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

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data

The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for the dynamic trend and correlation among longitudinal samples. We develop an efficient and stable algorithm to fit the NBMMs. We evaluate and demonstrate the NBMMs method via extensive simulation studies and application to a longitudinal microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of flexible framework for modeling correlation structures and detecting dynamic effects. We have developed an R package NBZIMM to implement the proposed method, which is freely available from the public GitHub repository http://github.com//nyiuab//NBZIMM and provides a useful tool for analyzing longitudinal microbiome data.

Negative binomial mixed models for analyzing microbiome count data

Background

Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.

Results

In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models.

Conclusions

We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data.

Bile acids at the cross-roads of gut microbiome-host cardiometabolic interactions

While basic and clinical research over the last several decades has recognized a number of modifiable risk factors associated with cardiometabolic disease progression, additional and alternative biological perspectives may offer novel targets for prevention and treatment of this disease set. There is mounting preclinical and emerging clinical evidence indicating that the mass of metabolically diverse microorganisms which inhabit the human gastrointestinal tract may be implicated in initiation and modulation of cardiovascular and metabolic disease outcomes. The following review will discuss this gut microbiome-host metabolism axis and address newly proposed bile-mediated signaling pathways through which dysregulation of this homeostatic axis may influence host cardiovascular risk. With a central focus on the major nuclear and membrane-bound bile acid receptor ligands, we aim to review the putative impact of microbial bile acid modification on several major phenotypes of metabolic syndrome, from obesity to heart failure. Finally, attempting to synthesize several separate but complementary hypotheses, we will review current directions in preclinical and clinical investigation in this evolving field.

Human microbiomes and their roles in dysbiosis, common diseases, and novel therapeutic approaches

The human body is the residence of a large number of commensal (non-pathogenic) and pathogenic microbial species that have co-evolved with the human genome, adaptive immune system, and diet. With recent advances in DNA-based technologies, we initiated the exploration of bacterial gene functions and their role in human health. The main goal of the human microbiome project is to characterize the abundance, diversity and functionality of the genes present in all microorganisms that permanently live in different sites of the human body. The gut microbiota expresses over 3.3 million bacterial genes, while the human genome expresses only 20 thousand genes. Microbe gene-products exert pivotal functions via the regulation of food digestion and immune system development. Studies are confirming that manipulation of non-pathogenic bacterial strains in the host can stimulate the recovery of the immune response to pathogenic bacteria causing diseases. Different approaches, including the use of nutraceutics (prebiotics and probiotics) as well as phages engineered with CRISPR/Cas systems and quorum sensing systems have been developed as new therapies for controlling dysbiosis (alterations in microbial community) and common diseases (e.g., diabetes and obesity). The designing and production of pharmaceuticals based on our own body’s microbiome is an emerging field and is rapidly growing to be fully explored in the near future. This review provides an outlook on recent findings on the human microbiomes, their impact on health and diseases, and on the development of targeted therapies.

Insights on the human microbiome and its xenobiotic metabolism: what is known about its effects on human physiology?

INTRODUCTION

Our microbiome harbours a metabolic capacity far beyond our own. Moreover, its gene pool is highly adaptable and subject to selective pressure, including host exposure to xenobiotics. Yet, the resulting adaptations do not necessarily follow host well-being and can therefore contribute to disease or unfavourable metabolite production.

AREAS COVERED

This review provides an overview of our host-microbiome relationship in light of bacterial (xenobiotic) metabolism, community dynamics, entero-endocrine crosstalk, dysbiosis and potential therapeutic targets. In addition, it will highlight the need for a systematic analysis of the microbiome's potential for substance toxification.

EXPERT OPINION

The influence of our microbiota reaches from primary metabolites to secondary effects such as substrate competition or the activation of eukaryotic Phase I and Phase II enzymes. Further on it plays a hitherto underestimated role in drug metabolism, toxicity and pathogenesis. These effects are partly caused by entero-endocrine crosstalk and interference with eukaryotic regulatory networks. On first sight, the resulting concept of a metabolically competent microbiome adds enormous complexity to human physiology. Yet, the potential specificity of microbial targets harbours therapeutic promise for diseases such as diabetes, cancer and psychiatric disorders. A better physiological and biochemical understanding of the microbiome is thus of high priority for academia and biomedical research.

HemI: a toolkit for illustrating heatmaps

Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under different conditions. In this work, we developed an easy-to-use tool named HemI (Heat map Illustrator), which can visualize either gene or protein expression data in heatmaps. Additionally, the heatmaps can be recolored, rescaled or rotated in a customized manner. In addition, HemI provides multiple clustering strategies for analyzing the data. Publication-quality figures can be exported directly. We propose that HemI can be a useful toolkit for conveniently visualizing and manipulating heatmaps. The stand-alone packages of HemI were implemented in Java and can be accessed at http://hemi.biocuckoo.org/down.php.

Accurate genome relative abundance estimation for closely related species in a metagenomic sample

BACKGROUND

Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.

RESULTS

We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn's disease.

CONCLUSIONS

By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Phylogeny, culturing, and metagenomics of the human gut microbiota

The human intestinal tract is colonised by a complex community of microbes, which can have major impacts on host health. Recent research on the gut microbiota has largely been driven by the advent of modern sequence-based techniques, such as metagenomics. Although these are powerful and valuable tools, they have limitations. Traditional culturing and phylogeny can mitigate some of these limitations, either by expanding reference databases or by assigning functionality to specific microbial lineages. As such, culture and phylogeny will continue to have crucially important roles in human microbiota research, and will be required for the development of novel therapeutics.

Functional environmental screening of a metagenomic library identifies stlA; a unique salt tolerance locus from the human gut microbiome

Functional environmental screening of metagenomic libraries is a powerful means to identify and assign function to novel genes and their encoded proteins without any prior sequence knowledge. In the current study we describe the identification and subsequent analysis of a salt-tolerant clone from a human gut metagenomic library. Following transposon mutagenesis we identified an unknown gene (stlA, for “salt tolerance locus A”) with no current known homologues in the databases. Subsequent cloning and expression in Escherichia coli MKH13 revealed that stlA confers a salt tolerance phenotype in its surrogate host. Furthermore, a detailed in silico analysis was also conducted to gain additional information on the properties of the encoded StlA protein. The stlA gene is rare when searched against human metagenome datasets such as MetaHit and the Human Microbiome Project and represents a novel and unique salt tolerance determinant which appears to be found exclusively in the human gut environment.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans

Many animal species live in close association with commensal and symbiotic microbes (microbiota). Recent studies have revealed that the status of gastrointestinal tract microbiota can influence nutrition-related syndromes such as obesity and type-2 diabetes, and perhaps aging. These morbidities have a profound impact in terms of individual suffering, and are an increasing economic burden to modern societies. Several theories have been proposed for the influence of microbiota on host metabolism, but these largely remain to be proven. In this article we discuss how microbiota may be manipulated (via pharmacology, diet, or gene manipulation) in order to alter metabolism, immunity, health and aging in the host. The nematode Caenorhabditis elegans in combination with one microbial species is an excellent, defined model system to investigate the mechanisms of host–microbiota interactions, particularly given the combined power of worm and microbial genetics. We also discuss the multifaceted nature of the worm–microbe relationship, which likely encompasses predation, commensalism, pathogenicity and necromeny.