A Metabolome- and Metagenome-Wide Association Network Reveals Microbial Natural Products and Microbial Biotransformation Products from the Human Microbiota

G protein-coupled receptors: A target for microbial metabolites and a mechanistic link to microbiome-immune-brain interactions

Human-microorganism interactions play a key role in human health. However, the underlying molecular mechanisms remain poorly understood. Small-molecules that offer a functional readout of microbe-microbe-human relationship are of great interest for deeper understanding of the inter-kingdom crosstalk at the molecular level. Recent studies have demonstrated that small-molecules from gut microbiota act as ligands for specific human G protein-coupled receptors (GPCRs) and modulate a range of human physiological functions, offering a mechanistic insight into the microbe-human interaction. To this end, we focused on analysis of bacterial metabolites that are currently recognized to bind to GPCRs and are found to activate the known downstream signaling pathways. We further mapped the distribution of these molecules across the public mass spectrometry-based metabolomics data, to identify the presence of these molecules across body sites and their association with health status. By combining this with RNA-Seq expression and spatial localization of GPCRs from a public human protein atlas database, we inferred the most predominant GPCR-mediated microbial metabolite-human cell interactions regulating gut-immune-brain axis. Furthermore, by evaluating the intestinal absorption properties and blood-brain barrier permeability of the small-molecules we elucidated their molecular interactions with specific human cell receptors, particularly expressed on human intestinal epithelial cells, immune cells and the nervous system that are shown to hold much promise for clinical translational potential. Furthermore, we provide an overview of an open-source resource for simultaneous interrogation of bioactive molecules across the druggable human GPCRome, a useful framework for integration of microbiome and metabolite cataloging with mechanistic studies for an improved understanding of gut microbiota-immune-brain molecular interactions and their potential therapeutic use.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

Multi-Level Optimization and Strategies in Microbial Biotransformation of Nature Products

Continuously growing demand for natural products with pharmacological activities has promoted the development of microbial transformation techniques, thereby facilitating the efficient production of natural products and the mining of new active compounds. Furthermore, due to the shortcomings and defects of microbial transformation, it is an important scientific issue of social and economic value to improve and optimize microbial transformation technology in increasing the yield and activity of transformed products. In this review, the aspects regarding the optimization of fermentation and the cross-disciplinary strategy, leading to the microbial transformation of increased levels of the high-efficiency process from natural products of a plant or microbial origin, were discussed. Additionally, due to the increasing craving for targeted and efficient methods for detecting transformed metabolites, analytical methods based on multiomics were also discussed. Such strategies can be well exploited and applied to the production of more efficient and more natural products from microbial resources.

Neuromicrobiology, an emerging neurometabolic facet of the gut microbiome?

The concept of the gut microbiome is emerging as a metabolic interactome influenced by diet, xenobiotics, genetics, and other environmental factors that affect the host's absorption of nutrients, metabolism, and immune system. Beyond nutrient digestion and production, the gut microbiome also functions as personalized polypharmacy, where bioactive metabolites that our microbes excrete or conjugate may reach systemic circulation and impact all organs, including the brain. Appreciable evidence shows that gut microbiota produce diverse neuroactive metabolites, particularly neurotransmitters (and their precursors), stimulating the local nervous system (i.e., enteric and vagus nerves) and affecting brain function and cognition. Several studies have demonstrated correlations between the gut microbiome and the central nervous system sparking an exciting new research field, neuromicrobiology. Microbiome-targeted interventions are seen as promising adjunctive treatments (pre-, pro-, post-, and synbiotics), but the mechanisms underlying host-microbiome interactions have yet to be established, thus preventing informed evidence-based therapeutic applications. In this paper, we review the current state of knowledge for each of the major classes of microbial neuroactive metabolites, emphasizing their biological effects on the microbiome, gut environment, and brain. Also, we discuss the biosynthesis, absorption, and transport of gut microbiota-derived neuroactive metabolites to the brain and their implication in mental disorders.

