl-β-d-glucopyranose (PGG) acted as a strong and broad-spectrum inhibitor on multiple gmβ-GUS (including EcGUS, CpGUS, SaGUS, and EeGUS). Inhibition kinetic assays demonstrated that PGG effectively inhibited four gmβ-GUS in a non-competitive manner, with the Ki values ranging from 0.12 μM to 1.29 μM. Docking simulations showed that PGG could tightly bound to the non-catalytic sites of various gmβ-GUS, mainly via hydrogen bonding and aromatic interactions. It was also found that PGG could strongly inhibit the total gmβ-GUS activity in mice feces, with the IC50 value of 1.24 μM. Collectively, our findings revealed that RCE and its constituent PGG could strongly inhibit multiple gmβ-GUS enzymes, suggesting that RCE and PGG could be used for alleviating gmβ-GUS associated enterotoxicity.

Novel Techniques and Models for Studying the Role of the Gut Microbiota in Drug Metabolism

The gut microbiota, known as the second human genome, plays a vital role in modulating drug metabolism, significantly impacting therapeutic outcomes and adverse effects. Emerging research has elucidated that the microbiota mediates a range of modifications of drugs, leading to their activation, inactivation, or even toxication. In diverse individuals, variations in the gut microbiota can result in differences in microbe-drug interactions, underscoring the importance of personalized approaches in pharmacotherapy. However, previous studies on drug metabolism in the gut microbiota have been hampered by technical limitations. Nowadays, advances in biotechnological tools, such as microbially derived metabolism screening and microbial gene editing, have provided a deeper insight into the mechanism of drug metabolism by gut microbiota, moving us toward personalized therapeutic interventions. Given this situation, our review summarizes recent advances in the study of gut-microbiota-mediated drug metabolism and showcases techniques and models developed to navigate the challenges posed by the microbial involvement in drug action. Therefore, we not only aim at understanding the complex interaction between the gut microbiota and drugs and outline the development of research techniques and models, but we also summarize the specific applications of new techniques and models in researching gut-microbiota-mediated drug metabolism, with the expectation of providing new insights on how to study drug metabolism by gut microbiota.

Microbial metabolites as modulators of host physiology

The gut microbiota is increasingly recognised as a key player in influencing human health and changes in the gut microbiota have been strongly linked with many non-communicable conditions in humans such as type 2 diabetes, obesity and cardiovascular disease. However, characterising the molecular mechanisms that underpin these associations remains an important challenge for researchers. The gut microbiota is a complex microbial community that acts as a metabolic interface to transform ingested food (and other xenobiotics) into metabolites that are detected in the host faeces, urine and blood. Many of these metabolites are only produced by microbes and there is accumulating evidence to suggest that these microbe-specific metabolites do act as effectors to influence human physiology. For example, the gut microbiota can digest dietary complex polysaccharides (such as fibre) into short-chain fatty acids (SCFA) such as acetate, propionate and butyrate that have a pervasive role in host physiology from nutrition to immune function. In this review we will outline our current understanding of the role of some key microbial metabolites, such as SCFA, indole and bile acids, in human health. Whilst many studies linking microbial metabolites with human health are correlative we will try to highlight examples where genetic evidence is available to support a specific role for a microbial metabolite in host health and well-being.

Gut Microbiota-Mediated Pharmacokinetic Drug-Drug Interactions between Mycophenolic Acid and Trimethoprim-Sulfamethoxazole in Humans

