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.

Impact of exclusive enteral nutrition on the gut microbiome of children with medical complexity.

BACKGROUND: Children with medical complexity (CMC) often require enteral tube feedings to meet their nutrition needs. Many, however, experience symptoms of feeding intolerance, such as vomiting and pain. The goal of this analysis was to examine the relationship between diet and the gut microbiome, controlling for medications, among CMC receiving enteral tube feedings, CMC consuming oral nutrition, and healthy controls. Given the variety of available commercial formula preparations, we were also interested in examining the impact of different formula types on the CMC microbiome. METHODS: Fecal samples from 91 children (57 CMC and 34 healthy controls) were collected and analyzed. Parents completed clinical and dietary questionnaires. 16S ribosomal RNA amplicon sequencing was completed using the QIIME2 pipeline. RESULTS: A significant decrease in alpha diversity among CMC receiving exclusive enteral nutrition (CMC EEN) compared with healthy controls (Shannon P = 0.006 and Faith's phylogenetic distance P = 0.006) was found that was not observed between CMC receiving oral nutrition and healthy controls. Significant differences in beta diversity were also observed between CMC EEN and healthy controls, with CMC EEN having a greater relative abundance of Enterobacteriaceae and obligate anaerobes. Differences were also noted between CMC EEN and CMC receiving oral nutrition (Aitchison distance P = 0.001); however, no differences were observed between CMC receiving oral nutrition and healthy controls. CONCLUSION: Despite similarities in medication profiles, CMC EEN have decreased alpha diversity and differences in beta diversity compared with healthy controls not observed in CMC receiving oral nutrition, highlighting the impact of diet over medications.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

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.

Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment.

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Searching for host immune-microbiome mechanisms in obsessive-compulsive disorder: A narrative literature review and future directions.

Obsessive-compulsive disorder (OCD) is disabling and often treatment-refractory. Host immunity and gut microbiota have bidirectional communication with each other and with the brain. Perturbations to this axis have been implicated in neuropsychiatric disorders, but immune-microbiome signaling in OCD is relatively underexplored. We review support for further pursuing such investigations in OCD, including: 1) gut microbiota has been associated with OCD, but causal pathogenic mechanisms remain unclear; 2) early environmental risk factors for OCD overlap with critical periods of immune-microbiome development; 3) OCD is associated with increased risk of immune-mediated disorders and changes in immune parameters, which are separately associated with the microbiome; and 4) gut microbiome manipulations in animal models are associated with changes in immunity and some obsessive-compulsive symptoms. Theoretical pathogenic mechanisms could include microbiota programming of cytokine production, promotion of expansion and trafficking of peripheral immune cells to the CNS, and regulation of microglial function. Immune-microbiome signaling in OCD requires further exploration, and may offer novel insights into pathogenic mechanisms and potential treatment targets for this disabling disorder.

Oral Microbial Species and Virulence Factors Associated with Oral Squamous Cell Carcinoma.

The human microbiome has been the focus of numerous research efforts to elucidate the pathogenesis of human diseases including cancer. Oral cancer mortality is high when compared with other cancers, as diagnosis often occurs during late stages. Its prevalence has increased in the USA over the past decade and accounts for over 40,000 new cancer patients each year. Additionally, oral cancer pathogenesis is not fully understood and is likely multifactorial. To unravel the relationships that are associated with the oral microbiome and their virulence factors, we used 16S rDNA and metagenomic sequencing to characterize the microbial composition and functional content in oral squamous cell carcinoma (OSCC) tumor tissue, non-tumor tissue, and saliva from 18 OSCC patients. Results indicate a higher number of bacteria belonging to the Fusobacteria, Bacteroidetes, and Firmicutes phyla associated with tumor tissue when compared with all other sample types. Additionally, saliva metaproteomics revealed a significant increase of Prevotella in five OSCC subjects, while Corynebacterium was mostly associated with ten healthy subjects. Lastly, we determined that there are adhesion and virulence factors associated with Streptococcus gordonii as well as from known oral pathogens belonging to the Fusobacterium genera found mostly in OSCC tissues. From these results, we propose that not only will the methods utilized in this study drastically improve OSCC diagnostics, but the organisms and specific virulence factors from the phyla detected in tumor tissue may be excellent biomarkers for characterizing disease progression.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages.

In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations.

Host-Microbial Interactions in Systemic Lupus Erythematosus and Periodontitis.