Cecal Metabolomic Fingerprint of Unscathed Rats: Does It Reflect the Good Response to a Provocative Decompression?

On one side, decompression sickness (DCS) with neurological disorders lead to a reshuffle of the cecal metabolome of rats. On the other side, there is also a specific and different metabolomic signature in the cecum of a strain of DCS-resistant rats, that are not exposed to hyperbaric protocol. We decide to study a conventional strain of rats that resist to an accident-provoking hyperbaric exposure, and we hypothesize that the metabolomic signature put forward may correspond to a physiological response adapted to the stress induced by diving. The aim is to verify and characterize whether the cecal compounds of rats resistant to the provocative dive have a cecal metabolomic signature different from those who do not dive. 35 asymptomatic diver rats are selected to be compared to 21 rats non-exposed to the hyperbaric protocol. Because our aim is essentially to study the differences in the cecal metabolome associated with the hyperbaric exposure, about half of the rats are fed soy and the other half of maize in order to better rule out the effect of the diet itself. Lower levels of IL-1β and glutathione peroxidase (GPX) activity are registered in blood of diving rats. No blood cell mobilization is noted. Conventional and ChemRICH approaches help the metabolomic interpretation of the 185 chemical compounds analyzed in the cecal content. Statistical analysis show a panel of 102 compounds diet related. 19 are in common with the hyperbaric protocol effect. Expression of 25 compounds has changed in the cecal metabolome of rats resistant to the provocative dive suggesting an alteration of biliary acids metabolism, most likely through actions on gut microbiota. There seem to be also weak changes in allocations dedicated to various energy pathways, including hormonal reshuffle. Some of the metabolites may also have a role in regulating inflammation, while some may be consumed for the benefit of oxidative stress management.

Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides

BACKGROUND

Depression and obesity are highly prevalent, often co-occurring conditions marked by inflammation. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how the microbiome mechanistically contributes to pathology remains unclear. Metabolomic investigations into microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we identified key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity.

RESULTS

Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was more highly predictive of depressive symptomatology-obesity co-occurrences than of obesity or depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among depressive symptomatology, obesity and comorbid obesity-depression. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin.

CONCLUSIONS

Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data

The human microbiome is a complex community of microorganisms, their enzymes, and the molecules they produce or modify. Recent studies show that imbalances in human microbial ecosystems can cause disease. Our microbiome affects our health through the products of biochemical reactions catalyzed by microbial enzymes (microbial biotransformations). Despite their significance, currently, there are no systematic strategies for identifying these chemical reactions, their substrates and molecular products, and their effects on health and disease. We present TransDiscovery, a computational algorithm that integrates molecular networks (connecting related molecules with similar mass spectra), association networks (connecting co-occurring molecules and microbes) and knowledge bases of microbial enzymes to discover microbial biotransformations, their substrates, and their products. After searching the metabolomics and metagenomics data from the American Gut Project and the Global Foodomic Project, TranDiscovery identified 17 potentially novel biotransformations from the human gut microbiome, along with the corresponding microbial species, substrates, and products.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Waistline to the gumline: Relationship between obesity and periodontal disease-biological and management considerations

Obesity is a pandemic and periodontitis is the sixth most prevalent disease in the world. These two noncommunicable diseases share several risk determinants. Epidemiologic evidence from the last 2 decades has established an increase in periodontitis prevalence in obese and overweight individuals. Biologic mechanisms potentially linking obesity and periodontal disease are adiposity-associated hyperinflammation, microbial dysbiosis, altered immune response, specific genetic polymorphisms, and increased stress. However, because of the lack of longitudinal interventional studies and randomized clinical trials, there is insufficient evidence to determine the cause-effect relationship between these two diseases. Despite this, the negative impact of obesity on oral health is well established. Several logistic and physiologic complications are associated with treating obese patients in a dental setting, and it requires an interprofessional team approach. Oral health care professionals need to be aware of the specific management considerations while rendering for this cohort, including modified practice facility and equipment, tailored supportive periodontal therapy, and heightened precaution during conscious sedation and surgical procedures.