Mycophenolic acid (MPA) and trimethoprim-sulfamethoxazole (TMP-SMX) are commonly prescribed together in certain groups of patients, including solid organ transplant recipients. However, little is known about the pharmacokinetic drug-drug interactions (DDIs) between these two medications. Therefore, the present study aimed to determine the effects of TMP-SMX on MPA pharmacokinetics in humans and to find out the relationship between MPA pharmacokinetics and gut microbiota alteration. This study enrolled 16 healthy volunteers to take a single oral dose of 1000 mg mycophenolate mofetil (MMF), a prodrug of MPA, administered without and with concurrent use of TMP-SMX (320/1600 mg/day) for five days. The pharmacokinetic parameters of MPA and its glucuronide (MPAG) were measured using high-performance liquid chromatography. The composition of gut microbiota in stool samples was profiled using a 16S rRNA metagenomic sequencing technique during pre- and post-TMP-SMX treatment. Relative abundance, bacterial co-occurrence networks, and correlations between bacterial abundance and pharmacokinetic parameters were investigated. The results showed a significant decrease in systemic MPA exposure when TMP-SMX was coadministered with MMF. Analysis of the gut microbiome revealed altered relative abundance of two enriched genera, namely the genus Bacteroides and Faecalibacterium, following TMP-SMX treatment. The relative abundance of the genera Bacteroides, [Eubacterium] coprostanoligenes group, [Eubacterium] eligens group, and Ruminococcus appeared to be significantly correlated with systemic MPA exposure. Coadministration of TMP-SMX with MMF resulted in a reduction in systemic MPA exposure. The pharmacokinetic DDIs between these two drugs were attributed to the effect of TMP-SMX, a broad-spectrum antibiotic, on gut microbiota-mediated MPA metabolism.

Characteristics of intestinal microbiota in infants with late-onset breast milk jaundice

Introduction

In this paper, microbiota analysis was determined to analyze the structure and difference of intestinal microbiota between LBMJ (late-onset breast milk jaundice) infants and healthy individuals.

Methods

We collected fresh fecal samples from 13 infants with LBMJ and 13 healthy individuals, then determined the intestinal microbiota by 16 s rRNA sequencing. The differences of microbiota structure, diversity and functional characteristics between the two groups were analyzed, and the correlation between dominant genus and TcB (transcutaneous bilirubin) value was calculated.

Results

In this study, there were no significant differences in maternal demographic characteristics, neonatal status and macronutrients in breast milk between the two groups (p > 0.05). There are differences in the structure of intestinal microbiota between LBMJ and the control group. At the genus level, the relative abundance of Klebsiella in the case group is high (p < 0.05). At the same time, correlation analysis indicates that the abundance of Klebsiella is positively correlated with TcB value. The intestinal microbiota richness and diversity (Alpha diversity and Beta diversity) of the two groups were significantly different (p < 0.05). LEfSe analysis showed that 25 genera including Klebsiella was significantly enriched in the LBMJ infants, and the other 17 species are enriched in the control group. Functional prediction analysis indicated that 42 metabolic pathways may be related to the occurrence of LBMJ.

Conclusion

In conclusion, characteristic changes are seen in intestinal microbiota compositions between LBMJ infants and the healthy controls. Klebsiella is closely associated with the severity of the disease, which may be due to enhanced β-glucuronidase activity.

Metabolically-targeted dCas9 expression in bacteria

The ability to restrict gene expression to a relevant bacterial species in a complex microbiome is an unsolved problem. In the context of the human microbiome, one desirable target metabolic activity are glucuronide-utilization enzymes (GUS) that are implicated in the toxic re-activation of glucuronidated compounds in the human gastrointestinal (GI) tract, including the chemotherapeutic drug irinotecan. Here, we take advantage of the variable distribution of GUS enzymes in bacteria as a means to distinguish between bacteria with GUS activity, and re-purpose the glucuronide-responsive GusR transcription factor as a biosensor to regulate dCas9 expression in response to glucuronide inducers. We fused the Escherichia coli gusA regulatory region to the dCas9 gene to create pGreg-dCas9, and showed that dCas9 expression is induced by glucuronides, but not other carbon sources. When conjugated from E. coli to Gammaproteobacteria derived from human stool, dCas9 expression from pGreg-dCas9 was restricted to GUS-positive bacteria. dCas9-sgRNAs targeted to gusA specifically down-regulated gus operon transcription in Gammaproteobacteria, with a resulting ∼100-fold decrease in GusA activity. Our data outline a general strategy to re-purpose bacterial transcription factors responsive to exogenous metabolites for precise ligand-dependent expression of genetic tools such as dCas9 in diverse bacterial species.