Background: Systemic lupus erythematosus (SLE) is a potentially fatal complex autoimmune disease, that is characterized by widespread inflammation manifesting tissue damage and comorbidities across the human body including heart, blood vessels, joints, skin, liver, kidneys, and periodontal tissues. The etiology of SLE is partially attributed to a deregulated inflammatory response to microbial dysbiosis and environmental changes. In the mouth, periodontal environment provides an optimal niche for local and systemic inflammation. Our aim was to evaluate the reciprocal impact of periodontal subgingival microbiome on SLE systemic inflammation. Methods: Ninety-one female subjects were recruited, including healthy (n = 31), SLE-inactive (n = 29), and SLE-active (n = 31). Patients were screened for probing depth, bleeding on probing, clinical attachment level, and classified according to CDC/AAP criteria with or without periodontal dysbiosis. Serum inflammatory cytokines were measured by human cytokine panel and a targeted pathogenic subgingival biofilm panel was examined by DNA-DNA checkerboard from subgingival plaque samples. Results: The results showed significant upregulation of serum proinflammatory cytokines in individuals with SLE when compared to controls. Stratification of subject's into SLE-inactive (I) and SLE-active (A) phenotypes or periodontitis and non-periodontitis groups provided new insights into SLE pathophysiology. Ten proinflammatory cytokines were upregulated in serum of SLE-I only and one in SLE-A only. Four molecules overlapped in SLE-A and SLE-I. Anti-inflammatory cytokines included IL-4 IL-10, which were upregulated in SLE-I sera (but not SLE-A), controlling clinical phenotypes. Out of 24 significant differential oral microbial abundances found in SLE, 14 unique subgingival bacteria profiles were found to be elevated in SLE. The most severe oral pathogens (Treponema denticola and Tannerella forsythia) showed increase abundances on SLE-A periodontal sites when compared to SLE-I and healthy controls. Inflammation as measured by cytokine-microbial correlations showed that periodontal pathogens dominating the environment increased proinflammatory cytokines systemically. Conclusions: Altogether, low-grade systemic inflammation that influenced SLE disease activity and severity was correlated to dysbiotic changes of the oral microbiota present in periodontal diseases.

Identification of the Bacterial Biosynthetic Gene Clusters of the Oral Microbiome Illuminates the Unexplored Social Language of Bacteria during Health and Disease.

Small molecules are the primary communication media of the microbial world. Recent bioinformatic studies, exploring the biosynthetic gene clusters (BGCs) which produce many small molecules, have highlighted the incredible biochemical potential of the signaling molecules encoded by the human microbiome. Thus far, most research efforts have focused on understanding the social language of the gut microbiome, leaving crucial signaling molecules produced by oral bacteria and their connection to health versus disease in need of investigation. In this study, a total of 4,915 BGCs were identified across 461 genomes representing a broad taxonomic diversity of oral bacteria. Sequence similarity networking provided a putative product class for more than 100 unclassified novel BGCs. The newly identified BGCs were cross-referenced against 254 metagenomes and metatranscriptomes derived from individuals either with good oral health or with dental caries or periodontitis. This analysis revealed 2,473 BGCs, which were differentially represented across the oral microbiomes associated with health versus disease. Coabundance network analysis identified numerous inverse correlations between BGCs and specific oral taxa. These correlations were present in healthy individuals but greatly reduced in individuals with dental caries, which may suggest a defect in colonization resistance. Finally, corroborating mass spectrometry identified several compounds with homology to products of the predicted BGC classes. Together, these findings greatly expand the number of known biosynthetic pathways present in the oral microbiome and provide an atlas for experimental characterization of these abundant, yet poorly understood, molecules and socio-chemical relationships, which impact the development of caries and periodontitis, two of the world's most common chronic diseases.IMPORTANCE The healthy oral microbiome is symbiotic with the human host, importantly providing colonization resistance against potential pathogens. Dental caries and periodontitis are two of the world's most common and costly chronic infectious diseases and are caused by a localized dysbiosis of the oral microbiome. Bacterially produced small molecules, often encoded by BGCs, are the primary communication media of bacterial communities and play a crucial, yet largely unknown, role in the transition from health to dysbiosis. This study provides a comprehensive mapping of the BGC repertoire of the human oral microbiome and identifies major differences in health compared to disease. Furthermore, BGC representation and expression is linked to the abundance of particular oral bacterial taxa in health versus dental caries and periodontitis. Overall, this study provides a significant insight into the chemical communication network of the healthy oral microbiome and how it devolves in the case of two prominent diseases.

Microbe and plant assisted-remediation of organic xenobiotics and its enhancement by genetically modified organisms and recombinant technology: A review.

Environmental problems such as the deterioration of groundwater quality, soil degradation and various threats to human, animal and ecosystem health are closely related to the presence of high concentrations of organic xenobiotics in the environment. Employing appropriate technologies to remediate contaminated soils is crucial due to the site-specificity of most remediation methods. The limitations of conventional remediation technologies include poor environmental compatibility, high cost of implementation and poor public acceptability. This raises the call to employ biological methods for remediation. Bioremediation and microbe-assisted bioremediation (phytoremediation) offer many ecological and cost-associated benefits. The overall efficiency and performance of bio- and phytoremediation approaches can be enhanced by genetically modified microbes and plants. Moreover, phytoremediation can also be stimulated by suitable plant-microbe partnerships, i.e. plant-endophytic or plant-rhizospheric associations. Synergistic interactions between recombinant bacteria and genetically modified plants can further enhance the restoration of environments impacted by organic pollutants. Nevertheless, releasing genetically modified microbes and plants into the environment does pose potential risks. These can be minimized by adopting environmental biotechnological techniques and guidelines provided by environmental protection agencies and other regulatory frameworks. The current contribution provides a comprehensive overview on enhanced bioremediation and phytoremediation approaches using transgenic plants and microbes. It also sheds light on the mitigation of associated environmental risks.