Metabolomics in Functional Interrogation of Individual Holobiont Members

Eukaryotes and their environments serve as petri dishes, hosting an abundant and a rich prokaryotic microbiome. The assemblage of a eukaryotic host and its microbiome is referred to as a holobiont. The holobiont’s microbiome interacts within itself, with the environment, and with the host at the chemical level through production of specialized metabolites resulting in homeostasis or dysbiosis. These interactions are triggered by a multitude of factors, such as community composition, age, presence of nutrients, xenobiotics, and change in physical conditions, such as temperature and oxygen. Understanding how holobionts respond and adapt to diverse triggers is necessary to uncover mechanisms of resilience or susceptibility to dysbiosis and to modulate the collective functioning of microbiome in health and disease. This article highlights the challenges associated with uncovering chemical contributions of individual holobiont members and the applicability of metabolomics-based approaches to uncover chemical signatures of microbial processes in the natural environment.

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.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Evidence of a hormonal reshuffle in the cecal metabolome fingerprint of a strain of rats resistant to decompression sickness

On one side, decompression sickness (DCS) with neurological disorders lead to a reshuffle of the fecal metabolome from rat caecum. On the other side, there is high inter-individual variability in terms of occurrence of DCS. One could wonder whether the fecal metabolome could be linked to the DCS-susceptibility. We decided to study male and female rats selected for their resistance to decompression sickness, and we hypothesize a strong impregnation concerning the fecal metabolome. The aim is to verify whether the rats resistant to the accident have a fecal metabolomic signature different from the stem generations sensitive to DCS. 39 DCS-resistant animals (21 females and 18 males), aged 14 weeks, were compared to 18 age-matched standard Wistar rats (10 females and 8 males), i.e., the same as those we used for the founding stock. Conventional and ChemRICH approaches helped the metabolomic interpretation of the 226 chemical compounds analyzed in the cecal content. Statistical analysis shows a panel of 81 compounds whose expression had changed following the selection of rats based on their resistance to DCS. 63 compounds are sex related. 39 are in common. This study shows the spectral fingerprint of the fecal metabolome from the caecum of a strain of rats resistant to decompression sickness. This study also confirms a difference linked to sex in the metabolome of non-selected rats, which disappear with selective breeding. Results suggest hormonal and energetic reshuffle, including steroids sugars or antibiotic compounds, whether in the host or in the microbial community.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers

It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.

Linking genomics and metabolomics to chart specialized metabolic diversity

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

The Microbiome in Cystic Fibrosis Pulmonary Disease

Cystic fibrosis (CF) is a genetic disease with mutational changes leading to profound dysbiosis, both pulmonary and intestinal, from a very young age. This dysbiosis plays an important role in clinical manifestations, particularly in the lungs, affected by chronic infection. The range of microbiological tools has recently been enriched by metagenomics based on next-generation sequencing (NGS). Currently applied essentially in a gene-targeted manner, metagenomics has enabled very exhaustive description of bacterial communities in the CF lung niche and, to a lesser extent, the fungi. Aided by progress in bioinformatics, this now makes it possible to envisage shotgun sequencing and opens the door to other areas of the microbial world, the virome, and the archaeome, for which almost everything remains to be described in cystic fibrosis. Paradoxically, applying NGS in microbiology has seen a rebirth of bacterial culture, but in an extended manner (culturomics), which has proved to be a perfectly complementary approach to NGS. Animal models have also proved indispensable for validating microbiome pathophysiological hypotheses. Description of pathological microbiomes and correlation with clinical status and therapeutics (antibiotic therapy, cystic fibrosis transmembrane conductance regulator (CFTR) modulators) revealed the richness of microbiome data, enabling description of predictive and follow-up biomarkers. Although monogenic, CF is a multifactorial disease, and both genotype and microbiome profiles are crucial interconnected factors in disease progression. Microbiome-genome interactions are thus important to decipher.

Metabolome-Microbiome Crosstalk and Human Disease

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these "omics" are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.