The role of gut microbial β-glucuronidase in drug disposition and development

Gut microbial β-glucuronidase (gmGUS) is involved in the disposition of many endogenous and exogenous compounds. Preclinical studies have shown that inhibiting gmGUS activity affects drug disposition, resulting in reduced toxicity in the gastrointestinal tract (GIT) and enhanced systemic efficacy. Additionally, manipulating gmGUS activity is expected to be effective in preventing/treating local or systemic diseases. Although results from animal studies are promising, challenges remain in developing drugs by targeting gmGUS. Here, we review the role of gmGUS in host health under physiological and pathological conditions, the impact of gmGUS on the disposition of phenolic compounds, models used to study gmGUS activity, and the perspectives and challenges in developing drugs by targeting gmGUS.

Fecal β-glucuronidase activity differs between hematopoietic cell and kidney transplantation and a possible mechanism for disparate dose requirements

The intestinal microbiota produces β-glucuronidase that plays an essential role in the metabolism of the immunosuppressant mycophenolate mofetil (MMF). This drug is commonly used in organ and hematopoietic cell transplantation (HCT), with variations in dosing across transplant types. We hypothesized that β-glucuronidase activity differs between transplant types, which may account for differences in dosing requirements. We evaluated fecal β-glucuronidase activity in patients receiving MMF post-allogeneic HCT and post-kidney transplant. Kidney transplant patients had significantly greater β-glucuronidase activity (8.48 ± 6.21 nmol/hr/g) than HCT patients (3.50 ± 3.29 nmol/hr/g; P = .001). Microbially mediated β-glucuronidase activity may be a critical determinant in the amount of mycophenolate entering the systemic circulation and an important factor to consider for precision dosing of MMF.

Usefulness of Optimized Human Fecal Material in Simulating the Bacterial Degradation of Sulindac and Sulfinpyrazone in the Lower Intestine

The first aim of this study was to evaluate the usefulness of optimized human fecal material in simulating sulforeductase activity in the lower intestine by assessing bacterial degradation of sulindac and sulfinpyrazone, two sulforeductase substrates. The second aim was to evaluate the usefulness of drug degradation half-life generated in simulated colonic bacteria (SCoB) in informing PBPK models. Degradation experiments of sulfinpyrazone and of sulindac in SCoB were performed under anaerobic conditions using recently described methods. For sulfinpyrazone, the abundance of clinical data allowed for construction of a physiologically based pharmacokinetic (PBPK) model and evaluation of luminal degradation clearance determined from SCoB data. For sulindac, the availability of sulindac sulfide and sulindac sulfone standards allowed for evaluating the formation of the main metabolite, sulindac sulfide, during the experiments in SCoB. Both model compounds degraded substantially in SCoB. The PBPK model was able to adequately capture exposure of sulfinpyrazone and its sulfide metabolite in healthy subjects, in ileostomy and/or colectomy subjects, and in healthy subjects pretreated with metoclopramide by implementing degradation half-lives in SCoB to calculate intrinsic colon clearance. Degradation rates of sulindac and formation rates of sulindac sulfide in SCoB were almost identical, in line with in vivo data suggesting the sulindac sulfide is the primary metabolite in the lower intestine. Experiments in SCoB were useful in simulating sulforeductase related bacterial degradation activity in the lower intestine. Degradation half-life calculated from experiments in SCoB is proven useful for informing a predictive PBPK model for sulfinpyrazone.

