Quorum sensing-based interactions among drugs, microbes, and diseases

Gut bacterial quorum sensing molecules and their association with inflammatory bowel disease: Advances and future perspectives

Inflammatory Bowel Disease (IBD) is an enduring inflammatory disease of the gastrointestinal tract (GIT). The complexity of IBD, its profound impact on patient's quality of life, and its burden on healthcare systems necessitate continuing studies to elucidate its etiology, refine care strategies, improve treatment outcomes, and identify potential targets for novel therapeutic interventions. The discovery of a connection between IBD and gut bacterial quorum sensing (QS) molecules has opened exciting opportunities for research into IBD pathophysiology. QS molecules are small chemical messengers synthesized and released by bacteria based on population density. These chemicals are sensed not only by the microbial species but also by host cells and are essential in gut homeostasis. QS molecules are now known to interact with inflammatory pathways, therefore rendering them potential therapeutic targets for IBD management. Given these intriguing developments, the most recent research findings in this area are herein reviewed. First, the global burden of IBD and the disruptions of the gut microbiota and intestinal barrier associated with the disease are assessed. Next, the general QS mechanism and signaling molecules in the gut are discussed. Then, the roles of QS molecules and their connection with IBD are elucidated. Lastly, the review proposes potential QS-based therapeutic targets for IBD, offering insights into the future research trajectory in this field.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

When smoke meets gut: deciphering the interactions between tobacco smoking and gut microbiota in disease development

Tobacco smoking is a prevalent and detrimental habit practiced worldwide, increasing the risk of various diseases, including chronic obstructive pulmonary disease (COPD), cardiovascular disease, liver disease, and cancer. Although previous research has explored the detrimental health effects of tobacco smoking, recent studies suggest that gut microbiota dysbiosis may play a critical role in these outcomes. Numerous tobacco smoke components, such as nicotine, are found in the gastrointestinal tract and interact with gut microbiota, leading to lasting impacts on host health and diseases. This review delves into the ways tobacco smoking and its various constituents influence gut microbiota composition and functionality. We also summarize recent advancements in understanding how tobacco smoking-induced gut microbiota dysbiosis affects host health. Furthermore, this review introduces a novel perspective on how changes in gut microbiota following smoking cessation may contribute to withdrawal syndrome and the degree of health improvements in smokers.

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.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Oral Cardiac Drug-Gut Microbiota Interaction in Chronic Heart Failure Patients: An Emerging Association

Regardless of the currently proposed best medical treatment for heart failure patients, the morbidity and mortality rates remain high. This is due to several reasons, including the interaction between oral cardiac drug administration and gut microbiota. The relation between drugs (especially antibiotics) and gut microbiota is well established, but it is also known that more than 24% of non-antibiotic drugs affect gut microbiota, altering the microbe's environment and its metabolic products. Heart failure treatment lies mainly in the blockage of neuro-humoral hyper-activation. There is debate as to whether the administration of heart-failure-specific drugs can totally block this hyper-activation, or whether the so-called intestinal dysbiosis that is commonly observed in this group of patients can affect their action. Although there are several reports indicating a strong relation between drug-gut microbiota interplay, little is known about this relation to oral cardiac drugs in chronic heart failure. In this review, we review the contemporary data on a topic that is in its infancy. We aim to produce scientific thoughts and questions and provide reasoning for further clinical investigation.

Targeting microbial quorum sensing: the next frontier to hinder bacterial driven gastrointestinal infections

Bacteria synchronize social behaviors via a cell-cell communication and interaction mechanism termed as quorum sensing (QS). QS has been extensively studied in monocultures and proved to be intensively involved in bacterial virulence and infection. Despite the role QS plays in pathogens during laboratory engineered infections has been proved, the potential functions of QS related to pathogenesis in context of microbial consortia remain poorly understood. In this review, we summarize the basic molecular mechanisms of QS, primarily focusing on pathogenic microbes driving gastrointestinal (GI) infections. We further discuss how GI pathogens disequilibrate the homeostasis of the indigenous microbial consortia, rebuild a realm dominated by pathogens, and interact with host under worsening infectious conditions via pathogen-biased QS signaling. Additionally, we present recent applications and main challenges of manipulating QS network in microbial consortia with the goal of better understanding GI bacterial sociality and facilitating novel therapies targeting bacterial infections.

The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine

Trillions of diverse microbes reside in the gut and are deeply interwoven with the human physiological process, from food digestion, immune system maturation, and fighting invading pathogens, to drug metabolism. Microbial drug metabolism has a profound impact on drug absorption, bioavailability, stability, efficacy, and toxicity. However, our knowledge of specific gut microbial strains, and their genes that encode enzymes involved in the metabolism, is limited. The microbiome encodes over 3 million unique genes contributing to a huge enzymatic capacity, vastly expanding the traditional drug metabolic reactions that occur in the liver, manipulating their pharmacological effect, and, ultimately, leading to variation in drug response. For example, the microbial deactivation of anticancer drugs such as gemcitabine can lead to resistance to chemotherapeutics or the crucial role of microbes in modulating the efficacy of the anticancer drug, cyclophosphamide. On the other hand, recent findings show that many drugs can shape the composition, function, and gene expression of the gut microbial community, making it harder to predict the outcome of drug-microbiota interactions. In this review, we discuss the recent understanding of the multidirectional interaction between the host, oral medications, and gut microbiota, using traditional and machine-learning approaches. We analyze gaps, challenges, and future promises of personalized medicine that consider gut microbes as a crucial player in drug metabolism. This consideration will enable the development of personalized therapeutic regimes with an improved outcome, ultimately leading to precision medicine.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion

MOTIVATION

Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed.

RESULTS

In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.