Development and mining of a volatile organic compound database

Automating methods for estimating metabolite volatility

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

Microplastics: What happens in the human digestive tract? First evidences in adults using in vitro gut models

Microplastics (MPs) are ubiquitous in the environment and humans are inevitably exposed to them. However, the effects of MPs in the human digestive environment are largely unknown. The aim of our study was to investigate the impact of repeated exposure to polyethylene (PE) MPs on the human gut microbiota and intestinal barrier using, under adult conditions, the Mucosal Artificial Colon (M-ARCOL) model, coupled with a co-culture of intestinal epithelial and mucus-secreting cells. The composition of the luminal and mucosal gut microbiota was determined by 16S metabarcoding and microbial activities were characterized by gas, short chain fatty acid, volatolomic and AhR activity analyses. Gut barrier integrity was assessed via intestinal permeability, inflammation and mucin synthesis. First, exposure to PE MPs induced donor-dependent effects. Second, an increase in abundances of potentially harmful pathobionts, Desulfovibrionaceae and Enterobacteriaceae, and a decrease in beneficial bacteria such as Christensenellaceae and Akkermansiaceae were observed. These bacterial shifts were associated with changes in volatile organic compounds profiles, notably characterized by increased indole 3-methyl- production. Finally, no significant impact of PE MPs mediated by changes in gut microbial metabolites was reported on the intestinal barrier. Given these adverse effects of repeated ingestion of PE MPs on the human gut microbiota, studying at-risk populations like infants would be a valuable advance.

Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm

BACKGROUND

Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications.

RESULTS

This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change.

CONCLUSIONS

Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

Breath Biopsy and Discovery of Exclusive Volatile Organic Compounds for Diagnosis of Infectious Diseases

Exhaled breath contains thousand metabolites and volatile organic compounds (VOCs) that originated from both respiratory tract and internal organ systems and their microbiomes. Commensal and pathogenic bacteria and virus of microbiomes are capable of producing VOCs of different chemical classes, and some of them may serve as biomarkers for installation and progression of various common human diseases. Here we describe qualitative and quantitative methods for measuring VOC fingerprints generated by cellular and microbial metabolic and pathologic pathways. We describe different chemical classes of VOCs and their role in the host cell-microbial interactions and their impact on infection disease pathology. We also update on recent progress on VOC signatures emitted by isolated bacterial species and microbiomes, and VOCs identified in exhaled breath of patients with respiratory tract and gastrointestinal diseases, and inflammatory syndromes, including the acute respiratory distress syndrome and sepsis. The VOC curated databases and instrumentations have been developed through statistically robust breathomic research in large patient populations. Scientists have now the opportunity to find potential biomarkers for both triage and diagnosis of particular human disease.

Systematic review: Effectiveness of combination of lactic acid attractants for control of dengue vector Aedes spp

BACKGROUND & OBJECTIVES

This study aimed to review the effectiveness of lactic acid when combined with other types of attractants for Aedes spp.

METHODS

A systematic review was conducted according to the protocol for a systematic review and meta-analysis (PRISMA). Literature search used Cinahl, Medline/PubMed, ScienceDirect, ProQuest and Ebsco electronic databases. Research articles used in the systematic review were experimental articles that reported the effectiveness of mosquito traps using lactic acid or a combination of lactic acid with other attractants.

RESULTS

From a total of 42 articles reviewed, there were 6 articles fulfilling the inclusion criteria. The highest synergistic combination of lactic acid in the ketone group was shown in the acetone compound, in the sulfides class, dimethyl sulfides, and in the chloroalkanes group, chloroform. The combination of lactic acid with two effective attractants can be seen in the incorporation of ammonia + caproic acid, and for the incorporation of lactic acid with three other effective attractants illustrated by combining ammonia + caproic acid + CO₂.

INTERPRETATION & CONCLUSION

Lactic acid as an attractant can be combined with other various compounds (ketone compounds, sulfides and chloroalkanes). Lactic acid increases its effectiveness in trapping Ae. aegypti and/or Ae. albopictus if combined with acetone, dimethyl sulfides, and/or chloroform.

BiClusO: A Novel Biclustering Approach and Its Application to Species-VOC Relational Data

In this paper, we propose a novel biclustering approach called BiClusO. Biclustering can be applied to various types of bipartite data such as gene-condition or gene-disease relations. For example, we applied BiClusO to bipartite relations between species and volatile organic compounds (VOCs). VOCs, which are emitted by different species, have huge environmental and ecological impacts. The biosynthesis of VOCs depends on different metabolic pathways which can be used to categorize the species. A previous study related to the KNApSAcK VOC database classified microorganisms based on their VOC profiles, which confirmed the consistency between VOC-based and pathogenicity-based classifications. However, due to limited data, classification of all species in terms of VOC profiles was not performed. In this study, we enriched our database with additional data collected from different online sources and journals. Then, by applying BiClusO to species-VOC relational data, we determined that VOC-based classification is consistent with taxonomy-based classification of the species. We also assessed the diversity of VOC pathways across different kingdoms of species.