β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

β-glucuronidases (GUS) of intestinal bacteria remove glucuronic acid from glucoronides, reversing phase II metabolism of the liver and affecting the level of active deconjugated metabolites deriving from drugs or xenobiotics. Two hundred seventy-nine non-redundant GUS sequences are known in the gut microbiota, classified in seven structural categories (NL, L1, L2, mL1, mL2, mL1,2, and NC) with different biocatalytic properties. In the present study, the intestinal metagenome of 60 healthy subjects from five geographically different cohorts was assembled, binned, and mined to determine qualitative and quantitative differences in GUS profile, potentially affecting response to drugs and xenobiotics. Each metagenome harbored 4–70 different GUS, altogether accounting for 218. The amount of intestinal bacteria with at least one GUS gene was highly variable, from 0.7 to 82.2%, 25.7% on average. No significant difference among cohorts could be identified, except for the Ethiopia (ETH) cohort where GUS-encoding bacteria were significantly less abundant. The structural categories were differently distributed among the metagenomes, but without any statistical significance related to the cohorts. GUS profiles were generally dominated by the category NL, followed by mL1, L2, and L1. The GUS categories most involved in the hydrolysis of small molecules, including drugs, are L1 and mL1. Bacteria contributing to these categories belonged to Bacteroides ovatus, Bacteroides dorei, Bacteroides fragilis, Escherichia coli, Eubacterium eligens, Faecalibacterium prausnitzii, Parabacteroides merdae, and Ruminococcus gnavus. Bacteria harboring L1 GUS were generally scarcely abundant (<1.3%), except in three metagenomes, where they reached up to 24.3% for the contribution of E. coli and F. prausnitzii. Bacteria harboring mL1 GUS were significantly more abundant (mean = 4.6%), with Bacteroides representing a major contributor. Albeit mL1 enzymes are less active than L1 ones, Bacteroides likely plays a pivotal role in the deglucuronidation, due to its remarkable abundance in the microbiomes. The observed broad interindividual heterogeneity of GUS profiles, particularly of the L1 and mL1 categories, likely represent a major driver of pharmacomicrobiomics variability, affecting drug response and toxicity. Different geographical origins, genetic, nutritional, and lifestyle features of the hosts seemed not to be relevant in the definition of glucuronidase activity, albeit they influenced the richness of the GUS profile.

Gut microbiota-drug interactions in cancer pharmacotherapies: implications for efficacy and adverse effects

INTRODUCTION

The gut microbiota is involved in host physiology and health. Reciprocal microbiota-drug interactions are increasingly recognized as underlying some individual differences in therapy response and adverse events. Cancer pharmacotherapies are characterized by a high degree of interpatient variability in efficacy and side effect profile and recently, the microbiota has emerged as a factor that may underlie these differences.

AREAS COVERED

The effects of cancer pharmacotherapy on microbiota composition and function are reviewed with consideration of the relationship between baseline microbiota composition, microbiota modification, antibiotics exposure and cancer therapy efficacy. We assess the evidence implicating the microbiota in cancer therapy-related adverse events including impaired gut function, cognition and pain perception. Finally, potential mechanisms underlying microbiota-cancer drug interactions are described, including direct microbial metabolism, and microbial modulation of liver metabolism and immune function. This review focused on preclinical and clinical studies conducted in the last 5 years.

EXPERT OPINION

Preclinical and clinical research supports a role for baseline microbiota in cancer therapy efficacy, with emerging evidence that the microbiota modification may assist in side effect management. Future efforts should focus on exploiting this knowledge towards the development of microbiota-targeted therapies. Finally, a focus on specific drug-microbiota-cancer interactions is warranted.

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.

Comprehensive investigation of RNA-sequencing dataset reveals the hub genes and molecular mechanisms of coronavirus disease 2019 acute respiratory distress syndrome

The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.

Human gut bacterial β-glucuronidase inhibition: An emerging approach to manage medication therapy

Bacterial β-glucuronidase enzymes (BGUSs) are at the interface of host-microbial metabolic symbiosis, playing an important role in health and disease as well as medication outcomes (efficacy or toxicity) by deconjugating a large number of endogenous and exogenous glucuronides. In recent years, BGUSs inhibition has emerged as a new approach to manage diseases and medication therapy and attracted an increasing research interest. However, a growing body of evidence underlines great genetic diversity, functional promiscuity and varied inhibition propensity of BGUSs, which have posed big challenges to identifying BGUSs involved in a specific pathophysiological or pharmacological process and developing effective inhibition. In this article, we offered a general introduction of the function, in particular the physiological, pathological and pharmacological roles, of BGUSs and their taxonomic distribution in human gut microbiota, highlighting the structural features (active sites and adjacent loop structures) that affecting the protein-substrate (inhibitor) interactions. Recent advances in BGUSs-mediated deconjugation of drugs and carcinogens and the discovery and applications of BGUS inhibitors in management of medication therapy, typically, irinotecan-induced diarrhea and non-steroidal anti-inflammatory drugs (NSAIDs)-induced enteropathy, were also reviewed. At the end, we discussed the perspectives and the challenges of tailoring BGUS inhibition towards precision medicine.