Correction: Qualitative analysis of biosurfactants from Bacillus species exhibiting antifungal activity.

[This corrects the article DOI: 10.1371/journal.pone.0198107.].

Secondary metabolite genes encoded by potato rhizosphere microbiomes in the Andean highlands are diverse and vary with sampling site and vegetation stage.

Potato (Solanum tuberosum) is an important staple crop worldwide, it has been cultivated in the Andean Altiplano under low-input farming practices at high altitudes and under harsh environment for centuries. We analyzed secondary metabolite (SM) gene diversity encoded in the potato rhizosphere microbiome during plant growth at three distinct sites located in the Andes at high altitudes by 454-pyrosequencing of non-ribosomal peptide and polyketide biosynthetic genes. Phylogenetic analysis indicated that the majority of rhizosphere SM-encoding sequences differed from previously known sequences and may have distinct ancestors. In particular, actinobacterial methyl-malonyl-CoA transferase and acyl carrier protein from Firmicutes, both involved in the synthesis of SMs, showed widespread distribution of clades which were clearly distinct from sequences deposited in public databases, and only 11% of these sequences could be linked to the production of specific classes of SMs. Although the same cultivar was analyzed, SM gene composition radically differed among plant growth stages and across sites, suggesting a distinct repertoire of SM genes that likely encode diverse SM structures. Also, great diversity of non-ribosomal peptide and polyketide biosynthetic pathways in potato-associated microbiomes in the Andean highlands may represent a rich source of novel natural products.

Surfactin variants mediate species-specific biofilm formation and root colonization in Bacillus.

Cyclic lipopeptides (cLP) and especially surfactins produced by Bacillus spp. trigger biofilm formation and root colonization and are crucial for biocontrol activity and systemic resistance in plants. Bacillus atrophaeus 176s isolated from the moss Tortella tortuosa produces the cLP fengycins, iturins and surfactins, possesses antifungal activities and can protect tomato, lettuce and sugar beet against Rhizoctonia solani infection. In B. atrophaeus we identified for the first time the variant surfactin C, which differs from surfactin A produced by B. subtilis and B. amyloliquefaciens by an isoleucine instead of a leucine at position 7 of the lipopeptide backbone. The analysis of the complete surfactin gene clusters revealed that the dissimilarity is encoded in the adenylation domain of srfC and show that surfactin variations are distributed in a species-specific manner in bacilli. We demonstrate that the surfactin A and C with subtle structural differences have varying signal strengths on biofilm formation and root colonization and act specifically on the respective producing strain. This became evident as biofilm formation and root colonization but not swarming motility in surfactin biosynthesis mutants was restored differentially in the presence of exogenously supplemented cognate and non-cognate surfactin variants.

The Draft Genome Sequence of Paenibacillus polymyxa Strain CCI-25 Encompasses High Potential for Secondary Metabolite Production.

We report here the draft genome sequence of Paenibacillus polymyxa strain CCI-25, which displays strong antifungal and antibacterial activities in vitro The genome encompasses nonribosomal peptide synthetases predicted to encode a tridecaptin, polymyxin, fusaricidin, an iturin-like synthetase, a lantibiotic similar to paenicidin A, as well as a type 1 polyketide synthase.

Genome mining: Prediction of lipopeptides and polyketides from Bacillus and related Firmicutes.

Bacillus and related genera in the Bacillales within the Firmicutes harbor a variety of secondary metabolite gene clusters encoding polyketide synthases and non-ribosomal peptide synthetases responsible for remarkable diverse number of polyketides (PKs) and lipopeptides (LPs). These compounds may be utilized for medical and agricultural applications. Here, we summarize the knowledge on structural diversity and underlying gene clusters of LPs and PKs in the Bacillales. Moreover, we evaluate by using published prediction tools the potential metabolic capacity of these bacteria to produce type I PKs or LPs. The huge sequence repository of bacterial genomes and metagenomes provides the basis for such genome-mining to reveal the potential for novel structurally diverse secondary metabolites. The otherwise cumbersome task to isolate often unstable PKs and deduce their structure can be streamlined. Using web based prediction tools, we identified here several novel clusters of PKs and LPs from genomes deposited in the database. Our analysis suggests that a substantial fraction of predicted LPs and type I PKs are uncharacterized, and their functions remain to be studied. Known and predicted LPs and PKs occurred in the majority of the plant associated genera, predominantly in Bacillus and Paenibacillus. Surprisingly, many genera from other environments contain no or few of such compounds indicating the role of these secondary metabolites in plant-associated niches.