Mass Spectrometry: A Rosetta Stone to Learn How Fungi Interact and Talk

Fungi are a highly diverse group of heterotrophic organisms that play an important role in diverse ecological interactions, many of which are chemically mediated. Fungi have a very versatile metabolism, which allows them to synthesize a large number of still little-known chemical compounds, such as soluble compounds that are secreted into the medium and volatile compounds that are chemical mediators over short and long distances. Mass spectrometry (MS) is currently playing a dominant role in mycological studies, mainly due to its inherent sensitivity and rapid identification capabilities of different metabolites. Furthermore, MS has also been used as a reliable and accurate tool for fungi identification (i.e., biotyping). Here, we introduce the readers about fungal specialized metabolites, their role in ecological interactions and provide an overview on the MS-based techniques used in fungal studies. We particularly present the importance of sampling techniques, strategies to reduce false-positive identification and new MS-based analytical strategies that can be used in mycological studies, further expanding the use of MS in broader applications. Therefore, we foresee a bright future for mass spectrometry-based research in the field of mycology.

Food Chemicals Disrupt Human Gut Microbiota Activity And Impact Intestinal Homeostasis As Revealed By In Vitro Systems

Growing evidence indicates that the human gut microbiota interacts with xenobiotics, including persistent organic pollutants and foodborne chemicals. The toxicological relevance of the gut microbiota-pollutant interplay is of great concern since chemicals may disrupt gut microbiota functions, with a potential impairment of host homeostasis. Herein we report within batch fermentation systems the impact of food contaminants (polycyclic aromatic hydrocarbons, polychlorobiphenyls, brominated flame retardants, dioxins, pesticides and heterocyclic amines) on the human gut microbiota by metatranscriptome and volatolome i.e. “volatile organic compounds” analyses. Inflammatory host cell response caused by microbial metabolites following the pollutants-gut microbiota interaction, was evaluated on intestinal epithelial TC7 cells. Changes in the volatolome pattern analyzed via solid-phase microextraction coupled to gas chromatography-mass spectrometry mainly resulted in an imbalance in sulfur, phenolic and ester compounds. An increase in microbial gene expression related to lipid metabolism processes as well as the plasma membrane, periplasmic space, protein kinase activity and receptor activity was observed following dioxin, brominated flame retardant and heterocyclic amine exposure. Conversely, all food contaminants tested induced a decreased in microbial transcript levels related to ribosome, translation and nucleic acid binding. Finally, we demonstrated that gut microbiota metabolites resulting from pollutant disturbances may promote the establishment of a pro-inflammatory state in the gut, as stated with the release of cytokine IL-8 by intestinal epithelial cells.

Machine learning for the meta-analyses of microbial pathogens' volatile signatures

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

Mining the Volatilomes of Plant-Associated Microbiota for New Biocontrol Solutions

Microbial lifeforms associated with land plants represent a rich source for crop growth- and health-promoting microorganisms and biocontrol agents. Volatile organic compounds (VOCs) produced by the plant microbiota have been demonstrated to elicit plant defenses and inhibit the growth and development of numerous plant pathogens. Therefore, these molecules are prospective alternatives to synthetic pesticides and the determination of their bioactivities against plant threats could contribute to the development of control strategies for sustainable agriculture. In our previous study we investigated the inhibitory impact of volatiles emitted by Pseudomonas species isolated from a potato field against the late blight-causing agent Phytophthora infestans. Besides the well-documented emission of hydrogen cyanide, other Pseudomonas VOCs impeded P. infestans mycelial growth and sporangia germination. Current advances in the field support the emerging concept that the microbial volatilome contains unexploited, eco-friendly chemical resources that could help select for efficient biocontrol strategies and lead to a greener chemical disease management in the field.

Volatile Metabolites Emission by In Vivo Microalgae-An Overlooked Opportunity?

Fragrances and malodors are ubiquitous in the environment, arising from natural and artificial processes, by the generation of volatile organic compounds (VOCs). Although VOCs constitute only a fraction of the metabolites produced by an organism, the detection of VOCs has a broad range of civilian, industrial, military, medical, and national security applications. The VOC metabolic profile of an organism has been referred to as its 'volatilome' (or 'volatome') and the study of volatilome/volatome is characterized as 'volatilomics', a relatively new category in the 'omics' arena. There is considerable literature on VOCs extracted destructively from microalgae for applications such as food, natural products chemistry, and biofuels. VOC emissions from living (in vivo) microalgae too are being increasingly appreciated as potential real-time indicators of the organism's state of health (SoH) along with their contributions to the environment and ecology. This review summarizes VOC emissions from in vivo microalgae; tools and techniques for the collection, storage, transport, detection, and pattern analysis of VOC emissions; linking certain VOCs to biosynthetic/metabolic pathways; and the role of VOCs in microalgae growth, infochemical activities, predator-prey interactions, and general SoH.

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.